CEOP centralized data system and integrated analysis tools
Kenji Taniguchi1, Toshihiro Nemoto2, Eiji Ikoma3, Masaki Yasukawa4, Toshio Koike5
and Masaru Kitsuregawa6
1,5

Dept of Civil Eng, Univ. of Tokyo
Institute of Industrial Science, Univ. of Tokyo
3
Center for Spatial Information Science, Univ. of Tokyo
E-mail: taniguti@hydra.t.u-tokyo.ac.jp
2,4,6

Abstract
The amount of earth environmental data has increased explosively because of recent advances in observational
techniques. On the CEOP (Coordinated Enhanced Observing Period) project, in order to improve our understanding ofwater and energy cycle, large amount of data is collected and archived. In this paper, we introduce the CEOP
centralized data system and integrated analysis tool. The centralized system enables us to retrieve, browse, analyze anddownload CEOP data set, and it improves accessibility and usability of the CEOP data. Three dimensional datavisualization system is developed with the fusion of various products and the cutting out of demanded arbitrary surface.
Developing an easy-to-use interface for non-computational researchers. The CEOP data system is developed at Instituteof Industrial Science, University of Tokyo. This paper also explains the design of the system.

1.

Introduction
Water and energy cycle is important for peoples’ life and the climate system. At the same time, there has been
drastic progress in techniques for observing the earth environment, and as a result, the volume of earth observation data
has increased. On the CEOP (Coordinated Enhanced Observing Period) project, large amount of data are being
collected and archived in order to improve our understanding of water and energy cycle. CEOP data consist of threekinds of data, those are in-situ data, satellite data and model output data. The in-situ data are a temporal series of air
temperature, pressure, humidity, precipitation and so on at 35 reference sites around the world. The satellite data areremotely sensed data from the operational satellites, such as TERRA, AQUA, TRMM, NOAA and so on. The model
output data are generated by numerical weather prediction centers. These data have various dimensions, spatial andtemporal resolutions, precision, formats, coordinate systems. The total amount of the data is almost 100TB per year.
This paper explains the design and implementation of data server in addition to the browse and analysis interface whichenable researchers to use our system easily.
It uses tape library system and disk arrays to store the data, however, the location of data is hidden from users. The
users can retrieve data without considering data location. The browse and analysis interface is the client of the dataserver and it provides the users with menu based integrated access tools to the data server. The connection between theclients and the server is based on HTTP. The users can access all kinds of data through the same interface without
taking account of data type. The users can view the retrieved data as graphic charts or bitmap images depend on theirdimension directly from the server. Some analysis operations such as average, difference, correlation, and so on can beapplied into one or more retrieved data on the server through the interface.

2. Data
2.1. CEOP data
Three kinds of data are planned to be archive on the CEOP project. They are in-situ data, model output data andsatellite data.

The in-situ data are a temporal series ofthe values observed at 35 reference sitesaround the world. Each reference site has one
or more stations. The in-situ data are divided
into three categories, namely surface
observation, subsurface observation and upper
air observation. The surface observationconsists of air temperature, pressure, humidity,
precipitation, heat flux, radiation and so on at
the ground level. The subsurface observationis composed of soil temperature, soil watercontent and soil heat flux for 2cm to 175cm
depth. The upper air observation consists of air
temperature, humidity, pressure and so on
measured by radiosonde. The all values are notalways observed at a reference site. The sorts of
Figure 1
the observed values and observation frequency
depend on the reference site. The total amount of in-situ data for two year and three months is almost 600MB.
The model output data is the gridded values from global forecast model or assimilation system generated by 10
num erical weather forecast centers. Two types of model output, namely gridded data and site-specific time series at
each of the reference site are planned to archive. The latter time series are designated as MOLTS (Model Output
Location Time Series). The gridded data are three dimensional data and each cell has several prognostic variables suchas air temperature, humidity, pressure and so on. The forecast length, assimilation intervals, the grid systems of themodels and also the variables in models are different each other. The MOLTS data are one dimensional time series ofvariables at the reference site extracted from gridded data. The total amount of model output data is almost 20TB.
The satellite data are remotely sensed data from the sensors on the operational satellites such as DMSP SSM/I, TRMM
TMI, TRMM PR, GMS S-VISSR, NOAA AVHRR, TERRA/AQUA MODIS, AQUA AMSR-E and so on. The satellite
data are two dimensional data and each sensor has one or more channels. Though these data are geometrically correctedby the data supplier, their resolutions depend on the sensor and the channel. The observation time may vary day by day
even if the same satellite. The total amount of satellite data is almost 200TB.
2.2. Data for integrated analysis tools
In addition to CEOP centralized data system, we have developed a prototype system of analysis tools for large
volume data set.
For the analysis system, monthly average data for global precipitation from 1979 to 2000 and daily average datafrom 1997 to 2000, which are in the GPCP(Global Precipitation Climatology Project) data set. The resolution is 1
degree per pixel on every side. And, because OLR (Outgoing Long-wave Radiation) is useful to describe summer
monsoons as an indication of convective activity in a tropical area, we use OLR data from 1975 to 2000 provided by
NOAA (National Oceanic and Atmospheric Administrator). The resolution is 2.5 degrees on every side. Reanalysis dataof NCEP/NCAR (National Center for Environmental Prediction / National Center for Atmospheric Research) for
atmospheric data is archived and used. The resolution is also 2.5 degrees on every side and frequency is daily. We use
sea surface temperature data provided by TMI (TRMM Microwave Imager) on TRMM (Tropical Rainfall Measuring
Mission). This data has a 0.25 degree spatial resolution, and three day average temporal resolution from December 1997.
Atmospheric Infrared Sounder (AIRS) onboard Aqua, NASA’s satellite, is designed by NASA Jet Propulsion Laboratory.
With other satellite sensors onboard Aqua, cloud effects can be reduced and vertical distribution of atmospherictemperature and water vapor and other atmospheric elements can be detected by AIRS. In this study, data of
atmospheric temperature and water vapor are used. Spatial resolution is 0.25deg*0.25deg in horizontal direction, and 28
layers in vertical direction. Details are described in the web site of “AIRS data support”.

3. System
3. 1. CEOP centralized data archiving system

- System architecture
The CEOP centralized data system is basedon a client-server model. The architecture of thesystem is shown in Figure 1. The communication
protocol between server and client is HTTP. The
requests from clients are at first received at the
HTTP server and then they are sent to the data
manager. The data manager is a servlet program.
It receives the requests from clients through the
HTTP server and then it generates SQL
Figure 2commands for data search or executes analysis
operations according to the user requests. One
dimensional data such as in-situ data and
MOLTS data are stored in DBMS, however, twoand three dimensional data such as gridded
model output data and satellite data are stored as
files on the hierarchical file system and only
their metadata are stored in DBMS. There areseveral reasons why we do not manage two or
three dimensional data as Large Objects (LOB)in DBMS. First, the accessing a LOB in DBMS
is slower than accessing a file. Second, existing
implementations of LOBs tend to lack support
for the hierarchical storage management system.
Although the gridded model output data and thesatellite data are stored on the hierarchical fileFigure 3system, small images around the reference sites
clipped from the global data are stored on disks. Generally the values around the reference sites are necessary tocompare the values from ground observations and that of model output or that of remotely sensed data. To store thesmall portions on the disks instead of hierarchical file system we can reduce the response time. The location of the datais hidden from the users. The users do not have to consider where the data are stored. The data server automaticallymigrates and retrieves the appropriate data from DBMS, disks or hierarchical file system as the user requests and sends
them to the clients. The DBMS manages the one dimensional data and the metadata for all data. We use a commercial
DBMS and JDBC for the connection between DBMS and the data manager.
The graphical user interface (GUI) in the client system roughly consists of two parts, HTTP browser and data
analysis interface. The communication between clients and servers are based on HTTP and the data analysis interface iswritten in JAVA. Accordingly, the client does not need any special hardware or software. Only HTTP browser and
JAVA runtime environment are required. Since many kinds of current computers and operating systems support HTTP
browser and JAVA runtime environment, the GUI for the data server works on many kinds of computers. The usage ofthe data analysis interface is described in the next section.
- Browse and analysis interface
The user accesses to the data server page through the HTTP browser at first and then authentication page is shown.
After the user passes the password check, the user can access to the data listed in his/her work space. The requested datais specified by three items, location (reference site name), data name and temporal period. The available location anddata name are listed in the menus. The user can select year and month in the menu as the temporal period or input thestart date and time and the end of those (Figure 2). Clicking the button to retrieve, the request is transferred to the server.
The server parses the requests, generates the SQL command, sends it to the DBMS and stores the result portion into theuser’s area. The items in the data management window correspond to the result portions in the data server. The retrieved
data can be displayed as a line chart (Figure 4) and a bitmap image (Figure 5). Users can specify any of the time, height,
latitude and longitude as x and y axis of the graph or the image. When the result portion has more than three dimensions,the values in the axis except x or y axis can be changed by the slider bar on the bottom of the line graph window or theFigure 4

Figure 6

Figure 5

Figure 7

bitmap image window. For example, when the user specify longitude as x axis and latitude as y axis to generate thebitmap images of the temporal series of global sea surface temperature, he can change the time by the slider bar on thebottom of the window. A scatter diagram can also be drawn (Figure 6). In the diagram, the regression lines are also
drawn. The regression coefficients and the correlation coefficients are shown in the coefficient information window(Figure 7).
The analysis operation is executed to the retrieved data. The user selects one or more retrieved data and thenpushes the appropriate analysis command button. The analysis request is sent to the data server and the data manager
executes the operation to the selected data in the user’s area. In the current preliminary version, only simple analysis
commands such as calculating average, maximum, minimum and so on are available (Figure 8). The data processed by
the analysis operation are also stored into the user’s area and they are listed in the data management window. They canbe targets of analysis operations again. The user can apply the analysis operations onto the processed data again andagain.

3.2. Integrated data integration tools
3.2.1. Correlation Coefficient Analysis Tools
Statistical analysis is used for extracting correlations between phenomena in meteorology. However, in the verylarge data sets that we targeted for this study, statistical analysis is quite difficult using general software. Moreover, thedata we are targeting has not only simple correlations between two data, but also various other kinds of correlation suchas spatial differences, temporal differences, and the difference of values; thus it requires tools which enable flexible
handling and analysis and the ability to specify conditions. In this study, we introduce a tool which can computecorrelation coefficient while specifying conditions such as time (term), space (area), value, temporal resolution andspatial resolution. With this tool, assuming that some natural phenomena may happen not at the same time but withsome delay (time lag), a user can analyze the correlation between phenomena with different times (time lag correlation)

Using this method, users can find not only
relationship between spatially separate points but also
temporally separate points (such as a phenomenon
happening a few days after the base phenomenon).
We developed a GUI which can operate these tools
via the Web. With this GUI, users can specify various
conditions and visualize the results. It also allows
multiple users to use it from remote locations.
When users log on our system, Top Page
(Figure 8) will appear.
In the right frame, user can specify as follows:- Choose 1 base data for correlation analysis- Choose some (multiple) target data forcorrelation analysis- Year, Month, Date
- Term- Specify base area for base data
- Choose the window for displaying results(Same window of this page or other)
Then, the user's specified area on a world map will
be displayed in the upper left frame for checking the area,
and user will be asked the value of the threshold forvisualization and the mode for processing. User can selectwhether results are immediately displayed or the system
will notify user by e-mail after processing.
In this window, the results of different days of lag
are lined up at horizontal direction, and the results ofdifferent value are lined up at vertical direction. In
other words, one result shows correlation coefficients
between spatial averaged time sequential and all points of
the whole globe for a time lag period, year, month, date.
Positive correlation points are shown as red points, andnegative ones are shown as blue points, the darkness ofcolor means it is strong and weak.
Such a display method enables users to understandchange of correlation as time goes on and which data has astrong correlation with the base data by comparing the result
at vertical direction.
If a user clicks interesting data on this window (Figure9), a new window will appear and a higher resolution image
is displayed, and the graph of area averaged time sequential
data is displayed in the lower window. Clicking a point suchas one with very high correlation on the global map of thiswindow, more detailed information on each point and eachtime will be shown in new window.

3.2.2 AIRS 3D Visualization system-Basic design of AIRS visualization system
In this study, following two functions are required
for the system;-3D visualization

Figure 8

Figure 9

Figure 10

-Data subset and visualization for arbitrary
curved surface
Two dimensional horizontal or vertical cross section
is not sufficient method to utilize three dimensional data.
For fully utilization of three dimensional data, virtual
reality markup language (VRML) is used in this system.
VRML does not require any commercial software andworks on web browser, and it enables to see three
dimensional data from arbitral view points.
At the same time, cross section is helpful tounderstand phenomena. However, software which havebeen used in meteorological research community has notallowed to make cross section in arbitral cross section.
Sometimes,
thislimitation
possiblyleads
misunderstandings of reality. Then, the second function isdeveloped in this study.
-VRML
In this system, three dimensional visualization of

Figure 11

AIRS data is realized with web browser using VRML 2.0.
By using VRML, users can view data from any direction
and distance in virtual reality space. Overlaying
geographical data, morphological feature and its effect onphenomena can be recognized at the same time (Figure 11).
VRML also enables to plot multiple variables in the same
space (Figure 12). Relationship among several elements
can be recognized and it is useful for analysis.
-Subset and plot on arbitral curved surface
In this system, users can select arbitral vertical section
by inputting longitude and latitude on the user interface(Figure 13). First, data is retrieved from data server.
Retrieved data is re-sampled by using selected curved crosssection and visualized by GrADS. Images are made forselected period (Figure 14). These images are viewed asanimation by choosing appropriate option. Plotted images
can be overlay on VRML and viewed with geographicalfeatures. This system can also make horizontal 2D plot andoverlay it in virtual reality space. These functions enable us
to understand vertical distribution of a variable andhorizontal 2D atmospheric condition in the same moment.

4.

Figure 12

Summary
Progress in observation and monitoring technology in meteorology has enabled researchers to find new knowledge
because of an increase in data volume. However, it has also brought some problems regarding methods andenvironments to use those huge volume data effectively and practically.
In this paper, centralized data system and integrated data using system for huge amount data developed by Instituteof Industrial Science, University of Tokyo in CEOP activity were introduced.
CEOP centralized data system has managing functions for huge and various formatted data set. Users can retrieve,
list and visualize data in their work space. At the same time, the data can be downloaded through the interface.

Moreover, the system enables us to process retrieved datawith basic mathematics functions. The data server has been
opened to the CEOP community tentatively.
At the same time, collaborating closely with theresearchers who are using earth environmental data,
analyzing tools for large volume data set are developed.
Tools are based on web technologies for easy access andusage.
Though our current preliminary data server supports
only simple operations, it may be helpful for studies related
on water and energy cycle. We are now implementing more
analysis functions and improving the system and also
develop more flexible and effective tools with the feedback opinion from active users.

References[1] Stolte E., Praun C., Alonso G. and Gross T.: Scientific Data
Repositories – Designing for a Moving Target, ACM SIGMOD

Figure 1313

2003.
[2] CEOP Home Page: http://monsoon.t.u-tokyo.ac.jp/ceop/.
[3] Nemoto T., Ikoma E. and Kitsuregawa M.: Design of dataserver for CEOP data, Proceedings of the 2nd Asia Pacific
Association of Hydrology and Water Resources Conference, Vol.2,
pp.558-565, 2004.
[4] Jet Propulsion Laboratory (NASA)， “AIRS - Atmospheric，”http://www-airs. jpl.nasa.gov/[5] Jeanne Behnke, Alla Lake，“EOSDIS: Archive and Distribution
Systems in the Year 2000，” Proceeding of 8th NASA Goddard
Infrared Sounder -Conference, pp.313-324, Mar. 2000.
[6] NASA Goddard Earth Sciences Data and Information Services
Center (GES DISC) / Distributed Active Archive Center (DAAC)，，” http://daac.gsfc.nasa.gov/atmodyn/airs/[7] Kitsuregawa Lab. ， “Satellite Image Archive at Institute ofIndustrial
Science,
UniversityofTokyo”,“AIRS data support

http://www.tkl.iis.u-tokyo.ac.jp/SIAIIS/

Figure 14
14， “Data Visualization System for EarthEnvironmental Researchers，” http://www.tkl. iis.u-tokyo.ac.jp:8080/DV/[9] U.S. Geological Survey (USGS)，“GTOPO30 - Global Topographic Data -，” http://edcdaac.usgs. gov/gtopo30/gtopo30.asp[10] Center for Ocean-Land-Atmosphere Studies (COLA)，“GrADS Data Server (GDS)，” http://grads.iges. org/grads/gds/gds.html[8] Kitsuregawa Lab.

[11] Nemoto, T. and M. Kitsuregawa, CEOP Data Server and Browse/Analysis Interface, Proceedings of CEOP/IGWCO Joint
Meeting, 2005[12] Ikoma, E., K. Taniguchi, T. Koike and M. Kitsuregawa, Development of a Visual Data Mining Application for EarthEnvironmental Data, Proceedings of CEOP/IGWCO Joint Meeting, 2005[13] Yasukawa. M., T. Nomoto, T. Koike and M. Kitsuregawa, Development of a Visualization System in Earth Observation
Satellite Data with Three-dimensional Information such as AIRS Data, DBSJ Letters, vol.4 (1), 2005 (in Japanese)[14] T. Koike, “The Coordinated Enhanced Observing Period – an initial step for integrated global water cycle observation”, WMO
Bulletin, vol.53, no.2, pp.115-121, April 2004.

TRMM PR, its possibility and limitation for the global mapping of
precipitation
Masafumi HIROSE
Earth Observation Research and Application Center, JAXA
E-mail: hirose@eorc.jaxa.jp
Abstract
Data potential of eight-year data observed by the spaceborne radar, TRMM PR, was examined in viewof the sufficiency of the sampling and the precipitation property. The degree of a biased climatology of monthly
rainfall and the detection ratio of the significant diurnal signature were investigated. The eight-year data clarified
a wide-range of the increasing possibilities and enhanced issues. Uncertainty of the precipitation structure
adjacent to the surface was addressed as one of the cross-cutting issues.
1.

Introduction
The Precipitation Radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) satellite is the firstand only spaceborne radar since the end of 1997. More than 12 TB of data had been stored. Given the growing
number of data samples without any deterioration, we can apply the data to the climate research where long-term
observations are essential to fundamental research needs. In this presentation, the increasing possibility and theperformance limitations are introduced.
2.

Unique strengths and the sampling issue of TRMM PR data
The primal uniqueness is the global 3-D observation of precipitation echo. It first observed accurate

stratiform and convective rainfall over land and ocean. The regional diversity of precipitation profiles isrecognized to be of great importance for any other conventional retrieval of precipitation. Main keywords ofother unique points would be the attenuation correction by using the path-integrated attenuation, observationfrom the non-sun-synchronous orbit, and the combination with other sensors on the same platform. On the other
hand, the biggest issue for most of possible users is the insufficient sampling. The bias of sampling for each local
hour is a critical problem for one-month data analyses. The seasonal data also contain this problem since itconsists of the insufficient intraseasonal dataset.
In order to mitigate this issue for the analyses of the seasonal and intraseasonal variation, theusefulness of the multiple-year data was examined. Figure 1 shows the variation of the sampling for each local
time, and the change by the data accumulation. The minimum number of the sampling was significantly
increased for 8 years. It is 1.4 times more than that of 7 years, and 8 times more than that of 3 years. The
accumulated number of samples for the minimum-sampling hour, 3-4 LT, was about 875 samples over 0.5 degree
box in August over Tibet. TRMM PR data are becoming climatologically significant and reliable dataset andenabled us to examine the precipitation variability in less time and in fine scale.

Fig. 1. The accumulated number of hourly samples over Tibet in August since 1998.
3. The temporal variation of rainfall depicted from the long-term data
The detection of the diurnal signature would be a good barometer for assessment of the impactof the long-term data accumulation since it needs sufficient hourly samples in fine spatial scale. We
considered the significant diurnal peak based on sufficient samples as the time of maximum rainfall withconsecutive positive anomalies for more than three hours. For one summer season, the diurnal signature
was ambiguous in most places. The occurrence frequency of the diurnal signal has been increasing
according to the increase of the sampling as shown in Fig. 2. For eight summer seasons, 20 % of regions
could be detected as the region with the significant diurnal signature. For all season during 8 years, thenumber became doubled. Looking into Tibet, 80 % of the region was detected, implying the uniform
mechanism therein. The TRMM satellite will be on orbit at least until 2009. There should be further
examined the increasing possibilities in utilizing the dataset. Each regional characteristics of the diurnal
signature prompt our speculation of possible research topics (not shown).

Figure 2. Year to year change of the percentage of significant features of time of maximum rainfall. Case
A-E corresponds to the detection rate of the diurnal feature with temporal continuity (case A), over Tibet(case B), over the eastern part of the south Atlantic (case C), and over the globe but for JJA (case D), andthat with spatial continuity in addition to the temporal continuity (case E).

Recently, a precipitation system climatology has it going on [e.g., Nesbitt and Zipser, 2003]. Its
concept is that climatic variability consists of individual factors and should be understood as
conglomerates of precipitation systems. The total rainfall amount is the sum of the individual pixel rain
intensities that are grouped into the precipitation systems. The eight-year observation captured millions ofprecipitation systems over the global tropics. For example, we can understand what kind of precipitation
systems makes the regional characteristics of the diurnal variation [e.g., Hirose and Nakamura, 2005].
The global and regional understandings of precipitation properties will deepen from the further data
accumulation and a diversity of researches.
4.

Remaining issues of the precipitation retrieval
A number of the ground validation of TRMM PR showed that adequate “truth” of global map of

rainfall is still in absence. Instead, the reduction of differences in rainfall estimates, about 5 % in average,
between TRMM PR and TMI has been discussed by evaluating each data properties. Major possible error
sources are listed: Regional variation of the drop size distribution, radar calibration, uncertainty ofattenuation correction for water vapor/cloud/precipitation, non-uniform-beam-filling effect, rain profiles
in the surface clutter, the temporal variation of the freezing level, the variation of the ice particles, and soon. Most of them are considered in the latest algorithm. However, they are still in controversy.
As an example of current activities, uncertainty in the surface rainfall estimates is focused on.
Any radar cannot observe rainfall rate at the surface due to the surface clutter. Around nadir and at theedge of the swath, the most frequent near-surface level was 500 and 1750 m, and the ninety-fifth
percentile was 750 and 2000 m, respectively (Fig. 3). Hence, in the latest algorithm, it has been estimatedby assuming several constant slope of dBZe in the clutter region. The slope was obtained from the windprofiler and TRMM PR observation. It is generally decreasing considering on the slower terminal velocity
near the surface. The height smearing effect of the near-surface levels on rainfall rate and the regionalvariation of the slope near the surface have not been clear yet.

Fig. 3. The number of the lowest levels by altitude diagram for each angle bin in case of no-rain overocean during 1998-2000.

As a comparative example, another surface rainfall was estimated by using the vertical gradient of
rainfall rate near the surface at nadir. Compared with our estimates utilizing the profiling capability of
TRMM PR, the difference between estimated surface rainfall and near-surface rainfall was almost sameon the global average but becomes large over the tropical land and over the mid-latitudes in winter (Fig.
4). Rain at 2 km was 6.6 % smaller than that at 1 km. There is few statistics of the vertical gradient below
the level of 1 km. With the collaboration of other ground-based observations, some kind of physicalmodel is needed, considering on the regional variation of the precipitation type and the environment.

Fig. 4. Altitudinal differences of averaged rainfall over the global tropics (left) and over land (right). Dark
colored lines indicate the observed and extrapolated vertical rain profiles. The other is estimated in thelatest algorithm.
5. Conclusion
The long-term data accumulation enabled us to investigate more accurate rainfall at various
temporal and spatial scales such as the intraseasonal and diurnal scales. Furthermore, it gives anopportunity to resolve the constituents of a particular climate regime as being congregations of various
precipitation systems. The comprehensive and interdisciplinary discussions are needed for further
scientific and algorithm benefits.
References
Boccippio, D. J., W. A. Petersen, and D. J. Cecil (2005), The tropical convective spectrum. PartI:
Archetypal vertical structures, J. Clim., 18, 2744-2769.
Hirose, M., and K. Nakamura (2005), Spatial and diurnal variation of precipitation systems over Asia
observed

by

the

TRMM

Precipitation

Radar,J.

Geophys.

Res.,

110,

D05106,

doi:10.1029/2004JD004815.
Nesbitt, S. W., and E. J. Zipser (2003), The diurnal cycle of rainfall and convective intensity according tothree years of TRMM measurements, J. Clim., 16, 1456-1475.

Necessary paths for developing
harmonized global land use classification systems
Christophe DUHAMEL
Landsis g.e.i.e. 49, boulevard de la Pétrusse L-2320 Luxembourg
Tel.: +352 26.84.78.18, E-mail: christophe.duhamel@landsis.lu

Abstract
Existing land use classification systems suffer from the proximity of a cumbersome neighbour - land cover- and from alack of theoretical background in terms of structuring information. This paper aims at describing the possible steps which
could constitute the necessary basis for a better individualisation of land use and therefore a better availability of land use
data sets in the future.
1. Introduction
Land use information is of significant value for a wide range of regional and global studies (George & Nachtergaele 2002).
However there is a paucity of global data sets that contain land use information and the quality of available information isvariable, often presenting a confused mixture of land use and land cover categories. The existing defined land use
categories are often inadequate for studies that focus on the collection of aspects of land use and on context related socioeconomic data.
Development of information systems on land use has considerably increased these last years under various initiatives atinternational level (UN, EU), research community level and national levels. Among the various possible sets of informationon land, land use is occupying a specific place but intertwined with land cover Although many initiatives have beenlaunched in order to improve the availability and the quality of land use information the result is extremely scarce anddiscouraging: many announced land use datasets are just adaptations or direct copies of land cover sets. This is partly
resulting from a lack of consciousness on the importance to build a sound theoretical framework together with a carefulanalysis of user’s requirement; or partly resulting from evidence: land use data is difficult to collect into global data sets orlastly partly resulting from a lack of conceptualization of the land use domain.
In terms of harmonization of data sets, one element is fundamental in order to structure the information: the question ofclassification. Since classifications are not built in order to create an aesthetic effect, there is a close development
between the development of scientific concepts and classifications. Classification systems are always the mirrors of theconceptualization of the domain to be investigated.
Objective of this paper will be therefore to discuss the main paths or the necessary prerequisites to propose classifications
for land use answering user’s requirements and taking into account capabilities of available data collection tools through anappropriate theoretical background. The presentation of the necessary elements to be taken into account for structuring land
use information will follow the general concepts of classification systems embracing (adapted from Sutcliffe 1993 andHull 1998):(Chapter 2) the demarcation of a universe of discourse (what is land use)
(Chapter 3) the establishment of a classification of all land use objects in this universe(Chapter 4) a system for naming the groups1 linked to the structure of the classification so-established(Chapter 5) the procedures for allocating any land use object to one and only one established group.

1
Words like family, kind, membership, category, cluster, set, collection, class, group have in common that they are all members of the “genus” of class-ofobjects words while at the same time they differ one from another according to their species within that genus. (Sutcliffe). They may be utilized indifferently
without any impact on the system itself

2 The demarcation of a universe of discourse (what is land use)
The demarcation of the universe of discourse (in our case the domain of investigation) implies a clear identification oftheoretical commitments (what is land use) and what are the subsequent land use objects.
Any given portion (Duhamel 1995) of Earth’s surface can be observed and described in various ways, which may
interact according to the distance separating the observer from the observation; the instruments utilized (human eye,aerial photography, satellite sensors); the way the observation is stored into information systems (lists, nomenclatures,
attributes); the observation units chosen (catchment’s areas, parcels, raster cells, specific objects) and the period of
observation. When observing a portion of Earth’s surface, several questions may arise: what is this, what is it for, why
and how is it like this, was it like this before, will it stay like this in the future etc…? The two first questions (what isthis, what is this for) are of interest since they are corresponding to two “easy ways” for observing Earth: land coverand ‘functional” land use.
2.1 LandLand is a term widely used throughout the world but definitions are not frequently given. The interdepartmental working
group on land use planning (IDWG-LUP) at FAO proposed in 1994 the following definition: “A delineable area of theearth's terrestrial surface, embracing all attributes of the biosphere immediately above or below this surface, including
those of the near surface climate, the soil and terrain forms, the surface hydrology including shallow lakes, rivers, marshes
and swamps, the near-surface sedimentary layers and associated groundwater and geohydrological reserves, the plant andanimal populations, the human settlement pattern and physical results of past and present human activity (terracing, water
storage or drainage structures, roads, buildings, etc.)”.
This definition has immediate practical consequences on land use: from a pragmatic point of view and considering theimportance and the significance in terms of economic value of multiple-use aspects for "urban" areas, the understanding ofland should also embrace uses above and below the ground level. Particular problems may be found with mine deposits,
subways beneath urban areas, water resources, mushroom beds, areas used for oil extraction. In order to limit potential
problems it has been recommended (Duhamel 1998) to restrict its application to reasonable cases. It is suggested to allow"urban" uses above and below the ground level (case of buildings with shops at ground level, flats and offices above, carparks below). For specific cases such as extraction activities of natural resources, it has been suggested to restrict theextension of such uses to their physical impact at ground level (oil well, entrance of mushroom beds etc.). This principle
may be generalized for any kind of uses. Given the context of global information systems and the difficulty to establish
clear thresholds between land and water (particularly for wetlands), it is also recommended to extend the concept of land toinland water areas and tidal flats.
Distinguishing the concepts of land cover and land use is not at all a complex task: however many information systems
are confusing the two dimensions as if the same patient (land) would have two simultaneous disturbances which could
not be separated. A simple solution -already described- (Duhamel 1995) is to look at the material the objects are
made of (land cover) and the function they serve (land use). In order to base the discussion on agreed concepts, ashort reminder on the concepts of land cover and land use is given.
2.2 Land coverLand cover, as previously mentioned, corresponds to a physical description of land. The definition proposed, adaptedfrom (Di Gregorio & Jansen 1997) is: the observed physical cover of the earth's surface This description enables
various biophysical categories to be distinguished - basically, areas of vegetation (trees, bushes, fields, lawns), bare soil
and hard surfaces (rocks, buildings) and wet areas/bodies of water (sheets of water and watercourses, wetlands). In
most cases, land cover is directly detectable by human observation or less directly from remote sensing. A very usefulcomparison can be made with approaches characterizing commodities where objects are just described according to thematerial they are made of, corresponding to the physical aspect of land Cover,
2.3 Land use
For land use, various approaches are proposed into the literature. Two main “schools” may be distinguished (Duhamel1998). The first one, termed functional corresponds to the description of land in terms of its socio-economic purpose(agricultural, residential, forestry etc...): this will be the approach referred as land use in this paper. This approach isextremely easy to handle since it has direct correspondences into widely utilized statistical nomenclatures such as ISIC.
The second one, termed sequential (Stomph & Fresco 1991) has been particularly developed for agricultural purposes: aseries of operations on land, carried out by humans, with the intention to obtain products and/or benefits through usingland resources. Without entering into the details of this approach, the sequential approach should be only treated if
necessary (and possible) as a modular approach below the agricultural function
2.4 Land cover vs Land use
Distinguishing between land cover and land use is in principle quite easy but not often reflected into the existing
systems. One major issue to be dealt with is that land cover and land use are often intertwined. It is sometimes possible(Duhamel 1998) to determine the functional aspect (land use) from the physical aspect (land cover). A parcel of land
covered by a field of wheat can reasonably be associated with agricultural use. Similarly, it is possible to infer physicalaspects from functional aspects. An area used for forestry purpose could reasonably be assumed to correspond to a“tree”-type class. However one physical class may correspond to a large number of functional classes. For example,
grassland may correspond to a lawn in a town, an airport runway, a sown meadow, rough pasture, a golf course—oreven a church roof in Iceland. Conversely, one and the same functional class may correspond to several physicalclasses: for example, a residential area consists of lawns, buildings, tarmac roads, trees, and bare soil.
Contrary to land cover, land use is difficult to "observe". For example, it is often difficult to decide if grasslands are used or
not for agricultural purposes. The information coming from the source of observation may not be sufficient and may
require additional information or existing maps/statistics or characteristics on the ground indicating the use (in the example
of grasslands mentioned above: the presence or absence of animals, the presence or absence of elements indicating thatanimals were grazing). Inference from land cover is therefore a possible (but not sufficient) solution when- global data setson land cover may be populated easily and regularly. Conversely land use data sets require enormous investments whencomplete coverage is desired.
Even if it is difficult to justify it when analyzing both user needs and the possible costs of simultaneously acquiring,using, and managing data obtained through separate approaches, there are strong methodological and technicalarguments in favor of systematic separation of land cover and land use. The importance of the knowledge for the two
aspects may be illustrated with one example (Table 1) adapted from Lund 1998.
Table 1: Observation of a portion of land at 4 different dates
Table 1LAND COVER
LAND USE

Date1
TREES
(Chestnut trees)
Forestry use(Timber)

Date2
TREES
(Chestnut trees)
Agricultural use(Chestnut
production)

Date3
TREES
(Chestnut trees)
Agricultural use(Grazing area)

Date4
TREES
(Chestnut trees)
Forestry use(Timber)

The observation of the land cover dimension could lead to a unique interpretation of no visible change throughout thewhole period considered. Conversely, the observation of land use sequences could be rapidly interpreted as adeforestation sequence between Date 1 and Date 2 and afforestation between Date 3 and Date 4. It is now easy tounderstand the difficulties to infer land use from land cover in some cases. With a combined system registering
separately the two dimensions, the simultaneous recording of both land cover and land use hampers any false
interpretation. It also brings a high richness in terms of information content.
2.5 Land use objects and observation units
As geographical information, land use data has also to deal with basic theoretical issues such as the question of land use
objects, land use observation units and the influence of scale on observation and data collection.
The meaning of land use object is a complex problem: the description (categorical classification) of a part of the earth’s
surface pre-supposes that the area is clearly defined in space. Objects are easily identifiable if the spaces are plots offarmland or built-up areas, as they have physical boundaries (enclosures, hedges, fences, clear division between crops).
One of the typical problems encountered is the problem of mixed objects. Three types of mixtures exist on land:

Mixtures “by juxtaposition” where many objects may be observed simultaneously within a portion ofland: for example mixed crops on a same parcel or orchards utilized for grazing. In general, a partition
into basic objects is recommended or, if not appropriate, the application of a pro-rata rule as applied inagricultural statistics for intercropping. Unfortunately the general tendency is to create mixed classes,
leading in general to a profusion of categories.
Mixtures “by superposition” are to be found for example in buildings, where floors could have a
different socio-economic purpose: a dominant use approach is recommended.



Temporal mixtures when for a specific period of observation (agricultural campaign) there is a
succession of crops or succession of bare soils and crops on the same parcel: changes are to e observedin terms of land cover, less frequently in terms of land use.

It is also important to address the issue of observation units. One observation unit may be an aggregate of objects.
Conversely, an object could be divided into various observation units. For example, an observation unit can be a parcel
composed of two different crops (mixture by juxtaposition). A big parcel of wheat may be observed through several
units of observation (points in a grid system, cells in a raster system). The choice of observation units is important since
it has impacts on the way the information systems will be built: impacts on the reproducibility (and therefore thereliability) of the observation over time.
2.6 Collecting data on land use
Two main approaches are considered for land use: geographic approaches (mapping) and statistical approaches (sample
surveys)Through mapping approaches, the coverage of the territory is exhaustive. Main source of information is generally
photo-interpretation or processing of aerial photographs and earth observation data together with ground truth. Choiceof observation units is driven by technical constraints: the scale of observation (you observe what you can) and thescale of restitution of information (you map what you can) leading to the concept of minimum mapable area. According
to scale, identification of objects will not be the same: what you observe and map at 1:10.000, is different from what
you observe and map at 1:250,000. There is no doubt (Eiden 2001) that remote sensing data represent a data source
which contributes to a deeper understanding of processes on the earth's surface and enables map production up to scalesof 1:5.000.. It is still important to remind that remote sensing images capture only land cover, i.e. the physical features.
Although one can interfere from some land cover categories to land use, remote sensing images are not really suitable
for this aim.
Suitable alternative approaches to acquire land use data are area frame surveys. Statistical surveys provide
information on samples from a population. Sampling theory is applied so that inference about the whole area can be
made. The sample is made of a set of area units: the statistical units may be of different size (points, areas) or different
shape (squares, circles). One significant advantage of sampling approaches is the possibility of being independent of
the difficult problem of observation units: the population (whole territory) may be divided up into a grid on asystematic basis; each area unit thus obtained being a statistical unit (observation unit) of the same size and same shape.
These approaches are commonly used in agricultural statistics and also widely applied in ecological monitoring surveys(e.g. Countryside Survey in United Kingdom).
A good illustration of the capabilities of area frame sampling surveys is given by the LUCAS project launched in 2000
by the Directorate General of Agriculture of the European Commission and Eurostat (the Statistical Office of theEuropean Communities). Objectives of the system are to carry out an area-frame survey based on points in order tocollect information on land cover and land use, to establish a common sampling base among Member States and toextend the scope of the survey from the normal agricultural domain to aspects relating to the environment, multi-use,landscape and sustainable development. A first survey was conducted in the 15 EU member States in 2001-2002,
followed by a new survey in 2003, including three new EU Accessing Countries. In 2006, the exercise will be repeatedin 23 countries with a revised methodology (initially an unstratified two phase design, now a two-phase sampling of
unclustered points with stratification):

The survey will be based on a systematic sample (base sample) of around 4.000.000 points defined througha 1-km grid which is covering the entire European Union, the regular grid being intersected withboundaries in order to extract points on the EU territory and allocate them by country.



The LUCAS master sample is a subset of the base sample. It corresponds to a 2-km grid and consists ofaround 1,000,000 points.



Each point of the master sample is photo-interpreted (recent ortho-photos or, when ortho-photos were notavailable, on satellite imagery) in order to obtain a stratification of land cover

From the stratified master sample, a sub-sample of points is extracted to be classified by field visit
according to a full land cover and land use classification system (around 250.000 points – field sample).



Surveyors will be collecting the land cover and land use information on the field sample. In addition, on a
250 m straight line (= transect) in an easterly direction from the point, land cover changes will be registered
and landscape pictures taken from each point.

In addition to the field work, a separate survey (“double-blind survey”) on 5% of points will be carried out
by independent surveyors.

Due to the fact that only parts of the territory are observed (in that specific case, just points), data can be gathered at avery detailed level and results can be extrapolated to larger reference zones (EU level or national level for big
countries). Moreover, the implementation of such area frame sampling is relatively simple and results may be madeavailable rapidly after the survey.
Compared to exhaustive mapping where, in principle, only land cover may be directly collected, sample surveys may
provide rapid results with accuracy estimates on both land cover and land Use, thus enabling periodic (annual orseasonal) surveys. Another advantage is the possibility to utilize the sample base for conducting specific surveys orincorporating specific issues such as noise, erosion, landscape. .
2.7 Additional prerequisites before classifying land use
Independently of the general rules that should govern the construction of classification systems (see chapter 3), specificconstraints due to the intrinsic nature of geographic data are to be taken into account. Two principles of consistency
have been identified (Duhamel 1995): spatial consistency (classification systems should be designed in a way to allow
compatibility of results between various geographical locations), temporal consistency (observation should be recorded
at time of observation2 in order to be able to measure changes between two stocks, the classification system should
avoid classes taking into account previous or future land uses). It is also important to ensure the independence of thesystem from the data collection tools (a lot of systems have been built on the basis of the technical capacities of thetools, e.g. remote sensing)
3. The establishment of a classification of all the land use objects
Since there is generally no natural or best classification of a set of objects as such (Spärck Jones 1970), the elaborationof a classification requires either formal criteria of goodness of fit, or, if a classification is required for a purpose, aprecise statement of that purpose. There is no easy way to decide whether an attribute is the most important criteria toutilize when classifying objects: it simply depends of the purpose of the classification. It is also important to accept(Hjörland 2002 ) working at the level of preciseness that we have access to, i.e. this is the condition under which wehave to work and we cannot change this by requiring something that is not available to us. This does not howeverchange the fundamental insight (Spärck Jones 1970): we have to base the classifications on knowledge about their
purposes, to satisfy both the internal criteria of classification and the external requirements of the users.
The establishment of a classification of all the land use objects has to result into the organisation of sub-classes of thedomain through a (hierarchical) series of nested categories that have been arranged to show relationships to one another.
Significant attributes of land use objects should be chosen taking into account that any set of land use objects may be
unfortunately classified in an unlimited number of ways according to our language and mental conceptual structures.
Any proposed land use classification system is conceptual, describing selected aspects of the real world: the same
reality might well be described according to several classifications (Duprat 1972). Groups of land use objects havetherefore to be described through the selection of shared characteristics that make the members of each group similar to
one another and unlike members of other groups. Each of the successive partitions means that “objective”
characteristics have been taken into consideration, and implies a conscious choice. Criteria of land use classification are
therefore deeply related to relevance: it is not the inherent qualities of the land use objects that determine the criteria ofclassification.
It is also important to remind that classification is both an information-losing process and an information gaining
process (Spärck Jones, 1970). Information is lost because the particular relationships among the objects are ignored indetail but gains may be found in the sense that it is made explicit that some objects are alike. Any member of a classcan be treated as if it possessed some or all properties of the group. The fact that an object is a member of a classenables to make an inference about it. The purpose for which the classification is designed necessarily shapes its
structure and content, this is why each user, in general, builds an individual classification adapted to specific needs:
spontaneous development of classifications therefore leads inevitably to incompatibility: this is frequent for land use
2This is a strong argument against sequential approaches of land use requiring a window of observation difficult to implement and to harmonizeclassifications. Two main schools of classification may be described: the classical theory, based on properties (datingback to Aristotle) and the prototype theory (Kühn 1970)
3.1 Classifications based on properties
The classical theory assumes that land use objects would be allocated according to a set of individually necessary andjointly sufficient properties. An important consequence is that although a class is agreed upon, the identification ofclass and non-class members need not always to be based on the same characteristics or set of characteristics.
Another important point is also to consider that links and relations between objects and/or groups are linked through acomplex net of relationships, likenesses, affinities or neighborhoods. In general, this net is transformed into a tree, somelinks considered as important or significant have to be chosen according to a particular point of view. These links or
relations are of different types in land cover and land use (Wüster 1971): logical links (industrial: chemical, irontransformation, car industry …); ontological links = whole and part (residential area - house); co-ordination links(maize-wheat); time links (bare soil - crop). These links and relations may be combined and presented from broader tonarrower concepts. To a certain extent links between land cover are mainly ontological (whole-and-part: forest > stand> tree) while land use links are mainly logical (industrial: chemical, iron transformation etc...). Two main approaches
may be highlighted as part of the classical approach: top-down tree approaches and classifier approaches.
The most known solution is a top-down tree. Many nomenclatures are built following this a-priori approach: thedomain of study is divided into categories and sub-categories, according to certain objectives and purposes. The
method has strong disadvantages: the tree is a rigid structure leading to difficulties if modifications are to be madewithout alteration to the former structure of information. The only possibility for modifying is creating more detailed
levels on the basis of the categories already existing (in this case when the existing tree is no more adapted to the needs,
additional levels do not solve the problem). Other disadvantages are the unequalled development of the sectors and theexaggerated importance of aggregated concepts created from the first partition
Another approach is to develop a tool consisting of a combinatory system applied on a common basis. This basis isjust a set of necessary characteristics to describe the objects. These characteristics, once identified and defined
uniformly (Ekholm 1996) allow, through combinations, the definition of the objects and the grouping of the objects for
all possible systems. Characters may have different “expressions”: character states and may be used in the decision ruleof classifying objects into a given classification system In order to avoid the problem of hierarchy of classifiers, it isrecommended to develop, instead of hierarchical and rigid schemes, a “flat” combinatory system (or faceted system)
applied on a common basis of classifiers of equal weight, this basis just being a set of necessary characteristics to thedescription of the objects (Duhamel 1995).
3.2 Classifications based on prototypes
The prototype theory proposes that land use objects in a similarity class need bear no more than a family resemblance
to their fellows (Kühn 1970), and hence that the concepts corresponding to these similarity classes are family
resemblance concepts. This method aims at bringing out empirically from different existing classification systems some
prototypes/kernels which will set up the beginning of main categories. It implies in general the gathering of existingclassification systems on the domains to be considered and their confrontation and analysis in order to answer thedifferent user requirements. This method has the objective to compare existing systems assuming that major aggregates
could be common for many users or approaches. General aggregates, commonly accepted through various
nomenclatures, may constitute the core (kernels/prototypes) and items which may be allocated to different aggregatesaccording to the different classification systems constitute the margins. The number of nomenclatures to consider ishowever to be limited since the more classifications are taken into account, the fewer kernels will be identified, since
the probability of discovering objects not belonging to the core increases as different points of view are encountered.
4. The system for naming and describing land use groups
A system for naming and describing land use groups linked to the structure of the classification so-established has also
to be clearly established. Six main basic components have been described (Duhamel 2001):

principle of completeness, a class must be found for any object to be classified: the union of all classes
in the first grouping must equal the original collection)
absence of overlap: anything can be classified in only one class, all classes of the same rank must be
pair-wise disjoint and an object may not belong to more than one class of the same rank
general rules of interpretation (cases of overlap, ontological or logical relations between objects,
problems of mixtures)
rules concerning the elaboration of headings and labels (kernel method of description, definitions byextension and intension, boundaries problems, cross-references of exclusions and inclusions)elaboration of index of objects
principles of coding

An example of a system for naming and describing groups may be found into the LUCAS system proposed by theEuropean Commission. An example of the textual description of a land cover class and its links with land use is given
herewith. Principles of completeness and absence of overlap are easily met through definitions by intension (the textual
definition of the class) and by extension (list of possible objects being part of the class), including boundaries rules(inclusions and exclusions), coding and relationships between that land cover class and land uses. Pictures ofprototypes of the class are also provided to the surveyor in order to facilitate the identification and the allocation to theclass.

Extract from LUCAS land cover – land use nomenclature
D

SHRUBLAND
Areas dominated (more than 20% of the surface) by shrubs and low woody plants generally below 5 meters ofheight. It may include sparsely occurring trees within a limit of a tree-crown area density of 10%

D01 Shrubland with sparse tree cover
Areas dominated (more than 20% of the surface) by shrubs and low woody plants generally below 5 meters ofheight, including sparsely occurring trees within a limit of a tree-crown area density between 5 and 10 %this class includes:
Scrub land (pines, rhododendrons, maquis, matorral and deciduous thickets)
Heathland with gorse, heather or broomthis class excludes:
Shrubland where tree cover is more than 10% (C)Shrub-like crops: orchards, vineyards in production (B7-B8))~

Principles of observation
Extended window of observation¾½

Links with Land Use (U)D01 ¾ U11
Agricultural use: grazing
D01 ¾ U12
Forestry (Wood production)
D01 ¾ U36x
Leisure areas
D01 ¾ U40 Wooded areas not utilized
D01 ¾ U50 Wetland

5. The procedures for allocating any land use object to a classThis corresponds to the procedures for allocating any land use object to one and only one previously classified andnamed class. The approaches for allocating any land use object to a group are directly linked with the types ofclassification system built. In the case of classifier approaches, objects will be systematically matched with candidateclasses through a predefined set of inherent traits utilizing for example a decision tree. In other cases (top-down trees orprototypes) the process is theoretically more empirical since the link between the object and the class has to deal withan intermediate level: the concept. This process, described theoretically through the so-called meaning triangle ofRichards-Ogden (Duhamel 1996), aims at establishing a link between the object and the class: this is why it isfundamental to have made available a textual part of the classification following the principles described into theprevious chapter (in particular the definitions by intension and extension). Fortunately, in many cases, an object may be
immediately identified and the allocation to a class may be done directly or through the utilization of an index ofobjects (a good example of index may be found into the UK Land use database).
6. Conclusions
Developing a land use classification system needs therefore (it was the objective of this paper) to be systematic:

the strict application of a series of precise procedures should ensure -through an appropriate
framework- that any land use objects may be classified into an appropriate class and should allow a
reduction of the initial population of objects into a set of nested categories.

However two pragmatic issues remain to be dealt with:

the purpose of the classification
it is a strong guideline for the construction of a classification system: user’s needs have to be strongly
reflected (sometimes a real challenge when policy and decision makers have to express their needs interms of data at various geographic levels (Croi 2001) and existing classification and information
systems should be taken into account.



the scarce opportunities to collect exhaustive data on land use on large areas
the increasing availability of remote sensing data has unfortunately low concrete impact on land use
data availability: if inference from land cover is sometimes possible, a direct observation on the groundremains invaluable. This is one of the most interesting aspects (and paradox) of land use information:
making available detailed and accurate information on land use is only possible when limited portionsof earth can be observed (statistical approaches); conversely detailed and exhaustive information onland cover is easily available at global level (mapping approaches).

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Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan

1

Harmonisation of land-use class sets to facilitate compatibility and comparabilityof data across space and timeLouisa J.M. Jansen
Land/natural resources consultant, Via Girolamo Dandini 21, 00154 Rome, Italy
Tel.: +39-065757719, E-mail: Louisa.Jansen@tin.it

Abstract
Harmonisation of land-use class sets should consider both space and time, as the objective should include
harmonisation of land-use change in order to analyse environmental processes and problems. Existing
systems make only a limited contribution to data harmonisation and data standardisation as they containmany inconsistencies in the adherence to the fundamental principles of classification and use a variety ofbasic units of measurement, in addition to the lack of agreement on the definition of land use. Though themeaning of land use varies among sectors, the set of key parameters used is limited. Analysis of major
existing class sets reveals that two parameters suffice: function that describes land use in an economic
context and activity that is defined as the combination of actions resulting in a certain type of product.
An example of land-use harmonisation in Albania illustrates how the creation of a reference system based
upon the principles of classification, using the synergy between classification and information technology
concepts and based upon the function and activity parameters can facilitate harmonisation between landuse class sets in parallel to achieving land-use change harmonisation. Comparison with remotely sensed
based land-cover related land-use data illustrates that land use contains many aspects that go beyond land
cover. Land-use change operates over a range of scales in space and time and analysis at different levels ofdata is necessary in order to detect the patterns at the different data levels.
The way forward to harmonisation of land-use class sets is adoption of a parametric approach. Further
research is necessary to define quantitative measures for the harmonisation result at class level andbetween two class sets.
Keywords: Land use; Land-use change; Classification; Data harmonisation; Data standardisation.
1. Introduction
Land-use change knowledge has become increasingly important in order to analyse environmental
processes and problems, such as uncontrolled urban development, deteriorating environmental quality, lossof prime agricultural lands, expansion of agriculture into areas that comprise either fragile ecosystems (e.g.,
wetlands and steep lands) or a high value with respect to biodiversity (e.g., humid tropical forests) or areas
with a high incidence of diseases (e.g., malaria, river blindness). These processes and problems must be
understood if living conditions and standards are to be improved or maintained at current levels (Anderson
et al., 1976; Dumanski and Pieri, 2000). Land-use change, as one of the main driving forces of (global)
environmental change, is central to sustainable development (Meyer and Turner, 1994; Walker et al., 1997;
Walker, 1998; Lambin et al., 2000). It is, therefore, essential to have detailed and in-depth knowledge of
not only land-use processes and problems but also of land uses. Such information is required at multiple
scales as support at the local, regional, state and cross-border co-operation levels.
Nowadays emphasis is shifting from static land-use data collection towards more dynamic environmental
modelling in order to understand the past, monitor the present situation and to predict future trajectories(Lambin et al., 2000; McConnell and Moran, 2001; Dolman et al., 2003). This implies the importance tore-examine existing land-use data sets and attempt to harmonise them to make comparisons within andbetween countries and to compile time series with which to analyse the change dynamics and detect trends.
Data harmonisation will be required as it is unrealistic to work only with new standardised class sets, with

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan2

major financial and intellectual investments having been made in existing class sets and survey
programmes that use established methods of classification (Wyatt et al., 1997; Wyatt and Gerard, 2001).
The Land Use Cover Change (LUCC) programme element of the International Geosphere-Biosphere
Programme (IGBP) and International Human Dimensions Programme on Global Change (IHDP)
mentioned in their science/research plan (Turner et al., 1995) that classification and data are a cross-cutting
integrating activity for which data availability and data quality need to be analysed and a classification
structure suitable for various requirements need to be devised. In addition, McConnell and Moran (2001)
highlight two key issues:• Both space and time considerations are essential for making land-use data compatible and hencecomparable.
• Harmonisation of land-use classifications includes harmonisation of land-use change, as we need tounderstand land-use change processes for decision making as explained above.
Therefore, any discussion on harmonisation of land-use class sets should address not only existing or
proposed classifications but also data quality, space and time dimensions and land-use change.
In this paper the harmonisation of land-use class sets, or correspondence between land-use class sets, willemphasize the semantic aspect of class sets consisting of the class definitions as these imply the parameters
used in the formation of classes. Class descriptions contribute to the definition of boundary conditions thatshould be applied unequivocally and consistently when establishing correspondence between classes
belonging to different class sets in order to avoid errors in data interpretation. The level of confidence withwhich such class correspondence is established is highest when the same parameters have been applied;differences in the applied parameters, and thus in boundary conditions, produce lower confidence levels.
Complete correspondence is not always obtainable when harmonising data, thus there is a need to establish
rules in order to reach the highest level of confidence possible.
2. Definition of the domain of interest
2.1 Land use
An international agreement on the definition and classification of land use does not exist though many
attempts have been made (Guttenberg, 1965; IGU, 1976; Kostrowicki, 1977 and 1980; UNEP/FAO, 1994;
Baulies and Szejwach, 1998; Duhamel, 1998; McConnell and Moran, 2001; Jansen and Di Gregorio,
2002). Consequently, a common terminology is lacking. The term “land use” has different meanings
across disciplines and, as a result, implies a set of mostly unidentified parameters. These different
perspectives on land use are, however, all valid. In the context of this paper land use is defined as “thetype of human activity taking place at or near the surface” (Cihlar and Jansen, 2001).
Land use is determined by natural, economic, institutional, cultural and legal factors. In general, possible
land uses are limited by biophysical constraints. These include climate, topography, soils and thegeological substrate, presence or availability of water and the type of vegetation. Agricultural practices
differ from one region to another and different types of land uses are practised on the same type of land indifferent areas, depending on the history, local traditions and way of life, apart from the biophysicalconstraints (Cihlar and Jansen, 2001). The location of an area with respect to other types of land uses, suchas residential and industrial areas, is also an important factor (e.g., the location of a commune close to main
urban centres and its proximity to, for example, an airport) (Jansen, 2003). Economic incentives as part ofpolicy (e.g., the EU Common Agricultural Policy) can affect land-use patterns.
2.2 Classification

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Classification is defined as “the ordering or arrangement of objects into groups or sets on the basis ofrelationships. These relationships can be based upon observable or inferred properties” (Sokal, 1974).
Thus, classification denotes a process. The term “classification” embodies two meanings (Duhamel, 1998):(1) establishment of groupings of all objects in a given field (according to Sokal’s definition); and (2) using
the established groupings in order to decide the membership status of other objects (e.g., in remote sensing
the imagery is used for the identification process of objects). The term “classification system” includes not
only the definition of the domain investigated and the classification process of the objects, but also a
considered set of principles, or methodology, to assign individual land uses to land-use classes and these
are arranged according to a set of adopted rules. Furthermore, it includes information for evaluating thereliability of assignment of objects to the various classes. Not only the quality of the data should be
documented, but also the quality of the harmonisation.
Classifying all the objects in the domain of interest requires some basic principles, which have been
described in detail elsewhere (e.g., EUROSTAT, 1991; UNEP/FAO, 1994; FAO, 1997; LANES, 1998;
Duhamel, 1998; Jansen and Di Gregorio, 1998; Di Gregorio and Jansen, 2000). The key principles are:• Completeness and absence of overlap of classes;• Existence of definitions and explanatory notes;• Existence of an index of objects;• Spatial and temporal consistency; and• Independence from scale and data collection tools.
Since many existing classifications and map legends do adhere only in part to these principles, as will bedemonstrated later on, the use of the term “class sets” has been preferred in this paper.
2.3 Data standardisation and data harmonisation
Data standardisation is defined as “the use of a single standard basis for classification of a specificsubject”, whereas data harmonisation is defined as “the intercomparison of data collected or organised
using different classifications dealing with the same subject matter” (McConnell and Moran, 2001). The
understanding between data standardisation and data harmonisation is fundamental:• Data standardisation will allow direct comparison of class sets but would disregard the financial andintellectual investments made in established methods and data sets; and• Data harmonisation will allow countries and institutions to continue to use existing data systems andclassifications but when definitions are imprecise, ambiguous or absent problems arise. Moreover, if
many class sets are involved the number of pair-wise class combinations becomes excessive because
comparison of n data sets requires n(n-1)/2 comparisons to be made.
The problem of excessive pair-wise class combinations can be resolved by developing a common reference
system. Correspondence between classes may then be inferred from the explicit record of how each classrelates to the reference system. The advantage is that translation of class sets into the reference system
would be required just once. In addition, such a reference system would be well suited to form the basis fora generally accepted classification that could be promoted as future standard. At the same time a reference
system could form the sound basis for a data model for use in geo-databases needed to manage informationon land (Wyatt et al., 1993; McConnell and Moran, 2001; Jansen et al., 2005b).
3. Previous attempts at land-use harmonisation and standardisation
An important effort for establishment of an international recognised statistical system was made by theUnited Nations Statistical Division with the publication of the International Standard Classification of all
Economic Activities (ISIC) in 1948 with three major revisions in 1958, 1968 and 1989 (UN, 1989).

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The International Geographic Union established the Commission on World Land-Use Survey in 1949
(IGU, 1976). A legend for a world map at a scale of 1:1,000,000 was developed combining land-covercharacteristics with function. This scale was quickly abandoned in favour of national land-use surveys atmuch larger scales in Great Britain, Italy, Japan, Malaysia, Poland and Sri Lanka. Furthermore, the IGU
established the Commission on Agricultural Typology that tried in the period 1964-1976 to produce asystem dedicated to agriculture. The work of this Commission was discontinued after 1976 though some ofits members continued and completed the Types of Agriculture Map of Europe in 1983 (Kostrowicki, 1977,
1982 and 1984). Contacts with FAO were made in the early 1970s when the interest in a world agriculturalclassification increased due to the growing food crisis. In 1983, Kostrowicki proposed a land-use
classification system, including non-agricultural land uses, which was a prime mover behind a proposal to
UNESCO in 1987 for a world land-use map (Kostrowicki, 1980, 1983a and 1983b). However, nothingcame of it.
The American Society of Planning Officials identified different dimensions of land use at an early stage(Guttenberg, 1959 and 1965). Guttenberg (1965) also identified different “modes” for classification:referential, appraisive and prescriptive (Figure 1). However, most of the existing classifications remain in
the referential mode, as it is the most neutral one, and frequently deal with observable characteristics, suchas land cover and actual activity, and derived characteristics, such as function and legal aspects. The
appraisive mode casts land use in the light of social interests and values that differ according to local
prevailing customs.
Insert Figure 1.
In the period 1969-1971, a study was made by the Commission on Geographic Applications of Remote
Sensing of the Association of American Geographers. The results were published in 1971 by Anderson
and further elaborated in 1976 (Anderson et al., 1976). This remote sensing driven classification was based
upon the World Land-Use Survey system (Paludan, 1976) and evolved in the period of the first
LANDSAT launch. The system represents the traditional subdivision in land-use terminology for built-up
and agricultural lands, and land-cover terminology for natural vegetation, water, snow and ice.
The Economic Commission for Europe of the United Nations proposed a Standard International
Classification of Land Use that would allow comparison of national land-use statistics (ECE-UN, 1989).
However, this is a mixture of land-cover and land-use terminology and the classes are not exhaustive.
The interest in reviewing and updating the U.S. Standard Land-Use Coding Manual (Urban Renewal
Administration, 1965) led to the initiative of the Land-Based Classification Standards (LBCS) project, coordinated by the Research Department of the American Planning Association in corporation with several
U.S. departments and agencies (APA, 1999). This effort is based upon recognition of various categories inwhich land use is traditionally classified: activity, function, structure-type, site development character andownership. These categories have each there own set of characteristics and classification takes place acrossthese multiple categories. The effort addresses many of the problems that previous systems had but
remains at the level of a system divided into several descriptive classes. The choice of categories may bedisputed.
The Land Utilization Type (LUT), as developed by FAO in the Framework for Land Evaluation (FAO,
1976) and in the Guidelines for Land-Use Planning (FAO, 1984), has been widely used as a generalised
description of agricultural land use in terms of inputs, two levels only, and outputs for which suitability
could only be defined imprecisely. This concept was based upon a shortened list of the land-use variables
identified by the IGU, the difference being the application of a qualitative land-use description in the
Framework. The concept was too imprecise to be applied at farm level or for production planning, it
contained only one (plot) level and reflected more a potential than an actual land-use class, while being
qualitative in nature. One should note, though, that this concept was adapted to the requirements of a landevaluation system and as such, it has been used in numerous regional or district crop suitability, capability

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan5and pre-feasibility studies (pers. comm. F.O. Nachtergaele, FAO). The matching of potential agriculturalland uses with the land through a series of decisions and ratings yielded into a quite complicated expert
system, thus the methodology became the reverse of being transparent. In the late 1980s, at FAO attempts
were undertaken to improve the LUT concept. The matching of precisely defined qualities andcharacteristics of the land unit with broadly, usually qualitative LUTs resulted in the limited use of thequantitative land resource data. A series of FAO commissioned studies was initiated as well as
collaboration with UNEP (Remmelzwaal, 1989; Adamec, 1992; Mücher et al., 1993; UNEP/FAO, 1994;
ITC/FAO/WAU, 1996; Wyatt et al., 1998). Adamec (1992) was the first to define the land-use type as “aseries (or sequence) of operations (or activities) carried out (or undertaken) to produce (or harvest)
products or benefits for consumption or sale” but he recognised at the same time the difficulty to apply
individual operations or their sequence and dates of execution as parameters plus the inputs already
employed. Nonetheless, this definition was adopted by the ITC/FAO/WAU effort resulting in the LandUse Database (1996). In this database, the primarily agricultural land-use class is independent from scale,the basic unit being the plot. The database permits user-defined hierarchical structures, comparison, and anumber of standardised parameters are included. However, the database allows users to add or change
parameters and definitions along with the order of parameters to fit a specific aim. If the objective ofclassification is a contribution to data harmonisation and data standardisation, another approach should beselected. The study by Wyatt et al. (1998) was an effort at outlining the parameters to be used for globally
applicable definition of land uses. The idea of analysis of existing systems in order to extract the set ofparameters to be used for building a reference system would have been valid if existing classifications
were used. However, the analysis was based upon a number of legends, hence indicating gaps in thecompleteness of land-use classes and parameters used. Duhamel (1998) clearly identified that the abovementioned studies and some selected national class sets suffer from the lack of systematic analysis of whatdefines land use, in addition to the insufficient adherence to the fundamental principles of classification
mentioned earlier (Table 1).
Insert Table 1.
The current view of the way forward is to promote a parametric approach to classification. The explicit
use of quantitative parameters will facilitate harmonisation between class sets if the same set of parameters
is used. In many existing class sets one will find (Jansen and Di Gregorio, 2002):• Inconsistent application of land-cover or land-use parameters, i.e. land-cover parameters are being
used to distinguish land uses and vice versa;• Inconsistent use of parameters at same level of classification, i.e. in one category a certain parameteris used and in a related category a completely different one is used;• Use of different parameters between classes, i.e. for subdivision of a class into three subclasses morethan one parameter is used; and• Use of non-inherent characteristics, i.e. using characteristics that are not related to the subject but
describe, for instance, its environment (e.g., climate, physiography, altitude from a DEM, etc.).
Although the underlying reasons for making subdivisions based upon different parameters may be valid,
they show that parameters do not always have the same weight in making distinctions. Such decisions areusually not well-documented in the accompanying reports of the class sets. This hampers harmonisation ofclass sets, as re-interpretations of not well-documented decisions are likely to differ between persons within
one country and between countries. The actual class sets make an insufficient contribution to data
harmonisation and standardisation. Efforts to increase harmonisation and standardisation do not necessarily
lead to loss of pragmatic decisions on the choice of parameters as the focus should be on the logical andfunctional consistent application of a set of inherent land-use parameters that are clearly separated from noninherent parameters (Jansen and Di Gregorio, 2002; Jansen, 2003).
However, if an international agreement on the definition of land uses will not be reached and a common
terminology found, data harmonisation will remain an impossible task, let alone attempting data

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standardisation. It is therefore important to underline commonalities in the existing approaches and identify
a set of commonly used parameters in the class sets.
4. Major parameters for harmonisation of class sets
An analysis of several existing class sets shows that statistical data are often collected on the basis of
economic purpose and/or activities (ECE-UN, 1989; UN, 1989; UN, 1998), natural resources related
disciplines tend to amalgamate land-cover characteristics with activity or function (Anderson, 1976; IGU,
1976; CEC, 1995; FAO, 1998), while legal aspects are described by land rights or patents and other related
legal conditions (FAO, 1998; UN, 1998). Table 2 provides an overview of the most commonly used major
parameters applied by various international systems. “Function” refers to economic purpose, “activity”
refers to a process resulting in a similar type of products, “biophysical” refers to the material andimmaterial environment (e.g., vegetation, land cover, geology, etc.) and “legal” refers to the context ofexisting laws and regulations.
Insert Table 2.
Table 2 shows that the major land-use parameters utilised by sectoral class sets are limited. Though themeaning of land use varies widely among sectors, the set of major parameters is apparently not so broad.
Just two parameters suffice to describe any land use: function and activity. The function approach describes
land uses in an economic context. This type of approach answers the aim of land uses and is commonlyused in sectoral land-use descriptions (e.g., agriculture, forestry, fisheries, etc.). The approach is able togroup land uses together that do not possess the same set of observable characteristics but serve the same
purpose, the so-called polythetic view (Sokal, 1977). An example of such land uses is “agriculture” that
may come in many forms, dealing with plants or animals, related to extraction, production or service
characteristics. These “agricultural” land uses share a large proportion of characteristics but do notnecessarily agree in any one characteristic (e.g., bee-keeping versus annual rainfed maize cropping). The
activity approach describes what actually takes place on the land in physical or observable terms. Activity
is defined as “the combinations of actions that result in a certain type of product” (UN, 1989) and refers to
a process. The term “activity” does not mean that one needs to witness the activity as observer at the
moment that it is being carried out, but one may observe the results and infer the activity. It is important tonote that the function approach is independent of the activity approach: a variety of activities may serve asingle function (e.g., both farm housing and farming activities serve agriculture).
Widely known and used systems for economic activities are: (1) the 3rd revision of the International
Standard Classification of all Economic Activities (ISIC) of the United Nations Statistical Commission(UN, 1989) (Table 3), which ensures harmonisation with other main economic classifications, such as theCentral Product Classification (UN, 1998) (the CPC was developed for the purpose to measure outputs, i.e.
products and services. Each category is accompanied by a reference to the ISIC class where the output ismainly produced (industrial origin parameter), classification of products is based on the physicalcharacteristics of the goods or the nature of services rendered); and (2) the Nomenclature des Activités dela Communauté Européenne (NACE rev. 1) of the Commission of the European Communities, which firsttwo levels are compatible with ISIC (CEC, 1993).
Insert Table 3.
The usefulness of the function and activity parameters is apparent. Function groups all land used for thesame economic purpose independent of the type of activities taking place, whereas activity groups all land
undergoing a certain process resulting in a certain type of product that may serve different functions. The
result of a combined approach will be a flexible data set where re-grouping of parameters can take place
for a wide variety of queries.

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The level of data collection increases notably from the function to the activity concept. The use of thefunction parameter as first level parameter is also a pragmatic choice as most major functional groupings
can be detected with limited investment of human and financial resources, whereas the activity parameter
would require substantial investments in data acquisition.
5. Basic units of measurement
Land use lacks a common unit of analysis, the so-called basic unit of measurement. The definition of thethis unit differs according to the purpose of data collection and/or analysis. Sometimes a statistical sample
area is used, sometimes the unit is based upon mapping units at a particular scale (e.g., minimum mapping
unit in the case of thematic mapping) and sometimes the cadastral parcel unit is used. These three basic
units of measurement are discussed in more detail below.
5.1 Cadastral land parcel
In many countries, the smallest land unit that one can define coincides with the cadastral land parcel unit,
which is the lowest-level unit of the cadastre and thus has a legal status. In the cadastral system not only
the spatial extent of these land parcels is recorded and their ownership but often also the occurring land-use
or land-cover related information (e.g. arable land with building). In order to have a flexible approach inwhich different units of measurement can be aggregated, the cadastral land parcel can be selected as thebasic unit of measurement for land use. These cadastral land parcel units can be regrouped according toownership, by cadastral zone and by the various levels of administrative units (e.g., village, commune or
district level). Furthermore, the land parcel units may be regrouped according to similar type of uses andsocio-economic properties in order to identify land-use systems (e.g., if the different cadastral parcels aregrouped at the level of ownership and/or leasing, the level of a socio-economic unit can be reached inwhich also the availability and use of technology can be incorporated).Thus, there is flexibility in the use
and regrouping of the data that will serve different levels of decision making in land-use planning andpolicy. Another advantage is that land-use change analyses will be possible at a level that corresponds withdecisions made by the individual landowner or landholder.
Land registration and the cadastre need to be seen as part of the process of natural resources planning andmanagement. They deal with two of the world’s major resources, i.e. land and information. Land
information is necessary in many government activities. The registers may be used for land taxation, therights over public utilities over private land or along public roads for facilities such as electricity and water
may need to be protected, infrastructures need to be maintained and/or improved, restrictions may benecessary where misuses occur, etc. The cadastre should therefore be seen as an integral part of the land
management system (Dale, 1995).
The use of the land is closely related to land rights, which may be associated with certain limitations orconstraints. In addition, the period over which certain land rights are held is important. An owner that hasland rights for a long period may be more inclined to make investments than one who has land rights for avery restricted period. Access to land and ownership may thus impede or restrict the use of the land. Landrights constitute a condition under which land use develops. Land rights may restrict the choice of thevarious options of land use and it is, therefore, an important determinant of what type of actual uses may befound in a particular place and time. The type of land rights and who is holding these land rights (e.g.,
individual, family or private company) are recorded in the cadastral system.
5.2 Land-cover polygon
Land use describes the use of the object “land” and thus needs to be tied to a methodology in which theobject is defined. This has led to the common practice to combine land use with land cover in the sameclass set, thereby attaching use to what you see because of what people do on the surface of the Earth.

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However, land use has many aspects that go beyond land cover (e.g., socio-economics, legal aspects).
Therefore, too much emphasis on land cover embodies the risk not to capture several aspects of land use.
The advantages of using land-cover polygons as basic unit of measurement are that cover can be observed
and that tools like remote sensing and geographic information systems can help in a first stratification of
the land-cover-related uses. Consequently, a spatial relationship is established between land use and land
cover. A problem arises where land-use delineations do not concur with land-cover polygons. Several
uses may take place within one land cover (e.g., in a building), as well as one land use may be applied tovarious land-cover types (e.g., certain types of free grazing). In the cases where the boundaries concur,
one can aggregate either the land uses or the land covers. However, a land use may be confined to partof a land cover or parts of several land-cover polygons. In such cases, a further analysis and delineation
would be required in order to define the basic unit of measurement. In practice, most of the land has
been designated a certain function that applies to the whole unit under consideration. The cases that aland cover with a specific function does not concur with the land use are rare (e.g., certain types ofrecreation or tourism) (Jansen and Di Gregorio, 2003 and 2004). A methodology for mapping land use
based upon available land-cover polygons is described by Cihlar and Jansen (2001). One should notethat the land-cover/land-use relationship may change with time, thus establishment of the relationshipalone is not enough.
5.3 Statistical sample unit
Statistics are often based upon a selection of areas that are representative for a much larger area, the socalled statistical sample unit. In Table 1 for instance the TER-UTI class set uses an area of 9m2 distributed
in a systematic manner over the country territory to do annual systematic observations. This methodology
has also been applied in Bulgaria besides France. This provided, among other projects, the experience
integrated into the Land Use/Cover Area Frame Statistical Survey (LUCAS) launched by EUROSTAT andthe Directorate General of Agriculture. LUCAS is making observations using a systematic grid: on aregular grid of 18 by 18km, each grid element contains 12x30 rectangular primary sampling units covering
90ha. In addition, there are 10 secondary sampling units per primary sampling unit. The secondary
sampling unit area is considered as being equal to 7m2 (a circle with a diameter of 3m). These sampling
units are revisited on a regular basis in order to describe them anew and analyse any changes. In 2005, thismethodology has been revised in a regular grid of 1 by 1km covering the entire EU providing the basesample. From this base sample, the LUCAS master sample is extracted corresponding to a regular grid of 2
by 2km (e.g., 1 million points) where each point is photo-interpreted in order to stratify the sample in seven
generic strata. From the stratified master sample, a sub-sample will be extracted for classification by fieldvisit according to the full LUCAS class set (pers. comm. C. Duhamel, Landsis g.e.i.e).
6. Data quality
Harmonisation of class sets requires the analysis of data quality because correspondence between two classsets having two very different qualities may not be meaningful. In the metadata of each class set,parameters should be described related to the positional and thematic accuracy. The positional accuracy
when using remote sensing can be divided into:• Geo-referencing, i.e. the technical solutions for projecting the imagery onto the selected projection
and spheroid aiming at providing for each pixel on the image its position on the ground by the meansof a tern of coordinates.
• Location control, i.e. the correspondence between the coordinates of any arbitrary chosen point on
the image and its position on the ground by the confrontation with better accuracy source data.
• Registration, i.e. the precision of the drawing/digitising system adopted defined as the difference
between the same lines when interpretation is repeated of the same feature.

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A statistically valid design for estimating accuracy parameters has three parts: (1) the response design
specifies which data are to be collected at each sample location; (2) the sampling design specifies thelocations at which the response data are to be acquired; and (3) the analysis lays out the formulas and tests
to be applied to the observations (Boschetti et al., 2005).
One of the most common means of expressing thematic accuracy in remote sensing is the preparation of aclassification confusion matrix, sometimes called error matrix or contingency table. The confusion matrix
compares on a class-by-class basis, the relationship between known reference data, i.e. the ground truth,
and the corresponding results of classification either in the form of pixels, cluster of pixels, polygons orgroups of polygons (Lillesand and Kiefer, 2000).
Semantic harmonisation of class sets should consider the data quality aspect in a comprehensive manner
and would need to address also the following two aspects that are still at the level of research (Jansen et al.,2005b):• A quantitative measure should be provided of the harmonisation result of a class. In existing
examples, the impression is often given that class correspondence is 100%, whereas more often than
not the result will be much lower.
• A quantitative measure should be provided for the overall correspondence between two class setssimilar to the overall accuracy calculated from the confusion matrix.
7. Example: land-use harmonisation in Albania
7.1 Use of a reference system and a data model
The land-use data harmonisation process is illustrated with an example form the EU Phare Land-Use
Policy II (LUP II) project in Albania based upon the cadastral parcel as basic unit of measurement (Jansen,
2003; Jansen et al., 2006). The LUP II results are compared to the World Bank Albanian National Forest
Inventory (ANFI) project based upon the land-cover polygon as basic unit of measurement and a class set
defined with the FAO/UNEP Land Cover Classification System (Jansen et al., 2005a). The Albanian
government needed an analysis of land-use change dynamics to better understand the past, monitor thecurrent situation and to predict future trajectories in order to plan land uses and develop and implement
appropriate policies. In the example data quality aspects have not been quantified as the basis for the
harmonisation effort is the cadastre, where in the past land use has been systematically recorded, implying
high data accuracy.
A standard hierarchical methodology for description of land use has been developed for Albania, as there isno such methodology available or an international land-use reference system. The developed Land-Use
Information System of Albania (LUISA) adopts the function and activity parameters for systematic
description and has been developed in complete synergy by the subject-matter specialist and information
technology specialist.
Harmonisation between class sets can be achieved on the condition that the data structure of existing data
sets is integrated in the newly developed class set. Here, problems may arise and if so they should beovercome. It may mean having to compromise and accommodate certain classes in a specific position in theclass set that is neither the most suitable when considering the concepts adopted nor enhancing the classset’s internal consistency. Adoption of a hierarchical system will allow the applicability at various scales,from national, regional, to local. In addition, the class set structure is linked to a data structure, so one should
not only be familiar with the subject matter of land use and the principles of classification, but also withinformation technology concepts (e.g., relational databases or object oriented approaches). In the above
discussion, it is assumed that a common set of attributes distinguishes the classes to be compared and thatclass differences are primarily due to differences in boundary conditions. In the case of land use, this is areasonable assumption (Wyatt et al., 1997).

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In the context of the LUP II project, four data sets covering the period 1991-2003 (e.g., under socialist
government, before and after privatisation) are important:
1. Statistical data from the Institute of Statistics (INSTAT) comprising seven classes;
2. Cadastral data from the Immovable Property Registration System (IPRS Kartela) comprising 41
classes (spatially explicit data);
3. Commune data comprising 14 classes (spatially explicit data); and4. LUISA data comprising 48 classes where the most detailed levels of the hierarchy were used forland-use data collection (spatially explicit data).
Correspondence between classes of the available class sets has been inferred from the explicit record of
how each class relates to LUISA using the available definitions. Three class sets would lead to threecomparisons to be made for each class, whereas four class sets would request six comparisons per class. It
was therefore more efficient to use LUISA as reference system. During its development, LUISA has beensystematically and thoroughly tested. For the purpose of the LUP II project the land-use categories havebeen limited to four that each are linked to a set of laws. Each of these categories branches out into
different levels, each level having its own set of classes and use of parameters, definitions and guidelines(Figure 2).
Insert Figure 2.
A link that is often ignored at an early stage of classification comprises the structure of data resulting fromclassification in a geo-database. The data model developed for the LUP II project distinguishes spatial
features (e.g., land-use and soils) from linear features (e.g., roads and channels). The latter two classes are
however also related to land-use because roads form the transport network, whereas channels form thedrainage and irrigation network. This division has important implications for the way in which roads andchannels appear in LUISA. In the data model linear features have been split into several segments; for eachroad or channel segment data is collected that deal with their actual state and maintenance. The advantageof having such segment information is that the user of the data can identify, for example, if anywhere on aroad used for transporting agricultural products to the nearby market there is a segment that is in such a badstate that a vehicle cannot pass. If the road would be a single feature in the database, such an analysis
would be impossible. Another example can be given using channels. In many class sets, one will find theclass “irrigated agriculture” where the parameter irrigation is applicable to the whole polygon. In
practice, irrigation channels may function only in part due to their maintenance state but such a polygon
would still carry the parameter irrigated. A much more flexible approach is to separate irrigation channels
from the agricultural fields and to split the channels in segments. Such a distinction permits the user to
identify those fields that are actually irrigated from those that cannot be irrigated due to segment
information on the state of the irrigation channels. One will thus not find every single possibility of a classin LUISA because of the data model adopted. It is sufficient to record roads and channels as land cover
because the segment information can be combined with these features at a later stage of the data integration
process in order to define land use.
Once correspondence with LUISA was established for each class of the class sets, land-use change could
be analysed using just LUISA. Using different class sets with several classes results in numerous land-use
changes making a meaningful analysis difficult. LUISA does not only act as a reference system forharmonisation of land-use class sets, it also acts as a reference system for harmonisation of land-use
change. The LUISA class structure, i.e. the data structure, is tailored in an efficient and logical manner inorder to identify land-use change processes. In principle, land-use modifications occur within a land-use
category and the degree of modification depends on the level of the class (e.g., at Level IV modification issmall, at Level III medium and at Level II high) and land-use conversion occurs between land-use
categories. The exceptions are the Non-agricultural land-use classes, where modifications occur within one
group (e.g., within Urban uses, within Transport, within Utilities, etc.) and conversions between groups(e.g., from Unproductive to Urban uses, or from Water bodies & waterways to Extraction & mining). In

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan11

the Agricultural, Forests and Pasture & Meadows land-use categories conversions occur between
categories, whereas modifications occur within a single category within and between groups (e.g., within
the Agricultural Land-uses modifications exist within Permanent Crop Cultivation or between Temporary
Crop Cultivation and Permanent Crop Cultivation, etc.). For the interpretation of land-use change a pieceof software was written, the Land-Use Change Analyses (LUCA), that groups the changes according to theland-use change processes modification and conversion as shown in Table 4.
Insert Table 4 and Figure 3.
The harmonisation process between the different class sets and for harmonisation of land-use change using
LUISA as reference system is shown in Figure 3.
7.2 Results of correspondence between the class sets
Correspondence between the classes of the four systems is important when using existing data sets comingfrom different sources at different levels of detail and trying to integrate and harmonise them in a geodatabase. A table of correspondence has been prepared (Table 5) that shows that correspondence is often of
the type one-to-many or many-to-many, especially when classes used at national level (e.g., INSTAT) arecorrelated with classes used at more detailed levels (e.g., IPRS Kartela, Commune and LUISA). However,
if one looks at the more detailed level of the cadastral parcel unit of the IPRS Kartela and LUISA class sets,
the many-to-many relationships occur less frequently and one gets a better idea about the correlation of
single classes (Table 6 and 7).
Insert Tables 5, 6 and 7.
Some classes of IPRS Kartela do not correspond with a class of LUISA because they either occur below
ground (e.g., 130, 332) and have not been included, or do not address a land-use (e.g., 344). Other classes
are of a more generic nature than the detail of the classes used in LUISA resulting in a one-to-many
relationship (e.g., 108, 110 and 118) or vice versa (e.g., LUISA classes 91, 92 and 93 with various IPRSKartela classes). Other classes are more closely related to a land cover than a land-use (e.g., 118, 119, 135
and 336) and the relation with land use is not always apparent.
LUISA classes 95, 113, 114, 122, 124 and 133 do not correspond with any IPRS Kartela class. More detailhas been introduced in the description of Agricultural and Non-agricultural land-uses. Suitable agriculturallands are limited in Albania and it is regarded as important to know why they might not be utilised for
production of agricultural goods and/or services in the current agricultural year or for longer periods. The
LUISA classes distinguished in the Forests and Pastures and Meadows categories have been introduced tobetter distinguish their range of uses instead of focussing mainly on their different land-cover type.
7.3 Comparison with the ANFI remotely sensed land-cover/use data set
The World Bank financed Albanian National Forest Inventory (ANFI) project provided an analysis ofspatially explicit land-cover/use change dynamics in the period 1991-2001 using the FAO/UNEP Land
Cover Classification System for codification of classes, satellite remote sensing and field survey for datacollection and elements of the object-oriented geo-database approach to handle changes as an evolution ofland-cover/use objects, i.e. polygons, over time to facilitate change dynamics analysis (Jansen et al.,2005a). Land-cover/use changes are the results of many interacting processes and each of these operates
over a range of scales in space and time (Verburg et al., 2003). The detailed LUISA land-use data can becompared to the coarser ANFI data (scale 1:2,500 and 1:100,000 respectively), as far as space and time
considerations both data sets represent more or less the same period (1991-2003 and 1991-2001
respectively), but the analysis of each data set gives a somewhat different view on the change dynamics at
detailed versus aggregated data levels. At aggregated data levels the local variability of spatially explicit

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan12

land-cover/use changes may be obscured, whereas patterns can be shown that at more detailed data levels
may remain invisible and vice versa (Veldkamp et al., 2001).
The LUISA data set permits analysis of changes at the level of the individual cadastral parcel unit, thereby
highlighting changes at the level of the landowner and/or land user. The ANFI data set provides a national
overview of the major change processes, such as deforestation, urbanisation and increased pasture, butcannot provide conclusive evidence on especially the use of agricultural land (Jansen et al., 2005a). The
LUISA data set provides an insight into the non-use of low productivity areas in hilly terrain and theextensive forms of agriculture practised on prime agricultural land because of the lack of fertilizer use andthe breakdown of irrigation systems (Jansen et al., 2006). These two spatially explicit data sets are
therefore complementary when analysing change dynamics.
It is important to note that the use of remote sensing for land cover is a common approach. Interpretation ofsatellite images can provide a quick overview of the type and location of different land-cover types. Often
land-use elements are inferred from land cover (e.g., detection of a field pattern results in the class“agriculture”). However, the above example clearly demonstrates that land use requires a different
approach because it contains many aspects that go beyond land cover. Even with the use of the mostdetailed satellite images, such aspects will not be covered.
7.4 Correspondence with an international class set
Reference should also be made to internationally established class sets used to describe national level landuse/cover data. These systems are not immediately related to the work of the LUP II project, but the valueof the project outputs will be enhanced if correspondence to especially EU wide operational systems isassured. This will facilitate accession of Albania into the EU and continuity in data collection routines.
Land use is of high importance in the definition and evaluation of common sectoral policies in the EU, e.g.
on environment, agriculture, transport and the integration of those policies in a comprehensive assessment
and planning of the territory. EUROSTAT, the Statistical Office of the European Communities, has themission to provide the EU with high quality statistical information service. To support policy formulation
EUROSTAT launched in co-operation with the Directorate General responsible for Agriculture in 2000 theLand-Use/Cover Area Frame Statistical Survey (LUCAS) project that has been applied in the period 20012005 and it will be applied in a revised form in 2006 in 23 EU Member States.
Overall objectives of this survey are (EUROSTAT, 2001):
1. Collection of harmonised data (i.e. unbiased estimates) at EU level of the main land-use and landcover areas and changes.
2. Inclusion not only of the usual agricultural domain but also the aspects linked with environment,
multi-functionality, landscape and sustainable development.
3. A common sampling base (e.g., sampling frame, class set and data management) that interested
Member States can use to obtain representative data at national, but also regional, level by increaseof the sampling rate while respecting the general LUCAS approach.
4. Evaluation of the strengths and weaknesses of a point area frame survey as one of the pillars of the
future Agriculture Statistical System (area frame means that the observation units are territorial
subdivisions instead of agricultural holdings as used in the Farm Structure Survey).
Insert Table 8 and 9.
The main LUCAS land-use categories (version 1.0 ) are shown in Table 8. Correspondence with LUISA isshown, although based upon a different basic unit of measurement, in Table 9 at the individual class level.
Only the LUISA classes related to land tenure (e.g., 11, 12 and 13) do not correspond with a LUCAS
category, which is logical as they are distinguished using a non-inherent land-use parameter (Jansen, 2003).

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan13

8. Discussion and conclusions
Land use has been defined and interpreted in many different ways depending on the sector. The multidisciplinary nature of the subject has hampered the development of a standardised methodology forclassification as well as harmonisation of land uses worldwide. Existing class sets have been reviewed inorder to distil the key elements but there is a genuine lack of consistency in applied methodology andadherence to the principles of classification, and a variety of basic units of measurement are used.
Evaluation of the main parameters used in the existing class sets leads to the conclusion that the
combination of just two parameters may suffice: function together with activity. Function is centred on the
purpose of land uses, whereas activity groups all land undergoing a certain process resulting in a certain
type of product.
The example in Albania shows how the use of a reference system, based upon the function and activity
parameters and using the cadastral parcel as basic unit of measurement, may facilitate harmonisation ofclass sets in parallel with the achievement of harmonisation of land-use change. This reference system canform the basis for future standardisation of land-use class sets in Albania. In addition, the use of synergies
between classification and information technology concepts (e.g., data model and resulting geo-databasestructure) should be enhanced.
Comparison of the cadastral-parcel-based class set of Albania with a polygon-based class set at coarser
resolution shows that different levels of detail are needed when analysing land-use change. Remote sensing
is a useful tool for gaining a quick overview of land-cover related land uses but the potential for a detailed
and in-depth knowledge of land use is limited as other aspects, such as socio-economics, institutional,
cultural and legal factors, are not captured by remotely sensed based land cover. Therefore, remote sensing
can make a valuable contribution but its limits should be clear and complementary approaches should be
used. Understanding land-use change dynamics does not only help to identify vulnerable places, but also
vulnerable (groups of) land users that on their own are incapable to respond in the face of environmental
processes and problems.
The way forward for harmonisation of land-use class sets is to promote and fully develop a parametricapproach to classification. Commonalities in existing approaches should be emphasized and a set ofcommonly used parameters should be identified. Lessons can be learnt from harmonisation attempts at
local, regional and national levels that are equally valid for a globally applicable land-use classification.
Furthermore, a quantitative measure should be defined to express the harmonisation result of a class andbetween class sets.
Acknowledgements
This paper was prepared for the 12th Center for Environmental Remote Sensing (CEReS) International
Symposium on Remote Sensing entitled “Maximizing the use of satellite data for understanding the Earthenvironment”, held 13-14 December 2005, Chiba, Japan. The author would like to sincerely thank theorganising committee.
In addition, Christophe Duhamel (Landsis g.e.i.e., Luxembourg) is thanked for his suggestions concerning
LUCAS and Giancarlo Carrai (SVALTEC S.r.l., Florence) for his comments on an earlier draft.
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Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan
Figure 1. Analysis of land-use planning (adapted from Guttenberg, 1965)

Planning: form and structure

Land-use planning

Components:

Structure theory

Functions:

Analysis

Classification
modes:

Referential

Goal theory

Land evaluation

ControlLand valuation

Land management

Appraisal

Prescription

17

1

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan
18
Figure 2. Overview of the LUISA class set with the four main categories Agricultural, Forests, Pasture & Meadows and Non-agricultural land uses (Jansen, 2003; Jansen et al., 2006)

NON-AGRICULTURAL LAND USESDATALEVELS

AGRICULTURAL LAND USESLEVELSDATALAND USESACTIVITYACTUALLY CULTIVATEDPERMANENT CROPCULTIVATIONII

ACTIVITYACTUALLY NOT CULTIVATED (IDLE)MIX OF PERMANENT/TEMPORARY
CROP CULTIVATIONTEMPORARY CROPCULTIVATIONIIIIUNPRODUCTIVE

EXTRACTION
AND MINING

RECREATION/TOURISM

MINERALSPROTECTION

GAS & OIL

RECREATION/TOURISMWETLANDS

TRANSPORTUTILITIESRESIDENTIAL

ROAD

POWER
SUPPLYSERVICES

RAILROADWATER
SUPPLYINDUSTRIALAIRPORT

SEWAGEMILITARY

PORT

WASTE
DISPOSAL

RECREATION/TOURISM
OLIVES

VINEYARDSCULTIVATION IN
GREENHOUSEFALLOWIVDATALEVELSARABLE LANDFOREST LAND USESACTIVITYIII

STREAM

LAKE
RESERVOIRCHANNELMIXEDCROPPINGGARDENSRIVER

IRRIGATIONCHANNELDRAINAGE
CHANNEL

NATURALARTIFICIALIVPASTURE & MEADOWS LAND USESDATALEVELSFRUIT TREESWATER BODIESAND WATERWAYSLAGOONGRAVELAND SANDIII

URBAN USESACTIVITYI

I
II

INDUSTRIAL USESWOOD/TIMBER

FUEL WOOD/
FIREWOOD

NATURAL RESOURCES

2

PROTECTION

RECREATIONENVIRONMENTAL
PROTECTION

MULTI-USE

GRAZING IN (SEMI-) NATURAL
AREAS

III

SUMMER GRAZINGWINTER GRAZINGGRAZING IN CULTIVATED/IMPROVED AREAS

ALL-YEAR-ROUNDGRAZINGII

III

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan

3
4

Figure 3. Harmonisation of class sets in Albania using a reference system (LUISA) and harmonising land-use change(LUCA)INSTAT

IPRS
Kartela

LUISA

LUCA

CommuneCorrespondence andharmonisation
5

Land-use change
analysis

19

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan

6

Table 1. Overview of adherence of selected systems to the general principles of classification (based upon Duhamel, 1998).
Land-use system
Principles
CompletenessAnderson(1976)
only applicable to
USA (asintended)

UK Land use(1972)
fulfilled

TER-UTI(1994)
fulfilled

Remmelzwaal(1989)
not fulfilled, toomuch geared
towards
agriculture

Adamec(1992)
fulfilledYoung(1993)
not fulfilled, someland uses are
missing

Mücher et al.
(1993)
fulfilledabsentECE-UN(1989)
more applicable
to northern
Europeancountries thanMediterranean
absentAbsence of overlap

fulfilled

fulfilledabsentnot addressed

not addressed

spatial unit,
hereditary andzone

a circle of 9m2

Tool independent

remote sensing
dependent
no systematic
reporting,definitions read
like comments
LC and LU aremixed

not addressedindependent

independent

not addressed

not addressed

potential
confusion
between certainclasses
unit ofmanagement;
population census
unit
not addressedfulfilled

Observation unit

potential
confusion
between mixed
classes
not addresseddefinitions exist;
unsystematic
explanatory notes

non-existentno definitions;explanations are
given

no systematic
definitions; no
explanatory notes

non-existentnon-existentnon-existent

general usage of
mixed classes;
dominance is notdefinedall uses arerecorded, noweighing inmixed classes

hierarchy of uses
requested but norules exist to
definemultiple uses are
not discussed

non-existent

not mentioned,
there is often an
Other category
non-existentnon-existentno systematic
approachfollowed
non-existenttentativecorrespondencewith socioeconomic systems

possiblepossible

not addressed

not addressed

multiple uses arerecognised:primary use refers
to the value added
to the holding
not addressed

multiples uses
exist; multiple
sequences ofoperations are notdealt withnot addressed

existing
national Standard
Industrial
Classification(SIC)

existing
table ofcorrespondencewith earlier
versions

non-existentnon-existentnon-existentnon-existentnon-existentnon-existentnon-existentnon-existent

Definitions andexplanatory notes
Interpretation rules

Inclusion of new
objects
Index of objects
Correspondencewith other systems

721

not addressed

unit ofbiophysicalmanagement(e.g., plot)
independent

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan

8

22

Table 2. Analysis of land-use characteristics used by several main class sets 1
Main sector
Agriculture
Fisheries
Forestry
Economics
Sociology
Statistics
Industry
HousingServices91Function
x
x
x
x
x
x
x

Land-use characteristics
Activity
Biophysical
x
x
x
x
x
x
x
x
x
x
x
x
x
x

Legal
x

x
x
x

Based upon: World Land-Use Survey (IGU, 1976), Anderson (Anderson et al., 1976), ISIC 3rd revision (UN, 1989),
Standard International Classification of Land Use (ECE-UN, 1989), NACE 1st revision (CEC, 1993), Central Product
Classification (UN, 1998), FAOSTAT (FAO, 1998), Land-Based Classification Standard (APA, 1999). For “forestry”,
use was also made of http://home/att.net/~gklund/DEFpaper.htm.

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan

10

Table 3. The main categories of ISIC, 3rd revision (UN, 1998).
International Standard Industrial Classification of All Economic Activities
Codes
Description of category
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q

11

01
02

Agriculture, Hunting and Related Service Activities
Forestry, Logging and Related Service Activities
Fisheries
Mining and Quarrying
Manufacturing
Electricity, Gas and Water Supply
Construction
Wholesale and Retail Trade
Hotels and Restaurants
Transport, Storage and Communication
Financial Intermediation
Real Estate, Renting and Business Activities
Public Administration and Defence
Education
Health and Social Work
Other Community, Social and Personal Service Activities
Private Households with Employed Persons
Extra-Territorial Organizations and Bodies

23

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan

12

Table 4. Grouping of the land-use changes according to LUCA (Jansen, 2003; Jansen et al., 2006)
Type of land-use change
No change
Low level

Modifications

Medium level

High level

Conversions

Unknown

Correspondence
Low level modification in Agriculture
Low level modification in Forests
Low level modification in Pastures
Low level modification in Non-Agriculture
Medium level modification in Agriculture
Medium level modification in Forests
Medium level modification in Pastures
Medium level modification in Non-Agriculture
High level modification in Agriculture
High level modification in Forests
High level modification in Pastures
High level modification in Non-Agriculture
Agriculture-to-Forest
Agriculture-to-Pasture
Agriculture-to-Non Agriculture
Forest-to-Pasture
Forest-to- Agriculture
Forest-to-Non Agriculture
Pasture-to-Agriculture
Pasture-to-Forest
Pasture-to-Non Agriculture
Non Agriculture-to-Agriculture
Non Agriculture-to-Forest
Non Agriculture-to-Pasture
No correspondence 2

132“No correspondence” means that the land-use change is unlikely to occur.

Code
1
201
202
203
204
301
302
303
304
401
402
403
404
5
6
7
8
9
10
11
12
13
14
15
16
99

24

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan

14

25

Table 5. Correspondence between land-use classes from different class sets at a generic level (Jansen, 2003)
Legal categories

Land-use classes-

1

1, 2, 3, 4, 5,
6, 7

Area with arable land
crops

c, d

101, 102

1a6, 7Area with permanent
crops

f

1b, 1c, 1d

1, 2, 3, 4, 5-

8, 9
51, 52, 53,
54, 5531, 32, 33,
34, 35, 36,37131, 132,
133, 134,
135, 136,
137, 138
81, 82

3

116, 125,
128, 131,
148
108, 110,
153

Non-utilised agricultural areae

Pastures andmeadows

Grassland and pasturesg

Forests

Forestsh

118

3

Water bodies-

107, 109,
111, 120,
138, 153

4a

Wetlands-

Non-agricultural
lands

15

LUISA 6b

Used agricultural area
Agriculture

INSTAT 3

Class sets
IPRS
Commune 5
Kartela 4

Built-up areas-

BarrenMining/extraction-

336
100, 103,
106, 114,
121, 129,
130, 136,
144, 152,
213, 261,
332, 337,
338, 339,
340, 341,
342
119, 135
117, 343

2, 3a-4b, 4c, 4d, 4e

91, 92, 93,
94, 95,111, 112,
113, 114,
121, 122,
123, 124

4f-

61
71, 72, 73

a=Total Area (not represented in the table), b=Used agricultural area (UAA), c=Cultivated area with arable land crops,
d=Main crops (the first ones), e=Non-utilised agricultural area, f=Area with permanent crops, g=Grassland and pasture,
h=Forests.
4
For explanation of the codes see Table 6. Classes 130, 332 and 344 not included.
51=Agriculture, 1a=Arable, 1b=Vineyards, 1c=Fruit trees, 1d=Olives, 2=Pastures, 3=Forest, 3a=Brush land, 4=Nonagricultural, 4a=Water body, 4b=Built-up, 4c=Cemetery, 4d=Roads, 4e=Railway, 4f=Barren.
6
For explanation of the codes, see Table 7.

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan

16

Table 6. Correspondence between land-use classes at the level of the cadastral parcel unit (Jansen, 2003)
IPRS Kartela land-use classes 7
CodeClass names
100
Apartment
101
Arable102
Arable + garden
103
Water treatment facility
106
Building non-residential
107
Channel108
Pasture
109
Lake
110
Meadows
111
River
114
Block of flats
116
Fruit trees
117
Oil well
118
Forest119
Barren120
Reservoir
121
Road
125
Garden (of private building)128
Olives129
Cemetery130
Tunnel, underground131
Vineyards
135
Rocky
136
Public area
138
Stream144
Transformer building (step-up or step-down)
148
Fruit trees + garden
152
Railroad
153
Barrier (natural or artificial)
213
Building for residential purpose
261
Sport field
332
Underground
336
Marsh
337
Sidewalk
338
Unit (consisting of small shop or bar)339
Garage340
Studio341
Power plant
342
Area associated to power plant
343
Mine area
344
Transport equipment

LUISA
Class codes 891
6, 7, 8, 9
6, 7, 8, 9
123
92, 93, 94
137, 138
51, 52, 53, 54, 55
134, 135
51, 52, 53, 54, 55
131
91
1
72
31, 32, 33, 34, 35, 36, 37
61
136
111
4
2
92
3
61
92
132
121
5
112
51, 52, 53, 54, 137, 138
91
92
81, 82
111
92
91
91
121
121
71, 73-17

7
8The IPRS Kartela classes do not have a hierarchical data structure, their structure is flat.
For the explanation of the codes see Table 7.

26

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan

18

Table 7. Correspondence between the land-use classes of LUISA and IPRS Kartela 9 (Jansen, 2003)
Category

Agriculturalland-uses

Forests

Pastures andMeadows

Non-agriculturalland-uses

9

LUISA
Code
1
2
3
4
5
6
7
8
9
31
32
33
34
35
36
37
51
52
53
54
55
61
71
72
73
81
82
91
92
93
94
95
111
112
113
114
121
122
123
124
131
132
133
134
135
136
137
138

Description
Fruit trees
OlivesVineyards
Gardens
Mixed cropping
Arable lands
Cultivation in greenhouse
Fallow lands
Actually not cultivated (idle and abandoned) lands
Industrial forests uses
Forests for wood/timber production
Forests for fuel wood/firewood
Protection of natural resources
Forests for environmental protection
Forests for recreation
Multi-use forests
Grazing in (semi-) natural areas
Summer grazing in (semi-) natural areas
Winter grazing in (semi-) natural areas
All-year-round grazing in (semi-) natural areas
Grazing in cultivated/improved areas
Recreation/tourism in unproductive areas
Mineral extraction and mining
Gas and oil extraction
Gravel and sand extraction/mining
Protection of wetlands
Recreation/tourism in wetlands
Residential area
Services
Industrial area
Military area
Recreation/tourism in urban areas
Road
Railroad
Airport
Port
Power supply
Water supply
SewageWaste disposal
River
StreamLagoonNatural lake
Artificial lake
Water reservoir
Irrigation channel
Drainage channel

IPRS Kartela
Code
116
128
131
125
148
101, 102
101, 102
101, 102
101, 102
118
118
118
118
118
118
118
108, 110, 153
108, 110, 153
108, 110, 153
108, 110, 153
108, 110
119, 135
343
117
343
336
336
100, 114, 213, 339, 340
106, 129, 136, 261, 338
106106
121, 337
152
144, 341, 342
103
111
138
109
109
120
107, 153
107, 153

For the hierarchical data structure of LUISA see Figure 2; for explanation of the IPRS Kartela codes see Table 6.

27

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan

19

Table 8. LUCAS version 1.0 (EUROSTAT, 2001)
Land-Use/Cover Area Frame Statistical Survey

20

Code

Land-use Category name

U11
U12
U13
U14
U21
U22
U31
U32
U33
U34
U35
U36
U37
U40

Agriculture
Forestry
Fishing
Mining – Quarrying
Energy Production
Industry – Manufacturing
Transport, Communication, Storage, Protective Works
Water, Waste Treatment
Construction
Commerce, Finance, BusinessCommunity services
Recreation, Leisure, Sport
Residential
Unused28

Jansen, L.J.M. / 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan

21

Table 9. Correspondence between the land-use classes of LUISA and LUCAS 1.0 (Jansen, 2003)
Category

Agriculturalland-uses

Forests

Pastures andMeadows

Non-agriculturalland-uses

LUISA 10
Code Land-use category code

LUCAS 1.0
Code

1
2
3
4
5
6
7
8
9
31
32
33
34
35
36
37
51
52
53
54
55
61
71
72
73
81
82
91
92
93
94
95
111
112
113
114
121
122
123
124
131
132
133
134
135
136
137
138

U11
U11
U11
U11
U11
U11
U11
U11
U11
U12
U12
U12
U12
U12
U12
U12
U11
U11
U11
U11
U11
U36
U14
U14
U14
?
U36
U37
U34, U35, U36
U22
U35?
U36
U31
U31
U31
U31
U21
U32
U32
U32
U13, U32
U13, U32
U13, U32
U13, U32
U13, U32
U13, U32
U32
U32

Fruit trees
OlivesVineyards
Gardens
Mixed cropping
Arable lands
Cultivation in greenhouse
Fallow lands
Actually not cultivated (idle and abandoned) lands
Industrial forests uses
Forests for wood/timber production
Forests for fuel wood/firewood
Protection of natural resources
Forests for environmental protection
Forests for recreation
Multi-use forests
Grazing in (semi-) natural areas
Summer grazing in (semi-) natural areas
Winter grazing in (semi-) natural areas
All-year-round grazing in (semi-) natural areas
Grazing in cultivated/improved areas
Recreation/tourism in unproductive areas
Mineral extraction and mining
Gas and oil extraction
Gravel and sand extraction/mining
Protection of wetlands
Recreation/tourism in wetlands
Residential area
Services
Industrial area
Military area
Recreation/tourism in urban areas
Road
Railroad
Airport
Port
Power supply
Water supply
SewageWaste disposal
River
StreamLagoonNatural lake
Artificial lake
Water reservoir
Irrigation channel
Drainage channel

22
10

For the hierarchical data structure of LUISA see Figure 2.

29Forest fire analysis for several years in Russia by using NOAA satellite
Jun-ichi KUDOH
Center for Northeast Asian Studies, Tohoku University, Japan
E-mail:kudoh@cneas.tohoku.ac.jp

Abstract
The main purpose of this work was to construct an efficient algorithm for forest fire detection method using
NOAA satellite images. The forest fire analysis using this NOAA satellite has been done by various researchers.
However, most of the method can detect the fire but a lot of false fire also appeared in the results and missing of actual
fire also happened in some cases. Here we analyzed four satellite based fire detection methods to compare thefundamental differences, problems and effectiveness. To do so we used the data from AVHRR of NOAA-16 of a periodof three to six months for Sakhalin and Japan region. Considering their problems and drawbacks an improved fire
detection method with statistical analysis has been constructed here. Our method has reduced the false fire detection
very significantly as well as detected the actual fire with high accuracy. This method can be applied to a real time forest
fire monitoring system for Russia and Southeast Asian region, which is very important for early warning and early
detection of fire for reducing the damages of forest fire. And also this study applied Siberian forest fire analysis forseveral years.
1. Introduction
A large-scale forest fire happens frequently in all parts of the world. Forest fire has serious economic implications:destruction of habitats, forests damage, costs of fire fighting and so on. Nowadays it is very important and sensitive
issue in Russia and Southeast Asian region since a large scale fire occurs frequently. A huge amount of exhaustion ofcarbon dioxide by the forest fires is thought to be a cause of global warming. The earth environment is changing
because of this global warm and air pollution [1]. At present specialist examines the damage situation after anoccurrence of large-scale forest fire and it is thought that wide-ranging monitor by the satellite remote sensing may havelots of advantages in fire fighting.
A wide range real time monitoring by remote sensing satellite is essential for the grasp of the fire occurrence situation.
The AVHRR (Advanced Very High Resolution Radiometer) observed on the NOAA satellite series is one of the mostwidely used satellite sensor for fire detection. Figure 1 shows an outline of NOAA weather satellite observation system
operated by National Oceanic and Atmospheric Administration. The AVHRR provides daily data (two scenes for each
operational platform) approximately at 1.1km spatial resolution, in five channels (Ch1= 0.58–0.68µm,
Ch2=0.723–1.10µm, Ch3=3.55–3.93µm, Ch4=10.5–11.3µm, Ch5= 11.5–12.5µm). Moreover, the long-term availabilityof AVHRR data provides a good information source for obtaining an indicator of seasonal fire activity on a global scale[2]. In this work we used the NOAA-16 AVHRR data that received in Sendai (Japan) and Novosibirsk (Russia), andtransferred to Tohoku University.
Here the problems of the early methods are enumerated so far and an improved fire detection algorithm is developedby using NOAA AVHRR images. Then the proposed method is applied to detect actual fire and compared with theresults of fire detection by other methods. This work was performed in two regions, Sakhalin and Japan.
First, NOAA-16 AVHRR data is processed geometric correction and radiometric correction by a program which isprovided by NEC [3]. This program processes geometric correction by using TBUS data. But this program cannot
process high accuracy geometric correction. Therefore, further geometry correction with higher accuracy is performedby using the method proposed by M. Nakano [4] In this method GCP is created automatically and perspective
transformation is used as the geometric correction algorithm.

Figure 1

NOAA Satellite Observation System.

2. Fire Detection methods with AVHRR Data
AVHRR data has been widely used for fire detection for more than a decade [5] and the accuracy of detection
criteria for both large and small fires has rarely been assessed using actual fire data [6] [7] [8]. In general the thermalvalue of Ch3 is used in most fire detection methods. It is known that the brightness temperature of Ch3 rises due to thefire point [9] [10]. Moreover, the rise like in Ch3 is not seen in Ch4 though the rise of the brightness temperature also
happens to Ch4. Most of the classical AVHRR fire detection procedures are based on fixed threshold values deduced byempirical analyses [9] [10] [11] [12] [7]. Limits in the adoptability of threshold methods have been suggested by several
authors (see for example Pozo et al. [13], Boles and Verbyla [6], Cuomo et al. [8]).
A contextual approach was devised to take into account the different observational and environmental conditions mainly
based on spatial statistics computed from pixels within variable sized windows around candidate fire pixels. The spatial
statistics are applied to pixels previously selected as potential fires from multi-spectral threshold tests [14] [15] [6].
Four fire detection methods (proposed by Flasse et. al., [4] Nakayama et al. [15], Boles and Verbyla [6], J.
Kudoh[17]) have been studied using NOAA AVHRR images for Sakhalin and Japan region. Sakhalin region is chosen
as large scale forest fire frequently happens. Japan region is compared to verify the false detection of fire where thecause of false detection can be made for the rising brightness value of Ch3 by the other factor rather than forest fire. It isthought that the tendency of rising brightness temperature of Ch3 as well as the possibility of false detection was high inthis region.
2.1 Method of Flasse
Flasse et. al.[14] proposed a contextual algorithm which principles approach were first reviewed by Justice andDowty [18]. The choice of thresholds used in these contextual approaches was first driven by Kaufman et. al. [9].
Further references were looked for Kennedy et. al. [10]. Flasse algorithm consists of two stages: first the selection ofcandidate pixels which could be potential fires (PFs) and second the confirmation of PF by comparing with their
immediate neighbors as a fire point [14]. A pixel is selected as a PF by considering the following conditions:T3 ≧ 316K(1)(2)
T3 －T4 ≧10K
T4 ≧ 250K(3)
where T 3 and T 4 (in deg K) are AVHRR brightness temperatures of Ch3 and Ch4 respectively.
A PF is classified and retained as a fire when it appears to be different enough from its background. For each PF
statistical information is calculated for a variable size context window (from 3 x 3 to 15 x 15 pixels) around the PF andat least 25% of neighboring pixels are considered as fire background. PF is confirmed as fire when
T3 > µ3bg + 2σ3bg +3K(4)(5)
T3－T4 ≧µ3-4bg+2σ3-4bg
Here, µ is the average value and σ is the standard deviation of the background.
2.2 Method of Boles
Usually a forest fire happens in the region, where NDVI (Normalized Deference Vegetation Index) is high e.g.
forest. Therefore, Boles first specify the forest by observing NDVI and added Fuel Mask algorithm to the fire condition
to reduce the false detection of the places like deserts or sea. Since the NDVI before a fire point indicates a high value,
NDVI for past one month is measured and the highest NDVI is assumed as MVC which is used as threshold to the firecondition.
Boles detect a fire by using the expressions mentioned below.
Fuel Mask Algorithm:
MVC ＞ 0.3
(6)Detection of PF
T3 ≧ 308K(7)(8)
T4 ＞ 284K
T3－T4 ＞ 10K(9)Fire detection
T3 ＞ µ3bg ＋1.5σ3bg(10)
T3－T4 ＞ µ3-4bg＋1.5σ3-4bg(11)
R2 ＞ µ2bg＋σ2bg(12)where R2 is reflectivity of Ch2.
Here the false detection may reduced because the place with the possibility of fire occurrence is specified from thesituation of past vegetation and a more certain fire may be detected.
2.3 Method of Nakayama
Nakayama et. al. improved the part where the standard deviation is used in Contextual algorithm by using the mean
deviation. Moreover, it tests the part that will be used as a background. The fire detection is done as follows:
Detection of PF

T3 ＞ 320K
T3－T4 ＞ ６K
R2 ＜ 25 %Background test
T3 ＜ 318K
T3－T4 ＜12K
Fire detection
T3－T4 ＞ µ3－4bg+ max(δ3－4bg,4K)
T4 ＞ µ4bg＋δ4bg－3K
Here δ is the mean deviation of background.

(13)
(14)
(15)
(16)
(17)
(18)
(19)

2.4 Method of Kudoh
Kudoh et. al. proposed the fire detection method that uses the feature of the fire point obtained from Three
Dimensional Histogram of Ch1, Ch3 and Ch5 which is made from past fire information. First of al, 14 years forest fires
information from NOAA AVHRR images were accumulated in a Three Dimensional Histogram. Accumulated area wasin Far East Russia about 1100 km square region with 681 scenes, 55983 points. Actually the fire is detected from thesepast AVHRR images by using the method of Flasse. Next, it looks for actual fire point as a result of confirmation by
watching the deterioration of NDVI, the presence of smoke and the changes of the temperature in time series. Obtained
fire point is collected as fire information and plotted in Three Dimensional Histogram (Kudoh et. al., 2003). To detect afire the threshold is assumed as the saturation value of 1023 for Ch3 ( Ch3 ≧ 1023 ). So, Two Dimensional
Histogram composed on Ch1 and Ch5 is obtained, which makes some looped area of the accumulated fire
corresponding to occurrence number as shown in figure 2. These circles provide reliable fire detection category. Thosepixels enter into this category are considered as actual fire. In this category the accuracy of the fire detection goes up by
using the number of frequency four or more.

Figure 2

The fire category in Two Dimensional Histogram.

3. Comparisons and Problems Finding
To compare the results of fire detection, above four methods were applied in two regions, Sakhalin and Japan. In
Sakhalin, the applied period was from June to August, 2003 as a large number of forest fire happened frequently. It isfrom May to September, 2004 in Japan when a marvelous heat was intense and high temperature was recorded invarious places. It is thought that the tendency of rising brightness temperature of Ch3 as well as the possibility of false
detection was high in this region especially during those periods. Therefore, the false fire detection by each method will
be verified.
3.1 Comparison in Sakhalin region
An image of NOAA 16 on June 17, 2003 where a fire broke out in the Sakhalin region has been applied for eachmethod. The obtained result of each method for Sakhalin region is shown in figure 3. Detected fire points indicated by
red dots in each image of the figures. It is clear that all method adequately detected the actual fire where a fire breaks
out in the center of the image and smoke is generated.

Flasse et al.

Nakayama et al.

Boles et al.

Kudoh et al.

Figure 3 Comparison among the results of early methods in Sakhalin region (NOAA-16 on June 17, 2003).
Moreover, the false detection also happened more or less for each method. False detection is appeared in the sea,
here and there for the method of Flasse. Since the fire occurrence place is specified by using MVC in the Boles method,
the false detection in the sea is not appeared. However, the false detection in the cloud region stands out probably forthe setting of low threshold. The false detection tendency for Nakayama method also looks like Flasse method. In
Kudoh method, comparing with others, the false detection is found very few though the actual fire has been detected
adequately. It is very clear to understand that in case of Flasse et. al. and Kudoh et. al. the false fire detection iscomparatively a little. Next these two methods have been applied for Japan region.
3.2 Comparison in Japan region
The results of fire detection for an image of NOAA-16 on 7 July 2004 is shown in figure 4 and figure 5 for Flasse
and Kudoh method respectively. Either methods detected huge red fire points which were actually false fire points. As
in reality there were happened no forest fire on that time. In case of Flasse method in figure 4, false detection happenedin the coastline and the cloud area. In figure 5, Kudoh method showed the false detection mainly in the central area ofTokyo and other area where the value of brightness temperature of Ch3 were very high.

200km

Figure 4 Result of Flasse method in Japan region.

200km

Figure 5 Result of Kudoh method in Japan region.

In the graph of figure 6 shows the comparison of detected fire points for each method applied in Japan region for theimage of NOAA-16 on July 7, 2004. In vertical positioned values are the fire points that actually are the false fire points,
because the fire that observed with NOAA in the graph has hardly happened in Japan.

The number of fire point

500000450000400000350000300000

FlasseBoles
Nakayama
Kudoh

250000200000150000100000
50000
0
May

June

July

Aug

Sept

Figure 6 Comparison of detected fire points in Japan region, 2004.
Since fire has been detected based on the brightness temperature of Ch3 in cases of Flasse, Boles and Nakayama,
the maximum number of false fire has been detected on the period of July when a high temperature was recorded inTokyo and other places of Japan. The least false fire has been detected by Kudoh et. al. among the other methods since
this method has detected a fire by the threshold processing for Ch3.
To find the reasons of this false detection gray scale images for Ch3 on July 7, 2004 of NOAA-16 in Japan region
has been plotted and analyzed as shown in figure 7 and figure 8. Since the method of Flasse have detected a fire by thecomparison with the surrounding background, a lot of false detection went out at the part where the brightness
temperature of Ch3 is turned such as the coastline and the cloud region as in figure 7. In case of Kudoh et. al. method afire has been detected by the threshold processing of Ch3. Therefore, a lot false detection is appeared in the part wherethe value of brightness temperature of Ch3 is very high (figure 8).

(39.9N,131.8E)(39.9N,144.2E)(39.9N,131.8E)(39.9N,144.2E)
200km
100km

200km

(29.8N,131.8E)(29.8N,144.2E)

low

high

Figure 7 Case of Ch 3 method of Flasse.

(29.8N,131.8E)(29.8N,144.2E)

low

high

Figure 8 Case of Ch 3 method of Kudoh.

From the above comparisons it is very clear that the accuracy of fire detection is highly dependent on the risingbrightness value of Ch3. The brightness value of Ch3 fluctuates largely with the change of temperature with latitude andseason. It can goes up by the reasons other than the fire such as 1) the rise of temperature by season in urban area or indesert, 2) the highly developed sun rise which makes reflection of the sun light on the sea. It can be confirmed here in
figure 8 that a very high value was produced by the reflection of the sun light on the sea and false fires were detected on
those spots.
Moreover, the rise of the brightness temperature of Ch3 is radiated by the rise of the temperature in summer inTokyo and its surrounding area which is thought to be a cause of the false detection in Japan region in July 2004 by
Kudoh method, figure 8. We presumed that the accuracy of fire detection may goes up if these problems can beconsidered in fire condition.
4. Proposed Method
From all the above comparisons and discussion it is clear that in case of Kudoh method, the difficulties of firedetection were comparatively low among the other methods. The main difficulties in fire detection were false detection.
That means the fire has been detected in places where fires did not appear in real. First of all, it is thought that the false
fire detection can be decreased by specifying the region where the possibility of fire occurrence is present (such as
forest), using NDVI (used in Boles et. al. method). So, the possibility of the false detection in the sea might be removed.
Moreover, the rise of brightness temperature of Ch3 goes up gradually due to seasonal factor (such as in summer)
whereas the rise goes up sharply for a forest fire. So it is thought that the fire can be detected more accurately if we
consider these natures in fire condition.
4.1 Algorithm
Here we proposed a new fire detection method by improving the Kudoh et. al. technique and by reducing the false
fire detection using statistical analysis. Figure 9 shows the flowchart of proposed algorithm. First of all, the image
where it wants to detect a fire is called a target image. From the target image, NDVI is observed for past one month and
MVC is calculated from the highest value of these NDVI.
It is thought that the false detection of the places such as sea, to which it comparatively rises the temperature of Ch3,
can be decreased by considering this MVC to the fire condition. Actually in this step the places with the possibility offire occurrence (such as forest) are specified from the situation of past one month vegetation.
Then we applied Kudoh method to detect the fire points from those specified places. It was assumed that some false
detection might be appeared due to the rise of brightness temperature of Ch3 by the seasonal factor rather than a fire. To
remove this false detection a statistical analysis is done over those fire points.
Start

MVC > 0.3

N

Y

Method ofKudoh et al.

N

Y
T3 > μ３ｔ＋2σ3t

N

Y

Fire
Not Fire

Figure 9 Flowchart of Proposed method.
In statistical analysis a threshold value has been fixed to remove this false detection and added in the fire condition.
First of all, from the target image the value of mean and standard deviation of each pixel for past 20 days from the target
day has been calculated for Ch3. At this time the part of cloud region in the target image has not been used. Since thebrightness temperature of Ch3 for cloud region is lower than the actual ground level, the average value might be lower
in calculation. For this purpose it has been avoided the influence of cloud region. Finally a threshold value is obtained
from these mean and standard deviation values of Ch3 by considering the equation (20).
T3(x, y) ≧ µt(x, y) + 2σt(x, y)(20)
Here, T3(x, y) is the value of Ch3 of target image on (x, y) coordinates, µt(x, y) is the mean value of Ch3 for past 20
days from target day and σt(x, y) is the standard deviation on (x, y) coordinates. When the value of Ch3 of a pixel was
higher than this threshold, it was considered as a fire point.
4.2 Results and evaluation
Our new method is applied in Sakhalin and Japan region by using the same images of NOAA-16 for same period
which were used previously in the comparisons on early methods. Then the evaluation has been done by comparing theresults of our method with the results of Kudoh method. This verification has been done for both Sakhalin and Japanregion as mentioned below.
4.3 Sakhalin region
Figure 10 shows the image of NOAA-16 for the fire detection results of our method in Sakhalin region on June 17,
2003. It is seen in the image of Sakhalin that fire points have detected adequately at the center with smoke as fires
mentioned by the red points. Moreover, it is understood that very few false detection appeared in the image of Sakhalin
region. No false detection appeared in the sea or cloud region as it was appeared for Flasse or Boles method. The maindifficulties of fire detection have been overcome in this method.
Figure 11 shows the graph of comparison between Kudoh et. al. and our proposed method for fire detection points
in Sakhalin on 2003. From this graph it is clear that our method detected the fire for Sakhalin region almost in the same
degree as it is detected by Kudoh method. It is confirmed that proposed method can detect the actual fire in the same
degree of accuracy as it can by the Kudoh method.

The number of fire points

4500
4000
3500
3000
2500

Kudoh et. al.
Proposed method

2000
1500
1000
500
0
June

July

Aug

Figure 11 Comparison of the fire points in Sakhalin region,
2003.

Figure 10 Result of proposed method in Sakhalin on June 17, 2003.
4.4 Japan region
The results of fire detection by proposed method for an image of NOAA-16 on 7 July 2004 in Japan region is shownin figure 12. It shows that very few false fire points have been detected here in Japan region by our method. It is clearthat the number of false fire points decreased greatly if we compare this result with the result of Kudoh as shown infigure 5.
A high temperature continued by the intense heat in Japan region from May to September 2004. Therefore, a lot offalse fire detection was found in Kudoh technique. Figure 13 shows the graphical comparison of fire points found in our
method and Kudoh method for Japan region in 2004. The fire points as shown in graph were actually false fire points.
Very little false detection appeared in our method. It is clear that in our method the number of false fire points has
decreased greatly in the comparison to Kudoh method. Our method is improved by decreasing the number of false fire
detection and makes fire detection much more efficiently than early methods. However a few number of false fires
appeared as it was not possible to remove all false fire to maintain the high accuracy of actual fire detection.

The number of fire points

70000
60000
50000
40000

Kudoh et. al.
Proposed method

30000
20000
10000
0
May

June

July

Aug

Sept

Figure 13 Comparison of false detection points in Japaneseregion, 2004.

Figure 12 Result of proposed method in Japan on July 7, 2004.

5.

Applied for Siberian Region
This study applied Siberian forest fire analysis from 2002 to 2004 for every April to September. The analysis area isdivided 15 regions including Siberia, Mongolia and north China shown in figure 14. Each region is 1024x1024 size for
AVHRR image.

Figure 14

Divided regions map for Siberia forest fire analysis.

2000
1800
1600
1400

1000
800
600
400
200

Date

Figure 15

Detected hot spot pixel in Siberia 2002.

2002/9/30

2002/9/16

2002/9/2

2002/8/19

2002/8/5

2002/7/22

2002/7/8

2002/6/24

2002/6/10

2002/5/27

2002/5/13

2002/4/29

2002/4/15

0
2002/4/1

Pixel

1200

4000
3500
3000

Pixel

2500
2000
1500
1000
500

2003/9/30

2003/9/16

2003/9/2

2003/8/19

2003/8/5

2003/7/22

2003/7/8

2003/6/24

2003/6/10

2003/5/27

2003/5/13

2003/4/29

2003/4/15

2003/4/1

0

Date

Figure 16

Detected hot spot pixel in Siberia 2003.

600
500

Pixel

400
300
200
100

2004/9/17

2004/9/3

2004/8/20

2004/8/6

2004/7/23

2004/7/9

2004/6/25

2004/6/11

2004/5/28

2004/5/14

2004/4/30

2004/4/16

2004/4/20

Date

Figure 17

Detected hot spot pixel in Siberia 2004.

The detected hot spot pixel is shown in figure15 – figure 17. This study is continued to more yearly analysis tofind the fire pattern, feature, and affecting Japan.
6. Conclusions
In this work a new fire detection method using NOAA AVHRR images have been constructed. Our method detected
the actual fire with the degree of high accuracy by overcoming the difficulties of actual fire detection. Moreover it
reduced the number of false fire detection very significantly. In this work to maintain the high accuracy of actual fire
detection it could not be possible to remove 100% false detection. Our method can be used to provide a real time firemonitoring system and can be calculated the burned area more accurately. A future subject is to find out the cause of

these few false fire points that appeared in our results.
7. References[1]FRA 2000 (Global Forest Resources Assessment 2000 - Main report), FAO forestry paper 140.
[2]EVA, H., and LAMBIN, E. F.,Remote Sensing of biomass burning in tropical regions: sampling issue andmultisensor approach. Remote Sensing of Environment, 64, 292–315(1998).
[3]NEC CORPORATION, NOAA receiving and processing manual (in Japanese)(1996).
[4]M. NAKANO, Y.HARAMOTO, J.KUDOH, An automatic GCP creation method for geometric correction for
Multi-time NOAA/AVHRR images, Journal of the remote sensing society of Japan, Vol.25, No.2, pp. 191-197(2005).
[5]LEVINE, J. S. (editor), Global Biomass Burning, Cambridge, MA: MIT Press (1991).
[6]BOLES S. H., and DAVID L. VERBYLA, Comparison of Three AVHRR-Based Fire Detection Algorithms forInterior Alaska, Remote Sensing of Environment, 72, 1-16(2000).
[7]LI, Z., NADON, S., and CIHLAR, J., Satellite-based detection of Canadian boreal forest fire: development andapplication of the algorithm. International Journal of Remote Sensing, 21, 3057–3069 (2000).
[8]CUOMO, V., LASAPONARA, R., and TRAMUTOLI, V., Evaluation of a new satellite based method for forest fire
detection. International Journal of Remote Sensing, 22, 1799–1826 (2001).
[9]KAUFMAN, Y., TUCKER, C. J. and FUNG, I., Remote sensing of biomass burning in the tropics. Journal ofGeophysical Research, 95, 9927-9939 (1990).
[10]KENNEDY, P. J. BELWARD, A. S., AND GREGOIRE, J. M., An improved approach to fire monitoring in West
Africa using AVHRR data. International Journal of Remote Sensing, 15, 2235-2255 (1994).
[11]RAUSTE, Y., HERLAND, E., FRELANDER, H., SOINI, K., KUOREMAKI, T., and RUOKARI, A.,
Satellite-based forest fire detection for fire control in boreal forests. International Journal of Remote Sensing, 18,
2641–2656 (1997).
[12]ARINO, O., and MELINOTTE, J. M., The 1993Africa fire map. International Journal of Remote Sensing, 19,
2019–2023 (1998).
[13]POZO, D., OLMO, F. J., and ALADOS-ARBOLEDAS, L., Fire detection and growth monitoring using a
multitemporal technique on AVHRR mid-infrared and thermal channels. Remote Sensing of Environment, 60,
111–120 (1997).
[14]FLASSE S.P., CECCATO P., A contextual algorithm for AVHRR.re detection, Int.J.Remote Sensing, Vol.17, No.2,
419-424 (1996).
[15]NAKAYAMA, M.MAKI, C.D.ELIVIDGE, S.C.LIEW, Contextual Algorithm adapted for NOAA-AVHRR.re
detection in Indonesia, Int. J. Remote Sensing,Vol.20, No.17, 3415-3421 (1999).
[16]BARBOSA, P. M., GREGORIRE, J., and PEREIRA, J. M. C., An Algorithm for Extracting Burned Areas fromTime Series of AVHRR GAC Data Applied at a Continental Scale. Remote Sensing of Environment, 69, 253-263(1999).
[17]J. KUDOH, and K. HOSOI, Two Dimensional Forest Fire Detection Method by Using NOAA AVHRR Images
IEEE 2003 International Geoscience and Remote Sensing Symposium 2003, 2494-2495 (2003).
[18]JUSTICE, C. O., and DOWTY, P. (editor), IGBP-DIS satellite fire detection algorithm workshop technical report.
IGBP-DIS working paper, 9, 88pp., February 1993, NASA/GSFC, Greenbelt, Maryland, USA

Agricultural Land-Use Change in Northeastern Asia and Climate Change
Katsuo OKAMOTO1, Junko SHINDO1 and Hiroyuki KAWASHIMA2
1
National Institute for Agro-Environmental Sciences2
Graduate School of Agricultural and Life Sciences, University of Tokyo
E-mail: kokamoto@affrc.go.jp
Abstract
The self-sufficiency of rice in China, which has the largest population in the world, is 100% and Chinese people tend toprefer quality over quantity. The total area of paddy field in the northeastern part of China has increased because theclimate conditions in this region are suitable for cultivating Japonica rice (short-grain rice). Large quantities ofirrigation water are consumed for cultivating rice. Rainfed rice cropping is possible in places with sufficientprecipitation, whereas rice cropping in regions with low precipitation is limited in places with easy access to water for
irrigation. Flatlands are advantageous for rice cropping because of their geographical and social conditions. The eastern
Heilongjiang province of China was selected as the test area for satellite image analysis. Land use/land cover wasclassified using the Landsat Thematic Mapper (TM) data of 1988 and the Enhanced Thematic Mapper Plus (ETM+)
data of 2002. According to the statistics of Heilongjiang Province, the total area of paddy field has increased rapidly
since 1995. On the basis of the 1988 land-use/land-cover classification map, areas suitable for rice cropping were
determined and compared with the distribution of paddy fields in 2002. Most of the paddy fields in 2002 were changed
from dry arable lands in 1988. The areas suitable for rice cropping are those with easy access to water for irrigation.
Moreover, as a result of climate changes, air temperature and precipitation may become unsuitable for the cultivation ofmajor cereals in Asia. Conversely, agriculture would affect the environment unfavorably. The major river basins withrice-cropping zones from East to Southeast Asia were studied. The possible changes in water quality in the future were
evaluated. The data used included the river course, land-use/land-cover classification, population, climate, nitrous oxide(NOx) emission, and country boundary. Statistical data on rice yield, harvested area, fertilizer usage, food production,
food consumption and food trade were also used to construct nitrogen load maps. The nitrogen load changes seasonallydepending on the cropping systems used and the amount of precipitation. In the future, nitrogen discharge from humans
and fertilizer use will increase along with the increase in population and cultivation intensity. Fertilizer use and NOx
deposition will also increase along with economic growth. Therefore, the nitrogen concentration in river water will also
increase. Excessive irrigation and fertilizer use during the dry season will result in the further deterioration of water
quality, creating nonsustainable ecosystems in river basins.
1. Introduction
The total area of arable cropland has steadily increased throughout the world over the past 300 years (Ramankutty andFoley 1999). This is the reason why food production has increased to sustain the population increase. Consequently, thetotal area of arable land has increased not only in highly productive areas but also in unfavorable areas.
Asians now represent 55 percent of the world population, and this figure has continued to increase in recent years(Figure 1). Farmers in Asia are increasing rice production to sustain the population increase (Figure 2). Large quantities
of irrigation water are consumed for cultivating rice. Hydrological conditions change according to changes in water use
in agriculture. Moreover, a paddy field is a source of methane which is one of the greenhouse effect gases (IPCC 1995).
Moreover, an abrupt change in land use/land cover and a rapid increase in the total area of paddy field have negative
effects on the ecosystem and environment.
In this study, the eastern Heilongjiang province of China was selected as the test area and satellite data acquired on
different dates were analyzed. The changes in the total area of paddy field were examined, and the geographical andsocial conditions of an area to be changed from dry cropland to paddy field were studied. Thereafter, the possible
impacts of agriculture to the ecosystem and environment were assessed.
2. Rapid land-use/land-cover change in Northeastern China using Landsat TM/ETM+ data
The self-sufficiency of rice in Asian countries has reached nearly 100%. The self-sufficiency of rice in China, which has
the largest population in the world, is 100% and Chinese people tend to prefer quality (eating delicious rice) overquantity (eating heartily). Therefore, the production of good-tasting Japonica rice (short-grain rice) has increased
compared with that of Indica rice (long-grain rice). The total area of paddy field in the northeastern part of China,
where the climate conditions fall within the marginal zone for cultivating Japonica rice originating from the subtropicalzone, has increased. Heilongjiang is now a major rice bowl in China. This province produces about 560 million metrictons of Japonica rice per year, according to the estimates from the statistics of FAO (2004) and USDA (1999). The total
area of paddy field in Heilongjiang has increased from 2.2 x 103 km2 to 16.5 x 103 km2 (7.5-fold) over the past 20 years(Heilongjiang Provincial Bureau of Statistics 2004) (Figure 3).
2.1 Test area of satellite image analysis
The land-use/land-cover change in the northeastern part of China was analyzed using multitemporal Landsat data. The

agricultural region (131˚ 35’-134˚ 33’ E, 46˚ 29’-48˚ 24’ N) that extends to the eastern Sanjiang Plain of Heilongjiang
province in China, which includes Fujin City, Tongjiang City, Fuyuan Prefecture and Raohe Prefecture, was selected as
the test area (Figure 4). The Heilong Jiang (the River Amur) flows north of this area, the Songhua Jiang flows west of
this area into the Heilong Jiang, and the Wusuli Jiang flows east of this area. This area has an annual precipitation of
550-600 mm and is located in a continental monsoon climatic zone. The highest temperature during summer reaches
22-27 ˚C.
2.2 Data used for satellite image analysis
The Landsat Thematic Mapper (TM) data acquired on 25th June 1988 and the Landsat Enhanced Thematic Mapper Plus
(ETM+) data acquired on 24th June 2002 (Path-Row=114-27) were used for the land-use/land-cover classification. In
the test area, rice seedlings are transplanted simultaneously within a short period from the end of May to the beginningof June. At the end of June, the rice plants are still small and the paddy fields are still submerged. At this time, the crops
planted on dry croplands are also small and the soil surfaces are exposed.
The 0.5-degree grid cell data of monthly mean air temperature from 1901 to 2000 (Mitchell et al. 2003), were used
to examine the changes in cumulative temperature for the rice-cropping season from June to September. The 1978-2000
statistics of the total areas of paddy field and dry cropland derived from the Heilongjiang Statistical Yearbook 2004
(Heilongjiang Provincial Bureau of Statistics 2004) were used to examine the changes in the wheat area. The 1991-2001
statistics of the producer prices of rice, wheat and maize in China (FAO 2004), were used to analyze the impact of thechange in the cultivated crop.
2.3 Method for satellite image analysis
The Landsat TM data acquired on 25th June 1988 and ETM+ data acquired on 24th June 2002 were assigned
geographical coordinates using the Lambert Azimuthal Equal area projection (origin=110˚ E, 45˚ N), the spheroid
WGS84 and the pixel size 30 m x 30 m. The TM/ETM+ data with bands 3 (red), 4 (near infrared), and 5 (middle
infrared), in which the middle-infrared band was used in distinguishing submerged paddy fields (Okamoto andFukuhara 1996), were prepared for the land-use/land-cover classification. These data were classified by the ISODATA
method, in which 30 initial classes, 2σ class radius, and 98% convergence limit were used as parameters. The portionsof misclassification in the land-cover maps for 1988 and 2002 were corrected by viewing interpretations of composite
images, in which bands 5, 4 and 3 of TM/ETM+ data are shown as blue, green and red, respectively. Land-cover classes
were reclassified into the following 12 classes: paddy field submerged, paddy field overgrown, bare dry cropland, dry
cropland overgrown, bare ground, grassland, woodland, wetland, water, built-up, shade and shadow, and clouds.
The land-use/land-cover changes between 1988 and 2002 were examined by overlapping the1988 and 2002
land-cover maps. Five-hundred- and 1,000-meter buffers around a water source were defined as the water availabile for
irrigation. The areas within 500 and 500-1,000 meters from a water source, which included paddy field, wetland andwater, were extracted. The land-use/land-cover changes between 1988 and 2002 were examined in terms of the distanceto a water source.
2.4 Land-use/land-cover change
According to the Heilongjiang Statistical Yearbook 2004 (Heilongjiang Provincial Bureau of Statistics 2004), the total
area of paddy field increased rapidly during both the mid-1980s and mid-1990s (Figure 3). The land-use/land-covermaps are shown in Figure 5. According to the results from a comparison of the land-use/land-cover map of 2002 withthat of 1988, the total area of paddy field in the test area increased from 166 km2 in 1988 to 3,925 km2 in 2002, that is, it
increased 23.6-fold (Table 1(a), Figure 6). The total area of dry cropland in the test area decreased from 11,065 km2 in
1988 to 8,988 km2 in 2002, that is, it decreased by 19%. However, the total area of arable land, which included paddy
field and dry cropland, increased from 11,231 km2 in 1988 to 12,913 km2 in 2002. On the other hand, the total area of
wetland decreased from 3,260 km2 in 1988 to 21 km2 in 2002. The total areas of woodland and grassland decreased
from 15,274 km2 in 1988 to 13,447 km2 in 2002. Of the 3,925 km2 of paddy field in 2002, 96.5 km2 was also classified
as paddy field in 1988; however, 2,523 km2 was classified as dry cropland in 1988, 509 km2 grassland, 485 km2
woodland, and 281 km2 wetland. It was assumed that wetland had been easily changed to paddy field because of its
proximity to rivers. However, only 9% of wetland in 1988 had been changed to paddy field in 2002, whereas 40% of
wetland in 1988 had been changed to dry cropland in 2002. That is, the total area changed from wetland to paddy field
was smaller than that from wetland to dry cropland.
The total area of dry cropland, grassland and bare ground within 500 meters from a water source was 10,539 km2
and that within 500-1,000 meters from a water source was 5,164 km2 (Table 1(b), Figure 7). In 2002, 2,650 km2 of
paddy field was located within 500 meters from a water source, whereas 690 km2 of paddy field was located within
500-1,000 meters from a water source.
These results suggest that most wetlands in 1988 were reclaimed and changed to dry cropland in 2002, whereas apart of the reclaimed wetland was changed to paddy field. It is possible that the abrupt increase in the total area of
paddy field between 1988 and 2002 resulted from the conversion of dry cropland, which was favorably situated near awater source, to paddy field.
The Landsat TM/ETM+ data, which were used in this study, for 1988 and 2002 were acquired on 25th June and24th June, respectively. As Heilongjiang is a granary of spring wheat and maize, it was concluded that the land-coverclass “dry cropland overgrown” was cultivated with spring wheat and “bare dry cropland” was cultivated with maize,

referring to a cropping calendar. According to the land-use/land-cover maps, the total area of bare dry cropland was
6,063 km2 and that of dry cropland overgrown was 5,002 km2 in 1988, whereas the area of bare dry cropland was 8,988
km2 and that of dry cropland overgrown was 0 km2 in 2002 (Table 1(a)). These results suggest that farmers, who
cultivated spring wheat in those croplands, stopped wheat cropping and changed their dry cropland to paddy field.
2.5 Climate impact and probable socioeconomic impact on land-use change
Temperature and precipitation are natural factors that have an impact on cereal cropping. The cumulative temperaturesof the rice-cropping season (June through September) in 1901-2000 are shown in Figure 8. All the cumulative
temperatures at Harbin City, Jiamusi City and Qianjin Prefecture were just above the lower limit for cultivating
Japonica rice and were nearly constant through the 21st century. On the other hand, the test area has geographically low
and flat features. The level of groundwater is high and the cropland in the test area is favorably situated near a water
source. Consequently, it is surmised that climate conditions, such as temperature and water, did not cause an abrupt
increase in the total area of paddy field.
The producer price and consumer price related to cereal demand are socioeconomic factors that have an impact on
cereal cropping and land-use change. According to the Heilongjiang Statistical Yearbook 2004 (Heilongjiang Provincial
Bureau of Statistics 2004), the total area of paddy field increased rapidly in both the mid-1980s and mid-1990s (Figure9). In particular, the total area of paddy field in the mid-1990s increased markedly. The producer prices of rice, wheat
and maize in China from 1991 to 2001 are also shown in Figure 9. These three producer prices are not correctedaccording to inflation. An abrupt increase in the total area of paddy field from 1996 to 1998 followed a sudden increasein producer price. In 1994, the Chinese Government increased the purchase price of cereals by 40% (Chien, X., personalcommunication, 22 November 2004). The prices shown in Figure 9 are the mean producer prices in China. The
producer price of wheat produced in Heilongjiang is lower than that of wheat produced in other provinces because thequality of wheat produced in Heilongjiang is lower than that of wheat produced in other provinces. The producer priceof rice produced in Heilongjiang is higher than that of rice produced in other provinces because the price of Japonica
rice produced in Heilongjiang is higher than that of Indica rice produced in the southern provinces. Therefore, thedifference between the producer prices of rice and wheat produced in Heilongjiang is larger than those shown in Figure
9. Consequently, it is probable that the high producer price of rice in comparison with the producer prices of othercereals, is one of the strong motivations for converting dry cropland to paddy field.
3. Impacts of agriculture on environment
The population in Asia has steadily increased, as mentioned earlier, and both irrigated cropland total area andagricultural production have also increased to sustain the population increase (Figures 1 and 2). The effects of thesechanges on the environment are considerable.
The use of nitrogen (N) fertilizer, for example, has increased more than eightfold since 1961 (Figure 10), posing
considerable problems for water quality. Japan now uses an average of 100 kg of N equivalent per hectare (ha) offarmland, whereas farms in China and Western Europe use more than 100 kg ha-1. The average yield per unit area in
Western Europe is 10 t ha-1, whereas that in China is only about 5 t ha-1. This means that the N fertilizer used in China isreleased into the environment at a faster rate than that in Western Europe (Kawashima and Okamoto 1999).
In Southeast Asia, the average usage is 70 kg of N ha-1 (Kaewthip et al. 2003). In some areas in China 500 to 1,900
kg of N ha-1 is consumed (Zhang et al. 1996). In Japan, 400-1,560 kg of N fertilizer is consumed per hectare (Tokuda
and Hayatsu 2001).
The nitrogen load from fertilizer has a considerable impact on water quality (Okamoto et al. 2003). The rapidly
increasing nitrogen load from fertilizer may even have impacts on the environment and climate through changes invegetative biomass (NPP: Net Primary Production) and nitrous oxide (N2O) emission. We constructed a nitrogen load
map on the basis of measurements of nitrogen concentrations in major river basins in the eastern part of Asia.
3.1 Test sites for impact analysis of agriculture on environment
Major river basins from East to Southeast Asia were selected as test sites. The major crop in these test sites is rice. Rice
production in these regions represent 90% of that in the world. The test sites are located 30-150° E longitude and 90°
N-12° S latitude. The test sites included the river basins of Amur, Huanghe (Hwang Ho, Yellow), Chang Jiang (Yangtze),
Xi (Hsi, Pearl), Red (Hong Ha, Hungho), Mekong, Chao Phraya (Mae Nam) and Irrawaddy in the eastern part of Asia
(Figure 11).
3.2 Data for impact analysis of agriculture
The 0.5° version of the Total Runoff Integrating Pathways (TRIP) data (Oki and Sud 1998) was used to determine rivercourses in test areas. The global 1-km land-cover data set DISCover (Belward et al. 1999) was used for theland-use/land-cover classification of test sites.
The population data of Asia and the Russian Federation (Dao and Eckert 1998, Deichmann 1996) and the statistical
data on food consumption and food trade (FAO 2004, National Bureau of Statistics 2000) were used to calculate thenitrogen load from humans. The statistical data on fertilizer use and food production (FAO 2004, National Bureau of
Statistics 2000) were used to calculate the nitrogen load from the agricultural sector. The amounts data of NOx emission
from the Center for Global and Regional Environmental Research (CGRER) and University of Iowa (Bouwman et al.
1995) were adopted for estimating the nitrogen load from the industrial and economic sectors. The monthly mean air

temperature and monthly mean precipitation from the mean 1961-1990 climatology values (New et al. 1999) were used
for calculating the amounts of available water and discharged water in each river basin. The results from a simulation
study (Shindo et al. 2003) were summarized for each river basin. They were also summarized for countries or provinces
in China, using the country boundary data from the World Boundary Databank (CGER 1994) and the provincial
boundary data of China (LREIS 1996).
3.3 Method of impact analysis of agriculture
The land-use/land-cover areas from the DISCover (Belward et al. 1999) were reclassified into eight categories: irrigatedpaddy field, rain-fed paddy field, irrigated cropland, rain-fed cropland, woodland, grassland, other land-use/land-cover
and water. The typical cropping season of rice in the test sites for each country was determined by referring to Country
Rice Facts (Crop and Grassland Service 2000) and Major World Crop Areas and Climatic Profiles (World Agricultural
Outlook Board 1994) (Table 2).
Using the results from a simulation study (Shindo et al. 2003), the changes in the amount of nitrogen discharged
artificially were discussed. The model is based on some assumptions on N discharge. The rate of nitrogen discharge
from cropland depends on the cropping systems used. For a single rice cropping, nitrogen is discharged from a paddy
field at the rate of 2:1:1 from May to July. For the rice cropping in rotation with wheat, one-half of the annual amount of
nitrogen consumption is discharged for rice cropping at the rate of 2:1:1 from May to July, and the rest is discharged
equally from October to April. For double rice cropping, nitrogen is discharged at the rate of 2:1:1 from April to Juneand from August to October. For triple rice cropping, nitrogen is discharged equally every month. From upland cropland,
nitrogen is discharged equally every month. Nitrogen from livestock and humans is discharged equally every month.
NOx is emitted and deposited equally in the same grid cell every month.
The model mainly consists of two sectors, that is, humans and agricultural land. Humans take in nitrogen fromagricultural and marine products such as meat, fowl, eggs and fish, and discharge nitrogen into agricultural land and theenvironment. Nitrogen is deposited into agricultural land by biological fixation and fertilizer use, depending on thecropping systems used, and is discharged from agricultural products. Agricultural products, particularly cereals, areeaten in by humans and livestock. Livestock takes in nitrogen from cereals and discharges nitrogen into agriculturalland and the environment. Livestock meat is eaten by humans. Agricultural and marine products such as meat, fowl,
eggs and fish are traded between countries. The above basic figures were distributed equally into countries or provinces.
The amounts of nitrogen discharged from arable land and humans in major river basins from 1970 to 2050 weresummarized.
3.4 Changes in nitrogen load from fields and humans from 1970 to 2050
The nitrogen load using the results from a simulation study (Shindo et al. 2003) was evaluated. The amount of nitrogen
discharged from arable land and that from humans were summarized for major river basins (Table 3). In East Asia, thatis, the basins of the Amur River and Huanghe River, the nitrogen concentration of river water increased with an increasein the amount of nitrogen load. These river basins have low precipitation and denitrification rates due to a lowtemperature (Okamoto et al. 2003). The efficient use of these water resources is important for sustainable agriculture inthese river basins. In Southeast Asia, that is, the basins of the Chao Phraya River and Mekong River, the present
nitrogen concentration of river water is low due to a high denitrification rate (Okamoto et al. 2003). However, thenitrogen concentration of river water in these river basins will increase as nitrogen load was predicted to increase further.
It is feared that the nitrogen concentration will increase during the dry season. The use of these water resources must be
efficient for sustainable agriculture.
In 1970, the amount of nitrogen discharged from humans in each river basin was larger than that from arable land
(Table 3). The amount of nitrogen discharged from humans increases with population growth. The amount of nitrogen
discharged from arable land increases rapidly with an increase in fertilizer use due to economic growth. The rate ofincrease in the amount of nitrogen discharged from arable land is incomparably larger than that from humans. In 2000,
the amount of nitrogen discharged from arable land increased, which is larger than that from humans, except that fromthe Irrawaddy River basin. In 2050, the amount of nitrogen discharged from arable land in most river basins is predicted
to increase markedly, compared with that from humans. Rivers flowing in Southeast Asia are generally abundant inwater due to a high precipitation. Nitrogen discharged from humans and arable land is diluted by abundant water.
However, it is feared that nitrogen concentration will increase in the basins of the Amur River and Huanghe River due
to a low temperature or a low precipitation. It is also feared that the water quality in these river basins will deteriorate
due to the overuse of irrigation water.
Humans and farmlands occupy very important positions within the nitrogen flow, both discharging nitrogen
directly to the environment. Humans take in nitrogen from crops and discharge it back to agricultural land and theenvironment. Nitrogen is deposited in farmlands by biofixation and fertilizer use, and is discharged in crops. From thismodel of nitrogen flow and environmental load in food production, our group simulated the nitrogen load and nitrogenconcentration of water at test sites from 1970 to 2050 (Figure 12). The nitrogen load increased rapidly in the northernpart of China from 1970 to 2000. Over the next half century, the nitrogen load will continue to increase in China,
Vietnam, and the Philippines. The nitrogen concentration in river water has also increased in Northern China, and willcontinue to increase. The nitrogen load increases in other parts of the world will probably be minimal. The increases inNorthern China are the expected result of changes in climactic conditions, namely, poor precipitation and a low rate ofdenitrification due to the low temperature in this river basin.

4. Conclusions
The Landsat TM/ETM+ data collected over the northeastern part of Heilongjiang, acquired in 1988 and 2002, wereclassified and the changes in the total area of paddy field were examined. The total area of paddy field increased from166 km2 in 1988 to 3,925 km2 in 2002. The total area of arable land, which included paddy field and dry cropland,
increased from 11,231 km2 in 1988 to 12,913 km2 in 2002. On the other hand, the total area of wetland decreased from3,260 km2 in 1988 to 21 km2 in 2002. Of the 3,925 km2 of paddy field in 2002, 2,523 km2 was dry cropland in 1988. It
was assumed that wetland had been easily changed to paddy field because of its proximity to rivers. However, mostwetlands in 1988 were reclaimed and changed to dry cropland in 2002. It is possible that an abrupt increase in the total
area of paddy field between 1988 and 2002 resulted from the conversion of dry cropland, which was favorably situatednear a water source, to paddy field.
The cumulative temperatures of the rice-cropping season in 1901-2000 were just above the lower limit for
cultivating Japonica rice and were nearly constant through the 21st century. Therefore, temperature did not probably
cause the abrupt increase in the total area of paddy field.
An abrupt increase in the total area of paddy field from 1996 to 1998 followed a sudden increase in producer prices.
In 1994, the Chinese government increased the purchase price of cereals by 40%. It is probable that the high producer
price of rice, compared with those of other cereals, is one of the strongest motivations for converting dry cropland to
paddy field.
Hydrological conditions change according to changes in water use in agriculture. It is also considered that a suddenchange in hydrological conditions and land-use/land-cover negatively affects the ecosystem and environment.
Nitrogen load is predicted to increase from 1970 through 2050. This trend is clear in the eastern part of China,
Vietnam and the Philippines. The reason is that N fertilizer usage is increasing. The concentrations of nitrogen (NO3-N)
are also predicted to increase from 1970 through 2050. This trend is clear in the northern part of China. The reason isthat N fertilizer usage is increasing and this region has a low precipitation. The nitrogen load from farmlands will be far
higher than that from humans (settlements), with increasing N fertilizer usage and economic growth.
Thus, agriculture has a negative impact on the ecosystem, from the viewpoint of nitrogen cycle. It is feared that theecosystem will be affected more severely. Therefore, we suggest that the use of water resources and fertilizer must be
efficient for sustainable agriculture.
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223-231.

rest
Bangladesh
Cambodia
China
India
Indonesia
Japan
Korea, Dem People's Rep
Korea, Republic of
Laos
Malaysia
Mongolia
Myanmar
Nepal
Pakistan
Philippines
Singapore
Sri Lanka
Thailand
Viet Nam

8 10 6

6 10 6

4 10 6

2 10 60Year

Figure 1
Population in Asia and the world.
Source: FAOSTAT Home Page (FAO 2004)
rest
Viet Nam
ThailandSri Lanka
Philippines
Pakistan
Nepal
MyanmarMongolia
Malaysia
Laos
Korea, Republic ofKorea, Dem People's Rep
Japan
Indonesia
India
China
Cambodia
BhutanBangladesh

6 10 8

4 10 8

2 10 80YearFigure 2
Rice production in Asia and the world
Source: FAOSTAT Home Page (FAO 2004)

2

Total areakm2) )
Area (x1000(x1000 km
120

Dryland cropland100

Paddy field

80
60
40
20

20
00

19
98

19
96

19
94

19
92

19
90

19
88

19
86

19
84

19
82

19
80

19
78

0

Figure 3
Changes in total areas of paddy field and dry cropland in Heilongjiang, China
Source: Heilongjiang Statistical Yearbook 2004 (Heilongjiang Provincial Bureau of Statistics 2004)

Figure 4

Test area: Eastern Sanjiang Plain, Heilongjiang Province, China (Map is cited from UT Library Online(2001) and is modified)(a) 25 June 1988(b) 24 June 2002

Figure 5Land-use/land-cover maps; (a) 25 June 1988 and (b) 24 June 2002.

Table 1
Total areas of land-use/land-cover in 1988 and 2002(a) Changes in total areas of land use/land cover between 1988 and 2002
2002 ∖ 1988
C 1: Paddy Field Submerged
C 3: Bare Dry CroplandC 4: Dry Cropland Overgrown
C 5: Bare GroundC 6: Grassland
C 7: Woodland
C 8: Wetland
C 9: Water
C 10: Built-up
Total

C1
97
26
0
17
17
0
0
4
2
166

C3
1,740
2,565
0
588
902
0
0
0
219
6,063

C4
783
1,812
0
547
1,540
0
0
0
273
5,002

C6
509
1,480
0
372
2,079
0
1
0
58
4,556

C7
485
1,702
0
365
51
7,600
2
41
96
10,718

C8
281
1,304
0
492
1,097
0
9
0
32
3,260

C9
30
98
0
104
155
0
8
580
22
1,014

Total
3,925
8,988
0
2,485
5,847
7,600
21
626
701
81,316(b) Changes in total areas of land use/land cover between 1988 and 2002 in terms of distance from water source
2002 ∖ 1988

C1

C3

C4

C6

C7

C8

C9

C13

C14

C15

C16

Total

C1
C3
C4
C5
C6
C7
C8
C9
C10
Total

97
26
0
17
17
0
0
4
2
166

48
631
0
71
285
0
0
0
34
1,075

18
436
0
66
315
0
0
0
39
881

16
421
0
60
380
0
0
0
8
891

84
538
0
132
50
5,436
1
14
22
6,601

279
1,293
0
481
1,014
0
8
0
31
3,149

30
98
0
104
155
0
8
580
22
1,014

11
285
0
28
0
454
0
0
9
793

2
91
0
22
269
112
1
0
4
509

2,650
2,871
0
1,057
2,288
1,152
3
27
359
10,539

690
2,298
0
448
1,066
446
0
1
170
5,164

3,925
8,988
0
2,485
5,847
7,600
21
626
701
81,316The changes in the total areas of classes “C2: paddy field overgrown” in 1988 and 2002, and “C5: bare ground” and“C10: built-up” in 1988 are 0 km2 and are thus omitted. The class “C13” denotes isolated woodland, “C14” isolated
dry cropland, grassland, bare ground and wetland, “C15” regions within 500 meters from a water source, and “C16”regions within 500-1000 meters from a water source.

C 10: Built-up
C 9: Water
C 8: Wetland
C 7: Woodland
C 6: Grassland
C 5: Bare GroundC 4: Dry Cropland Overgrown
C 3: Bare Dry CroplandC 2: Paddy F. Overgrown
C 1: Paddy F. Submerged

22
Total area
Area(km(km) )
10,000

8,000
6,000
4,000
2,000
0
C1

C2

C3

C4

C5

C6

C7

C8

C9

C 10

Land
in 2002
Land use/Land
use/Land cover for
2002

Figure 6

Changes in total areas of land-use/land-cover from 1988 to 2002

C 16: Access to Water <500-1000m
C 15: Access to Water <500m
C 14: Isolated SpaceC 13: Isolated Woods
C 10: Built-up
C 9: Water
C 8: Wetland
C 7: Woodland
C 6: Grassland
C 5: Bare GroundC 4: Dry Cropland Overgrown
C 3: Bare Dry CroplandC 2: Paddy F. Overgrown
C 1: Paddy F. Submerged

2 2
Total
Areaarea(km(km) )10,000
8,000
6,000
4,000
2,000
0
C1

C2

C3

C4

C5

C6

C7

C8

C 9 C 10Land
coverforin 2002
Land use/Land
use/Land cover
2002

Figure 7

Changes in total areas of land-use/land-cover from 1988 to 2002 in terms of distance from water source:
Class “C14: isolated space” is a class of isolated dry cropland, grassland, bare ground and wetland.

Cumulative Temperature (degree day)
3000
2500
2000
1500
1000
500

Qianjin
Jiamusi
Harbin
Qianjin: y=-0.0965x+2462.9

R=0.03601900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Year
Figure 8

Cumulative temperature changes in Heilongjiang from 1901 to 2000

Procucer
Producer Price
price(Yuan Renminbi)

2

Total
Areaarea(x1000(x1000
kmkm
) 2)

18

1,800

Paddy field

16

1,600

Rice Producer Price(Yuan Renminbi)
Wheat Producer Price(Yuan Renminbi)
Maize Producer Price(Yuan Renminbi)

14
12
10

1,400
1,200
1,000
800

6

600

4

400

2

200

0

0

19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00

8

Figure 9

Changes in total area of paddy field in Heilongjiang and producer prices of rice, wheat and maize in China
Source: Heilongjiang Statistical Yearbook 2004 (Heilongjiang Provincial Bureau of Statistics 2004)

8

1 10

rest
Bangladesh
Cambodia
China
India
Indonesia
Japan
Korea, Dem People's Rep
Korea, Republic of
Laos
Malaysia
Mongolia
Myanmar
Nepal
Pakistan
Philippines
Singapore
Sri Lanka
Thailand
Viet Nam

7

8 10

7

6 10

7

4 10

7

2 10

0

Year

Figure 10
Nitrogen fertilizer usage in Asia and the world.
Source: FAOSTAT Home Page (FAO 2004)

Figure 11

Major river basins in continental Asia (River basin data is cited from the TRIP data (Oki and Sud 1998)
and is modified)

Table 2
Typical rice-cropping seasons. Information was obtained from Crop and Grassland Service (2000) andWorld Agricultural Outlook Board (World Agricultural Outlook Board 1994) with modification.
Country or Region
First Rice-Cropping Season
Second Rice-Cropping Season
Bangladesh
May - October
January - April
Cambodia
July - OctoberDecember - April
Northern Part of China
May - AugustSouthern Part of China
March - June
July - OctoberIndia
May - October
January - April
LaosJuly - October
Malaysia
October - December
MyanmarJuly - OctoberDecember - April
Nepal
July - September
PakistanJune - September
ThailandJune - September
November - March
VietnamJune - September
January - April* The northern part of China is defined as the region north of the Chang Jiang River, and the southern part of China as
the region south of the Chang Jiang River.
Table 3
Changes in nitrogen load from arable land and humans from 1970 to 2050. The amount of nitrogen
discharged from arable land and (/) that from humans are listed in each column. The amount of nitrogen is expressed inunits of 1,000 ton N.
River Name/ Country
Amur/ China and Russian Fed.
Huanghe/ China
Chang Jiang/ China
Xi/ China
Red/ China and Vietnam
Mekong/ China, Laos, Cambodia and Vietnam
Chao Phraya/ Thailand
Irrawaddy/ Myanmar

1970
103/ 137
157/ 233
563/ 908
113/ 184
29/ 5822/ 159
0/ 66
2/ 75

2000
1,386/ 304
2,125/ 516
7,633/ 2,014
1,527/ 408
368/ 137
790/ 306
336/ 136
34/ 2292030
3,227/ 450
4,951/ 765
17,778/ 2,982
3,562/ 604
938/ 207
2,214/ 481
776/ 181
124/ 299

2050
3,277/ 466
5,028/ 790
18,054/ 3,082
3,630/ 625
1,167/ 237
2,831/ 559
892/ 196
172/ 342

(a) Nitrogen load(b) Nitrogen concentrations

1970

1970

2000

2000

2030

2030

2050

2050

Figure 12

Impacts of food production to water quality in Asia; (a) Nitrogen load (t year-1), and (b) nitrogenconcentrations (NO3-N ppm).

Utilization of Satellite Imagery for Vegetation Drought Monitoring in Indonesia
Eleonora RUNTUNUWU
Indonesian Agroclimate and Hydrology Research Institute
Email: runtunuwu2001@yahoo.com
Abstract:
Drought is a natural hazard that has significant impact on environmental, economic and socio economic aspects. The
Indonesian Agroclimate and Hydrology Research Institute (IAHRI) is currently developing a vegetation droughtmonitoring system by using either the meteorological or satellite imagery as part of its effort to combat the effects ofdrought in Indonesia. The project methodology is depended on six main steps: (1) Development of meteorological
database system, (2) Analysis of vegetation drought using Water and Agroclimate Research Management model(WARM ver. 1.0), (3) Determination the drought index based on meteorological data, (4) Building the ground station
High Resolution Picture Transmission (HRPT) AVHRR receiver, (5) Application the remote sensing for vegetation
drought monitoring, and (6) On-line spatial drought information delivery. The primary goal of this project is todevelop an efficient system in Indonesia to analyze and deliver drought related information to the stakeholders andagricultural industries on the ground for drought preparedness and management.
1. Introduction
There are increasing pressures on water resources as a result of global population growth, economic development
and the adverse effects of land use and climate change. Any deficit or limitation in water supply will be most criticalin drought periods, and competing water needs maybe the cause of conflicts. Therefore, in order to design andmanage water resources schemes, it is essential to monitor the spatial and temporal variability of droughts.
The essential for quantification of drought impacts and monitoring is of critical importance in politically,
economically, environmentally of Indonesia. Several users such as top level policy makers at the national level, thegovernments in province, researchers, farmers, trades and water managers and national/international relief agencies
are interested in reliable and accurate drought information in order to mitigate impact on surface and groundwater
resources.
Traditional methods of drought monitoring rely on rainfall data, which are limited in the network of stations andincomplete climate data, often inaccurate caused human error or crash equipment and most importantly, difficult toobtain data in near real time either spatially or temporally. In this sense, remote sensing technology has greatly
enhanced our ability to monitor and manage the natural resources, especially in the areas of water resources.
2. What is drought?
Tallaksen and Lanen (2004) noted that in general drought is a sustained and regionally extensive occurrence of
below average natural water availability, and can thus be characterized as a deviation from normal conditions ofvariable such as precipitation, soil moisture, groundwater and streamflow. It is a reoccurring and worldwide
phenomenon, with spatial and temporal characteristics that differ significantly from one region to another. Jeyaseelan(2003) and Tallaksen and Lanen (2004) illustrated in Figure 1 the different types of droughts, there are:meteorological droughts, agricultural droughts, and hydrological droughts. The impacts can be classified as
economic, social, and environmental and it depends on the complex interaction between the physical environment
and the socio-economic system.
The primary cause of a drought is the lack of precipitation over a large area and for an extensive period of time,called a meteorological drought. The meteorological drought identification is usually carried out on the basis ofhistoric data analysis of rainfall or combined rainfall and evaporation as recorded at meteorological stations. For
example is the Standardized Precipitation Index, SPI (McKee et al. 1995, Agnew, C.T, 1999). The SPI is a statisticalindicator evaluating the lack or surplus of precipitation during a given period of time as a function of the long-term“normal” precipitation to be expected during the period.
The agricultural drought is occurred when soil moisture is insufficient to support crops which the water deficit
propagates through the hydrological cycle and combined with the high evaporation rates. This drought could bemonitored direct to the vegetation since the vegetation condition reflects the overall effect of rainfall, soil moisture,weather and agricultural practices. Therefore, satellite based monitoring of vegetation plays an important role indrought monitoring and early warning. Many studies have shown the relationships of vegetation index andagricultural drought. Thenkabail et al. (2004) developed methods that allow two generations of sensors (AVHRR and
MODIS) to be combined beneficially for drought assessment and monitoring on a regional and a near-real-time. Parket al. (2004) determined the influences of hydrologic soil characteristics on early detection of drought with MODIS
thermal emission data and response time of vegetation index to the thermal signals.
Within the hydrological cycle, groundwater is normally the last to react to a drought situation, unless surface water ismainly fed by groundwater. In deep aquifers the slow reaction of groundwater implies that only major
meteorological droughts will finally show up as groundwater droughts. The lag between a meteorolical and agroundwater drought may amount to months or even years, whereas the lag between a meteorological andstreamflow drought varies from days in a flashy catchments to months in a groundwater-fed catchments.
3. Recent Drought Event
The area of Indonesia is 2 million sq. km (736,000 sq. mi.),included the maritime area 7,900,000 sq. km. It consists ofmore than 17,000 tropical islands in an archipelago across the
Pacific Ocean, south of Malaysia and the Philippines and northof Australia. Only 1,000 of Indonesia's islands are permanently
settled. It has the world's fourth biggest population, with morethan 224 million (2004). Its climate is equatorial, but is cooler
in the highlands. The climate of Indonesia is generally
characterized by two seasons: dry (April to September) andwet (October to March). Based on the number of dry and wetmonths, the country is divided into 14 agroclimatic zones, ofwhich 11 are considered as sensitive to extreme climate
variability since rain fluctuations can upset establishedcropping patterns.
Figure 1. Propagation of drought through thehydrological cycle (Source: Jeyaseelan, 2003)
From 1877 to 1997, 93 percent of the drought years in Indonesia have been linked to El Niño events(http://www.isse.ucar.edu/un/indonesia.html). Several studies show a clear positive correlation between normalized
Indonesian rainfall anomalies and the Southern Oscillation Index (SOI). While in El Niño years the onset of themonsoon is later than normal, during La Niña years the onset is earlier in most areas. Therefore, an El Niño event
causes delayed planting and consequently a delayed and reduced harvest. A La Niña year offers the possibility ofadvancing the planting season with an early increased harvest, as well as the possibility of planting an additional crop.
4. Development of Vegetation Drought Monitoring System in Indonesia
Drought monitoring mechanism exists in most of the countries based on ground based information on drought relatedparameters such as rainfall, weather, crop condition and water availability, etc. Earth observations from satellite arehighly complementary to those collected by in-situ systems. Satellites are often necessary for provision of synoptic,
wide area coverage and frequent information required for spatial monitoring of drought conditions. The present stateof remotely sensed data for drought monitoring and early warning is based on rainfall, surface wetness, temperatureand vegetation monitoring.
Since 2003, Indonesian Agroclimate and Hydrology Research Institute (IAHRI) has been developing the vegetation
drought monitoring system which could be divided into six main steps: (a) Development of meteorological databasesystem, (b) Analysis of vegetation drought using WARM ver 1.0, (c) Determination the drought index based on
meteorological data: Standardized Precipitation Index, (d) Building the ground station High Resolution Picture
Transmission (HRPT) AVHRR receiver, (e) Application the remote sensing for drought monitoring, and (f) On-line
spatial drought information delivery.
a.

Development of meteorological database system in 2003

In the process of implementing vegetation drought monitoring, the acquisition of state-of-the-art equipment forstrengthening the national meteorological network of observing stations became necessary. Presently IAHRI has

2009
2008
2007
2006
2005
2004
2003

UPDATING AND EVALUATION
Implementation and validation

On-line spatial drought information delivery
Application the remote sensing for droughtmonitoring

Determination the drought index based on meteorological data
and building the ground station HRPT receiver

Analysis of vegetation drought using WARM ver 1.0 model

U
P
D
A
T
I
N
G

Development of meteorological database system

Figure 3. The roadmap of drought monitoring system development 2003-2009
only 75 automatic weather stations (AWS), 4 automatic rainfall stations (ARS), and 26 automatic water level
recorders (AWLR), therefore, collecting meteorological data from other institutions (approximately 5000 manual
rainfall gauges but 1200 stations collected yet) became important in order to get climate information whole Indonesia(Figure 4). The meteorological data are air temperature minimum (°C), air temperature maximum (°C), mean air
temperature (°C), minimum humidity (%), maximum humidity (%), mean humidity (%), rainfall (mm), wind speed(m/s), and global radiation (MJ/m2).

Figure 4. The Indonesian meteorological database systemb.

Analysis of vegetation drought using WARM ver 1.0 model (2004).

In 2004, IAHRI has developed a water balance model to analyze the crop water management which connected to themeteorological database. In soil moisture drought condition the model is able to recommend the volume and time forsupplemented irrigation information.

Figure 5. The water balance analysis to (a) estimate the water stress condition and (b) volume and time of
supplemented irrigation

c.

Determination the drought index based on meteorological data

The SPI is used to study the relationships between drought duration, frequency, and time scale, as described by
Agnew (1999). The SPI for each station was determined using this equation: SPI = ( X ik − X i ) / σ i , where σ i is
standardized deviation for the ith station, X ik is rainfall for the ith station and kth observation, and X i is mean
rainfall for the ith station. All negative SPI values are taken to indicate the occurrence of drought, while all positive
values show no drought. To determine drought intensity, SPI values of equal to or less than -0.50 were used. The
frequency of occurrence of drought years within each decade was determined on a station-by station basis by simple
statistical means, and the result was then summed over the 40year period.
MONTHLY SPI 1997
SPI < 0 : 71%

MONTHLY SPI 2004
SPI < 0 : 58%

4

2

4

1

3
2

0
SPI

SPI

3

-1
-2

1
0
-1

-3

-2

0

100

200

300

400

500

600

700

STATIONS

-3
0

100

200

300

400

500

600

700

STATIONS

Figure 6. (a) The monthly SPI and (b) The Spiline SPI for 630 stations of Center of Java Provinced.

Building the ground station High Resolution Picture Transmission (HRPT) receiver (2005/2006)

In 2005/2006 IAHRI is conducting to build the HRPT ground station. The HRPT service installed on the NOAA
satellites which is the main source of high quality data from polar orbiting meteorological satellites. The data stream
not only contains full resolution images in digital format from the Advanced Very High Resolution Radiometer(AVHRR) instrument but also the atmospheric information from the suite of sounding instruments. Through HRPT
reception the user site can acquire data from three or more consecutive overpasses twice each day from each satellite,
giving high resolution data coverage of a region extending to about 1500 km radius from the user station. Compare
to other satellites, the HRPT is more coarse spatial resolution, but very high temporal resolution that useful indrought monitoring activities. The imagery gives a snapshot of the meteorological conditions and can also be used
for many land applications, while the sounding data gives detailed atmospheric data that may be processed and usedin regional weather prediction models.
e.

Application the remote sensing for drought monitoring

Kogan (2002) described the world drought condition using Vegetation Condition Index (VCI, Kogan, 2002).
Marshall (2003) applied the Apparent Thermal Inertia (ATI) and vegetation indices to determine drought in Cordoba,
Argentina. In other case, Bhuiyan (2004) applied various drought indices for monitoring the drought, i.e.
Standardized Precipitation Index (SPI), Standardized Water–Level Index (SWI), Normalized Difference Vegetation
Index (NDVI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI) over Aravalli Terrain ofIndia. Table 1 showed the drought index for monitoring three types of drought using meteorological and satellite data.

Figure 7. The HRPT function diagram
Many different satellites are used to analyze and monitor drought. For example, Park et al. (2004) used Moderate
Resolution Imaging Spectroradiometer (MODIS) combined with a climatic water budget model and the STATSGO
database within a GIS technology to determine hydrologic soil properties for detecting drought in western – central
Kansas.
Table 1. Various drought index for monitoring drought using meteorological data and satellite imagery
No

Drought
index

FormulaNormalcondition

Severedrought

Healthy
vegetation

Types ofdrought

SPI

SPI = X ij − X im / σ)-1 to +1

0

-1

+1

Meteorological

2.

NDVI

NDVI = (λ NIR − λ RED ) / (λ NIR + λ RED )-1 to +1-1+1Agricultural

3.

*DEVNDVIDEVNDVI = NDVIi − NDVImean-1 to +1

Depends on
the location
0

-1

+1

Agricultural

4.

VCI

VCI = 100 NDVI − NDVI min / NDVI max − NDVI min

0 to
100%

50%

0%

100%

Agricultural

5.

TCI

TCI = 100(Tmax − Tcur ) / Tmax − Tmin

0 to
100%

50%

0%

100%

Agricultural

6.

VHI

VHI = 0.5(VCI ) + 0.5(TCI )<10 to >
40

< 30

< 10

> 40

Agricultural

7.

SWI

** SWI = (Wij − Wim )/ σ

<0 to >4

>1

>4

<0

Hydrological

1.

(

Range

(

)(

(

)

)

*Drought severity index; ** Wij is the seasonal water level for the ith well and jth observation, Wm is seasonal mean, and σ is its standardization.

Figure 8 (left) shows the August (the driest month) spatial distribution of DEVNDVI over Indonesia on 1996 (wetyear), 1997 (driest year) and 1998 (pasca El Nino) where areas in green is healthy vegetation and areas in red isdrought affected. In 1997, most of the pixels in the study area have red colour, indicative of the negative deviationfrom NDVI mean. This result match well with the actual condition in Indonesia since at the time a large part of thecountry suffered from severe drought, resulting in a huge shortfall in rice production that necessitated the import ofover five million metric tons of rice to ensure food availability to the economically weaker sections of the society. In
the forestry sector, the effects of large-scale forest fires during 1997-98 were unprecedented, damaging more than 9.7
million ha of forest area. The smoke and trans boundary haze from these fires affected not only Indonesia but also
other Southeast Asian countries, in particular Brunei Darussalam, Malaysia and Singapore.
Figure 8 (right) shows the VCI that describes the moisture condition. The high value of VCI (green area)
corresponded to unstressed vegetation or undrought condition and the VCI value close to zero percent reflects an
extremely dry month. The duration of the successive months period below normal conditions and magnitude of thedeviation are two powerful indicators of drought severity. In this case, the VCI and DEVNDVI have similar pattern,
therefore we expected they have strong correlation that could be used to monitor the drought.

f.

On-line spatial drought information delivery (2007)

The last step of this project is delivering the drought information using internet technology to stakeholders or
agricultural industries, since at present, there is no efficient system in Indonesia to analyze and deliver a near-realtime drought-related information. An information system should also allow drought development in the region to bemonitored at different scales, such as provinces level and smaller administrative subdivisions.

Figure 8. Indonesia drought severity index (left) and vegetation condition index (right) for August
1996, 1997, 1998 based on NOAA image during 1982-1998.
5. Conclusions:
Droughts is one of the most devastating natural hazards in the world, and causing extensive damage to agriculture aswell human and wild live and local economies. Therefore, IAHRI is currently developing a vegetation droughtmonitoring system by using either the meteorological or satellite imagery as part of its effort to combat the effects ofdrought in Indonesia. The ground meteorological data and remote sensing as well GIS technologies significantly
contributes in the vegetation drought analysis. In this paper, brief review of the drought vegetation monitoring
system development in Indonesia by IAHRI for drought preparedness and prevention.
References:
Agnew, C.T. 1999. Using the SPI to Identify Drought. Drought Network News, 12(1), pp. 6-12.
Bhuiyan, C. (2004). Various drought Indices for monitoring Drought Condition in Aravalli Terrain of India. http://
www.isprs.org/istanbul2004/comm7/papers/243.pdf.
Marshall, G. (2003). Drought Monitoring using remote Sensing. http://www.ees.nmt.edu/zhou/Geoffrey% 20Marshall.ppt
http://www.isse.ucar.edu/un/indonesia.html
IPCC (2001). Climate Change 2001. The scientific Basis. IPCC Third Assessment Report, Intergovernmental Panel of Climate Change (IPCC),
UNEP, WMO, Cambridge University Press, Cambridge, UK.
Jeyaseelan A.T. (2003). Droughts and Floods Assessment and Monitoring using remote Sensing and GIS. Satellite Remote Sensing and GIS
Applications in Agricultural Meteorology pp.291-313.
Kogan, F. N. 1997. Global Drought watch From Space.- Bull. Am. Met. Soc., 78, 621-636.
Kogan, F. N. 2000. Contribution of Remote Sensing to Drought Early Warning. In Early Warning Systems for Drought Preparedness and Droughtmanagement. Ed. D.A. Wilhite and D.A. Wood. 75-87. Geneva: world Meteorological Organization.
Kogan, F. N. 2002. World Droughts in the New Millenium from AVHRR-based Vegetation Health Indices. Eos, Transaction, Amer. Geophy.
Union, 8(48), pp. 562-563.
McKee, T. B., Doesken, N. J., Kleist, J. 1993. The Relationship of Drought Frequency and Duration to Time Scales. Proc. 8th Conf. on Appl.
Clim., 17-22 Jan. 1993, Anaheim, CA, 179-184.
McKee, T.B., Doesken, N. J., Kleist, J. 1995. Drought Monitoring with Multiple Times Scales. Proc. 9th Conf. on Appl. Clim., 15-20 Jan. 1995,
Dalas, TX, 233-236.
Tallaksen, L. M., and Lanen, H. A. J. (2004). Hydrological Drought. Processes and estimation methods for streamflow and groundwater.
Development in Water Science 48. 579 pages.
Thenkabail, P. S., Gamage, M.S. D. N., and Smakhin, V.U. (2004). The Use of Remote Sensing Data for Drought Assessment and Monitoring inSoutheast Asia. Research Report 85. IWMI. Colombo, Sri Lanka; International Water management Institute. 27 pages.
Zhang P., Anderson, B., tan B., Huang D., Myneni R. (2005). Potential monitoring of crop production using a satellite-based Climate-Variability
Impact Index. Agricultural and Forest Meteorology. Article Press. http://www.elsevier.com/locate/agrformet-3440. 15 pages.

Characteristics of dust event in East Asia:
Focus on the Gobi Desert, and Mongolia regions
Yasunori KUROSAKI
Center for Environmental Remote Sensing (CEReS), Chiba University
E-mail: ykurosak@faculty.chiba-u.jp, ykurosaki2004@yahoo.co.jp
Abstract
This study investigated the effect of snow and vegetation covers on dust emission by the correlation
analysis of strong wind frequency and dust emission frequency, where the strong wind is defined with a constantthreshold 6.5 m/sec. This correlation should be high (low) where the variance of land surface environment is low(large). In addition to this idea, referring to the parameterizations of threshold wind speed by NDVI and snow
cover fraction, we built four hypotheses as shown in section 3.1. However, our obtained results disagreed withthese in many points, and this indicates problems in the current parameterizations. We will discuss the reasons ofthese disagreements and some methods will be proposed to clarify these problems.
1. Introduction
Dust gives impacts on our lives from many aspects, which are climate change via radiation process,changes in ice cap albedo, respiratory disease, cropland losses, damages on transportation, ocean fertilization,neutralization of acid rain, disease transmission, etc. Many researchers have developed numerical models of dust
cycle (i.e., emission, born, and deposition processes), which have played an important role in assessing suchimpacts of dust. However the current models have insufficient accuracy especially in the treatment of emission
process. One of the reasons of such problem is the immature skill of the treatment of geographic information
system (GIS) data concerning land surface environment (e.g., soil particle distribution, soil wetness, vegetation
cover, snow cover, land cover type) in dust emission model. In some models, the threshold wind speed of dust
emission is parameterized by leaf area index (LAI) derived from Normalized Difference Vegetation Index(NDVI) and snow cover fraction from satellite data (Kurosaki and Mikami, 2004). When we use theseparameterizations, the threshold wind speed is high in summer and winter because NDVI and snow cover
fraction are large, respectively. Moreover, the interannual variance of threshold wind speed is large as wellbecause of the large variance of NDVI and snow cover fraction.
Kurosaki and Mikami (2005) discussed the variation of land surface environment with the correlation ofstrong wind frequency and dust emission frequency where the strong wind is defined by a constant threshold
wind speed 6.5 m/sec. This correlation should be high (low) where the variance of land surface environment islow (large). Although their analysis period is limited to spring (March, April and May), they obtained following
results: (1) the correlation is high in March and April but low in May in the Gobi Desert; (2) they are high inMarch but low in April and May in the Northern and the Western Mongolia, whose major land cover type isgrassland. These results suggest that the vegetation cover begins to suppress dust emission from April in
Mongolia and from May in the Gobi Desert.
This study discusses the effect of snow and vegetation covers on dust emission with the correlation ofstrong wind frequency and dust emission frequency. Although the analysis period is limited to spring in Kurosaki
and Mikami (2005), we calculated the correlation coefficient of each month from January to December. From
this analysis, we discuss the interannual variance of land surface environment in each month in each region.
2. Data and method
In the discussion of dust emission and surface wind, we used the present weather and the wind speed at a 10
m height included in SYNOP, which is a routine report of 3-hourly surface meteorological data. The period is
March 1988 to June 2005. The regions are the Gobi Desert, the Northern Mongolia, and the Western Mongolia,
which are given in Kurosaki and Mikami (2005). According to the data from U.S. Geological Survey, the major
land cover types are semi desert shrubs in the Gobi Desert, grassland in the Northern Mongolia, and the semi
desert shrubs and grassland in the Western Mongolia.
The frequency of dust emission is defined as the percentage of the number of dust emissions to the total
number of observations in the given period and the given region. Similarly, the frequency of strong wind isdefined, where a strong wind is defined with a constant threshold 6.5 m/sec, which is the threshold wind speed
for a dust emission in some numerical models.
Our used snow cover and vegetation cover data are derived from satellite images. The snow cover data isderived from the Special Sensor Microwave/Imager (SSM/I). This product, whose title is Near Real-Time SSM/I
EASE-Grid Daily Global Ice Concentration and Snow Extent, is distributed by the National Snow and Ice Data
Center (NSIDC). We define the snow cover fraction as the ratio of the number of snow cover days to the total

days during the given period. In the discussion of vegetation cover, we used the NDVI of the NOAA/NASA
Pathfinder AVHRR Land (PAL) program.
3. Results and discussion
3.1 Seasonal variation
In spring, dust emissions are frequent in the Gobi Desert, and they are frequent as well in the Northern andWestern Mongolia, though the dust emission frequencies are lower than those of the Gobi Desert. From summer
to winter, dust emissions are observed in the Gobi Desert, though the frequency is low. On the other hand, they
are seldom or never observed in the Northern and Western Mongolia. The NDVI are almost the same and very
low in these three regions during spring, while they are much larger in the Northern and Western Mongolia thanthat in the Gobi Desert during summer. In winter, snow cover entirely extends over the Northern and the Western
Mongolia. In March, the major dust source regions are not covered by snow. These results suggest that snow andvegetation covers suppress the dust emission in Mongolia during winter and summer. In comparison with thesegrassland regions (i.e., Mongolia), as the NDVI is not so large and snow cover is seldom observed throughout
the year in the Gobi Desert, we can guess that the frequency of strong wind might control the dust emission
frequency.
From these results, we can built four hypotheses; (1) the correlations are low in the Northern and theWestern Mongolia during winter because of high snow cover fraction; (2) the correlations are low in the same
regions during summer because of vegetation cover; (3) the correlations are high in the same regions during
spring and autumn because of less snow and vegetation covers; (4) the correlation is high throughout the year in
the Gobi Desert because of less snow and vegetation covers throughout the year.
3.2 Correlation of strong wind frequency and dust emission frequency
In the Northern and the Western Mongolia, the correlation of strong wind frequency and dust emission
frequency is high in spring and low in summer. This result agrees with the hypotheses given in the previous
section. However, the following all results disagree with the hypotheses. The correlation coefficient reaches thehighest in winter, although the snow cover fraction is high. Moreover, the correlation is not so high in autumn,
although NDVI and the snow cover fraction are not so high. In the Gobi Desert, the correlation coefficient
clearly varies with the seasonal march as well as in Mongolia.
These results suggest that the current parameterization of threshold wind speed should not be applied in the
dust emission model. Some potential reasons of the above disagreements occur to us. For example, the land
surface environment frequently differs in the place and in the season, even if NDVI is the same. Although NDVI
is low in Mongolia in both spring and autumn, we can guess possible land surface environment, which are rarevegetation cover in spring and broadly distributing dry grass in autumn. Similarly, although the snow cover
fraction is high in winter, if the quality of snow is dry and powdery, the threshold wind speed is not so high. On
the contrary, although the snow cover fraction is not so high just like in spring, the threshold wind speed might
be very high, if snow is wet. By parameterizing the threshold wind speed by such factors, we can newly define
the strong wind by a variable threshold in accordance with snow quality and LAI including dry grass. The
correlation coefficient using such variable threshold wind speed can be a good index to validate thisparameterization. To achieve this goal, we need GIS data concerning land surface environment obtained fromfield surveys and satellite observations.
4. Summary
This study investigated the effect of land surface environment on dust emission by the correlation analysisof strong wind frequency and dust emission frequency, where we define the strong wind with a constantthreshold. This correlation should be high (low) where the variance of land surface environment is low (large). In
addition to this idea, referring to the parameterizations of threshold wind speed by NDVI and snow cover
fraction data, we built four hypotheses indicated in section 3.1. However, our obtained results disagreed withthese hypotheses, and this pointed out the problems in the current parameterizations. This study presented somepotential reasons of these disagreements, which are snow quality and dry grass. One of the methods to validate
the effect of these factors on dust emission is to build a new parameterization using such factors. The correlation
coefficient using such new parameterization can be a good index to validate this parameterization. To make aparameterization, we need GIS data concerning land surface environment obtained from field surveys andsatellite observations.
References
Kurosaki, Y. and M. Mikami (2004), Effect of snow cover on threshold wind velocity of dust outbreak, Geophys.
Res. Lett., 31, L03106, doi:10.1029/2003GL018632.
Kurosaki, Y. and M. Mikami (2005), Regional difference in the characteristics of dust event in East Asia:
relationship among dust outbreak, surface wind, and land surface condition, J. Meteor. Soc. Japan, 83A, 1–18.

Land Cover Monitoring over Yellow River Basin in China
using Remote Sensing
Masayuki MATSUOKA1, Tadahiro HAYASAKA1, Yoshihiro FUKUSHIMA1,
Yoshiaki HONDA2 and Taikan OKI3
1
Research Institute for Humanity and Nature
2
Center for Environmental Remote Sensing, Chiba University
3
Institute of Industrial Science, University of Tokyo
E-mail: matsuoka@chikyu.ac.jp
Abstract
The current status of the study on the land cover monitoring over the Yellow River in China is outlined inthis paper. Three kind of optical sensors, MODIS, AVHRR and Landsat have been used in order to understandthe recent status and the change for 20 years since 1980 of the land cover. Land cover map is generated fromtime series of MODIS in 2000. Time series of AVHRR is in production for the change detection. Landsat
data will be applied to drastically changed area such as irrigation districts.
1. Introduction
Yellow River in China (figure 1) has been dried up for many days in a year and long distance since 1970s.
It is mainly due to the decrease of precipitation in the upstream of the basin and excess water use for theirrigation. Therefore the hydrological model which can deal with the human activity such as dam operation andirrigation has been being developed in Research Revolution 2002 project [1] for the optimal management ofriver water. Land cover is one of the most important parameter in the model since it reflects human activity as
agriculture and it also provides the hydrological characteristics of land surface. The objective of this study is toanalyze the land cover and its change for 20 years from 1980 over the Yellow River basin using remote sensingdata.
2. Method of the study
The method of this study is shown in figure 2. Three kinds of optical sensor are used depend on its
temporal and spatial characteristics. Because the MODIS onboard Terra is new sensor which has been operated
since 1999, it is used to understand recent status of the land cover. Land cover classification map has been
generated using time series of MODIS product in 2000 combined with DMSP/OLS product. AVHRR has been
operated continuously over 20 years and historical data is available since early 80s, hence it is used to detect thechange of land cover. The time series of AVHRR data is generated with the spatial resolution of 1 kilometerfrom the HRPT, LAC and GAC data. Landsat series of sensor i.e. MSS, TM, and ETM+ have quite higher
spatial resolution relative to previous two sensors, even though temporal resolution is low in contrast. These
data are used for the validation of land cover classification and change detection, and local analysis over the
drastically changed area.

Fig. 1. Yellow River basin.

Fig. 2. Method of the study.

3. Land cover classification using MODISLand cover map was generated by the classification using time series of MODIS 250 meter resolution
reflectance product (MOD09Q1), 500 meter resolution snow product (MOD10A2) and DMSP/OLS nighttime
light product in 2000. Eleven kinds of land surface features i.e. annual maximum and minimum NDVI, annual
maximum and minimum reflectance in band 1 and 2, annual average reflectance in band 1, monthly average
NDVI in April and June, number of snow days in summer season, and human settlements, are derived from
MODIS and OLS products (figure 3), and these were input to the simple decision tree classification method
shown in figure 4. The threshold values used in decision tree were adjusted manually by comparison ofclassification result to the other land cover maps. Sixteen types of land cover which include five kinds ofagricultural categories were adopted from the hydrological point of view. Classification result is shown infigure 5. This map was compared to two reference data, existing classification map [2] and Chinese census[3][4]. Land cover map categories were aggregated and compared in province base. The result is shown infigure 6. Forest shows good agreement with land cover map, but overestimation relative to the census. Total
agricultural field, grassland and barren show the good agreements in both comparison, but individual agricultural
categories i.e. paddy field, dry field, and irrigated field resulted in poor agreements. This classification map isused as the base map in the hydrological model.
:True:FalseInput data
Decision 1

Decision 2

Decision 3

Decision 4

Decision 5

Decision 6

Snow and Ice

Unclassified

Water Bodies

Urban and
Built-up Lands

BarrenOpen
Shrublands

Grasslands

Decision 10

Decision 9

Decision 8

Decision 7

Decision 13

Irrigated
Fields

Decision 13

Croplands(non-paddy)

Croplands(inc. paddy)

Double-cropping
Fields (non-paddy)

Decision 12

Decision 11

Evergreen
Evergreen
Needleleaf Forests Broadleaf Forests
DecisionFig. 3. Land surface features.

Fig. 5. Classification result.

Double-cropping
Fields (inc. paddy)
Decision 12

Deciduous
Deciduous
Broadleaf Forests Needleleaf Forests
Criteria

Thresholds

1

Sum_Day_Snow > Threshold

15 days2

All features but Apr_Ave_NDVI and Jun_Ave_NDVI include error

None

3

Ann_Min_Ref2 < Threshold OR
Ann_Min_NDVI < Threshold

5.00 %-0.05

4

Ann_Max_NDVI < Threshold AND
Hum_Set_OLS > Threshold

0.45
55

5

Ann_Max_NDVI <= Threshold

0.15

6

Ann_Max_NDVI <= Threshold

0.25

7

Ann_Ave_Ref1 <= Threshold

5.50 %

8

Apr_Ave_NDVI and Jun_Ave_NDVI have no error AND(Apr_Ave_NDVI - Jun_Ave_NDVI) > Threshold

None
0.00

9

arctan(Ann_Max_NDVI/Ann_Min_NDVI) >= Threshold AND
Ann_Min_NDVI < Threshold AND
Ann_Max_Ref1 < Threshold

74.0 deg.
0.15
25.0

10

Ann_Max_Ref1 < Threshold10.5 %

11

Ann_Min_NDVI > Threshold

0.44

12

Ann_Max_Ref2 > Threshold

28.0 %

13

Ann_Min_Ref1 < Threshold AND

3.75 %

Fig. 4. Decision tree classification method.

Fig. 6. Comparison to reference data.

4. Change detection using AVHRR
4.1 Production of AVHRR data set
Time series of AVHRR data for 20 years has been being produced from HRPT, LAC and GAC data with 1
km resolution. HRPT was supplied by Kitsuregawa Laboratory [5], University of Tokyo, and LAC and GAC
were downloaded from CLASS, NOAA [6]. The radiometric calibration is applied using time varying
calibration coefficients to derive the top of atmosphere reflectance for channel 1 and 2. Channel 3 to 5 areconverted to blightness temperature by non-linear calibration method[7-10]. In geometric calibration, initialcorrection is applied by means of Two-Line Element orbital information [11] followed by precise correction
based on the ground control points (GCPs) derived from MODIS product. Precise correction is composed oforbital correction and attitude correction. Orbital correction is applied daily several paths at the same time forthe purpose to keep the number of available GCPs, and attitude correction is applied by each path in order toallow the frexibility of attitude. Optimized orbital information and attitude angles are derived by least squareestimation.
The HRPT data is not available especially in the western region around upstream of the Yellow River,
because it is outside of the receivable range of University of Tokyo receiving station. Therefore, GAC is usedas base product and HRPT and LAC are overlaid if these are available, though the spatial resolution of GAC islower as 4 kilometers. The outline of the overlay is shown in figure 7.
4.2 Preliminary study using Pathfinder AVHRR Land Data Set
Change detection method was applied for Quingtongxia and Hetao irrigation districts as the feasibility study

by means of 8 kilometers resolution AVHRR data set, Pathfinder AVHRR Land data set. Annual maximum
NDVI (Ann_Max_NDVI) is derived by averaging of second to sixth maximum NDVI among the daily data for
one year. Time series of regional distribution of Ann_Max_NDVI for both irrigation districts are shown infigure 8. Ann_Max_NDVI has been increased gradually in both districts through 20 years. The trend isslightly different, that is, widely distributed Ann_Max_NDVI has been concentrated to higher value in
Quingtongxia districts, and lower Ann_Max_NDVI has been totally increased in Hetao district. The reason ofthese increase seems to be the increase of agricultural fields, however, the detail of the change is not interpreted
from 8 kilometer resolution data set.

GAC

NDVI

HRPT, LAC

1 km
Yellow River mouth

4 km
HRPT, LAC

GAC

Fig. 7. Overlay of HRPT, LAC (1 km) and GAC (4 km).

Quingtongxia

Hetao

Fig. 8. Time series of the distribution of the annual
maximum NDVI for two irrigation districts.

5. Detailed analysis using Landsat
The change of the Ann_Max_NDVI in irrigation districts has been analyzed in detail using higher resolution
data, 1 kilometer resolution AVHRR combined with Landsat. The method of analysis is shown in figure 9.
Ann_Max_NDVI over Quingtongxia districts in 1999 was derived from 1 kilometer daily AVHRR data
described in section 4. Land cover map was derived by simple decision tree classification using Landsat/ETM+
acquired on 12th Aug. 1999 when the status of the vegetation is most active. The data was downloaded from
GLCF, University of Maryland [12]. Since the spatial resolution of AVHRR and ETM+ is about 1 kilometerand 28.5 meters respectively, 1 pixel of AVHRR corresponds to 32 by 25 pixels of ETM+. Therefore, fractionof agricultural area (Frac_Agri_Area) in one AVHRR pixel is calculated from the land cover map of ETM+.
Finally Frac_Agri_Area is estimated from the Ann_Max_NDVI.
MethodAVHRRTime series of daily data
Ann_Max_NDVI

Path

ETM+

2 scenes

Land coverclassification

Relation of Ann_Max_NDVI
and agricultural area Estimation of agriculturalarea using AVHRR

Fig. 9. Method of the analysis.
The decision tree classification method applied to ETM+ is shown in figure 10. NDVI, reflectance in band
5 and digital elevation model GTOPO30 [13] were used to categorize the pixel into the five types of land cover.
The classification result is shown in figure 11. As the assessment of the classification accuracy, only
agricultural field is compared to county based census value, that is, agricultural area in each county is extractedusing county mask (figure 12) and it is compared to the sowing area in 1999 recorded in the Statistical Yearbookof Ningxia province. The result is shown in figure 13. The overestimated counties are located in midstreamof the district, and relatively underestimated counties are in downstream. Excess underestimated county is
Yanchi county, which is located outside of the irrigation districts.
Ann_Max_NDVI in Quingtongxia district is shown in figure 14. The relation of Ann_Max_NDVI and
Frac_Agri_Area is shown in figure 15. The linear equation of [Frac_Agri_Area = 2.54 * Ann_Max_NDVI 0.33] is derived from the least square regression. The Frac_Agri_Area estimated from AVHRR using thisequation is shown in figure 16. The mountainous area was masked by GTOPO30. Figure 17 shows thecounty based comparison of agricultural area with ETM+ classification. This figure indicates that AVHRR

estimation is comparable to the ETM+ estimation in county base. This method is specialized to irrigation
districts in arid region where the contrast of the NDVI in agricultural area and background is high. The method
will be applied to the time series of AVHRR for detecting the seamless change of the land cover in irrigation
districts.
ETM+

True
False
700

Decision 1 Decision 2 Decision 3 Decision 4
Classification (km2)

600

Agricultural Dense
SparseBare
field
vegetation vegetation ground** Including built-up area
Water

Decision
12
34

Criteria
NDVI < Threshold AND
Ref5 < Threshold
Elevation < Threshold AND
NDVI > Threshold AND
Ref5 < Threshold
NDVI > Threshold
NDVI > Threshold

Thresholds
0.400
3.75 %
1800 m0.350
25.0 %
0.375
0.250

Fig. 10 Classification method.

500
400
300
200
100
0
0

100 200 300 400 500 600 700

Census (km2)

Fig. 11. Land cover map.

Fig. 12. County mask

Fig. 13. Comparison result.

700

2

AVHRR estimation (km )

600
500
400
300
200
100
0
0

100

200

300

400

500

600

700

ETM+ estimation (km2)

Fig. 14. Ann_Max_NDVI

Fig. 15. Relation of Ann_Max_NDVI
and Frac_Agri_Area.

Fig. 16. Frac_Agri_Area.
derived by AVHRR

Fig. 17. Comparison

6. Conclusions and future plans
The land cover monitoring on the Yellow River in China is in progress under the hydrological project.
Land cover map in 2000 was produced using 250 meter MODIS product and it is used as the base map of thehydrological model. The time series of AVHRR for 20 years has been being produced from HRPT, LAC and
GAC with the spatial resolution of 1 km. Land cover change especially concerned with the agricultural field
will be detected using this data combined with Landsat.
Acknowledgement
This study has been carried out as a part of the Research Revolution 2002 project supported by the Ministryof Education, Culture, Sports, Science and Technology (MEXT) in Japan.
Authors appreciate the valuable distribution of HRPT by Kitsuregawa Lab., LAC data by Comprehensive
Large Array-data Stewardship System, Landsat by Global Land Cover Facility, and GTOPO30 by USGS.
References[1] The Yellow River Studies. (2003). http://www.chikyu.ac.jp/yris/.
[2] CASW Data Technology, 1 km land-use & land cover raster data of China, http://www.casw.com.cn/.
[3] Chinese Forest Science Data Center website, http://www.cfsdc.org/.
[4] National Bureau of Statistics of China website, http://www.stats.gov.cn/.
[5] Kitsuregawa Laboratory, http://www.tkl.iis.u-tokyo.ac.jp/english/.
[6] NOAA's Comprehensive Large Array-data Stewardship System, http://www.class.noaa.gov/nsaa/products/welcome.
[7] Kidwell, K. B., "NOAA Polar Orbiter Data Users Guide" (November 1998 revision), http://www2.ncdc.noaa.gov/.
[8] Goodrum, G., et al., "NOAA KLM User's Guide" (September 2001), http://www2.ncdc.noaa.gov/.
[9] Agbu, P.A., and M. E. James. (1994). "The NOAA/NASA Pathfinder AVHRR Land Data Set User's Manual", Goddard
Distributed Active Archive Center, NASA, Goddard Space Flight Center, Greenbelt.
[10] Ouaidrari, H., et al. (2002). " Land surface temperature estimation from AVHRR thermal infrared measurements An
assessment for the AVHRR Land Pathfinder II data set", Remote Sensing of Environment vol. 81, pp. 114-128.
[11] Celes Track, http://www.celestrak.com/.
[12] Global Land Cover Facility, University of Maryland. http://glcf.umiacs.umd.edu/data/.
[13] GTOPO30 Global 30 Arc Second Elevation Data Set http://www1.gsi.go.jp/geowww/globalmapgsi/gtopo30/gtopo30.html.

ANALYSIS OF POPULATION DENSITY DISTRIBUTION
WITH IMAGE SATELLITE
Ketut Wikantika1,2, Farid M. Rahman.1, Andri Hernandi2, Hadi, F.1,2
1

Expertise Group on Remote Sensing and Geographical Information Science2Center for Remote Sensing
Faculty of Civil Engineering and Environment, Institut Teknologi Bandung
Jl. Ganesha 10 Bandung (40132), Indonesia
Telp. 022-2530701 Ext. 3644, Fax. 022-2530702
Email : ketut@gd.itb.ac.id

ABSTRACT
Current demographic data from Badan Pusat Statistik (BPS) is only calculated and displayed based on administrative
boundary without knowing the actual density. This make the derived information is not complete, tend to behomogenuous and is not represent the real facts. In the regional planning, demographic aspect is one of the mostimportant aspect because planning is basically for and by the citizen itself. Remote sensing technology currently
provide alternative solution on the problem of time-series spatial data. The excellence of remote sensing which canprovide continuous data can be utilized to study population distribution on certain area. This research studies the role ofremote sensing to analyze the population distribution in several villages in Bandung with Quickbird satellite images.
The method in this research is land use density according to least square principles. The output of this method is a newthematic map which describe population density distribution on a more informative map and have the ability torepresent the actual condition.
Keywords: distribution, population density, land use density, least square principles

1. INTRODUCTION
Data on current population density from Badan
Pusat Statistik (BPS) is only calculated anddisplayed based on administration area. Due tothis condition, the information is not complete,tend to be homogeneous and visually not
represent the real condition. While in the planningof an area or city, demographic aspect is one of
the important components because planning isdesigned for and by the citizen itself.
Every steps taken by the government during thedevelopment process is basically to improve thecitizen's wealthiness. Information on population
density is one of the most important data in theregional planning. Due to this reason, there is aneed to search for new techniques that produce
information with a better quality.
This research utilize a high spatial resolution
imagery, Quickbird, to analyze population density
distribution in several villages in Bandung. Landuse density method with least square principle

was chosen as the research method (Min et al.,2002).
2. DATA AND STUDY AREA
Data used in this study consists of Quickbird
satellite imagery acquired on August 2003, GPSpoint that were taken in 2004, administrative
boundary with 1:25.000 scale from topographic
map and Bandung statistical data of the year 2003.
Six villages chosen as study case are Cipedes,
Sukabungah, Pasteur, Cipaganti, Pajajaran andPamoyanan. These six villages area assumed torepresent several type of land in Bandung.

Figure 1. Quickbird satellite image (left) andvillage administration boundary (right)

3. METHODOLOGY
The research methodology consists of several
steps:
a. Reference study, covers study on
fundamental theories and methods which will
be used to analyze population density
distribution with satellite images.
b. Secondary data collection.
c. Data processing which includes image
geometric correction, population density
calculation, digitization and result displays.
d. Research analysis and publication.
Validation step is carried out by taking picture oneach habitation during the field check.

population density from sample is the estimation
population of every kind of inhabitation type. The
total sum of every kind of inhabitation type is thetotal population in study area.
The excellence of the land use density method is
it's idea and calculation is simple; thedisadvantage of the method is that the selection ofsampled region is much more difficult. Is there
method the population density of every habitation
type is estimated but the sampled region need not
be selected. In some region, there is mathematical
relation among the The advantage of this method
is the relatively easy calculation process, while thedifficult are in choosing the sample area andwhether those areas will memenuhi population
density formula or not.
Further description on this method is shown in thenext paragraph below. Suppose there are j regions
which the population is known, j = 1,2,...,m. The
population sum of every regions is Pj. There are ikinds of habitation types, i = 1,2,..,n. The
population density of every habitation type is Di,
then

4. LAND USE DENSITY METHOD
In this method, population density assessment isobtained from land use type. The approach
method is the presentation of population density
represented by the different land use type,especially land habitation type. Mathematics
model which is used as the approach (Min et al.,
2002)
n

P=
(1)

Ai Di

i= 1

n

Pj =
A ji D i(2)
i= 1
When several villages in the study area were
sampled, the above formula is became to :P1 = A11D1 + A12D2 + … + A1nDn
P2 = A21D1 + A22D2 + … + A2nDn
Pm = Am1D1 + Am2D2 + … +AmnDn

After those equation are rearranged in matrix typeis like below :P = A.D

where:
P = total population
Ai = size area on each landuse type
Di = population density on each landuse typeThe land use type is obtained from different of
population density. The flow of this method is:first, the inhabitation and the non-inhabitation is
distinguished with remote sensing imagery. Everykind of inhabitation type is distinguished ininhabitation area. The boundary of every kind of
inhabitation is measured. The area of every kindof inhabitation type multiplying corresponding(3)

where :

(4)

⎡ P1 ⎤⎢P ⎥2
P= ⎢ ⎥⎢... ⎥⎢ ⎥⎣ Pm ⎦⎛ a11⎜⎜ a 21
A= ⎜
...
⎜
⎜a
⎝ m1

a12
a 22
am2

... a1n ⎞⎟... a 2 n ⎟⎟⎟... a mn ⎟⎠⎡ D1 ⎤
⎢D ⎥
2 ⎥
D= ⎢
⎢... ⎥⎢ ⎥⎣ Dm ⎦(5)

When m > n, that is, the region count known
population is more than habitation type count. The
least square principle is used to calculate the best
population density estimation of every kind of
habitation which the errors of statistical
population is least.
T

-1

T

D = [A A] . A P(6)

5. HABITATION DEFINITION
According to the WordNet which was released by
Princeton University in 2001, the word habitation
has three definition, (1) the native habitat or homeof an animal or plant , (2) housing that someone isliving in, (3) the act of dwelling in or living
permanently in a place (said of both animals andmen).
Indonesia uses none of these definition forclassifying its human habitations. This result in nostandard classification for human habitations.
While in land use method, this definition is one of
the needed parameter to assess the population
density. As an example, China classifies its
human habitation to several types such as highbuilding, multilayer building, simple building andcompositive building. The high building indicatesa building with more than 7 layers. The multilayer
building indicates a building with from 3 to 6
layers. The simple building indicate a building
with less than 3 layers. The compositive building
indicate a building which used for business andhabitation.

Based on the definition above, the way to
overcome this problem is to make someassumption which is relevant to the definition andto the study area. The assumptions which werechosen are:a) Habitation is defined only to those building
used as a place for permanent stays.
b) The building is not design for a work placec) The building is not design for temporary staysuch as hotels, motels and etc.
d) The building is not design to educate people
After the habitation type is defined, the next stepis to carried out interpretation of satellite image.
In this research, habitation is only divided into
structured habitation and unstructured habitation.
6. RESULTS AND DISCUSSION
RGB composite Quickbird satellite image wasused in this study. This image was firstly
geometrically corrected with 9 GCPs that were
distributed evenly in the study area. The accuracyof GCPs distribution was checked by independent
check points (ICP). The value of RSME from ICP
shows a value of 0,5 pixel. The datum that wasused in the process of rectification is WGS 84
with map projection South UTM 48.
Tranformation method that was used is first
degree polynomial transformation with nearest
neighbor as the interpolation method.
After the data on size area of each habitation andpopulation are obtained, the next step is the data
calculation with mathematic model to assess
population density. The data which will be used inthis calculation is shown in Table 1.
To make the process easier and to fit the conceptsof population density calculation with the chosenmathematics model, the data in Table 1 isrearranged in matrix.

Tabel 1. Data for calculation of population density
No.

Village1

Cipaganti

0,040357548

Area (Km2)

2

Cipedes

0,593817838

3

Pajajaran

0,298752457

4

Pamoyanan

0,358116354

5

Pasteur

0,663372739

6

Sukabungah

0,382620902

Jumlah Total

2,337037838

Habitation typeArea (Km2)

Structured habitation
Unstructured habitation
Structured habitation
Unstructured habitation
Structured habitation
Unstructured habitation
Structured habitation
Unstructured habitation
Structured habitation
Unstructured habitation
Structured habitation
Unstructured habitation

0,040357548
00,152017917
0,441799921
0,185674304
0,113078152
0,167232574
0,19088378
0,05785
0,60552113
0,125909076
0,256711826

The calculation shows that the number of
population density for structured habitation isapproximately 89586 people / km2, while for
unstructured habitation is about 24146
people/km2. By seeing these numbers, population
density for structured habitation has higher
number. This is not because of the wrong data or
calculation but arise as the result of calculationfrom least square principle. This analysis isreasonable because when the calculation isretaken in the reverse direction by using D valueon the mathematical model, the total value of P
and total value of Pc is nearly the same,
111.694 and 104138, 3155 people. The differenceis about 7556 people or about 6,8 % fromstatistical data.
The foundations of population density assessmentwith this method is the dividing of eachadministrative region into several habitatation
types. After that, each habitation on every
administration region is further classified andunited into one tipe of habitation. The number of
habitation will influence the detail of informationthat we will have. The more class of habitation,
the more class of population density that will bederived and this will result in the better quality of
population density information.
By using this method, we can provide a newpresentation model to describe the population
density distribution. The population density isvisualized not just by translating numeric data into
administrative boundary but also visualized by its
density on each administrative region.

Population (P)
11664
23698
22242
9309
19723
25058
111694

Figure 2. Conversion ofpresentation model on population density

The difference between the use of previous way
and this model in describing population density
information is that by using land use density
method the presentation is more representing thereal condition.
7. CONCLUSIONS
Based on the result and analysis, there are several
conclusions :a.
The population density method with landuse density can not be used on low spatial
resolution satellite image due to the need of
detail classification process.
b.
The information on population densitythat will be gained by this method is depended
on the number of habitation classes. There is
no standard habitat classification in Indonesia,
so the application of this method is difficult.

c.

Satellite image can be applied in thesocial mapping research as a data resource tocalculate and to present information on
condition and social characteristic of certain
region.

RECOMMENDATION
There is a need to make a standard habitation
classification in Indonesia. If this classification
exist, it will be easier to define the type of humansettlement from satellite images.
REFERENCES
Min, Lu An., Ming, Li Cheng, Jian, Li-zhong,2002. Modelling Middle Urban Population
Density With Remote Sensing Imagery.
Proceedings of ISPRS Symposium on
Geospatial
Theory,Processing
andApplications, Ottawa, Canada
WordNet, www.answers.com/library/WordNet(2001)

Mapping soil degradation by topsoil grain size using MODIS data
Jieying Xiao1, Yanjun Shen2, Tateishi Ryutaro1
1

Center for Environment Remote Sensing, Chiba University, 1-33, Yayoi, Inage, Chiba 263-8522, Japan
Tel: 043-290-2964; Fax: 043-290-2963 Email: xiaojy@graduate.chiba-u.jp

2

Institute of Industrial Science, the University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo 153-8505, Japan

Abstract:
MODIS BRDF reflectance data at the end of April 2004 was selected to make a desertification map baseon topsoil grain size by using Gain Size Index at arid and semiarid Asia. After data processing, GSI wasapplied into desertification mapping, and we find that high GSI area distributed at the desert and its’
marginal area, degraded grassland, desert steppe. The desertification map was output according to thecorrelation between GSI and grain size distribution, the classification of desertification is no
desertification, light desertification, middle desertification, and severe desertification. The result canmanifest the soil degradation very well.
1 Introduction
Desertification is land degradation, manifested by “desert-like” conditions in dryland areas. Climaticconditions together with geomorphologic processes help to mould desert-like soil surface features in aridzones. The identification of these soil features serves as a useful input for understanding thedesertification process and land degradation as a whole (Shrestha et al,. 2005). Soil degradation mapping
is very useful for land management and soil evaluation, the changing of soil properties like texture,organic matter (OM), pH, phosphorus and potassium is important for the world because it has close
relation with the food and living condition for human being. Grain size distribution is one of importantcharacteristics of soil texture, and coarsen of the soil surface is an indicator of soil degradation.
Soil reflectance spectra have a direct relationship with soil texture, as well as to other parameters such as
soil moisture, and organic matter. Remote sensing technique is proved as an available method for
detecting and monitoring the change of land cover. Analysis of remotely sensed data involves identifying
features and correlating ground-based measurements with recorded reflectance or emittanee values(Mattikalli, 1997). Earlier studies have identified significant relationships between some of theseproperties and spectral reflectance of soils in the visible and near infrared portions of the electromagnetic
spectrmn (e.g., Gerbermam et al., 1979; Stoner anti Baumgardner, 1981; Horvath et al., 1984;
Baumgardner et al., 1985; Jeyasingh, 1986; Mattikalli, 1997). And recently, also many studies concern
about this topic, Shrestha et al (2005) analyzed absorption feature parameters in the spectral region
between 0.4 and 2.5 µm wavelengths and correlated with soil properties such as soil colour, soil salinity,
gypsum content and applied Linear unmixing and spectral angle matching techniques to assess theirsuitability in mapping surface features for land degradation studies. Chikhaoui et al., (2005) characterized
the state of land degradation in a small Mediterranean watershed using ASTER data and ground-based
spectroradiometric measurements, developed land degradation index, and applied this index for assessing
and mapping land degradation.

Very few studies concerned about soil degradation mapping by topsoil grain size in arid and semiarid area,even recently there isn’t one grain size distribution map or a desertification map base on the topsoil grain
size. And it cost time and resource to obtain the map of soil physical properties in regional or continental
scale using traditional method, in this study, it will become very quick and economical to get the topsoil
grain size content map. The present study will apply the grain size index which base on the field survey
and laboratory analyses, to the mapping of soil degradation by manifesting the topsoil grain size
distribution using remote sensing images.
2 MODIS data and processing
2.1 MODIS data
In the present study, the surface reflectance data production name MODIS/TERRA Nadir
BRDF-Adjusted Reflectance 16-DAY L3 Global 1 KM SIN Grid Product (MOD43B4 NBAR) werechosen for mapping the soil degradation in arid and semiarid area of central east Asia. Every 16 days, the
MODIS BRDF/Albedo Product (MOD43B) algorithm relies on multidate, atmospherically corrected,
cloud-cleared data and a semiempirical kernel-driven bidirectional reflectance model to determine a
global set of parameters describing the Bidirectional Reflectance Distribution Function (BRDF) of theland surface. These one kilometer gridded parameters are then used to determine directional
hemispherical reflectance, bihemispherical reflectance, and nadir BRDF-adjusted reflectance (NBAR) for
seven narrow spectral bands and (in the case of albedo) three broad bands. Since the parameters of thesimple kernel-based BRDF model (RossThickLiSparseR) are also provided, along with extensive quality
information, the MODIS BRDF/Albedo Product offers members of the global observation and modelingcommunity the additional flexibility to derive reflectance and albedo measures particularly suited to their
specific applications. MOD43B is a MODLAND Level 3 Product and is provided globally as discrete
1200 by 1200 element tiles in an Integerized Sinusoidal Grid projection in a HDF-EOS format.
MOD43B1 provides the BRDF model parameters, MOD43B2 provides an alternative set of model
parameters, MOD43B3provides the black-sky albedo (at a solar zenith angle of 45 degrees) and thewhite-sky albedo, and MOD43B4 provides the NBAR value at the mean solar zenith angle of the 16 day
period (source from: http://edcdaac.usgs.gov/modis/mod43b4.asp).
The BRDF gives the reflectance of a target as a function of illumination geometry and viewing geometry.
The BRDF depends on wavelength and is determined by the structural and optical properties of thesurface, such as shadow-casting, mutiple scattering, mutual shadowing, transmission, reflection,
absorption and emission by surface elements, facet orientation distribution and facet density. (Lucht,
http://geography.bu.edu/brdf/brdfexpl.html, last updated: 1, Aug 2000)
MOD43B4 NBAR Product is computed for each of the MODIS spectral bands (1-7) at the mean solar
zenith angle of each 16-day period. Since the view angle effects will have been removed from thedirectional reflectance, this will result in a more stable and consistent product. NBAR values could bedirectly used as the primary input to the advanced technique classifiers used in the production of the
global MODIS Land Cover Product. It is expected that the user community will be quick to takeadvantage of the NBAR data for those situations where composited surface reflectances may have been
traditionally used (Schaaf et al., 2002 and http://geography.bu.edu/brdf/userguide/nbar.html, last update,
Apr 2004). The Original Data Set Characteristics are:Area = ~ 10 x10 lat/longSize = 1200 x1200 rows/columns
Volume = ~31MB
Resolution = 1 kilometer
Projection = Integerized Sinusoidal
Data Type Reflectance = 16-bit Signed Integer
Data Type Quality = 32-bit Unsigned Integer
Data Format = HDF-EOS

Science Data Sets (SDS) = 2(http://edcdaac.usgs.gov/modis/mod43b4.asp last update at 13 June, 2005)

2.2 Data processing
The data set is come from Land Processes Distributed Active Archive Center (LPDAAC) of NASA’s
Earth Observing System (EOS). The chosen area showed by the MODIS/TERRA Nadir BRDF- Adjusted
Reflectance 16-DAY L3 Global 1 KM SIN Grid Product by RGB color composition, the date range ofthis image is 10-25, June 2004, Fig.1, vegetation cover is in green color while the desert, sandy land andbare ground is in pink, orange or thistle. The location is at arid and semiarid area of Asia, including China,
Mongolia, Kazakhstan, Uzbek, Turkmen, Afghan, Pakistan, Tajikistan, Kyrgyzstan and so on. Most ofthese countries in this area suffering the severe land degradation, especially China. The images for GSI
and soil texture mapping were from 23, April to 8, May in 2004.
The original data set was preprocessed before using, including into image mosaic one by one by using
MODIS reprojection tool (MRT) software, and then change the original Integerized Sinusoidal projection
into geographic Latitude and Longitude WGS 84. Geometric correction was done to the images, Landsat
ETM+ images were used and considered as a reference. Landsat ETM+ images were downloaded from
Global Land Cover Facility Earth Science Data Interface of University of Maryland(http://glcfapp.umiacs.umd.edu: 8080/esdi/index.jsp). The images are georeferenced to Geographic map
projection for WGS84 Reference datum. (The work of mosaic, reprojection and geometric correction has
been done by Al-Bilbisi et al., 2005). And then made a spatial subset to the image to choose arid andsemiarid area of Asia only using ENVI software.

RussiaKazakhstan
Uzbek.
Turkmen
IranMongolia

Kyrgyzstan
China

Tajikistan
Afghan.Pakistan

Fig. 1 Arid and semiarid area in Asia.
There are some fill values in this data set, so the mask was applied into the images to delete noisy, andthen calculated the reflectance data of every band for the preparation for the calculation of GSI. In thisstudy, the data of 23 Apr to 8 May 2004 was chose from MOD43B4 NBAR data set for desertification
mapping.
3. Soil degradation mapping by using GSI
Topsoil Grain Size Index (GSI) was developed by Xiao et al in 2005, it is base on the field survey andlaboratory analyses. After the analyses of the correlation between grain size distribution and reflectancedata of topsoil samples, we found that the reflectance data changing accompany with the changing ofgrain size distribution, especially fine sand content and clay+silt content. Then GSI was applied into local
area, we obtain the GSI map of the local area, high GSI area means fine sand rich area. Then according to
the correlation between GSI and fine sand content, fine sand content distribution map was output as theresult, it could manifest the fine sand content very well.

It is calculated by the equation (1):
GSI=(R-B)*(R+B+G)(1)

Where R, G, B, mean the red, green, and blue visible band of MODIS image that band 3, 4, and 1. In thisstudy, GSI will be applied into the arid and semiarid area of Asia to describe the land degradation of thisarea.
4. ResultGrain size of topsoil is one of the important characteristics of the soil, and coarsen of the topsoil
manifested the soil degradation turning serious. In this study, the fine sand (0.0075-0.425mm in diameter)
content of topsoil was obtained by using the GSI map in arid and semiarid Asia.
GSI was classified into seven classes according to the value (Fig.2), and higher GSI value points
concentrated at the desert area and sandy land while the highest value points are not located at the large
area desert. From the GSI map of 2004, the white color area (GSI is in the range of 0.1-0.15) is only lieson the North part of China and the South part of Mongolia, while it can be found at the south part andsome part of Taklimakan desert in China.
Also the desertification maps based on the GSI map were drawn in this study. The Grain Size Index wascoming from the correlation between the reflectance and the grain size content, so in this study, the finesand percentage was found through calculating the reflectance, classified into <15%, 15-30%, 30-45%,
and >45%. Then the desertification map were made according to the content of fine sand, classified into
no desertification, light, middle and severe desertification, showed by Fig.3, the desertification map
manifested the content of the particle which grain size from 0.0075mm to 0.425mm in diameter of arid
and semiarid Asia in 2004. From the desertification map, we can find the severe desertification area (ingray color) is in desert area and northeast part and north part of Inner Mongolia, China, some south partof Mongolia and north part of Tibet high plateau of China. If long-term data are possible, change
detection can be conducted. In addition, this desertification map can be used for other analyses. It will
benefit for analyzing the correlation between desertification and other contributing factors.
This is the first desertification map which base on the topsoil grain size. It is very easy to find the topsoil
grain size content in large scale by very economical and quick way. The topsoil grain size map can beused as a tool for land degradation process assessment and change detection.

Fig. 2 GSI map of arid and semiarid area of Asia at 2004

Fig. 3 Desertification map of arid and semiarid area of Asia at 2004

Reference:
Shrestha D.P., Margate D.E., Meer F., Anh H.V. 2005, Analysis and classification of hyperspectral data
for mapping land degradation: An application in southern Spain. International Journal of Applied Earth
Observation and Geoimformation. Volume 7, Issue 2, pp85-96.
Gerbermann, A. H., and Neher, D. D. 1979, Reflectance of varying mixture of clay and sand.
Photogramm. Eng. Remote Sens. 45, pp1145-1151.
Stoner, E. R., and Baumgardner, M. F. 1981, Characteristic variations in reflectance of surface soils. Soil
Sci. Soc. Am. J. 45, pp1161-1165.
Horvath, E. H., Post, D. F., and Kelsey, J. B. 1984, The relationships of Landsat digital data to theproperties of Arizona rangelands. Soil Sci. Soc. Am. J. 48, pp1331-1334.
Baumgardner, M. F., Silva, L. F., Biehl, L. L., and Stoner, E. R. 1985, Reflectance properties of soils.
Adv. Agron. 38, pp1-44.
Jeyasingh, V. K. tt. 1986, Development of a technique of modeling and predicting soil characteristics
based on spectral reflectance. Ph.D. thesis, Indian Institute of Technology, Powai, Bombay.
Chikhaoui M., Bonn F., Bokoye A.I., Merzouk A., 2005, A spectral index for land degradation mapping
using ASTER data: Application to a semi-arid Mediterranean catchment. International Journal ofApplied Earth Observation and Geoimformation, Volume 7, Issue 2, pp 140-153.
http://modis.gsfc.nasa.gov/about/
http://modis.gsfc.nasa.gov/about/specifications.php
Lucht, http://geography.bu.edu/brdf/brdfexpl.html, last updated: 1, Aug 2000
Schaaf, C. B., Gao F., Strahler, A. H., Lucht W., Li X., Tsang T., Strugnell N. C., Zhang X., Jin Y.,
Muller J.-P., Lewis P., Barnsley M., Hobson P., Disney M., Roberts G., Dunderdale M., Doll C.,
d'Entremont R., Hu B., Liang S., and Privette J. L., First Operational BRDF, Albedo and Nadir
Reflectance Products from MODIS, Remote Sens. Environ., 83, 135-148, 2002.
http://geography.bu.edu/brdf/userguide/nbar.html, last update, Apr 2004
http://glcfapp.umiacs.umd.edu: 8080/esdi/index.jsp
Al-Bilbisi Hussam, Tateishi Ryutaro, Rokhmatulah and Arihara Kota, et al., 2005, Pre-processing of
global MODIS data form USGS, Proceedings of the 39thConference of the Remote sensing society ofJapan (submitted).
Mattikalli, N, M., 1997, Soil color modeling for the visible and near-infrared bands of Landsat sensors
using laboratory spectral measurements. Remote Sensing of Environment, Volume 59, Issue 1,
pp14-28.
http://edcdaac.usgs.gov/modis/mod43b4.asp last update at 13, June 2005
xiao, J, Y., Shen, Y.J., Tateishi R. Development of topsoil grain size index for monitoring desertificationin arid land using remote sensing. International Journal of remote sensing, (conditional accept, 2005).

Land cover change and land use of oases surrounding Taklimakan Desert
in Xinjiang Uyghur, China derived from satellite images
T. Ishiyama*, N. Saito*, S. Hujikawa** and H. Ohkawa***Center for Environmental Remote Sensing, Chiba University, Japan**Geotechnos Co. Ltd, Japan
Abstract
Land cover change of the oases surrounding Taklimakan Desert in Xinjiang Uyghur in the past 40 years was examined bymeans of satellite images. From the results of the analysis of these images, the following have been explained. The farmland
utilization ratio in average stands at 0.28 in the northern edge of Taklimakan Desert. In the meantime, the ratio stands at
0.13 in the southern edge, whereas 0.31 in the western edge. It is revealed that in the northern edge, area of the irrigation
land is vast enough to be utilized. Small oases are much noticed generally in the southern edge, and accounted for usuallyless than 0.15 resulting in lowness in the utilization ratio of the farmland. However the smaller the value of the said ratio is,
the higher the potential of the farmland development is. It is explained that the vegetation land is slightly expanded in theupper reaches of the river in the small oases in the northern edge, but the vegetation land is drastically changed to barren
land in the lower reaches of the river. On the other hand, it is explained that the vegetation regions are being expanded year
by year in the oases in the northern edge. Especially in Aksu, the farmland area was expanded accompanied with increaseof the number of the development settlers.
1. Introduction
In recent years, it is reported that wide area of the land has been rapidly converted into sandy sites in the edges of the oases
in Taklimakan Desert in the northern part of Xinjiang Uyghur, China and is suffering from violence of dust storms. Although
the cause of the land being converted into sandy sites are not yet explained, it is widely conceived that overlapped actions
between the change of weather or climate and human activities, e.g. overgrazing of livestock, outrageous lumbering, landdegradation brought about by execution of inadequate irrigation onto farmland, etc. are the causes.
To explain how the land cover change in the regions surrounding Taklimakan Desert is, analysis was made by using the dataof the earth observation satellites with the investigation of the distribution of the oases surrounding Taklimakan Desert andthe land cover change in Pishan Oases in the southern edge and in the vicinities of the Aksu Oases in the northern edge,
especially with the fluctuation in the vegetation regions.
2. Distribution of the oases in Taklimakan Desert and the district surrounding them
The oases of Takilamakan Desert comprise the river water from the mountains in the vicinities of the desert and a tiny
amount of the underground water, and change of the horizontal distribution of the vegetation density of the farmland of theoases and desert regions is quite drastic. In Fig. 1, a distribution map of the vegetation indices of the Takilamakan Desert
obtained by analyzing TERRA/MODIS data is shown. Great oases are noticed on an alluvial fan in the western end on Pamir
Plateau and Tianshan Mountains in the western end and northern edge of Takilamakan Desert. On the other hand, the oaseson the southern edge that have developed on an alluvial fan of Qunlun Mountains are relatively small in comparison withthe one referred to above. Thus it is understood that the longitudinal width is shorter than the one of the western edge and of
the northern edge.

Fig. 1. Distribution of vegetation index of whole Taklimakan Desert derived fromTERRA/MODIS data (May 1, 2000). The circles indicate the test sites.

In Table 1, the area of the oases surrounding Takilamakan Desert that is calculated from MODIS data is shown. The area inquestion is obtained from the number of the pixels of the vegetation indices calculated from the satellite data. As theintroduction equation of the vegetation indices, normalized vegetation indices (NDVI) were used. At this stage, NDVI
(0.4-0.9) takes up the district ranging from a region of open vegetation to a region with high vegetation density as an
objective. In this study, the regions shall be expressed as cultivatable land. On the other hand, the vegetation indices(HDNDVI: high density NDVI, 0.6-0.9) indicates an inner oases farmland with high vegetation density. The area ratio of boththe former and the latter can be expressed as farmland utilization ratio (= irrigation land/cultivatable land). The values of theland utilization ratio in average are 0.28 on the northern edge of Takilamakan Desert, 0.13 on the southern edge, and 0.31
on the western edge, according to Table 1. Akus and Qucha in the northern edge and Yarkand and Kashgar in the westernedge are oases exceedingly immense, and the utilization area of the irrigation land is shown to be particularly vast. On thecontrary, oases rather small in comparison with those in the northern or western edge are generally found in the southernedge. The ratio of the area of the individual oases is usually lot less than 0.15, and the utilization ratio of the farmland is very
low. However the smaller the value of the ratio is, the higher the potential of the farmland development is. It is made known
that the utilization of the farmland is also low in Hotan in the southern edge, which is a relatively great oasis.

Fig. 2. Relationship among total water resources and irrigation, cultivated land in drainage of
Taklimakan Desert. The areas of irrigation and cultivated land derived from MODIS data.

Needless to say, the oasis utilization ratio is dependent on the amount of the water supplied from rivers and on the volumeof the underground water. In Fig. 2, a relation among the total amount of the water resources of the principal rivers forming
the oases, cultivatable land shown in Table 1, and irrigation land is shown. The irrigation land area and total amount of thewater resources are almost in a linear relation. However with correlation between the cultivatable land (including naturalvegetation land) and total amount of the water resources, it is noticed that the both factors are in a linear relation to theextent that the total amount of the water resources is almost 5 billion cubic square kilometers. However when the amountexceeds the said value, the cultivatable land is saturated. On condition that all the water resources are utilized with goodefficiency, it is to be expected that the cultivatable land will also be increased accompanied with expansion of the irrigation
land. However as a matter of fact, no increase can be seen with the cultivatable land despite the fact that the riverside
district is enlarged as seen in Yarkand and Kashgar in the southern edge. This might mainly stem from the reason that in theseregions, under development of the irrigation channels and inferiority in water control technology compel the water
resources to be wasted exceedingly purposelessly. Also the land where salts were accumulated owing to inadequate
irrigation laws is much seen in Yarkand, Kashgal, Akus, etc. This is linked with the difficulty to develop cultivatable landbecause of the lowness of the level of the area.
3. Land cover change in the southern and northern edges in Takilamakan Desert
3.1 Land cover change in the oases in the southern edge
To examine the land cover change in the southern edge in Takilamakan Desert, comparison was made among the different
images obtained from the earth observation satellite launched into space in 1972 at the time of observation. However in thenorthern edge, almost none of regions with great land cover change that was vast enough to be identified from the images ofthe satellite could be extracted. However on-the-spot investigation reports that the tiny land cover change that is hardly
discernible from images of the satellite, e.g. invasion of the sand of moving dunes into the oases or erosion of the vegetation
regions by domestic animals is found. On the other hand, expansion of the farmland by irrigation or enlargement ofvegetation by afforestation is seen. Thus it can safely be said that change of the land surface is a quite complicated one
owing to complexity of natural phenomena and human activities(1, 2).

Fig. 3. Comparison of land cover condition of the small oasis of eastern edge of Lop based on Landsat MSS(1973), TM(1988)
and Terra/ASTER (2001).Fig. 3 (a). Landsat/ MSS on July 24, 1973., Fig. 3 (b).LANDSAT/ TM on July 27, 1988., Fig. 3 (c). The
extraction of 56% rise in vegetation areas (light blue) derived from composite imagery composed of MSS band 4 image (July24,1973) and TM band 1(July 27, 1988), (d). The land cover condition derived from TERRA/ ASTER on April 27, 2001.
In Fig. 3, change of small farmland in the eastern edge of Lop District, Hotan City is shown. The figure reveals that the city is
separated from Lop District as is seen in Fig. 3a (Landsat MSS, 1973), but it is explained from the analytical result that Lop
District is partially connected with the vegetation region in 1988, i.e. 15 years after the separation as is seen in Fig. 3b

(Landsat TM). Thus it is made known that the area of the connected part is expanded as widely as 56% (Fig. 3c). Furthermore
it is explained from comparison of ASTER image in 2001 in the same region with Landsat TM in 1988 referred to above thatLop District is again divided, but the vegetation region is increased 7% in a downstream direction (Fig. 3d). Thus at least in
the southern edge of Takilmakan Desert as seen above, change of land surface situation complicated enough to repeat
expansion and shrinkage of the vegetation region in the oases is shown. In the oases developed along a great river, such
districts where vegetation regions are being expanded accompanied with expansion of the farmland or afforestation are also
in existence. Land degradation as seen in conversion into desert rather appears outwardly to be in a state of stagnation.

Fig. 4. Comparison of CORONA photographic image (1960) and ASTER image (2001) in land cover condition of southernedge of Taklimakan Desert. Top gray scale images are shown CORONA satellite image.
In Fig. 4, an outline is shown with the change of the land cover of the whole of the southern edge of the newest
TERRA/ASTER images taken place after 40 years in the same region where the images of CORONA satellite (KH-4B
resolution: 15m) manifested itself in the primary period of 1960. The result obtained by visually discerning both the types of
the images is depicted on the image of ASTER. It is ascertained from the depiction that with the small oases such as Pishanor Muji on the districts surrounding the boundary on the desert, vegetation regions are in a declining trend. Meanwhile on
the upstream district of these oases or on the place close to the irrigation canal, situations are quite reverse and thevegetation region is expanded. In the north of Lop Region (No. 1, district) of Hotan City in Fig.4, sand of the dunes rushed tothese districts starting from the beginning of 1980. The farmland could not but be deserted because of such rush of sand,according to the local farmers. However in Moyu located in the west of Hotan City, the farmland was developed byintroducing the water from 5 irrigation cisterns situated along Karakash River. The area of the said 5 irrigation cisterns is thegreatest in the southern edge. On the other hand, it is also made known from the on-the-spot investigation in September
1991 that sand protection by means of afforestation in recent years prevents the dunes from being invaded. In Fig. 5,
geographical positions of the change of the area of the vegetation region of the oases in the southern edge are shown. From
the figure, it is made plain that regression of the vegetation region is remarkable in the downstream part of the oasis. On theother hand, it is understood that the great expansion of the vegetation region seen in the center of the figure is dependent on
the existence of large cisterns in the vicinity of Moyu District, Hotan City.

Fig. 5. Three dimensional image of distribution of fluctuation of vegetation areas during
1960's and 2001 around southern edge of Taklimakan Desert.

3.2 Distribution of the land cover change on a district surrounding PishanComparison between ASTER image of the CORONA satellite in the beginning of 1960 and the one obtained 40 years latermade it possible to extract the change regions where land cover was in progress. The extraction was in accordance with thefollowing method. The CORONA image, which was printed on 70mm monochrome film, was digital-converted by means ofa scanner. The image in question, in which the geometrical distortion brought about during the observation is contained,
was deprives of its geometrical distortion by referring to the GCP (ground control point) obtained from ASTER image in thesame region with which geometrical correction is made. The images obtained in this manner were classified into 10 clusters
in accordance with the ISODATA classification method (Non supervisor classification). From all the clusters, the clusters
believed to be in vegetation region were extracted by visually discerning them. By the extracted clusters, which were
furthermore re-classified into the ones in the vegetation region and the ones in the non-vegetation region, extraction of thevegetation region was successfully made at the final stage. On the other hand, the area of the vegetation region, barren land,
etc. were respectively calculated from the number of the pixels of the analyzed satellite images.

Fig. 6. Distribution of the land cover change on the district surrounding Pishan was obtained fromthe images of ASTER image of the CORONA satellite in the beginning of 1960 and the one obtained
40 years later. The green line shows border of the test site in this study.
Table 2. Land cover change in Pishan oasis of during 1960
and 2000 derived from CORONA and ASTER data.

Distribution of the land cover change on the district surrounding Pishan that was obtained from the images of the 2 periods
referred to above (Fig. 6) is shown. With the small oases such as Pishan, Muji, etc. expanding on the fan of Qunlun
Mountains, slight expansion of the farmland owing to existence of the small-sized irrigation cistern and irrigation channel
was noticed as a result of the analysis of the satellite images. Expansion of the farmland was seen in the upper reaches of thesmall rivers, but contrarily vegetation is in regression on the down reaches of these rivers. A single piece each of small
cistern is respectively in existence in Pishan and Muji at present, but each of them appear to have been not constructedaround 1978. For this reason, a sufficient amount of irrigation water was not provided in the lower reaches and expansion of
farmland is believed to have been a difficult one. Especially on the tip of the oasis, invasion of sand from dunes plunges theoases into a crisis of being converted into desert. Meanwhile on the southern edge, Hotan that developed on the alluvial fanof the Karakax River, the largest river, and the Yurunkaxi River are blessed with utilizable water in comparison with Pishan.

Several large-sized cisterns were constructed, and they contribute to expansion of farmland (Fig. 4). Area of the 40-year-old
change of the land cover that is obtained from satellite data is shown in Table 2. In Pishan or Muji, things are dependent on
the small rivers and a tiny amount of underground water. However such water of the rivers and underground water are very
scanty in their absolute amount, and therefore expansion of farmland will be hard to be accomplished in future as well. In
such an environment, there is a possibility that the pressure applied onto the agriculture in the lower reaches and landdegradation will be expedited. It can safely be said that Xinjiang Government’s water resources control, e.g. adequate
distribution of water to the regional residents, will be a key factor to promote a countermeasure for oases to be converted
into desert.
3.3 Aksu in the northern edge
The oases in the northern edge are blessed with the utilizable river water derived from the Tiansian Mountains. The area isvast enough generally in comparison with that of the oases in the southern edge. Especially Aksu is the largest oasis in thenorthern edge.
The satellite data used for this study are CORONA (KH-4B, Aug. 8, 1969) and multi-temporal Landsat images, whereas thesensors are MSS, TM, and ETM. The Landsat data were converted from the digital number to brightness value by referring to
the meta-data of the individual sensors. Composite images were formulated from the blue, red, green, and near-infrared
bands of the obtained brightness values, and the images obtained in a manner the same as that of the CORONA images areclassified into 10 clusters in accordance with the ISODATA classification method (Non supervisor classification). A cluster
believed to be in the vegetation region is visually discerned to be extracted from all the clusters, and furthermore is
re-classified into the ones in the vegetation region and in the non-vegetation region. Thus at the final stage, the one in thevegetation region was extracted. The images of the 3 scenes in a district surrounding Aksu were mosaic-processed to cut out
the range as wide as the CORONA image. Thus the vegetation cover area was calculated. The vegetation region extracted
from the CORONA image of 1969 is shown in Fig. 7(a), whereas the vegetation regions of 1976, 1990 and 2002 extracted
from the Landsat image are shown in Fig. 7(b), Fig. 7(c) and Fig. 7(d). It is understood from these that the cover area ofvegetation is continuously increased every year starting from 1969. It is also understood that the area is being expanded
especially along the district surrounding Aksu City and its eastern side. The developing farm is expanded from the left bankof the Aksu River to Aral to reach the Tarim River. This corresponds to the vegetation region as the farmland extracted fromthe satellite image. In Table 3, aging (year variation) of the vegetation cover area is shown.

Fig. 7. The vegetation region extracted from multi temporal satellite images in Aksu, the CORONA image of 1969 in Fig.
7(a), whereas the vegetation regions of 1976, 1990 and 2002 extracted from the Landsat image in Fig. 7(b). Fig. 7(c). andFig. 7(d). , Fig. 7(e) is shown the land cover change of Aksu obtained from composite of the image of CORONA satellite in1969 and Landsat image 33 years later.

Table 3. Fluctuation of vegetation areas in Akus oasis
derived from satellite data.

The population that was changed in Takilamakan Desert in recent 40 years, which was 3,047,300 in 1949, was 1,104,200 as
the whole of the northern edge(3). However the values referred to above were reversed in 1980 to be 5,138,600 in thesouthern edge and 6,919,400 in the northern edge. Meanwhile in Fig. 8, population fluctuation in Aksu City in recent years
is shown. It is understood from this that the population of 7,000,000 in 1953 was increased to reach 1,500,000.
Furthermore the population became 1,715,900 in 1990. The population increase starting from the middle of 1900 was
brought about owing to the fact that the Chinese Government shifted the development settlers from the other regions for the
purpose of farmland development in the northern edge. It is comprehended that aging of the vegetation cover area shown
together with the change curve of the population well corresponds to the increase in population. Shown in Fig. 7(e) is theland cover change of Aksu obtained from composite of the image of CORONA satellite in 1969 and Landsat image 33 years
later on the region the same as that of the former. It is comprehended from the figure that the vegetation region is increasedin the left coast of the Aksu River, whereas the vegetation region remains almost unchanged in the right bank in the vicinityof Awat the same as in 1969. Contrarily to the above, the region where the vegetation region is in regression was also
observed.

Fig. 8. Recent fluctuations of population and vegetation areas in Aksu oasis.

4. Conclusions
Papers concerning the study on land degradation such as conversion of oases into desert in Takilamakan Desert are
publicized mainly by Chinese researchers. However very few papers describing change of the land cover throughout thewhole of Takilamakan Desert quantitatively are available. In this paper, land cover or land use in oases surrounding
Takilamakan Desert was analyzed by using satellite data. On the other hand, quantitative analysis was made with thechange of land cover of the representative oases in the southern and northern edges of Takilamakan Desert. Thus several

fruitful outcomes have been obtained. This is the first report concerning the land cover and land use on the district
surrounding the oases in the whole of Takilamakan Desert.
With the change of land cover and land use in a vast region where natural condition is very severe as seen in Takilamakan
Desert, the whole situation cannot be grasped until analysis is successfully made with satellite data that can makeobservation of wide region for a long-term. Conversion of oases into desert makes progress not only by climate change but
also by human environmental change. Concurrently with this, it is also possible to prevent such progress from being
accomplished to a certain degree of extent. However with the oases dependent on small rivers or a tiny amount ofunderground water, it is exceedingly difficult to prevent the sand from dunes from invading to the oases when the social
foundation such as irrigation cisterns, water course, windbreaks, etc. are poorly provided. This can commonly be said
especially with the oases in the southern edge that are short of river water and underground water.
Environmental dynamics in Takilamakan Desert is considerably complicated, and long-term and multi-temporal observationis required. In this region as well in recent years, frequency of dust storm is increased resulting in severe damage in humans
or livestocks. On the other hand, more than 90% of water resources are supplied from glaciers. Thus when global warming
is intensified in future resulting in shrinkage of the glaciers in Qunlun Mountains, Tianshan Mountains, and Pamil Plateau,drastic change of the environment will be caused in the oases and their surroundings.
(Acknowledgment)
Part of this study was supported under the auspices of a subsidy for Japan society for the promotion of science.
(References)(1) Sugihara,S., T. Ishiyama and M. Naya: Desertification monitoring based on the satellite data., RIKEN Review, No. 5,
April, p. 15-16, 1994.
(2) Ishiyama, T., S. Fujikawa, H. Ohkawa and S. Tanaka: Observation of vegetation distribution of Hotan oasisd in southernpart of Xinjiang Uyghur, Proceedings of the 13th conference of Japanese association for arid land studies, pp. 66-67, 2002.
(Text in Japanese)(3) Yoshino, M., : Desertification in China, Published by Aichi University , pp.301., 1997 . (Text in Japanese)Ice breakup dates on 18 Eurasian lakes estimated by MODIS datafrom 2001 to 2005.
Takashi NONAKA1, Tsuneo MATSUNAGA2, and Akira HOYANO3
1
2

PASCO Corporation

National Institute for Environmental Studies
3

Tokyo Institute of Technology

Abstract
Ice breakup date is one of the important indices showing climate changes, and we had developed the ice
breakup date estimation method using satellite data to infer the ice breakup date of the lakes with noavailable in-situ data. In this study, ice breakup dates on 18 Eurasian lakes from 2001 to 2005 were
estimated using water temperature trend derived from MODIS data. The annual changes of the ice
breakup dates were different by the location, elevation, local climate, and so on. In the next stage, we
evaluated the relationships between ice breakup dates and mean air temperature. The result showed that 1
day change of the ice breakup date represented the 0.2 ℃ changes of the mean air temperature.
1. Introduction
Snow and ice is widely taken up as one of the important indices showing climate change (IPCC 2001). It
is reported that the ice breakup date of lake and river has become early about fewer weeks over the past
century (Magnuson et al. 2000). Here, the ice breakup date means that a first day when a specific area oflake surface is completely free of ice. However, few ice breakup date data of the lakes on the central partof the Eurasian continent, for example Lake Baikal (Shimaraev et al. 1994), are recorded. We havedeveloped ice breakup date estimation method using water temperature trend derived from satellite
thermal infrared data to obtain the information for the lakes with no available records (Nonaka et al.
accepted). In this study, we estimated the ice breakup date on 18 Eurasian lakes from 2001 to 2005 using
Moderate Resolution Imaging Spectroradiometer (MODIS) data. From the time series analysis, therelationships between ice breakup dates and local climates were also discussed.
2. Ice Breakup Date Estimation Method
The ice breakup date estimation method we developed was based on Bussieres et al. (2002), and using
the water temperature trend. The method uses the slow seasonal lake surface temperature variation, andice breakup date is estimated by going back from a time series of the surface temperature data derived
from satellite thermal infrared data (Fig. 1).
Fig. 2 shows the flowchart of the developed ice breakup date estimation method and used data whenthe ice breakup date is estimated by MODIS data. It consists of two parts, the threshold surface
temperature estimation and water temperature trend analysis. First, the surface temperature when the lake
surface is completely free of ice (threshold surface temperature, TTH) is determined. The surface
temperature data of the lake are derived from the Sea Surface Temperature (SST) products (Brown andMinnett 1999). In the process, we use the reflectance data of near infrared band as well as SST data.
Secondly, lake surface temperature data on a clear day are derived using the cloud mask, and the surface
temperature (ST) is plotted against date (t). In the next stage, ST data after snow and ice on the lake iscompletely melted and ST rises are selected, and expressed as a quadratic regression equation by t
ST = at2 + bt + c(1)

where a, b, and c are coefficients determined by least square fit. Finally, the intersection of the regression
equation and TTH is the ice breakup date.
The accurate TTH estimation is important for the method because 1 ℃ change of the TTH corresponds
to more than 10 days change of the ice breakup date for some study lakes. The estimated TTH for
freshwater was 2 ℃, while that for seawater was 0.5 ℃ (Nonaka et al. accepted). When we evaluatedthe developed method using in-situ data of Saroma-ko Lagoon and Lake Baikal, the accuracy was better
than 3 days.
First Part

Second Part

SurfaceTemperatu

TTH

Time series data
Ice Breakup Date

Date

Fig. 1. Schematic of the ice breakup date estimation method using water temperature trend.
Threshold Surface
Temperature Estimation

Water Temperature
Trend Analysis

DATA

SST (Level 2)
Reflectance (Band 2)
Cloud mask

SST (Level 2, 3)
Cloud mask

METHOD

Estimation of Threshold
Surface Temperature(TTH)

Selection of Data Used for
Regression Analysis(data from March to June)

Quadratic Regression Equationof Water Temperature Trend
ST = at 2+ bt + c

Ice Breakup Date
(intersection of the regression equation and TTH)

Fig. 2. The flowchart of the ice breakup date estimation method and used data when ice breakup date isestimated by MODIS data.

3. Ice Breakup Date on 18 Eurasian Lakes from 2001 to 2005
3.1 Used Data
Satellite sensor data are generally only useful for estimating ice breakup dates if the satellite has a shortrevisiting time. When using sensors with thermal infrared bands, such as the MODIS and the Advanced
Very High Resolution Radiometer (AVHRR) satisfy this condition. In this study, we rely on MODIS data
to estimate the ice breakup dates in order to take the advantages of thoroughly prepared information on
products such as cloud mask, SST, and so on.
The sensor performs quite well estimating SST, with an estimation accuracy of 0.25 ˚C within therange of -2 ˚C to 32 ˚C (Evans 2002). Our estimation of the ice breakup dates on the 18 lakes investigatedin this study were performed using MODIS Level 3 mapped SST data products with 4.89 km resolution
collected during daytime from 2001 to 2005.
3.2 Study Lakes
Using ILEC (2001), we selected 18 freshwater lakes (Table 1, Fig. 3) in the central part of the Eurasiancontinent for investigation in this study. All of the lakes are larger than about 2000 km2. Some aremanmade reservoirs, and one is partially saltwater (the east part of Lake Balkhash).
Table 1. Features of the 18 Eurasian lakes investigated in this study.
No

Lake

Country

Latitude, Longitude

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

Peipsi
LadogaOnega
Rybinsk
Tsimlyansk
Volgograd
Kuibyshev
Kama
Balkhash
Chany
ZaisanKrasnoyarsk
Khubsugul
BratskBaikalVilyuisk
Zeya
KhankaRussia/ Estonia
RussiaRussiaRussiaRussiaRussiaRussiaRussiaKazakhstan
RussiaKazakhstan
RussiaMongolia
RussiaRussiaRussiaRussiaRussia58.4 oN, 27.4 oE
60.5 oN, 31.2 oE
61.5 oN, 35.2 oE
58.0 oN, 38.5 oE
48.2 oN, 43.1 oE
50.2 oN, 45.4 oE
54.5 oN, 49.4 oE
58.3 oN, 56.1 oE
45.4 oN, 76.2 oE
54.5 oN, 77.4 oE
48.0 oN, 83.5 oE
54.5 oN, 91.4 oE
51.0 oN, 100.2 oE
54.4 oN, 102.1 oE
53.4 oN, 106.4 oE
62.5 oN, 111.1 oE
54.2 oN, 128.1 oE
44.5 oN, 132.2 oE

3
Area2
1 2

12

8

4

Area1

10

14

6

15

Volume (10
km3)
25
908
280
25
24
31
57
12
106
4
53
73

3

Not obtained

169
23000036
68
18

Surface
Area (km2)
3558
18135
9890
4550
2702
3120
5900
1910
18200
2010
5510
2000
2760
5478
31500
2170
2119
4190

17

16

7
5

Elevation
(m)
30
5
35
102
36
15
53
109
341
106
341
243
1645
402
456
244
315
69

9

11
Area3

13
Area4

18

Fig. 3. The location of the study lakes. The each number correspond to the lake in table 1.

3.3 Results
The ice breakup dates on the 18 Eurasian lakes were estimated using the developed method (Fig. 2).
The threshold of all of these lakes was assumed to be 2.0 ˚C, the estimated value for freshwater lakes (inthe case of Lake Balkhash, only the western freshwater was selected), and the daily Level 3 mapped SST
data from March to June were used for all estimation, and the water temperature trend was evaluated as aquadratic equation (1) for each lake (coefficients a, b, and c of each lake are not reported). Surface
temperature was defined as the mean value of 3 × 3 pixels at the center of each lake.
Fig. 4 shows the ice breakup dates (Julian day) of these lakes for 5 years. The ice breakup dates in 2002
and 2004 are relatively earlier for some of the study lakes, especially lakes in area 1 and area 2). In
contrast, the ice breakup date in 2005 is earlier than in 2004 for Lake Chany, Zaisan, Vilyuisk and Zeya inarea 3 and 4. Moreover, clear trend of the time series is not observed for lakes at high elevation of area 3,
except Lake Khubusgol. We postulate these features are mostly related with the air temperature trend in
spring, so the relationships between ice breakup dates and mean air temperature is dealt in the next
section.
(a) Area l(b) Area 2
140

160
Peipsi
LadogaOnega
Rybinsk

140
130
120
110
100
90
80

130

Ice Breakup Date

Ice Breakup Date

150

110
100
90
80
70
60

2001 2002 2003 2004 2005

Tsimlyansk
Volgograd
Kuibyshev
Kama
Balkhash120

2001 2002 2003 2004 2005

Year

Year

(c) Area 3(d) Area 4
180

180
Chany
ZaisanKrasnoyarsk
Khubsugul

160
150
140
130
120
110
100

2001 2002 2003 2004 2005

170

Ice Breakup Date

Ice Breakup Date

170

BratskBaikalVilyuisk
Zeya
Khanka

160
150
140
130
120
110
100

2001 2002 2003 2004 2005

Year

Year

Fig. 4. Ice breakup dates of 18 study lakes on the Eurasian continent from 2001 to 2005.
3.4 Relationships between ice breakup dates and air temperature
The relationships between ice breakup dates and local air temperature are discussed in this section. Air

temperature data were derived from National Center for Environmental Prediction/ National Center for
Atmospheric Research (NCEP/NCAR) data set (Kistler et al. 2001). It is noted that the effects of air
temperature is different by the time of the ice breakup dates. Palecki and Barry (1986) showed that the ice
breakup date is correlated most with air temperature in previous month. Accordingly, we used the meanvalues from March to May in this study taking into consideration of the ice breakup date of study lakes.
Fig. 5 shows the time series of the mean air temperature for each area from 2001 to 2005. The airtemperature was averaged for the all lakes of each area. We can see that the air temperature in 2002 and2004 are relatively higher than other years in area 1, corresponding to the ice breakup dates. Further, thefact that the ice breakup dates in 2005 is earlier than in 2004 for some of the lakes in area 3 and 4 can be
explained from the increase the air temperature from 2004 to 2005.
Fig. 6 shows the relationships between ice breakup dates (IB) and mean air temperature (T). The linear
regression equation was expressed as:
T = 27.05 - 0.20 IB(2)

The correlation coefficients of the equation (2) were –0.93, and the rms error was about 1.8 ˚C. Thisequation means that a day change of the ice breakup date corresponded the 0.2 ℃ changes of the mean
air temperature from March to May. These results suggest that annual changes of the ice breakup datemostly can be explained from the mean air temperature.
Air Temperature (oC)

12.0
Area1
Area2
Area3
Area4

10.0
8.0
6.0
4.0
2.0
0.0
-2.0
-4.0

2001 2002 2003 2004 2005

Year

Fig. 5. Time series of the mean air temperature for each area from 2001 to 2005.

Ice Breakup Date

180
2001
2002
2003
2004
2005
G

160
140
120
100
80
60
-10.0 -5.0

0.0

5.0

10.0 15.0

Air Temperature (oC)Fig. 6. The relationships between ice breakup date on 18 Eurasian lakes and mean air temperature foreach year.

4. Summary
Ice breakup dates on 18 Eurasian lakes from 2001 to 2005 were estimated using water temperature
trend derived from MODIS data. The annual changes of the ice breakup dates were different by thelocation, elevation, local climate, and so on. In the next stage, we evaluated the relationships between ice
breakup dates and mean air temperature. The result showed that 1 day change of the ice breakup daterepresented 0.2 ℃ changes of the mean air temperature from March to May. We need long term of theice breakup dates to grasp the relationships between ice breakup dates and local climate more detail, and
it is left for future studies.
Acknowledgements
The authors are grateful to GES DAAC for providing MODIS SSC data, cloud mask data, andreflectance data.
References
Brown, O., and Minnett, P., 1999. MODIS infrared sea surface temperature algorithm theoretical basis document
version 2.0, April 30, 1999, pp. 91. Available online at: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod25.pdf.
Bussieres, N., Verseghy, D., and MacPherson, J., 2002. The evolution of AVHRR-derived water temperatures overboreal lakes. Remote Sens. Environ., 80, 373-384.
Evans,

R.,

2002.

MODIS

sea

surfacetemperature(SST)

products.

http://podaac.jpl.nasa.gov:2031/DATASET_DOCS/modis_sst.html “.
ILEC, 2001. World lakes database. http://www.ilec.or.jp/database/database.html.
IPCC, 2001. Climate Change 2001: The Scientific Basis. Cambridge University Press, Cambridge UK, pp.
Kistler, R., Collins, W., Saha, S., White, G., Woollen, J., Kalnay, E., Jenne, R., and Fiorino, M., 2001. The
NCEP–NCAR 50–Year Reanalysis: Monthly Means CD–ROM and Documentation, Bulletin of the American
Meteorological Society: Vol. 82, No. 2, 247–267.
Magnuson, J., Robertson, D., Benson, B., Wynne, R., Livingstone, D., Arai, T., Assel, R., Barry, R., Card, V.,
Kuusisto, E., Granin, N., Prowse, T., Stewart, K., and Vuglinski, V., 2000. Historical trends in lake and river ice coverin the northern hemisphere. Science, 289, 1743-1746.
Nonaka, T., Matsunaga, T., and Hoyano, A., Estimating ice breakup dates on Eurasian lakes using watertemperature trend and threshold surface temperature derived from MODIS data. Int. J. Remote Sens. accepted.
Palecki, M., and Barry, R., 1986. Freeze-up and break-up of lakes as an index of temperature changes during thetransition seasons: A case study for Finland. Journal of Climate and Applied Meteorology, 25, 893-902.

Schimaraev, M., Verbolov, V., Granin, N., and Sherstyankin, P., 1994. Physical limnology of Lake
Baikal: A review. Baikal International Center for Ecological Research, Irkutsk.

Estimation of Miyakejima volcanic gas hazards using vegetation index images
Naoko Iino1, Kisei Kinoshita2, Toshiaki Yano3 and Shuichi Torii4
1
Faculty of Engineering, Kagoshima University, Japan
E-mail: iino@mech.kagoshima-u.ac.jp
2
Research and Development Center, Kagoshima University, Japan
3
Kagoshima University, Japan
4
Faculty of Engineering, Kumamoto University, Japan
Abstract
The eruptive activity of Miyakejima volcano started on 8 July 2000, and the ejection of enormous amounts ofsulphur dioxide (SO2) has been continued since August 2000. The Tokyo Metropolitan Government started
monitoring volcanic gas concentrations at the foot of the volcano from the end of 2000. We studied the seasonal andregional characteristics of high concentrations of SO2 using the data. In order to estimate the volcanic gas hazards forthe whole Miyakejima, a hazard mapping method using the Normalized Difference Vegetation Index (NDVI) images
is proposed.
1. Introduction
Volcanic gases and aerosols released into the atmosphere have adverse impact on air quality, vegetation, animals,
and human health. Miyakejima volcano, about 170 km south of Tokyo (Fig. 1), has been ejecting enormous amountsof sulphur dioxide (SO2) from the summit (775 m a.s.l.) since mid-August 2000.
The Tokyo Metropolitan Government stated the monitoring of volcanic gas concentrations from the end of 2000
at three gas-monitoring stations at the foot of the volcano, and expanded into fourteen stations as of April 2004 (Fig. 2
and Table 1). We have been analyzing the SO2 concentrations to investigate the mechanism of high concentration
occurring at the volcano. The following results were obtained from our previous studies (Iino et al., 2003, 2004a, b).
(i) High SO2 concentrations at the ground surface on Miyakejima are mainly caused by downdraft owing to strongwind. (ii) The seasonal and regional characteristics of high SO2 concentrations correspond well to the 925 hPa windobserved at Hachijyojima, the nearest upper air observatory from Miyakejima (Fig. 1). (iii) There is notable differenceof environment of SO2 concentrations owing to a slight directional difference from the crater. This should beconsidered in making a volcanic gas hazard map at the volcano. (iv) The frequency distribution of high SO2
concentration events corresponds well to the distribution of vegetation shown in an image of Terra/ASTER.
In this study, we propose a method to estimate the volcanic
gas hazards for the whole Miyakejima using satellite imagery whichshows the distribution of vegetation before and after the 2000
Miyakejima eruption. The resultant hazard map is examined bycomparing with the occurrence frequency of high SO2
concentrations at each gas-monitoring station and with the groundobservations by visible (VIS) or near-infrared (NIR) cameras.
Here, the NIR photographs are obtained based on our previous
studies (e. g., Kinoshita et al, 2003).
In addition, we briefly discuss the long-term trend and theseasonal and regional characteristics of the occurrence frequencyof high SO2 concentrations, defined above 0.1 ppm and 10 ppm,
using SO2 concentration data during January 2001 - September 2005.
Fig. 1 Location of Miyakejima volcano.

Symbol
A1

Fig. 2 Locations of volcanic gas-monitoring
stations on Miyakejima as of April 2004 ( ), thesummit vent ( ), and the district names.

Table 1 Gas-monitoring stations
Observation start
Station name
Abbreviation(month/year)
Branch OfficeBO
12/2000

D1

Mimoi

MM

04/2004

B1

Ainohama

AH

09/2001

C1

Miike

MI

03/2002

C2

YakubaYK

05/2002

A2

AirportAP

12/2000

D2

Mitake-Jinjya

MJ

04/2004

C3

TsubotaTS

03/2002

B2

AkacoccoAC

09/2001

C4

Usugi-Namakon

UN

03/2002

D3

Usugi-Bus Stop

UB

04/2004

A3

Ako

AK

12/2000

D4

FurusatoFU

04/2004

B3

Igaya

IG

09/2001

2. Sulphur dioxide concentrations at Miyakejima
Figure 3 shows occurrence frequency of high SO2 concentrations of all gas-monitoring stations since January
2001. The environmental standard of SO2 concentration, which was prescribed by the Ministry of Environment inJapan, is less than 0.1 ppm for one-hour averaged value. However, much higher concentrations, several ppm, were
often recorded at Miyakejima. Thus, we defined here the two standards of high concentration levels of SO2 as 0.1 and1 ppm. In each figure, solid and dotted lines indicate the high concentrations frequencies of 0.1 and 1 ppm,
respectively.
The occurrence frequency of high SO2 concentrations is not in the obvious decreasing trend though five years
have passed since the Miyakejima 2000 eruption. The seasonal and regional characteristics of high concentrations
derived from a ten stations analysis (Iino et al., 2004b) are essentially the same as the results including four stations,which were added from April 2004. These are summarized as follows. (i) The stations located between east andeast-southeast directions from the vent, C1, C2 and A2, observe very high rate in winter. (ii) At east-northeast station,
B1, the rate of high concentrations is high not only in winter but also in summer. (iii) At southwest stations, C4 andD3, the rates of high concentrations are relatively high throughout a year. (iv) At north and southeast stations, A1 andC3, respectively, the high concentrations rates are not high.
Here, we focus on the four stations added in April 2004, D1-D4. The Mimoi station, D1, located at northeast
is interesting, because there had been no continual gas-monitoring station though southwesterly wind is dominant inspring and summer. As expected from the wind, the rate of high concentrations defined as 0.1 ppm at the station ishigh in spring and summer, while low in autumn and winter (Fig. 3b). Whereas the rate of high concentrations
defined as 1 ppm, the rate of high concentrations is low though in spring and summer. It may depend on thecharacteristics of wind of which the rate of strong wind in summer is lower than that in winter. For the Mitake-Jinjya
station, D2 (Fig.3g), located at southeast, the characteristics of high concentrations is similar to the Airport station,
A2 (Fig. 3f), but the rate is obviously lower, about one third. The characteristics of the Usugi-Bus Stop station, D3(Fig.3k), located at southwest and near the Usugi-Namakon station, C4 (Fig. 3j) is similar to the tendency andfrequency levels of the C4 station. For the Furusato station, D4 (Fig. 3m), located at west-southwest near the Ako
station, A3 (Fig. 3l), the tendency of high concentrations is similar to the A3 station but the levels are about one half.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

(l)

(m)

(n)
Fig. 3 The occurrence frequency of high SO2 concentrations during January 2001 - September 2005.

3. Hazard mapping for high sulphur dioxide concentrations
In order to estimate the volcanic gas hazards for the whole Miyakejima, we propose a new hazard mapping
method using the Normalized Difference Vegetation Index (NDVI) images. This method is based on a hypothesis thatthe distribution of vegetation at Miyakejima volcano well reflects the influence of volcanic gases. The satellite data
used are as follows. Before the 2000 Miyakejima eruption is JERS-1/OPS on 3 April 1994, while after the eruption is
Terra/ASTER on 7 April 2003. We selected the same season data to eliminate the effect of seasonal change in
vegetation.
First, the JERS-1/OPS was corrected geometrically to fit to Terra/ASTER data, by taking eight GCP-points.
Second, the NDVI is calculated as the difference between the near-infrared and visible reflectances divided by thesum of the two. The NDVI images of JERS-1/OPS (OPS-NDVI) and Terra/ASTER (ASTER-NDVI) are shown in
Figs. 4a and b, respectively. As shown in Fig. 4a, Miyakejima had been covered with vegetation except for the lava or
artificial areas. After the 2000 Miyakejima eruption shown in Fig. 4b, the east and southwest regions are shown as
dark-colour areas, that is the area with low NDVI values. Therefore, it can be considered that vegetation there wasdamaged by volcanic gases. The difference between OPS-NDVI and ASTER-NDVI was calculated to visualize therelative change in vegetation, and classified into four steps of hazards by referring with one-year averaged value ofSO2 concentrations at each station (Table 2), during May 2002 - April 2003. Finally, originally less vegetation areas,such as open water, the lava, artificial areas, were masked by using near-infrared image of JERS-1/OPS though themask is not enough, e.g., for the areas of airport or crater. The resultant volcanic gas hazard map is shown in Fig. 4c.
The color image of Fig. 4c is shown at our web site (in Japanese). http://ese.mech.kagoshima-u.ac.jp/miyake/.

(a)

(c)

(b)

Fig. 4 NDVI images. (a) JERS-1/OPS on 3 Apr. 1994, (b) Terra/ASTER on 7 Apr. 2003. (c) Volcanic gas hazard map.
Table 2 The one-year averaged SO2 concentrations
during May 2002- April 2003.
Gas-monitoringSO2 concentrations
station[ppb/year]
Branch Office
11
Ainohama

143

Miike

279

Yakuba280

Airport166

Tsubota17

Akacocco

29

Usugi-Namakon

120

Ako

46

Igaya

40

Fig.5 The rate of SO2 concentration levels during May 2002 April 2003.

In Fig. 4c, the difference of NDVI values becomes large with the decrease of the vegetation, that is, the gas
hazard level is evaluated to be high. Figure 5 shows the rate of SO2 concentration levels at each station during May
2002 - April 2003. At the foot of the volcano, the gas hazard levels, e.g., the highest level is seen in east andfollowings is in southwest, correspond well to the environment of SO2 concentrations shown in Fig. 5. It is also foundthat the hazard level becomes higher at the mountainside near the crater.
4. Ground observations
Figure 6 shows ground observations in May 2005 from the coastal loop-line road around Miyakejima and from aship with visible (VIS) and near-infrared (NIR) cameras. Vegetation is shown as dark in VIS images (Figs. 6a-c), andas white in NIR images (Figs. 6d-f). In the high volcanic gas concentration districts, Tsubota in the east (Fig. 6a) andAko in the southwest (Fig. 6b), many wilted trees were observed at the foot of the volcano. In contrast, vegetation inother districts (e.g., Figs. 6d-f) was almost normal at the foot of the volcano, except for some wilted Japanese cedar.
We confirmed that the volcanic gas hazard map (Fig. 4c) is consistent with ground observations of Miyakejima island.
The observation results containing animation files are displayed at our web site mentioned above.

(a) Tsubota high concentration district (VIS)(b) Ako high concentration district (VIS)(c) Izu-Kamitsuki (VIS)(d) The Tsubota station, C3 (NIR)(e) Ako-Sabigahama (NIR)(f) Igaya-Izu (NIR)

Fig.6 Ground observations from the coastal loop-line road around Miyakejima, (a)-(d) and from a ship, (e)-(f).
Figs 6(a)-(c) are visible photographs. Figs. 6(d)-(f) are near-infrared photographs by SONY DCR-TRV30
with night-shot mode and Fujifilm IR84 and Kenko ND400. The district names are shown in Fig.2.

5. Concluding remarks
In order to estimate the volcanic gas hazards for the Miyakejima island as a whole, a hazard mapping method
using the Normalized Difference Vegetation Index (NDVI) images was proposed. The resultant hazard map wasconsistent with the occurrence frequency of high SO2 concentrations at ten gas-monitoring stations as of 2003, andwith the ground observations by visible or near-infrared cameras.
However, it should be noted that volcanic gas behaviour depends on the wind around the crater and that the highconcentrations have the seasonal characteristics as shown in Fig. 3. Therefore, the volcanic gas hazard map should be
used together with the meteorological information. In order to improve and complete the Miyakejima volcanic gas
hazard map, further studies will be done in near future.
Acknowledgements
We thank very much to the Tokyo Metropolitan Government for providing the SO2 measurement data in
Miyakejima. Terra/ASTER data by ERSDAC, Japan, was provided as a part of ASTER-AO activity. N. Iino is
grateful to Mr. C. Kanagaki for technical supports of the NIR-camera observation, and to Mr. T. Shiba forcollaborative works on hazard mapping.
References
Iino, N., K. Kinoshita, M. Koyamada, C. Kanagaki and A. Terada, 2003. Analysis of high volcanic gas concentrations
at the foot of Miyakejima volcano, Japan, J. Natural Disaster Science 25 (2), 85-91.
Iino, N., K. Kinoshita, T. Yano and S. Torii, 2004a. Dispersion of volcanic clouds at Miyakejima and gas
concentrations at surface stations, CD-ROM Proc. 1st Int. Symp. Micro&Nano Tech., Honolulu, USA, Mar.
2004, XXIII-C-01, pp.1-6.
Iino, N., K. Kinoshita and T. Yano, 2004b. Regional characteristics of high concentration events of volcanic gas at
Miyakejima, J. JSNDS 23 (4), 505-520 (in Japanese with English Abstract).
Kinoshita, K., C. Kanagaki, A. Minaka, S. Tsuchida, T. Matsui, A. Tupper, H. Yakiwara and N. Iino, 2003. Groundand Satellite Monitoring of Volcanic Aerosols in Visible and Infrared Bands, Proc. CEReS Int. Symp. Remote
Sensing, Chiba, Japan, Dec. 2003, pp.187-196.

Vegetation and Water Quality Analyses of Industrial Waste Using Remote Sensing
Wenhui ZHAO 1, Takanori SASAKI 2 and Shigetaka FUJITA 3
1, 2, 3
Division of Electrical and Electronic Engineering, Graduate School of Engineering,
Hachinohe Institute of Technology
E-mail: d05202@hi-tech.ac.jp
Abstract
After an illegal dumping site was found on the border between Iwate and Aomori Prefectures, an environmental
monitoring system using artificial satellites and ground observation apparatus was established to constantly monitor
and analyze environmental changes. In this study, the data collected using this system, including satellite data, on-site,
infrared camera images, and water quality data, was analyzed to establish the level of on-site contamination and stateof recovery. Some SPOT images were compared to establish the on-site vegetation changes using the NDVI(Normalized Difference Vegetation Index). The relationship between changes in water quality and rainfall wasestablished. It was confirmed that the removal of fluid waste and extensive work carried out on the site reduced thealkalinity of the water.
Key Words: industrial waste, remote sensing, vegetation analysis, water quality
1. Introduction
One of the biggest illegal dumping sites in Japan was found on the border between Iwate and Aomori Prefectures.
Its area is 27 hectares, and the total amount of waste is about 820,000 m3. It caused major social problems such asenvironmental contamination 1). Fig. 1 is an aerial photograph taken on April 25, 2005 showing the state of the illegalsite and the various methods used to treat the waste. The waste is taken away in trucks, but this will take several years
to complete. In the meantime, the waste has been covered with tarpaulins to avoid pollution spreading before disposal.
Waterproof walls will be set up around the site. An environmental monitoring system was established to collect andanalyze the environmental data on-site. The satellite data such as multispectral SPOT image was analyzed forenvironmental analyses in the wide area, and the data from on-site sensors was analyzed in detail. This system is very
useful for establishing the level of contamination and state of recovery on and around the site.

Fig. 1 Aerial photograph of illegal dumping site (April 25, 2005)
2. Remote sensing systems and analysis methods
Artificial satellites and ground observation apparatus were used in this environmental monitoring system.
Data from NASA’s Terra-1 and Aqua EOS satellites was received directly by an antenna installed on the roof of abuilding on our campus. SPOT images, DEM (Digital Elevation Model) data, and Quickbird images wereanalyzed by ENVI software. Two high spatial resolution SPOT images were compared to establish the changes
in on-site vegetation using the NDVI (Normalized Difference Vegetation Index).
Five water quality analyzers, weather measuring equipment, and two infrared cameras were set up at the
dumping site. Data was then transmitted to the monitoring PC in the university every three minutes. The items
measured were pH, electric conductivity, temperature, flow rate, wind direction, wind velocity, air temperature,and rainfall. Fig. 2 shows an example of an on-site image taken by infrared cameras that can be operated
remotely. Environmental changes and on-site work progress can be checked using this data. This groundobservation system and field work back up the satellite images for better accuracy.

Fig. 2 Image from infrared camera (November 4, 2005)
3. Result and discussion
3.1 Vegetation analysis using SPOT images
Two high spatial resolution SPOT images were analyzed in this study. One is a SPOT 2 image of 20 m
spatial resolution taken on July 20, 1992, and the other is a SPOT 5 image of 10 m spatial resolution taken on
June 17, 2004. The NDVI was used to transform multispectral data into a single image band representing
vegetation distribution 2). The NDVI values indicate the amount of green vegetation present in the pixel —near-infrared radiation minus visible red radiation divided by near-infrared radiation plus visible red radiation 3).
This formula is called the Normalized Difference Vegetation Index. Written mathematically, the formula is:
NDVI = (NIR - RED)/ (NIR + RED). For SPOT data, NIR represents Band 3 (0.78-0.89 µm) and RED
represents Band 2 (0.61-0.68 µm).

(a) July 20, 1992(b) June 17, 2004

Fig. 3 Changes in NDVI data on dumping site

Fig. 3 (a) shows the on-site vegetation on the site before the waste was dumped and Fig. 3 (b) shows theon-site vegetation after the waste was dumped. Very low NDVI values (0.1 and below) correspond to barren
areas of rock, sand, or water. Moderate values (0.2 to 0.4) represent shrub and grassland, while high values (0.6
to 0.8) indicate forest. The on-site NDVI value was lower, meaning that there was less on-site vegetation than in
the surrounding area. There are signs of human activity such as narrow paths in Fig. 3 (b), in contrast to thenatural montane vegetation shown in Fig. 3 (a). Furthermore, these figures show a reduction in vegetation, andwe can determine those areas that had changed in that period. This vegetation analysis will be a useful tool for
identifying and confirming illegal dumping sites.
3.2 Analysis of water quality using ground observation apparatus
Water quality data plays an important role in observation and analysis of the level of on-site contamination.
Five water quality analyzers were installed at the former water supply source (Point 1), the new water supply
source (Point 2), in the vicinity of the Kumahara River (Point 3), at the intake (Point 4) and the outlet (Point 5)of the water purifying plant as shown in Fig. 4 4). Fig. 5 shows the changes in water quality in September 2005.
In this figure, the pH and electric conductivity of on-site water (Point 4 and 5) were higher than normal water(Point 1, 2, and 3), and the pH at Point 4 reduced on rainy days (September 2, 7, 14, and 22).

Fig. 4 Distribution of water quality analyzers

Fig. 5 Changes of pH in September 2005

The removal of waste began in September 2004, and 34,428 tons of waste (26,668 tons of solid waste and7,760 tons of fluid waste) was removed by November 2005. Although only 4% of the total waste in Aomori
Prefecture has been removed, almost all the fluid waste has been removed. Furthermore, the extensive work
carried out as mentioned above has improved the water quality. The changes in pH at Points 4 and 5 are shown in
Table 1. We found that the alkalinity of the water was reduced by the water purifying plant — the mean pH in
September 2004 fell from 7.92 (Point 4) to 7.41 (Point 5), and in September 2005 fell from 7.34 to 7.18. The
mean pH at Point 4 also fell from 7.945 (September and October 2004) to 7.46 (September and October 2005).
Table 1 Changes in water quality (pH)
September
2004
Point 3 (Kumahara

October
2004

September
2005

October
2005

7.34

7.15

6.93

6.48

7.92

7.97

7.34

7.58

7.41

7.27

7.18

6.85

River)
Point 4 (entrance of thewater purifying plant)
Point 5 (exit of thewater purifying plant)In addition, it is thought that the water quality will improve further after waterproof walls are built aroundthe dumping site as shown in Fig. 1.
4. Conclusions
A remote sensing system was established to monitor and analyze the environmental data at the illegal
dumping site on the border between Iwate and Aomori Prefectures. In this paper, the data collected using thissystem, including satellite data, images from on-site infrared cameras, and water quality data, was analyzed toestablish the level of on-site contamination and state of recovery.
Multispectral SPOT images of high spatial resolution were analyzed using the NDVI to establish changes invegetation at the dumping site. We found signs of human activity such as narrow paths and a substantial
reduction in on-site vegetation when the waste was dumped. This system will help identify and confirm otherillegal dumping sites. Changes in water quality are important in assessing contamination and were analyzed,
along with rainfall, as the waste was removed. The removal of waste and other on-site work reduced thealkalinity of the water. In the future, new satellite data and ground data will be sequentially collected by thissystem. This will be compared with current data to understand and forecast environmental changes.
References
1) S. Fujita et al.: Environmental monitoring using remote sensing technology, Abstracts of International
Symposium on APGC, p. 308, (2004).
2) Research Systems Inc: ENVI Tutorials, Research Systems Inc. pp. 668-670, (2004).
3) Garguet-Duport, B., et al.: The Use of Multiresolution Analysis and Wavelets Transform for Merging SPOT
Panchromatic and Multispectral Image Data, Photogrammetric Engineering and Remote Sensing, Vol. 62, no. 9,(1996).
4) Wenhui Zhao, Takanori Sasaki and Shigetaka Fujita: Environmental Monitoring of Industrial Waste using
Satellite and Ground Observation Apparatus, Proceedings of International Conference on Materials Engineeringfor Resources, pp. 332-335, (2005).

Satellite image presentation system for education SiPSE based on DEM data

Kisei KINOSHITA1, Nobuya TOMIOKA2 and Hirotsugu TOGOSHI3
1
2

Research and Development Center, Kagoshima University, Kagoshima 890-0065, Japan

Learning Information Center, Kagoshima City Board of Education, Kagoshima 890-0816, Japan
3

Oumaru Junior School, Kawanabe-cho, Kagoshima 897-0131, Japan
E-mail: sipse-adm1@edu.kagoshima-u.ac.jp

Abstract
A data set for 3D presentation of the LANDSAT image for any part of Japan and its viewer are provided by the
SiPSE homepage via the Internet. The digital elevation model data covering Japan with 50 m resolution areembedded in the data set. In addition to the on-line system described in Japanese for the domestic use, off-line
systems are developed with larger file size and other functions for research and international uses. The 3D images
are compared with aerial and ground photographs especially with near-infrared mode.

1. Introduction
In order to drive a satellite 3D image in real time movement as a flight simulator in a personal computer, the SiPSE
system has been developed [1-3], where SiPSE is an abbreviation of the Satellite image Presentation System for
Education. The SiPSE homepage in Japanese started in September 2000 covering Kyushu, Okinawa and Izu Islands,
and extended to cover Japan in 2001, with the address
http://sipse.edu.kagoshima-u.ac.jp/sipse/ .
SiPSE off-line systems have been also developed for research
purposes so as to include new functions: Some of them are covering
submarine topography, embedding the volcanic eruption cloud,simulation of the sea level rise and so on [2]. For the internationaluse, the CD packages of the viewer in English and some data havebeen tested in China, The Philippines, Australia, U.S.A., and Italy.
The satellite image for 3D presentation may be selected from true or
natural color, and single band modes. Especially, monochromatic
view of TM band-4 is very useful to recognize water and land areas,
to see the vegetation coverage and mountainous topography, and tocompare with aerial and ground photographs with near-infrared(NIR) mode.
2. The SiPSE system
The data set, called the SiPSE data, is composed of digital

Fig. 1.

elevation model (DEM) data and the satellite data of the land

The construction of the SiPSE system.

coverage, as shown in Fig. 1. The land coverage data is obtained from the LANDSAT TM 1-4 data with the pixel
size 28.5 m by reducing the brightness from 8 bits into 4 bits. The reduction of the TM data and the discard of the
bands 5-7 are done so as to reduce the file size for the Internet use. The land coverage is expressed by the true-color
mode with TM 1, 2 and 3, or by the natural-color mode with TM 1, 2+4 and 3 corresponding to B, G and R,
respectively. TM 4 is also utilized for the gray scale presentation of the NIR image. This form of the land-cover
data in pre-processed form for the web use started in 1996 as the SiNG data covering Kagoshima area in southern
Japan [4], where TM 6 was maintained converting into the temperature scale.
Geographical Survey Institute (GSI) provides the DEM data with the spatial resolution 50 m covering Japan in
CD-ROMs. In the SiPSE data, the DEM information is converted to fit with the land-cover data with higher spatial
resolution by linear interpolation.
The SiPSE data can be handled by means of the SiPSE viewer, which is also provided via the Internet. The 3D
presentation in a Windows computer with a selected land-cover image of a scene can be done in still or motion
modes, specifying ways and the speed of the motion. The vertical/horizontal ratio of scales in a 3D image can be
adjusted, as well as the overall scale. The standard size of a scene is 512 pixels with 512 lines corresponding to 15
km squared, while it is 1024 * 1024 corresponding to 30 km squared for specially registered users. Wider areas up
to six times in length can be obtained by lowering the spatial resolution. The viewer has other functions such as freedrawing on the land-cover image, measuring a distance between specified points and the size of a specified area.
3. Volcanic topography and land coverage
There are a lot of beautiful volcanic sceneries in Japan, which are nice subjects of the SiPSE imagery [2, 3]. Let us
visit Unzen volcano located in the center of Shimabara peninsula in western Kyushu, Japan, as shown in Fig. 2. The
volcano started eruption in November 1990, and a lava dome developed at the Fugen-dake summit accompaniedwith pyroclastic flows endangering lives of the inhabitants during 1991-1995. We see the lack of vegetation on thedome and the passages of the flows very clearly both in the true color and NIR images. We may see the topographic
structure of the volcanic complex, including old volcano Mayu-yama on the east side of the main peak, by changing
the viewing direction. Thus, the SiPSE system may be utilized for the prevention of the volcanic disasters withrespect to the improvement of the understanding of the topographic situation.

Fig. 2. Unzen volcano observed from south-eastern sky in true-color and NIR images (a and b), from the south intrue-color image (c), and from the east near the horizon in NIR image (d), respectively. LANDSAT: 1998.10.4.

4. NIR images of SiPSE-3D and aerial photographs
In a NIR image of rather flat scene
in daytime, we may distinguishvegetated area, bare land and water
areas as light gray, dark gray andalmost black, respectively, such as
shown in Fig. 3, which is a
SiPSE-3D NIR image of three big
rivers in the west of Nagoya city
pouring into Ise Bay, seen fromeastern sky. In this image, urban
areas of Nagoya, Ogaki and other
cities are relatively dark. In the areasof Suzuka and Ibuki mountains withrich vegetation in the upper part of

Fig. 3. A SiPSE-3D NIR image of three big rivers, Ibi-, Nagara- andtheKiso-gawa, from top to down pouring into Ise Bay, seen from eastern

figure,mountainous

topographies can be observed by thesky. Original LANDSAT data: 1997.10.21.

shadows of the sunshine. The oldbattlefield Sekigahara can be foundin between the two mountainous
areas.
Fig. 4 is an aerial NIR photographtoward the north from above Ise Bay.
We may clearly see the three big
rivers, and the difference of thevegetation in urban and rural areas.
In such a slant view near the horizon,
we may see very wide NIR viewcomparable to a full scene ofFig. 4. An aerial NIR photograph toward the north from above thecenter of Ise Bay, by the night-shot mode of Sony DSC-V3 with IR84
filter of Fuji-film on 4 Nov. 2005 at 12:17. Shin-Nagoya airport is seenon the down right.

LANDSAT data, while conventional visible image is usually very much obscured by aerosol and moisture. See [5]for more about NIR photographs.
In Fig. 5, we compare southern part of Kyoto basin in true-color and NIR images by SiPSE-3D observed fromsouthern sky (a and b), and a mosaic of NIR photos from northern sky (c). In two SiPSE-3D images (a and b), plains
and mountainous areas exhibit opposite brightness in true-color and NIR owing to the difference in vegetation, while
the rivers are clearly seen in both images as relatively dark lines. Three big rivers, Kazura-gawa from the north,
Uji-gawa from the east and Kizu-gawa from the south, join together into Yodo-gawa that flows down toward
south-west into Osaka Bay through the Osaka plains. Kamogawa, joining Kazura-gawa, is also seen clearly in Fig. 5a.

(a) SiPSE-3D true-color image(b) SiPSE-3D NIR image (LANDSAT data: 1989.5.31)(c) A mosaic of NIR photographs by thenight-shot mode of Sony DCR-TRV30
with Fuji-film IR84 and Kenko ND400.

Fig. 5.
Southern part of Kyoto basin observed fromthe southern sky (a, b), and northern sky (c).
The NIR photograph (c) is very similar to the satellite-based image (b), except for the directions of the observationand the sunlight. In Fig. 5c, we may see big bridges crossing the rivers, and also the shadow of the clouds in thedownside of the picture.
It is interesting to note that we may find big battlefields decisive to Japanese history in Fig. 5: On 13 June 1582,
Mizuhide Akechi was defeated by Hideyoshi Hashiba at the decisive Yamasaki battle around the upstream of
Yodo-gawa, and Hideyoshi turned out to be the governor of Japan. On 3 January 1868, the battle of Toba-Fushmi
started around the down streams of Kazura-gawa and Uji-gawa, and ended after three days resulting the collapse of
the Tokugawa regime. Topographical importance of these areas to become historical scenes may well be understoodby the satellite 3D images.
5. Estimate and simulation of volcanic eruption cloud heights
The height determination of volcanic eruption cloud is very important for the dispersion forecasts of ash clouds so
as to avoid the airline hazards. In order to improve the pilot reports of the cloud height observed, two methods havebeen developed as off-line systems. One of them is to estimate the cloud height of an aerial photograph by thesimulation of the land topography with a height scale [6], and the other one is to embed a model of eruption cloud
as a 3D object in a SiPSE 3D image, such as shown in Fig. 6. By changing the cloud height, the wind direction andthe viewpoint in the latter case, we may simulate various situations for the training.

Fig. 6. A model of eruption cloud as a 3D object embedded in a SiPSE 3D image of Sakurajima volcano.
The box of parameter control buttons is shown on the left hand side.
6. Unification of land and submarine topographies
Marine Information Research Center provides the submarine topography data around Japan in a CD-ROM with theresolution 1 km. In order to join with the land topography and land-cover data with much higher spatial resolution,
special care is necessary for the handling of the coastal lines. Instead of the sea surface data or some coverage dataof the sea bottom, it may be reasonable to use the sea depth data with a graduation scale. Fig. 7 is a scene of Amami,
Tokara and Kumage Islands up to the southern Kyushu observed from southern sky, thus constructed.

Fig. 6. From Amami Islands to southern Kyushu, Japan, observed from southern sky. Relatively big islands
from the top to the down are Tanegashima, Yaku-shima, Amami-Oshima, Tokunoshima, Okinoerubu-jima andYoron-jima. This SiPSE-3D image is composed of the true-color image for the land and the sea depth
graduation image, with the DEM information. Equi-depth lines are also drawn on the sea. Horizontal scale isenhanced by the facter 5.

In Fig. 6, we may see a part of very deep Nankai Trough on the right-hand side as very dark blue area. This 3D
presentation with submarine topography is developed as an off-line system with special handling of the data.
In general, we may try new approaches in off-line systems, while we should keep the constancy of the format in theon-line system open to public through the Internet.
7. Concluding remarks
In order to construct a land image database of a satellite for the 3D presentation, a large archive of the original datais necessary so as to obtain cloudless scenes. To avoid the snow-cover in northern areas is also an important
requirement for the scenes. The SiPSE database is thus constructed during 1996-2001, by selecting relatively newscenes if available.
In order to unify neighboring LANDSAT scenes to get wide images, it is better to use the original data with similarseasons and years. However, this requirement is not always easy to fulfill, because of the clouds and/or snow-cover.
Handling of very wide scenes is investigated as an off-line activity of the SiPSE group.
Acknowledgements
We are grateful to JAXA (previously NASDA) for allowing the use of LANDSAT data in the SiPSE format, and
GSI for the use of the DEM data of Japan in the SiPSE database.
References[1] H. Togoshi, N. Tomioka and K. Kinoshita, Development of 3D image presentation system for education using
digital elevation model and remote sensing data, Proc. 27th Japanese Conf. Rem. Sens., 1999, p.273-274 (inJapanese with English abstract).
[2] K. Kinoshita (ed.), Construction and application of the satellite image providing and presentation system foreducation, Kagoshima Univ., 2004, 141p. (in Japanese).
[3] K. Kinoshita, N. Tomioka and H. Togoshi, “How to create and see 3D satellite images by SIPSE system, Observation of landscapes of Japan from the sky –“, Kokon-shoin, 2005, 126p. (in Japanese:).
[4] N. Tomioka, K. Takayama, A. Minaka and K. Kinoshita, Construction of Satellite Image Providing System for
Education on the Internet, Proc. 22nd Japanese Conf. Rem. Sens., 1997, p.215-218 (in Japanese with English
abstract).
[5] K. Kinoshita, C. Kanagaki, A. Minaka, S. Tsuchida, T. Matsui, A. Tupper, H. Yakiwara and N. Iino, Ground andSatellite Monitoring of Volcanic Aerosols in Visible and Infrared Bands, Proc. CEReS International Symposium on
Remote Sensing, Monitoring of Environmental Change in Asia, Chiba, Japan, pp. 187-196, 2004.
[6] K. Kinoshita, C. Kanagaki, N. Iino, M. Koyamada, A. Terada and A.C. Tupper, Volcanic plumes at Miyakejima
observed from satellites and from the ground, Proc. SPIE 4891(2003), Optical Remote Sensing of the Atmosphere
and Clouds III, part of SPIE's 3rd Int. Asia-Pacific Environmental Remote Sensing Symp., October 2002,
Hangzhou, China, 227-236

Hydrologic image interpretation on small-scale on farm pond
using high resolution satellite imagery

Kenji SUZUKI 1 and Yukiyo YAMAMOTO 2
1, 2
Japan International Research Center for Agricultural Sciences
E-mail: suzukik@jircas.affrc.go.jp
Abstract
This paper describes the use of satellite image interpretation for analysis of small-scale on-farmponds in northeastern Thailand based on field knowledge of hydrology and water use. Image
interpretation on high-resolution QuickBird satellite imagery revealed that 1) the type of pond can be
categorized visually from the viewpoint of its location, 2) the catchment location in relation to thepond can be estimated from the intake point of water and the layout of the pond. Through a series ofimage interpretations, the effectiveness of high-resolution satellite imagery for field sciences andinvestigations was confirmed.
Keywords: Field sciences, Image interpretation, Northeastern Thailand, QuickBird
1. Introduction
In field science, which focuses on the survey of actual sites, a technique of juxtaposing field study
observations with interpretations of aerial photography or high-resolution satellite imagery iseffective in the investigation of area-specific phenomena. For example, in a study of riverengineering, aerial photographs of the area around the channel taken immediately after flooding
provided invaluable information when combined with the site survey and hydraulic model tests(Miwa et al., 2003).
High-resolution imagery produced typically by IKONOS or QuickBird satellites provide images
that are as detailed as aerial photographs, with a resolution of up to 1 m. Since these satellite images
became available, some researchers have been testing them for use in surveys of relatively small
areas in so-called site-based field studies (Suzuki et al., 2005a).
This paper describes an example of image interpretation regarding the hydrological location ofsmall-scale ponds using field knowledge and high-resolution satellite imagery.
2. Overview of the study area and data used
For this study, we selected the village of NS in Ban Haet District, Khon Kaen Province, in
northeastern Thailand. The village is located about 30 km south of the city center of Khon Kaen. The
area is characterized by a gently undulating terrain. Paddy fields dominate the lower site, the valley,while the upper site features sugar cane and cassava farms.
The soil is sandy with poor water retention capacity and nutrient content, and is susceptible toerosion. Heavy rain often causes soil erosion in the upper site, where the lands are practically bare.
The wet seasons and dry seasons are distinct in northeastern Thailand. Precipitation is minimal in thedry season. In the wet season, the precipitation is variable because the timing of seasonal onset andthe amount of rainfall are unstable. The rain generally comes in the form of localized squalls,
contributing to the uneven temporal and spatial distribution of water resources. As a result,agricultural productivity has remained low and unstable.
Under such circumstances, there have been many small-scale water resources developments.
Small ponds adjacent to cultivated fields, such as paddy fields and farmlands, and irrigation weirs
built in small rivers are typical examples of such measures. The study site, the village of NS, hasalready had more than 260 ponds developed in the area, and is known to have more ponds than otherareas (Suzuki et al., 2005b).
This study used panchromatic images (60 cm/pixel) and multispectral images (2.4 m/pixel) taken
by QuickBird in the dry seasons of April 2002 and January 2004 for visual interpretation. In addition,
we conducted field surveys in the subject area over the period from 2002 to 2005 to facilitate theobservation of precipitation and water levels.
3. Examples of interpretation and application
3.1 Identification of types of ponds
Farmers own small-scale ponds adjacent to their farms, and take water primarily by pump for avariety of uses, including rice planting and drought relief, as well as for fish farming and vegetable
growing. The ponds in the study area are roughly divided into 2 categories according to theirlocation and construction methods.
The most common ponds in the lower site are made of earth bunds built in the lowest part in thevalley bottom (Photo 1). The bunds run for lengths of some 60 to 80 m. Water levels available forstorage are less than 1.5 m, although some ponds have their upstream side dug down somewhat
deeper. Since there is little precipitation during the 6-month dry season, the water level keeps falling
during this time due to an evaporation rate of about 5 mm/day. These ponds usually remain wet even
without stored water because they are situated in the valley. However, their water budget suggeststhat water may not be available year-round because of their inherently shallow storage depth.
Contrastingly, the ponds in the upper site are predominantly dug into the ground by heavymachinery. One side of the pond runs about 20 to 30 m in length. The total height of the surrounding
embankment and the depth of the dug-out pond provide the storage depth, which can be as high as
3 m (Photo 2). Many of these ponds have water available for use year-round because sinking theminto the ground ensures a sufficient storage depth. Some of these ponds are replenished by shallowsubsurface water.
On the images, the bunds of the ponds that dominate the lower site are visually identifiable. The
images taken in the dry season in particular often present the ponds in an irregular shape that reflect
falling water levels (Fig. 1). On the other hand, the in-ground ponds in the upper site retain theirrectangular shape, and do not change even when their water level falls. The embankments
surrounding these ponds are also recognizable in the images (Fig. 2). These differences presented byhigh-resolution satellite imagery provide the possibility of identifying the type of pond from theimages. For convenience, this study refers to these respective types of ponds as the “lower ponds”
and the “upper ponds”.
Many of these ponds are of a rectangular shape, with the short sides normally running parallel tothe direction of the grade. The paddy fields are also rectangular and laid out in a similar orientation.
This layout reduces the difference in water levels in ponds (and in paddy fields) when the water isstored on a sloped terrain. Nevertheless, even this layout may cause unevenness, especially in paddy
fields, if the land is not leveled sufficiently. Accordingly, the layout of ponds and paddy fields (theorientation of the short sides) provides clues which make it possible to estimate the direction of thesurface grade at the site.

Photo 1 Typical pond in the lower site

Fig. 1

Photo 2 Typical pond in the upper site

Typical pond in the lower site

(QuickBird, Panchromatic, 0.6 m/pixel)

Fig. 2

Typical pond in the upper site

(QuickBird, Panchromatic, 0.6 m/pixel)

3.2 Identification of intake points in the upper ponds
The water level may vary among a number of ponds present within a small watershed even withthe same usage of water. What factors regulate the difference in the water levels of the upper ponds?
Figure 3 illustrates the concept of the water budget of a pond. Because there is little difference
between the ponds in terms of their surface area, factors such as precipitation and evaporation arevirtually the same. On the other hand, factors such as surface flow, lateral inflow (and outflow), andseepage loss of the ponds may vary depending on their location. The lateral inflow (and outflow) andseepage loss are relatively small. Consequently, the inflow of surface water from the catchment area
regulates the water budget of the ponds, and is the biggest factor causing locational differences.
Substantial rainfall in the wet season causes a rapid rise in the water level (Fig. 4). Especially, adaily rainfall of more than 100 mm raised the water level by more than 55 cm in a day in
mid-September. Subtracting the 10 cm of direct rainfall on the pond surface, the water level rose by
45 cm. Assuming that 40 cm of this rise was due to surface inflow from the catchment area, and thatthe runoff ratio was 0.4, a calculation-based water budget estimates the size of the catchment area tobe 10 times as large as the surface area of the pond.
ET

SurfacerunoffThrough-flow

Deep loss

Shallow
ground
water

3

ponds

Concept of water budget of upper

50

Fig. 4

Rain
Pond

100

2.5

150

2

200

1.5

250

1
2003/8/1

Fig. 30

4
3.5

Rainfall (mm/day)

Rainfall

Water level (m)

Water
use

300
2003/9/1

2003/10/1

Observed changes in water level of an

upper pond

If the inflow of water depends on the size of the catchment area, farmers will be motivated tosecure the catchment area. Many of the upper ponds are equipped with an intake point or a small
channel to secure an inflow of water from their catchment area. The passage of water, however,makes the ground around the intake point prone to erosion. If the volume of water passing is too
high, there is a risk that the embankment near the intake point may collapse. Furthermore, if
farmlands or bare lands occupy a large portion of the catchment area, and these lands are adjacent toa pond directly, sedimentation from the inflow of sediment-laden surface water becomes a problem.
An examination of images identified some ponds with recognizable sedimentation indicating thelocation of their intake points. Figure 5 is a satellite image that provides a relatively clear view of thearea near the intake point. The area marked by the dotted line indicates the extent of thesedimentation, presented as a triangle with its apex at the intake point in the upper right corner.
Photo 3 is a photograph of the intake point of this pond and the actual condition of the sedimentation,
confirming how well the satellite image corroborated them.

Fig. 5 Sedimentation around intake point of

Photo 3 Sedimentation around intake point ofwater (QuickBird, Panchromatic, Jan. 2004,

water (Photograph taken from position marked

Arrow: intake point, Dotted line: sedimentation,} in Fig. 5 in July 2005; Arrow: intake point,

Photograph taken from position marked })Dotted line: sedimentation)

A comparison of images taken at 2 different times also confirms the process of sedimentation overtime. The white area in Fig. 6a is a newly constructed pond. The embankment and the periphery of
the new pond are presented as a bright white area in contrast to the surroundings as it is bare land
covered by dry soil. Another image of the same pond taken 2 years later (Fig. 6b), however, shows aclear sign of sedimentation due to the inflow of sediment.

(a) 2002
Fig. 6(b) 2004

Process of sedimentation in a pond (QuickBird, Panchromatic, 0.6 m/pixel)

Figure 7a shows an upper pond in a rare dried-up condition, which provides us an opportunity tomake close observation of the condition of its bottom. Examining the bottom surface of the pond
indicates that its intake point is located at the lower right corner. The sediment-laden water flowed
into the pond from this point, but has since dried, appearing in white in the image. The 2004 imageof the same pond (Fig. 7b) shows a higher water level than is shown in the 2002 image (Fig. 7a). The
shape of the water surface clearly confirms the area of sedimentation in the lower right corner of thepond. The intake point itself is not very clear in Fig. 7b. If the water level were slightly higher, thesedimentation would have been hidden beneath the water surface, making it difficult to identify theintake point. In other words, the presence of sedimentation provides convincing evidence of thelocation of the intake point. Accordingly, the sedimentation on the bottom of a pond exposed by low
water level supports the identification of the location of the intake point.

(a) 2002(b) 2004

Fig. 7 Observed sedimentation at low water levels (QuickBird, Panchromatic, 0.6 m/pixel)
4. Conclusion
This study examined hydrological information that can be extracted from visual interpretation ofhigh-resolution satellite images of small-scale ponds in northeastern Thailand. It was found that (1)it was possible to classify the ponds according to their location or their type (ponds in the upper site,
and in the lower site); (2) for some of the ponds, it was possible to estimate the location of the main
catchment from the layout of the ponds and the location of the intake point. These interpretation
techniques are believed to provide valid information when conducting an investigation of suitablelocations for irrigation ponds.
With respect to the collection of hydrological information using remote sensing methods, the roleof visual interpretation is very important from the point of view of region-specific information(Kondo, 2003). To this end, a researcher must possess sufficient understanding of local conditions
and expert knowledge of the visual interpretation of satellite imagery. A future task is to accumulate
examples to further develop and refine the analytical techniques that utilize field knowledge.
References
Kondo, A., 2003. Application of remote sensing and GIS technology to hydrology; Geography
Review, 76(11), pp. 788–799, in Japanese.
Miwa, H., Kurashima, E., 2003. Present situation and cause analysis of diversion dam failures inJapan, Paddy and Water Environment, 1(4), pp. 207–214.
Suzuki, K., Y. Yamamoto, M. Ando, C. Ogura, 2005a. Evaluation of land and water resources inrainfed agricultural areas using high-resolution satellite data and GIS, Journal of the Japanese
Agricultural Systems Society, 21(3), in press.
Suzuki, K., Y. Yamamoto, M. Ando, 2005b. Evaluation of small-scale on-farm ponds in northeast
Thailand using hydrologic model and GIS, Journal of the Japanese Agricultural Systems Society,21(1), pp. 59–64.

Processing and interpretation of JERS-1 Synthetic Aperture Radar (SAR) imageof Cepu and its surrounding areas, Central Java Province, Indonesia
Wahyudi W. PARNADI1, Salim RUSLI1, Ketut WIKANTIKA2, Josaphat TETUKO S. S.3
1
Department of Geophysical Engineering, Institute of Technology Bandung
2
Department of Geodetic Engineering, Institute of Technology Bandung
3
Center for Environmental Remote Sensing, Chiba University
E-mail: wparnadi@bdg.centrin.net.id
Abstract
Geological as well as lithological interpretations of subsurface condition from Radar image are of great interest.
Conventionally, geological features are derived visually from the images. Patterns associated with geological structures/features are recognized. Attempts to interpret lithological as well as structural geological features deeper than 1 - 10
meters are of course challenging. This research is in the early stage of an attempt to derive lithological and geologicalfeatures from Radar image conducted in the Department of Geophysical Engineering of Institute of Technology
Bandung, Indonesia. In this stage, we tried to derive geological structures and other features visually from JERS-1 SAR
data from Cepu area in Central Java Province, Indonesia, in which a huge oil reservoir located. Conventional image
processing flows applied on the data could improve data quality that lead to more interpretability of the data. Elevenanticlinal folds and one faulted anticlinal fold were detectable. These folds could be identified based on theircharacteristics: coarse textures and high brightness values, folded form or horizontally folded and symmetrical or
repetitive patterns. Shear fault structures could also be identified based on the appearance of lineaments intersecting
folds. A small difference in structure orientation interpretation from image-derived structures and the geologicalstructure map could be resulted from the error in the process for geometric correction, especially for structures locatedoutside ASTER image coverage acting as geometrical correction reference. Even for structures inside the ASTER image
coverage, Digital Elevation Modeling (DEM) technique should be incorporated in the processing for better results in thedetermination of structures orientation.

1. Introduction
Interpretation of subsurface condition from Radar image is of great interest, covering surface as well subsurface featureswith resolution much higher than resolution offered by radar technique. Lithological as well as geological informations
extracted from the image could be of higher value. Conventionally, geological features are derived visually from Radar
images (Sabins, 1997). Patterns associated with geological structures/features could be detected. Oil seeps occurrence
under ocean could also be recognize in radar image. This leads sometimes to the discovery of oil and gas fields infrontier areas.
In nature, Oil and gas were trapped at a depth below 400m – 700m. In Indonesia, their occurrence could be in shallow
reservoir at a depth of 200m-500m and in reservoir at deeper depth. In other side, informations extracted from remotesensing data provide just some 10 centimeters – 1 meter thick subsurface condition. That makes remote sensing data
used only in the reconnaissance survey. Traditionally, seismic technique is the only primary method applied for oil andgas exploration. However, some fieldworks recognize the use of remote sensing data in the late stage of oil and gas
exploration for some purposes. Seismic surveys, which are the routine method for defining drilling prospects can not be
carried out in the most of Petroleum Prospecting Licenses (PPL) operated by Chevron Corporation in Papua New
Guinea for two reasons: (1) inaccessibility and rugged topography causing expensive exploration, and (2) scattering ofseismic energy which results in very poor data (Sabins, 1997). It is therefore better to incorporate remotely sensed datain late stage of exploration. An attempt to interpret lithological as well as structural geological features deeper than 1 10 meters is therefore a challenging task. That depth is, in the first approximation, appropriate for geotechnical andenvironmental purposes. This could more likely be carried out using correlation between remotely sensed data and data
derived from surface measurement. Another better method would be derivation of physical mechanisms between
electromagnetic waves and their interaction with subsurface media. We are now in the early stage of our attempts toobtain techniques that make deeper interpretation of radar data possible.
In this research, we tried to derive geological structures and other features visually from JERS-1 SAR data from Cepu
area, Central Java Province, Indonesia, in which a huge oil reservoir located. Figure 1 and figure 2 show the study area
and its geological structures. The study area located in the coordinates 6˚ 29’ 14.09” S – 7˚ 17’ 39.42” S and 111˚ 27’
4.32” E – 112˚ 18’ 9.19” E. This area comprises of part of Randblatung and Rembang zones. This zone is geologicallydominated by anticlinal folds structure with East-West orientation. In this zone is more than 20 oil fields, which aredeveloped since 1893, located. This zone has potentially the biggest hydrocarbon accumulation among other zones inthe surrounding areas. The use of geological map is intended to get better understanding of geological features andstructures that could be recognized in the Radar image. Since we are still in the first stage to work with radar data, weapplied conventional image processing flows. We hope that such image processing flows are sufficient to improve data
quality that lead to more interpretability of the data.

Fig. 1: Cepu Study Area is located in Central Java Province, Java Isle, Indonesia, in the coordinates 6˚ 29’ 14.09” S – 7˚
17’ 39.42” S and 111˚ 27’ 4.32” E – 112˚ 18’ 9.19” E. A huge oil and gas reservoir was found to be at 200-400 m depth.

Fig. 2: Geological Structure Map of study area showing anticlinal folds and their orientations (Darman & Sidi, 2000).
The study area is located in some part of Randublatung and Rembang areas. This zone has potentially the biggest
hydrocarbon accumulation among other zones in the surrounding areas.
2. Data and Data Processing
JERS-1 as well as ASTER images for this research were available from Remote Sensing Research Center - Panditho
Panji Foundation, Bandung – Indonesia and ERSDAC of Japan. Combining with data from field observation, we
processed the JERS-1 image using following data processing flows: radiometric correction (speckle reduction) with
low-pass filtering, geometric correction (image rectification) with least-square polynomial regression, image sharpening
with linear contrasting and high-pass filtering. Speckle reduction was carried out using two types of filters: 3x3 and 5x5
median filters. Beside visual appearance, we used ratio between deviation standard σ and statistic mean µ (= σ/µ ratio)
as other criteria. Images with the smallest σ/µ ratio were taken to further processes. Geometric corrections were appliedwith image rectification. ASTER image was acted as reference image in this process. Objects which clearly identified in
the images were selected as ground control points (GCPs). Distribution of GCPs as well as their correspondence points
in the SAR image could be easily done since we marked all the points during field observations. Image sharpening was
carried out through 2 consecutive techniques: image contrasts sharpening and special filtering. Autoclip transform wasadopted for the image contrast sharpening. 3x3 high-pass filtering was then applied after image contrast sharpening. All
these processing steps were conducted using ER Mapper 6.4 software available in our Department. Figure 3 shows raw
image of study area. The area is quite easily accessible. Rectangle in red denotes area used for comparison in speckle
reduction processing step. Geometric correction is applied using JERS-1 SAR and ASTER images. For this purpose,
distribution of control points in the JERS-1 SAR image and their reference points in the ASTER image is shown in the
figure 4. Figure 5 shows processed JERS-1 SAR image after image sharpening. Image histogram after application ofhigh-pass filtering is shown in the right side of the figure.

Fig. 3: Raw SAR image of study area. Area for speckle reduction
processing step is shown in red rectangle.

Fig. 4: Distribution of ground control points (GCPs) in the JERS-1 SAR image (left) and their reference
points in the ASTER image (right) for image rectification in the geometric correction processing step.
These points could be easily recognized in field observation.

Fig 5: processed JERS-1 SAR image after image sharpening (left) and an example of applied parameter forthis image sharpening. Data histogram of the image after high-pass filtering application is shown (right).

3. Data Interpretation
Processed image combining with geological structures map were available in the interpretation stage. Two types ofgeological structures were identifiable in the processed image: anticlinal folds and faults. Eleven anticlinal folds andone faulted anticlinal fold were detectable. Four and five anticlinal structures have NW-SE and SW-NE orientation
respectively. Only 1 anticline was found with W-E orientation. These folds were identified based on their characteristics,
i.e. coarse textures and high brightness values, folded form or horizontally folded and symmetrical or repetitive patterns.
Figure 6 shows interpreted anticlinal folds and fault. White solid lines denote fold structures and white dashed line
denotes shear fault. Identification of these anticlinal structures was quite easily carried out in the images.
Shear fault structure was identified based on the appearance of lineaments intersecting folds. Figure 7 is image fraction
showing area in the adjacent of interpreted fold no. 1 and shear fault. Symmetrical axes are denoted by blue arrows.
These results have been compared with geological structure map. Based on this comparison, 3 anticlinal structures, i.e.
anticline 4, 5, 6, were found to be declined from their orientations in the geological structure map. The difference instructure orientation interpretation from image-derived structures and the geological structure map is more likely
resulted from the error in the process for geometrical correction, especially for structures located outside ASTER image
coverage acting as geometrical correction reference. Moreover, for structures inside the ASTER image coverage, Digital
Elevation Modeling (DEM) technique should be incorporated in the processing flows in order to get better results in thedetermination of structures orientation.
4. Conclusion
Interpretation of subsurface condition from Radar image is of great interest, either structurally/geologically or
lithologically. In conventional way, geological features and structures are derived visually from the Radar images.
Patterns in the image are tried to be identified and recognized as objects associated with geological structures/features.
Attempts to interpret lithological as well as structural geological features from the images for subsurface media deeper
then, say 1-10 meters are of course challenging. This 1-10 meters range is, in the first approximation, suitable forgeotechnical and environmental purposes. This research is in the early stage of an attempt to derive lithological andgeological features from Synthetic Aperture Radar (SAR) image conducted in the Department of GeophysicalEngineering of Institute of Technology Bandung, Indonesia. In this stage, we tried to derive geological structures andother features visually from JERS-1 SAR data from Cepu Area, in which a huge oil reservoir located. Cepu area situatedsome part in the Randublatung and other part in the Rembang zones. This zone is well-known as a zone which has
potentially the biggest hydrocarbon accumulation among other zones in the surrounding areas. Application of
conventional image processing flows on the data has improved data quality that lead to more interpretability of the data.
Eleven anticlinal folds and one faulted anticlinal fold were then detectable. These folds were identifiable based on theircharacteristics: coarse textures and high brightness values, folded form or horizontally folded and symmetrical or
repetitive patterns. Shear fault structures are also be identifiable based on the appearance of lineaments intersecting
folds. A small difference in structure orientation interpretation from image-derived structures and the geologicalstructure map was found. This could be resulted from the error in the process for geometric correction, especially forstructures located outside ASTER image coverage acting as geometrical correction reference. In order to provide better
results in the determination of structures orientation, even for structures inside the ASTER image coverage, Digital
Elevation Modeling (DEM) technique should be incorporated in the processing steps.

Fig. 6: Interpreted folds (denoted by white solid lines) and fault (white dashed line) in the study area. These anticlinal
folds are some of 11 anticlines structures found in the study area. Most anticlines have E-W orientations.

Fig. 7: Image fraction showing more detailed area in the adjacent of interpreted fold no. 1 and shear fault.
Arrow signs denote symmetrical axes.
References[1] Darman. H. and Sidi, H. (eds.), 2000. An outline of The Geology of Indonesia : Indonesian Assoc. Geologists(HAGI), Jakarta.
[2] Jensen, J., 1986, Introductory to Digital Image Processing: A Remote Sensing Perspective: Prentice-Hall, New
Jersey, USA.
[3] Permana, T. W., 2003, Analysis of Soil Backscattering Value using Transmission Lines Approach and Data
Processing of JERS-1 SAR Data (in Indonesian language): BSc. Thesis, ITB, Bandung.
[4] Rimayanti, A., 2001, Geological Mapping with fusion method of SPOT-HRV and JERS-1 SAR Data (inIndonesian language): BSc. Thesis, ITB, Bandung.
[5] Sabins, F. F., 1997, Remote Sensing, Principles and Interpretation: W. H. Freeman and Company, New York, 3rd ed.
[6] Tateishi, R., Al Bilbisi and J. Tetuko S. S., 2003, A Technique to Estimate Topsoil Thickness in Arid and Semi-Arid
Areas of North-Eastern Jordan using Synthetic Aperture Radar Data: Int. J. Remote Sensing, 25, No. 19.
[7] Tetuko S. S., J, Tateishi, R. And Takeuchi, N., 2003, Estimation of Burnt Coal Seam Thickness in Central Borneo
using JERS-1 SAR Data: Int. J. Remote Sensing, 24, No. 4.
Acknowledgment
We thank Remote Sensing Research Center - Pandhito Panji Foundation of Indonesia and ERSDAC of Japan forproviding the JERS-1 SAR and ASTER image data.

Impacts of the sea surface temperature anomaly in the Pacific andIndian Oceans on the Indonesian climate
Bannu1,2), Hiroaki Kuze1), Nobuo Takeuchi1) and D.A. Suriamihardja2)
1)
Center for Environmental Remote Sensing, Chiba University,
1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
2)
Center for Environmental Studies (CES), Hasanuddin University,
Makassar 90245, South Sulawesi, Indonesia
Abstract: Impacts of the El Niño-Southern Oscillation (ENSO) and Indian Ocean
Dipole (IOD) events on the Indonesian rainfall were studied using observational data
and the NCEP/NCAR reanalyzing global data. ENSO and IOD variability in thetime-periods are detected by using wavelet analysis, and we attempt composite analysisof the rainfall amounts over Indonesia during ENSO and IOD events. The correlation
between strong El Niño intensities and several regions in Indonesia with rainfall below
normal (< 85%) are high, but when the intensities are weak the correlation becomes low.
In this case other phenomena such as IOD can contribute to drought in Indonesia. Our
analysis also indicates that during El Niño and positive IOD events, the southeast
monsoon (Australian monsoon) over Indonesia is intensified, causing the dry season
longer than rainy season.
Keywords: NCEP/NCAR reanalyzing data, observational rainfall data, ENSO, IOD, andwavelet analysis.
1.Introduction
Sea surface temperature (SST) variability in the tropical Pacific Ocean such as El
Niño-Southern Oscillation (ENSO) phenomena is associated with drought and flooding inmany parts of globe. El Niño (La Niña) represents the warm (cool) phase of the ENSO
cycle. The term El Niño (La Niña) refers to a periodic warming (cooling) in SST across thecentral and east-central equatorial Pacific (Fig. 1).
Recently it has been pointed out that conditions in the Indian Ocean might also affectclimate variability in Indonesia. The El-Niño like temperature anomaly in the Indian Ocean
is called the Indian Ocean Dipole, or IOD (Saji et al., 1999, Webster et al., 1999; Hendon,
2003). The IOD event has raised a number of new questions about its generation andpossible interactions with other climate phenomena. During a positive IOD event, the SST
drops in the southeastern part of the Indian Ocean, off the northern coast of Australia, theeastern coast of Japan and throughout Indonesia. At the same time, the SST rises in the1western equatorial Indian Ocean and off the eastern coast of Africa. This SST increase
leads to heavy rainfall over the east Africa and severe droughts/forest fires over Indonesia(Fig.2, left). The negative IOD event, on the other hand, is in effect the reversal of thepositive IOD (Fig.2, right).

Fig.1 Sea surface temperature anomaly pattern on the El Niño and La Niña events.

Negative IOD event

Positive IOD event

Fig.2 Illustrative of the sea surface temperature pattern on the positive IOD and thenegative IOD events (source: http://indianocean.free.fr/links.htm)

A number of studies have reported that Indonesian rainfall is strongly affected by the
ENSO phenomena (Philander, 1990; Hendon, 2003), and also by the IOD event (Saji et al.,
1999; Webster, et al, 1999; Hendon, 2003). Geographically, Indonesia is not a country that
seriously suffers from droughts. However, when affected by the warm episode of ENSO
and IOD, rainfall becomes much lower than the normal amount.
In this paper we demonstrate the clear impact of the ENSO and IOD events on theIndonesian rainfall. Wavelet analysis is used to analysis the time-periods of ENSO andIOD variability, and we attempt composite analysis of the rainfall amounts over Indonesia
during ENSO and IOD events.
2

2.Data and methodology
For sea-surface temperature (SST), wind and rainfall, we use monthly data in thisstudy. The ENSO index is measured by the area-average SST anomaly in Niño 3.4 region
o

o

o

o

o

o

(5 S–5 N, 120 –170 W), and obtained from the NOAA Climate Prediction Center web site(http://www.cpc.ncep.noaa.gov/). The IOD index as reported by Saji et al (1999) is definedas the SST anomaly difference between the tropical western Indian Ocean
o

o

o

o

o

(10 S–10 N;50 –70 E) and the tropical south-eastern Indian Ocean (10 S–Eq;90 –110 E).
The global data on SST, rainfall and surface wind are derived from the NCEP-NCAR
reanalysis data. For validation of result, rainfall data from ground observations at 7
meteorology stations in Indonesia (in Fig.7) are used in this analysis (Indonesian
Meteorology and Geophysics Agency (BMG), 2002, personal communication).
To detect the time-periods, the ENSO and IOD indices are analyzed with wavelet
transform. Wavelet analysis is a useful tool for analyzing time series with many different
timescales or changes in variance. Here we employ the Morlet wavelet (MW), and thetransform is performed in Fourier space using the method described in Torrence and Compo(1998). The MW consists of a complex exponential function modulated by a Gaussian. The
wavelet power spectrum (WPS) is defined as the square of the absolute value of the wavelet
transform, giving a measure of the time series variance at each period and at each time. To
study the non-stationary change of a variance, it is most appropriate to choose the global
wavelet spectrum (GWS). The GWS is equivalent to the Fourier power spectrum smoothedby the Morlet wavelet function in the Fourier domain. The steps involved in the wavelet
analysis are given as a schematic flow chart below:
for ω 0 = 1
Analysis of Intensity
Wavelet Transform (WT)+∞

Wn (s) = ∫ x(t )ψ (t )dt

Morlet Wavelet (MW)

ψ 0 = π −1 / 4 e i ω t e − t / 2
0

2

−∞

Domain Fourier of times series

1xˆ k = ∑ x n e −i 2πkn / N
N n=0
N −1

Normalize of the MW⎛ 2π s ⎞ψˆ ( sω k ) = ⎜⎟⎝ δt ⎠

WT in Domain Fourier−1

2

for ω = 6
ψˆ 0 ( sω k ) Analysis of0 Period

WPS2
Wn (s)N −1

Wn ( s ) = ∑ xˆ kψˆ * ( sω k )e iω nδtk

k =0

Fig.3 Flow chart of the Wavelet analysis
3

GWS
2

W =

1 N −1
2
Wn ( s )∑N n =03. Results and discussion
Since the ENSO and IOD indices are non-orthogonal (Yamagata et al, 2002), it would
be reasonable to investigate the possible interactions between ENSO and IOD. Bannu et al,2005 has reported that the ENSO and IOD events show the strong 2 – 8 year inter-annual
cycles. This result suggested that some IOD events coincide with some ENSO events. Using
wavelet analysis, we find that three of the major warm ENSO events (i.e., those in 1972,
1982, 1997) are accompanied with positive IOD events (Fig. 4a and 4b).
Accordingly, WT analysis
Wavelet Power Spectrum
GWS(a)can detect variations of power
spectrum by decomposing the
ENSO/IOD
indices
intotime–periods. As a result, one
can determine the dominantmodes of variability andunderstand how those modes(b)vary in time.
In Indonesia, drought
conditions during the dry season(June – November) typicallyoccur in conjunction with thedevelopment of El Niño in thePower (oC2)
Pacific. This is typicallyFig.4 Wavelet power spectrum and Global Wavelet
represented by cold anomalously
Spectrum (GWS) for: (a) ENSO index and (b)SST around Indonesia while
IOD index.
warm anomalies develop in theeastern Pacific and western
Indian Oceans. Conversely, enhanced Indonesian rainfall during the dry season often occurs
during the development of La Nina, with SST anomalies opposite to those of El Niño.
Seasonal relationships between Indonesian rainfall throughout the Indo-Pacific basin are
explored by calculating seasonal anomaly. In contrast to the IOD 1994 event where thedipole is prominent only in the Indian Ocean (Fig.5, left), a similar phenomenon in 1997
was accompanied by another dipole pattern in the Pacific due to the co-occurrence of thewell-known El Niño event (Fig.5, right). The correlation between strong El Niño intensities
and several regions in Indonesia with rainfall below normal (< 85%) are high, but when theintensities are weak the correlation becomes low. In this case other phenomena such as
IOD can contribute to drought in Indonesia. Our analysis also indicates that during El Niño
and positive IOD events, the southeast monsoon over Indonesia is intensified (Fig.8),
causing the dry season longer than the rainy season (Fig.6 and Fig.7).
4

Fig.5 Anomalies of the rainfall on September – November, for 1994 (left) and1997 (right). Values are in (mm.month-1).
Jun – AugSep – Nov
ManadoAmbon

Makassar

Dec – Feb

Mar – May
Pontianak
SurabayaJakarta
Padangin %Fig.6 Seasonal rainfall percentage during ENSO and IOD events. Below normal(< 85%), normal (85 – 115%), and above normal (>115%).

140

Jun – AugDec – Feb

Sep – Nov

Mar – May

120
100
80
60
40
20
0
Jakarta

Makassar

ManadoSurabaya

PadangPontianak

Ambon

Fig.7 Seasonal rainfall percentages for ground observations data during ENSO and IOD
events. Below normal (85%; dotted line) and above normal (115 %; solid line).
5Jun - Aug

DJF

Sep - Nov

Interestingly, the positive
IOD and the warm episode of
ENSO
have
opposite
influences in the Far East

including Japan (Fig. 2a and2b); positive (negative) IOD
events give rise to warm anddry (cold and wet) summerm/sowing to enhancement ofoC
downdraft in the troposphere
Fig.8 Composite analysis of the SST and surface(Yamagata et al, 2002). The
wind anomaly during El Nino and positive
Indiansummermonsoon
IOD events.
rainfall (ISMR) is enhanced(decreased) during positive(negative) IOD events; the recent weakening of ENSO-ISMR relation may be interpreted in
Dec – Feb

Mar – May

terms of frequent occurrence of the positive IOD in the recent decade (Ashok et al., 2001).
References:
Ashok, K., Z. Guan, and T. Yamagata, 2001: Impact of the Indian Ocean Dipole on the
Decadal relationship between the Indian monsoon rainfall and ENSO. Geophysics
Research Letter, 28, 4499-4502.
Bannu, D. Sudiana, H. Kuze, N. Takeuchi, M.A. Ratag, and B. Tjasyono, 2005, Analysis
on the impacts of ENSO, IOD, and monsoon over the Maritime Continent ofIndonesia, Proceedings of the 2005 Autumn Meeting of the Meteorology Society ofJapan.
Hendon, H.H, 2003, Indonesian rainfall variability: impacts of ENSO and local air-sea
interaction, J. Climate, 16, 1775-1790.
Philander, S.G., 1990, El Niño, La Niña, and the Southern Oscillation, Academic Press, Inc.
New York, USA.
Saji, et al, 1999, A dipole mode in the tropical Indian ocean, Nature, 401, 360-363.
Torrence, C. and G.P. Compo, 1998, A practical guide to wavelet analysis, Bull. Amer.
Meteor. Soc., 79, 61-78.
Yamagata, T., S.H. Behera, S.A. Rao, Z. Guang, K. Ashok, and H.N. Saji, 2002, The
Indian Ocean Dipole; a Physical Entity, CLIVAR Exchanges: selected research
papers, 24, 1-6.
Webster, P.J., A.M. Moore, J.P. Loschnigg, and R.R. Leben, 1999, Coupled oceanAtmosphere Dynamics in the Indian Ocean during 1997-98, Nature, 401, 356-359.

6

Identification of the climate control factors on carbon cycle variations oftropical forests combined analysis of ground and satellite observations
Shin NAGAI1, Kazuhito ICHII2 and Hiroshi MORIMOTO1
Graduate School of Environmental Studies, Nagoya University
E-mail: s010116d@mbox.nagoya-u.ac.jp
2
San Jose State University
1

Abstract
An estimation of the responses of tropical rainforests for climate changes is important problem to evaluate theterrestrial global carbon cycle. In the present study, we analysed the cross-correlation functions of vegetationindexes to climate parameters for the satellite-based global scale data in the southeastern Asia and theground-observed point data at Bukit Soeharto, Borneo. Cross-correlation functions show various signals for theregional and temporal scale. Our conclusion is that tropical rainforests in the southeastern Asia havespatio-temporally various responses for climate variations.
Keywords: NDVI, tropical rainforests, climate change, southeastern Asia, cross-correlation function
1. Introduction
Tropical forests play an important role in the global terrestrial carbon cycle. Identification of the response ofvegetation activities for climate changes needs to estimate the tropical ecosystem. In special, tropical rainforests
are significant. There are three methods to evaluate the relationship, that is, (1) terrestrial ecosystem model (e.g.
Nemani et al. 2003), (2) field measurement study (e.g. Potts 2003) and (3) analysis using satellite-based data (e.g.
Nagai et al. 2005). Nemani et al. (2003) show that radiation is the most important climate factor. On the contrary,Potts (2003) presents that a severe drought due to strong El Niño events restricts vegetation activities. Nagai et al.
(2005) detect that interannual variations in NDVI (Normalized Difference Vegetation Index) are inclined todecrease in El Niño events due to dryness. A reason for different interpretations is that tropical rainforests haveno distinct phenology and the climate system consists of various time scale modes. Although ground-based data
provide a good explanation, there are few field stations. Therefore, we need an analysis using the satellite-based
global scale data.
In the present study, we evaluated the responses of tropical rainforests for climate variations in thesoutheastern Asia using the cross-correlation functions for the satellite-based global scale data, and comparedwith the analysis of ground-based point data.
2. Data
2.1 Study area
Our target area is the tropical rainforests in the southeastern Asia, where includes the field station Bukit Soeharto(0º51'41"S, 117º02'41"E) (Figure 1). We made two conditions to select tropical rainforests that the vegetation
map of DeFries et al. (1998) shows evergreen broadleaf forests and monthly mean precipitation from 1971 to2000 is over 100 (mm). We roughly divided the southeastern Asia into seven areas as shown in Figure 1.
The flora of Bukit Soeharto is secondary tropical rainforests recovered from the forest fires in 1983 and 1998
due to severe droughts. There is a dry season from July to October (Gamo 2003).

Figure 1. Our study areas in the southeastern Asia (A: 4ºN-Eq., 95ºE-104ºE, B: 7ºN-Eq., 109ºE-114ºE,
C: 7ºN-Eq., 114ºE-119ºE, D: Eq.-6ºS, 98ºE-105ºE, E: Eq.-5ºS, 109ºE-119ºE,
F: Eq.-4ºS, 131ºE-143ºE and G: 4ºS-10ºS, 135ºE-150ºE).
The selected tropical rainforests colour black. A cross marker shows the field site Bukit Soeharto.

2.2 Global scale data
We used the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data (Pinzon 2002, Pinzon et al.
2004, Tucker et al. 2005) distributed by the ISLSCP Initiative II data archive (Hall et al. 2005) with a 0.25º
spatial resolution. Precipitation and temperature data based on ground observations are the Climate Research
Unit (CRU) TS 2.0 data with a 0.5º spatial resolution (Mitchell et al. 2004). Incoming surface solar radiation
data derived from satellites is the Goddard Institute for Space Studies (GISS) downwelling shortwave full sky at
surface radiative flux data with a 2.5º spatial resolution (Rossow and Schiffer 1999). We used monthly
composite data from January 1984 to December 2000.
2.3 Ground observation data
The LAI (Leaf Area Index) calculated from attenuation of PAR (Photosynthesis Active Radiation), soil watercontent at 10 (cm) underground, air temperature at 30 (m) above ground, and PPFD (Photosynthesis Photon Flux
Density) data from January 2001 to December 2004 with a 30 minute temporal resolution were used. We
reconstructed weekly composite data from the daily average of soil water content and temperature, and thedaytime (8:15-17:45) average of LAI and PPFD. Deficit data were complemented by an autoregressive model.
3. Method
The cross-correlation functions of NDVI to climate parameters were calculated as shown in equation (1) after
applying three month moving average for each data to remove high frequency components.

C NDVI − climate parameter (k ) =1 N∑ NDVI(t ) climate parameter(t − k )N t = k +1

(1)

where N is the length of time-series.
In the same way, we evaluated the cross-correlation functions of LAI to climate factors for the ground-based
point data after applying four week moving average.
4. Results and discussion
We compared the monthly average of NDVI with the ground-observed LAI at Bukit Soeharto to test the seasonalvariation in the satellite data. Although NDVI data in tropical rainforests include noise components due to cloudcontamination (e.g. Kobayashi and Dye 2005), the seasonal cycle of NDVI coincides with those of LAI well.

Figure 2. The monthly average of NDVI from 1984 to 2000 (bar) and ground-observed LAI (thin line) from2001 to 2004 at Bukit Soeharto. We extracted the NDVI data from a grid (0º50'S-0º75'S,
117º00'E-117º25'E). The LAI data show daytime (8:15-17:45) average.
Figure 3 shows the cross-correlation functions of NDVI to climate parameters. Tropical rainforests havevarious responses for climate factors with different lag time and there are regional differences. We can roughly
divide five groups, that is, (1) Malaysia (area A, B and D), (2) the northern Borneo (area C), (3) the southern
Borneo (area E), (4) the northern New Guinea (area F) and (5) the southern New Guinea (area G). NDVI has
higher positive correlations with temperature and radiation with no lag time in Malaysia and the northern Borneo.
On the contrary, NDVI has a higher positive correlation with precipitation with some lag time in Malaysia, thesouthern Borneo and the northern New Guinea. In the northern Borneo, precipitation has no correlation with
NDVI. NDVI-temperature and radiation show higher positive correlations with some lag time in New Guinea.
These results mean as following sensitivities of tropical rainforests to climate parameters. Temperature andradiation are important at a point of time in Malaysia, which coincides with Nemani et al. (2003). However,precipitation is significant with some lag time in Malaysia, the southeastern Borneo and the northern New
Guinea. It presents that water stress restricts vegetation activities, which is consistent with such as Gamo (2003),
Potts (2003) and Nagai et al. (2005).
Similarly the cross-correlation functions of LAI to climate parameters at Bukit Soeharto are shown inFigure 4. The correlation of LAI-soil water content denotes the same tendency of the NDVI-precipitation in thesouthern Borneo (area E), while those of LAI-temperature and PPFD are different from the NDVI-temperatureand radiation respectively. It’s assumed that ground-based point data include the local meteorology, in special,variations of amount of cloud cover. However, this result means that water stress limits vegetation activities, andagrees with the estimation of the satellite-based global scale data.
We detect that tropical rainforest in the southeastern Asia have different responses to climate parameters
spatio-temporally. However, we should be concerned that the climate of southeastern Asia has variations due tothe interaction between ENSO and Asian monsoon. More detailed analysis is necessary.

Figure 3. Cross-correlation functions of NDVI to precipitation (bold line), temperature (thin line) and radiation(dotted line) for selected tropical rainforests regions (a: area A, b: area B, c: area C, d: area D, e: area E, f: area F
and g: area G).

Figure 4. Cross-correlation functions of LAI to soil water content (bold line), temperature (thin line) and PPFD
(dotted line) at Bukit Soeharto.

Acknowledgement
We wish to express our gratitude to Dr. Gamo, National Institute of Advanced Industrial Science and Technology(AIST), Japan, for providing the ground-based point data and constructive advice. A grant from the Ministry of
Education, Culture, Sports, Science and Technology, Japan (Dynamics of the Sun-Earth-Life Interactive System,
No.G-4, the 21st Century COE Program) supported our study.
References
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8km spatial resolution: the use of training data derived from Landsat imagery in decision tree classifiers. Int. J.
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GAMO, M., 2003, CO2 balance in the secondary tropical rain forest, available online at
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HALL, F.G., COLLATZ, G., LOS, S., BROWN DE COLSTOUN, E. and LANDIS, D., (Eds.), 2005, ISLSCP Initiative II.
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519-525.
MITCHELL, T.D., CARTER, T.R., JONES, P.D., HULME, M. and NEW, M., 2004, A comprehensive set ofhigh-resolution grids of monthly climate for Europe and the globe: the observed record (1901-2000) and 16
scenarios(2001-2100).
TyndallCenterWorking
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available
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http://www.tyndall.ac.uk/publications/working_papers/wp55.pdf.
NAGAI, S., ICHII, K. and MORIMOTO, H, 2005, Relatinoship between interannual variations in satellite-based
vegetation index and ENSO phase over tropical forests, J. Agri. Meteorol., 60(6), pp. 1211-1214.
NEMANI, R.R., KEELING C.D., HASHIMOTO, H., JOLLY, W.M., PIPER, S.C., TUCKER, C.J., MYNENI, R.B. and
RUNNING, S.W., 2003, Climate-driven increases in global terrestrial net primary production from 1982 to 1999.
Science, 300, pp. 1560-1563.
PINZON, J., 2002, Using HHT to successfully uncouple seasonal and interannual components in remotely sensed
data, In SCI 2002, 14-18 July 2002, Orlando, Florida (Conference Proceedings).
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empirical mode decomposition, In Hilbert-Huang Transform: Introduction and Applications, HUANG, N. (Ed.),
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POTTS, M.D., 2003, Drought in a Bornean everwet rain forest. Journal of Ecology, 91, pp. 467-474.
ROSSOW, W.B. and SCHIFFER, R.A., 1999, Advances in understanding clouds from ISCCP. Bull. Amer. Meteor.
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SALEOUS, N., 2005, An Extended AVHRR 8-km NDVI Data Set Compatible with MODIS and SPOT
Vegetation NDVI Data. Int. J. Remote Sens., in press.

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· . fhg]iX[Zk dE_EnWrsbe"d !s_EdY^r«XW #u %$µIÈ!¶=ÇWÄÈIÅ .I· ¸ 4ÜI¦o®§°Y`Iµ¾ · -q·µÉ=¶=¶!¶Iç3ÖªYk¦E"ªIª.¥`= ¯W"¦Y"o=ª#"£q§o¥Ijjk¦mj!ª`§¿.!ª#¦Y= ª£e= KW¯=B¦mÛB­`"z"ª`©# Y§h¦Yk¢Wo=ªDYo`:§¿!¥oIB¦oªD!§Ya¢"¢Wo=ª* ªs©a¹&· _ag_EXWVkÁfhg]ijXWZa& 'q^S[Za (u $ µ`ÉÅkÄÉ=ÈI·Comprehensive evaluation of Leaf Area Index estimated by several methods― LAI-2000, SunScan,
Fish-eye, and littertrap ―Midori Kurata1, G.A.Sanchez-Azofeifa2, Wang Quan1*, Yoshitaka Kakubari1
1

Faculty of Agriculture, Shizuoka University
E-mail: zoorin@agr.shizuoka.ac.jp*E-mail: wangquan@agr.shizuoka.ac.jp

2

Earth & Atmospheric Sciences University of Alberta,Canada

Abstract
The leaf area index (LAI), was estimated in mountaineous beech forest stands different estimation
techniques. The effective LAI (Le) was estimated optically by measurements with the gap fraction
instrument LAI-2000, SunScan, and Fish-eye. The aim of this study was to find out the most reliable
estimation technique in mountaineous beech forest stands.
1.

Introduction

Leaf Area Index (LAI) is a key biophysical variable influencing land surface processes such as
photosynthesis, transpiration, and energy balance and is a required input for various ecological models. LAI
is defined as the projected area of leaves per unit ground area. There are several techniques for estimating
LAI. Remote sensing offers the possibility of providing relatively accurate estimations of LAI at areasonable cost for most regional projects. However, remotely sensed vegetation indices at present needsite- and stand-specific calibration against ground-based measurements of LAI and still do not yieldsuitable results for complex canopies such as forests with a high LAI. It is necessary to rely first on
ground-based LAI estimates if remotely sensed vegetation indices need cross-calibration.
Direct and indirect methods are used to estimate LAI in the field. Direct methods mainly include
destructive sampling, application of allometric equations based on tree diameters (and tree height), and leaf
litter fall collection. On the other hand, LAI can also be estimated indirectly from the measurement of lighttransmission through plant canopies. This method takes use of a number of commercially available
instruments, such as LAI-2000, DEMON, TRAC, hemispherical photography and so on.
The aim of this study is to establish a practical technique for LAI estimation suitable to mountaineous
beech forest ecosystems.

Table 1 structural description of each sites
type
site namealtitude (m)age of stands (yr)†
DBH (cm)mean tree height (m)stand density (trees ha-1)
basal area (m2/ha)
percent of Fagus Crenata (%)†DBH, diameter at breast

old-beech
1300y
900X3
1500ｋ
1500y
1500
1500
1300
900
300
300
300
150
17.6
13.8
11.5
28.9
22
20
20
22
450
229
250
383
30
21
31
42
71
72
86
59
○
○
○
○
○; destructive sampling were made in 1970s

700n2
700
200
36.9
30
361
52
82

young-beech
900X1
900X5
900
900
70
85
23.1
17.3
21
21
1033
1400
51
46
94
74

600k
600
250
30.2
27
433
46
74

550ｋ
550
260
37.1
34
246
36
86
○

1200y
1200
250
20.0
20
539
35
33

1100y
1100
250
11.8
19
625
54
38
○

mix-wood
900y
900X4
900
900
250
150
16.6
15.8
31
21
535
829
45
31
41
28
○

700ｎ
700
200
16.0
31
425
31
31
○

mix-wood
900y
900X4

700n

young-beech
900X1
900X5

700
3.8
-

900
7.4*
4.9
0.2

Table 2 Mean and standard deviation values of LAI based on direct methods
type
site name

1500k

1500y

1300y

old-beech
900X3

700n2

600k

550k

1200y

1100y

altitude (m)allometric equation
litter collection( standerd deviation )

1500
3.6
4.1
0.7

1500
2.3
-

1300
3.1
-

900
4.9
-

700
6.1
5.3
0.4

600
5.5
5.4
0.7

550
4.6
4.6
0.7

1200
3.6
5.9
0.9

1100
5.3
-

900
5.4
-

900
4.5
4.9
0.6

900
7.5*
5.2
0.7

* is calculated by Eq.(3), LAI of the other sites are calculated by Eq.(2).

2.

Method

2.1 Study site
Measurements were made on the northern slope of the Naeba Mountain (2145m, 36°51’N, 138°41’N),
located in southern Niigata Prefecture of the Japan Sea side of central Honshu in Japan. 14 sites (Table 1) were
deployed along the altitude gradient from 550 to 1500m, the beech dominates the forest over range from 550m
to 1550m. In all these sites, the canopy upper layers are consisted mainly by beech (Fagus Crenata) withoccasional mixture of a small number of other species. These 14 sites are roughly categorised into 3 types bystand ages and the ratio of understory trees (old-beech, young-beech and mix-wood).
Seven sites of them are permanent sites (since 1960’s) at different altitudes, as one of the IBP/PT programs(Table 1). Among these sites, destructive samplings were made for parameterising allometric at each altitude(see Table 3).
2.2 Estimating LAI by direct methods
2.2.1

litter collection

5-10 litter traps were set randomly at 8sites respectively (Table 2). And they were collected occasionallythrough the whole season. The traps had a circular area (0.25m2) and were placed about 1 m above the ground.
Collected litter was separated into leaves and other non-leafy materials such as branches. They were air driedout and weight. Some of each the collected litters were taken sampled for area and dry weight, which SLA is
calculated as;
SLA=ΣAi /Σwi

(1)

where SLA is the specific leaf area, Ai is the one-sided projected area of the given leaf ‘‘i’’, and Wi is the dryweight of it. LAI was then calculated by multipulying SLA with the dry weight.
2.2.2

allometric aquation

The stand leaf area was estimated based on allometric correlations between leaf area and D2H or Dπ. The
allometric equation was as following ;log F = h log ( D2H ) + K(2)

F = 0.006 × ( Dπ ) 2+ 0.4656 ( Dπ )(3)

where F is the total leaf area of each tree, while, D is diameter at breast height and H is its tree height. h and K
are fitted coefficients of the equation (Table 3). Then, summation of leaf area of all trees and devided is by theplot area to get LAI of each site. Eq.(2) is used for old-beech and mix-wood, while Eq.(3) is used foryoung-beech.
Table 3 Coefficients (h and K) of Eq.(2) used for the calculation of leaf area at different altitudes

Altitudes(m)
1500
1300
1100
900
700

h
0.656
0.629
0.709
0.594
0.669

k
-0.793-0.684-1.152-0.466-0.852

SourcesKakubari et al. (1970)
Maruyama and Yamada (unpublished)
Kakubari (unpublished)
Yamada and Maruyama (1962)
Maruyama et al. (1970)

2.3 Estimating LAI by indirect methods
The effective LAI (LAIe) was estimated indirectly by using LAI-2000, SunScan, and Fish-eye. All of them are
optical instruments, measuring radiation based on gap fraction and calculating LAI from the Beer-Lambertextinction law. LAI-2000 and SunScan measure both incoming radiation and transmitted through the canopy.
For both instruments, 10~25 sample points were set in each site. Measurements were made onetime at thepeak of the growing season (August and September) in order to estimate the maximum PAI (Plant Area Index),
and repeated again after leaf fall for WAI (Wood Area Index). Then LAI was calculated from the following
equation ;LAI = PAI - WAI
2.3.1

(4)

LAI-2000

The Plant Canopy Analyser, LAI-2000 (LI-COR Inc. , USA) uses a fish-eye light sensor to measure thediffuse radiation below 490nm. The simplest way is to measure below- and above-canopy radiation is to use
two cross-calibrated sensors simultaneously. However, we have only one sensor to work with. Below- andabove-canopy measurements were made with view caps of 22.5°and 90°. Although LAI-2000 is compact andeasy to use in the dumpy forest, it requires a diffuse radiation environment which eventually determined by theweather condition.
2.3.2

SunScan

The SunScan Canopy Analysis System (Delta-T Devices Ltd, UK) measures the incident photosynthetic
active radiation (PAR) above canopy and the transmitted PAR through the canopy. The SunScan probe can also
be connected to a Beam Fraction Sensor (BFS) measuring both direct and diffuse incident radiation above thecanopy and simultaneously connected to the common logger of the linear probe. In this way, it is available in all
weather conditions. But in forest stands, the cable length becomes alimiting facter.
2.3.3

FisheyeHemispherical photograghs were taken in overcast sky condition by using digital cameras(COOLPIX990,
3.34 megapixels, Nikon) with Fisheye converter (FC-E8, Nikon). This method does not require above-canopy
measurements. From the digital images, gap fraction and
8

Hemiview (Delta-T Devices Ltd, UK). PAI and WAI were
calculated by equation according to Norman and Campbell(1989).
3.

Results and Discussion

3.1 Estimated LAI by direct methods
The relations between LAI estimated by the litter collection
and by allometric equation are shown in Fig 1. For the

LAI estimated by litter collection

canopy openness were calculated by a specific software( 1: 1 )7
6
5
4

R2 = 0.95

3old-beech
mix-wood
young-beech

2
1
0
0

1
2
3
4
5
6
7
8
LAI estimated by allometric equation

Fig. 1 Relationships between LAI estimated by allometricequation and by litter collection.The dotted line is 1:1

old-beech forest, the relationship was almost 1:1. However, big deviations were found for mixed and young
beech forests. This may partly due to the less destructive samplings in these types. Moreover, the leaf fall fromthe understories in the mix-wood stands may contribute to the total leaf collection which finally level to thelarge LAI values in these stands.
One of the most reliable methods the LAI estimation is to collect leaves in litter traps as made in this study.
And from the above result, allometric equation is also reliable if there are enough data and use that equation insimilar stands.
In this study, LAI determined from litter collection was treated as true values, from which all other methods
were compared and validated. The destructive sampling was made for sites with no litter traps, from which theestimated values then used as ground truth.

8( 1: 1)

7

LAI estimated by direct method

LAI estimated by direct method

865

R2 = 0.22

43

2

R = 0.71

2
1

old-beech
mix-wood
young-beech

5

R2 = 0.57

4
3

R2 = 0.92

2
1
0

1
2
3
4
5
6
7
8
LAI estimated by LAI2000 (22.5°)

old-beech
mix-wood
young-beech

1
2
3
4
5
6
7
8
LAI estimated by LAI2000 (90°)

8( 1:1)

7
6

R2 = 0.52

54
3

R2 = 0.59

2
1

old-beech
mix-wood
young-beech

LAI estimated by direct method

8
LAI estimated by direct method

6

0

0
0

( 1: 1)7( 1:1)

7
R2 = 0.09

6
5
43

old-beech
mix-wood
young-beech

2
1
0

0
0

1

2
3
4
5
6
7
LAI estimated by SunScan

8

0

1

2
3
4
5
6
7
LAI estimated by Fish-eye

8Fig. 2 Relationships between LAI based on direct methods and LAIe estimated by LAI-2000, SunScan andFish-eye. Thick line shows the correlation of all samples, while thin line only for old-beech.

3.2 Estimated LAI by indirect methods
Fig. 2 shows the relationships between LAI based on direct methods and LAIe estimated by LAI-2000,
SunScan and Fish-eye. Among these indirect methods, LAI-2000 has the strongest correlation with the direct
estimated LAI, Especially for the LAIe from LAI-2000 with 90°view-cup. In this sense, wide field-of-view
is much desired for the heterogeneous radiation environment as below canopy.
Although LAI-2000 estimated LAIe has strong linear relationship with the direct methods derived LAI, it
was underestimated. Therefore, it needs connection before it can be used.
SunScan estimated LAIe of old-beech has the linear relationship to direct LAI, although in young-beech
LAIe was underestimated.
LAIe estimated by Fisheye is saturated around LAIe =2. Classification of images involves using digital image
process to distinguish canopy opening from foliage, which is achieved by determining a threshold intensity
value.
4.

Conclusion

We found that the method based on LAI-2000 was suitable for LAI estimation in mountain beech forests. And
broad view angle is required for measuring. In addition, it usually underestimates LAI and thus needs conection
before it can be used.
Although it is very portable, Fish-eye measuring requires more technique. SunScan is hard to use in the forest
stands if the cable is connected to BFS. However, SunScn is resistant to weather changes and can keep
measuring under versatile weather conditions.

Reference
Campbell GS. 1986. Extinction coefÆcients for radiation in plant canopies calculated using an ellipsoidal inclination
angle distribution. Agricultural and Forest Meteorology 36, 317-321. sunscan
Norman JM, Campbell GS. 1989. Canopy structure. In: Pearcy RW, Ehleringer JR, Mooney HA, Rundel PW, eds.
Plant physiological ecology: feld methods and instrumentation. London: Chapman and Hall, 301-325.

hemiview

Applying the Remote Sensing in a Decision Support Tool for Food Security
Eleonora RUNTUNUWU, Fadlullah RAMADHANI, and Setyono HARI ADIIndonesian Agroclimate and Hydrology Research Institute
Email: runtunuwu2001@yahoo.com
Abstract
One of the most significant challenges for improved the national food security programme is availability oftimely, up-to-date, and accurate data for planners and decision makers. Such information needs to be available toconcerned government official, regulators, donors, NGO and other interested organizations in a comprehensive,
consistent, regular, and easy to understand format. This research attempts to (1) develop a food security databasesystem included an analytics instrument to improve the quality of paddy field data using remote sensing and GIS
technologies, (2) compute the food balance of each region using DSS model, and (3) design the web-based foodsecurity information system. Remote sensing (RS) technology and geographical information system (GIS) havebeen used to delineate the crop area for estimating paddy yield based on cropping areas and productivity.
Integrating all kind of spatial, both image and vector as well tabular data in a database system, then transformed
into information to enhance the knowledge based as a basis for decision making for food security are discussed
in this paper.
1. Introduction
The concept of food security has developed over the past three decades. Concerns about food security up to theend of the 1970s were directed more at the national and international level, and concerned the ability of countries
to secure adequate food supplies. Only later did the level of analysis shift to include a focus on food security at
local level, even down to households and individuals (Young, et al., 2001). In this paper, the food security was
discussed for sub national level and it possible to scale up to national level.
In Indonesia, there are many institutions concerned in food security data, which each institution has own
analytical tools and methods to determine the food security parameters; therefore, we often find a dissimilar
published data for a same parameter such as the paddy field area of a certain province. In addition, it takes time
for collecting the historical data from local until national levels; consequently, it is awfully difficult to access thereal time food security information.
Therefore, this research attempts to develop an analytics instrument to improve the quality of paddy field datausing remote sensing technology and geography information system. Integrating all kind of spatial, both image
and vector with tabular data in a database system, then transformed into information to enhance the knowledge
based as Decision Support System (DSS) model for food security are discussed in this paper. Althoughdefinitions of a DSS are abundant and varied, there is a basic consensus that a DSS must be a helpful system fordecision makers (Freeland, 1999). In Choi et al. (2002) and Bohanec (2002), the decision support area has been
defined as the development of approaches for applying information systems technology to increase theeffectiveness of decision makers in situations where the computer can support and enhance human judgment in
the performance of tasks that have elements which cannot be specified in advance. Several researches thatutilized DSS in food security are Young (1998), Young at al. (2001), and Young (2001).
2. Data and methodology
The procedure began with collection and pre-processing of required digital image layers (Figure 1). The type ofsatellite imagery data used here is selected Landsat 7 ETM in planting time of 2002-2004 period. To allow the
web-based food security information system development, the method was defined in three main steps (Figure
2).

Figure 1. The path/row Landsat 7 ETM of study area

Figure 2. Schematic procedures used on this research.
The first is food security database system development. It included also the modified the quality of paddy fielddata by using RS technologies, Global Positioning System (GPS) and theodolite measurements. In this case, we
found the relationship between the remote sensing to GPS as well to theodolite results using regression analysis.
The samples were obtained from various classes of slope, there is: <8, 8-16, 16-45, and >45%. The relationships
were then utilized to calculate the paddy field area in order to get more accuracy data.
The second step is development of DSS model to calculate the food balance of each region using the remotesensed paddy field as well the population data. The estimated paddy area (ha) was converted to rice yield (kg) –as supply – by multiplying it with productivity (kg/ha). The population data have been used to estimate the rice
requirement of a certain district (as demand), Figure 3. by comparing the supply and demand data, the foodbalance could be identified (surplus, deficit or sufficient of each region. All these data and results were thendesigned in a web-based food security information system.
Population(Statistical Bureau)

Rice Consumption(32,65 Kg/head/month)

Rice recruitment

Paddy field area
(Satellite Images)

Estimated rice

Paddy productivity(Statistical bureau)

Rice stock

Rice stock remains= estimated rice + rice stock - rice recruitment< 0 = Deficits, addition of rice levying> 0 = Surplus, next month stock or distribute to other region

Recommendation= 0, sufficient

Figure 3. The DSS model to calculate the food balance
3. Result and Discussion
a. The food security database development
The first result of this research is food security database system on district level. The database can be divided
into five main groups, are (i) Geographic data such as administrative boundary, road, river, etc; (ii) biophysics
data such as, land use/land cover distribution; (iii) Food commodities data such as production, harvesting area,
productivity, etc; (iv) Civilization and agriculture infrastructure data; and (v) Socio-economic data such as
population; The feature of food security DSS model is strongly depended on quality and quantity of data input.
All of those parameters is related each other and could be modified depend on technology and essential purposein the future.
Beside developed the food security database system, this study tried to develop an analytics instrument toimprove the quality of paddy field data using remote sensing and GIS technologies. The regression result of RS
vs Theodolite and GPS vs theodolite are illustrated in Figure 4 and Table 1, respectively.

< 8%luas citra vs theodolite 0-8%8.000

luas citra

6.000

16-45%

y = 0.9598x

8.000

R2 = 0.9907

7.000
6.000

5.000

y = 0.9307x + 0.0999
R2 = 0.9925

4.000
3.000

citra

7.000

the o v s citra 16-45%

2.000
0.000
0.000

2.000

3.000
2.000

y = 1.1243x - 0.199
R2 = 0.9355

4.000 luas theo 6.000

1.000

1.500
1.000
0.500
1.000

1.500

6.000

7.000

>45%

2.000

0.500

5.000

2.000
lu as citra

2.500

4.000

luas theo vs citra >45%

y = 1.0956x
R2 = 0.7891

3.000

3.000
the o

8-15%

3.500

2.000

8.000

citra vs theo 8-15%

4.000

citra

5.000
4.000

1.000
0.000
0.000

1.000

0.000
0.000

y = 1.0588x
R2 = 0.9307

2.000

2.500

3.000

theodolite

y = 0.9713x
R2 = 0.6116

1.500
1.000

y = 0.9766x - 0.0049
R2 = 0.6116

0.500
0.000
0.000

0.500

1.000

1.500

2.000

luas theo

Figure 4. The regression analysis between RS and theodolite
Based on the Figure 4 and Table 1, the remote sensing technology using Landsat 7 ETM have been successfully
used to estimate the paddy field area (range of R2 is 60-99%) for all groups of slope (<8%, 8-16%, 16-45%,>45%). On the next analysis, we used the RS and theodolite relationship equation.
Table 1. The recapitulation of regression analysis: RS vs theodolite and GPS vs theodoliteNo

Slope (%)

a constanCorelation Coeficeint
(R2)

RS

GPS

RS

GPS

1.

<8

0.95980.96670.99070.98742.

8 - 16

1.0956

1.10720.78910.81063.

16 - 450.94191.05880.96580.93074.

> 45

0.97130.92420.6116

0.7710b. DSS model for food balance analysis
Figure 5 shows the one example of estimated the paddy field area in Sragen Distric of Central of Java Province
for 4 phase of paddy growth (water, vegetative, generative, harvest phases). The area of each phase could be
used to estimate the harvest area for one or two months later, and then were converted to paddy yield (as supply,
for two months later. Table 2 shows some examples that the paddy field area have been converted to paddy yield(kg) by multiply the RS area (ha) to rice productivity (kg/ha) around 6000-7000 kg/ha depended of season andarea.
The population data could be used to estimate the rice requirement as demand in food balance analysis. The DSS
model, in this case, was focused on examine the rice stock recommendation of each district of each month. By
applying the DSS model in Figure 3, the food condition in each district could be identified: surplus, sufficient, or
deficit. This DSS is very useful for decision makers to make a decision. For deficit area, it has to increase therice stock from other district; otherwise for the surplus area, it could be used for the next month stock or transfer
to another district.

Generative
phase
Vegetative
phase
(a)

(b)

Bulan

Fase pertumbuhan
Air
Vegetatif
17625
3896
14437
12600
8810

Bera
913
23259
20745

Apr (a)Jul (b)Okt (c)Harvestphase

Generatif
27449
4395
3832

(c)

Water
phase

Figure 5. The analysis estimate of paddy field area on four phases of crop growth
Table 2. The area paddy area and estimated paddy fieldNoNO

D e saK a b u p a te n

Location

Distric

L e re n g

Slope
(%)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

D u ku h K a rya
K a ry a M u ly a
K a ra n g J a y a
Pagadungan
C ira n g g o n
Ca m pa ka
C ik u m p a y
N a g ri T e n g a h
S a w a h K u lo n
P a sa w a h a n
K e r ta M u k ti
K a ra n g L a y u n g
B a b a ka n S a ri I
Sam payW a ru n g J a m b e
C ik o n d a n g
B a b a k a n S a ri II
L i n g g a M u k ti I
L i n g g a M u k ti I I
M a r g a A si h

K a ra w a n g
K a ra w a n g
K a ra w a n g
K a ra w a n g
K a ra w a n g
P u r w a k a r ta
P u r w a k a r ta
P u r w a k a r ta
P u r w a k a r ta
P u r w a k a r ta
P a d a la ra n g
P a d a la ra n g
P u r w a k a r ta
C ia n ju r
P a d a la ra n g
P a d a la ra n g
P u r w a k a r ta
P u r w a k a r ta
P u r w a k a r ta
P a d a la ra n g

0 -8
0 -8
0 -8
0 -8
0 -8
8 -1 58 -1 58 -1 58 -1 58 -1 5
1 6 -4 5
1 6 -4 5
1 6 -4 5
1 6 -4 5
1 6 -4 5
> 45
> 45
> 45
> 45
> 45

Luas
Luas
GPS
Theodolite
G P S (H a ) T h o d o l i te
(ha)
(ha)
(H a )
6 .9 6 0
7 .2 9 5
2 .4 5 6
2 .8 6 5
1 .8 8 0
1 .7 3 5
2 .5 0 3
2 .2 4 7
2 .6 0 3
2 .5 6 5
0 .5 1 0
0 .5 2 0
2 .3 1 2
2 .1 2 5
1 .7 8 7
1 .7 4 5
2 .0 2 4
2 .0 4 5
1 .5 2 3
1 .6 2 5
2 .2 7 2
2 .0 5 5
1 .4 0 4
1 .3 9 5
0 .8 3 0
0 .8 9 5
1 .3 4 1
1 .2 5 5
2 .3 0 8
2 .9 4 5
1 .2 1 6
1 .1 2 5
0 .2 8 1
0 .3 7 5
0 .5 9 3
0 .6 4 5
0 .4 2 5
0 .4 7 5
0 .5 1 6
0 .6 8 5Luas
P r o d u k si
RS
Paddy yieldC i tr a
Padi
(ha)
(kg)
(H a )
2001
6 .9 7 2
3 9 3 .7 4
2 .4 7 7
1 4 3 .8 5
2 .0 1 8
1 0 1 .4 92 .2 9 3
1 2 7 .4 1
2 .5 6 9
1 6 6 .6 6
0 .6 4 2
3 8 .0 6
2 .2 0 2
1 3 0 .5 52 .1 1 0
1 2 5 .0 9
2 .2 0 2
1 3 0 .5 5
1 .2 6 0
7 4 .7 0
3 .0 6 0
1 8 1 .4 1
1 .2 6 0
7 4 .7 0
0 .9 0 0
5 3 .3 6
1 .2 6 0
7 4 .7 0
2 .1 6 0
1 2 8 .0 6
1 .2 6 0
7 4 .7 0
0 .2 7 0
1 6 .0 1
0 .4 5 0
2 6 .6 8
0 .4 5 0
2 6 .6 8
4 2 .6 9
0 .7 2 0c. Web based food security information system development
In order to deliver the food security information to numerous users, the system should be launched by using
internet facility. Figure 6 shows the capture of web based food security information system. The system is user
friendly, therefore, everybody will be able to access the information.
Tabular data: Production (kg) Paddy field area (ha) Harvest area (ha)
 Rainfall (mm)
 Etc.

Remote sensing data
 Land use Etc

Supporting data Administration
 Accessibility
 River
 Etc.

Figure 3. Illustration of food Security system information development

3. Conclusion
One of the most strategic challenges in Indonesia is how to achieve food security through optimization of
the available resources. For this purpose, quantification of the characteristic of resources and themagnitude of events both spatially and temporally in the best quality and format is imperative. Satellite
imagery with broad coverage and timely information can be fully utilized for multi purposes programs withmultiple stakeholders to develop a decision tool.
Food security has to be achieved at national, local and household levels by securing the access to food by
the population and to information by policy makers. The decision support system (DSS) for food security
that has been developed for Central Java province was carried out in three main phases: i. e. (a) identify anddevelop required variable in the database such as demographical, statistical, satellite data, etc; (b) estimate
paddy production of each district using high resolution images and compute food balance based on the
supply and demand, and (c) design and operate the information delivery system to multiple users.
With the DSS the different variables and criteria commonly used by various institutions dealing with foodsecurity can be standardized. The utilization of multi-spectral and multi-temporal satellite imagery
integrated in geographic information system as components of the DSS will facilitate the efforts of all theinstitution as well as minimizing the cost.
Furthermore, the DSS is very useful to formulate strategic policy in achieving food security; avoid
duplication in program and budget; and develop monitoring system to ensure food availability. When
expanded to cover national or regional level as ASEAN the system can promote inter-regional trade.
Considering the different peak of harvest of rice crop (March in the southern hemisphere andNovember-December in the north), the trade will be mutually beneficial. The trade will provide theconsumers with freshly produced food and minimize cost of storage as well as getting better price to the
producers.
References
Bohanec, Marko, 2002, What is Decision Support?, Department of Intelligent systems, Jožef Stefan Institute,
Ljubljana, Slovenia.
Choi, J Y, B. Engel, J. Harbor. 2002. GIS and Web-based DSS for Preliminary TMDL Development. In Total
Maximum Daily Load (TMDL) Environmental Regulations: Proceedings of the ASAE Publication Number
701P0102, ed. Ali Saleh. March 11-13, 2002 Conference, (Fort Worth, Texas, USA) Publication Date
March 11, 2002. Pp. 477-484
Freeland, N, 1999, Malawi: Review of food security information systems, Masdar Technology Ltd, UK.
Sean B. EOM, 2002, Decision support systems, Southeast Missouri State University, USA.
Young H, 1998, Food Security Assessment In Emergencies; Theory And Practice Of A Livelihoods Approach,
Paper, London, UK.
Young, H, 2001, Food-security assessments in emergencies: a livelihoods approach, DS Print & ReDesign,
Enfield, London.
Young H, S. Jaspars, R. Brown, J. Frize and H. Khogali, 2001. Food-security assessments in emergencies: alivelihoods approach. Network Humanitarian Practice Network (HPN). No 36. 40 pages.

The environmental problem of the Dead Sea using remote sensing and GIS
techniques
Ahmad Al-Hanbali*1, Hussam Al-Bilbisi*2, and Akihiko Kondoh*2*1

Graduate School of Science and Technology, Chiba University, Yayoi 1-33, Inage, Chiba, 263-8522, Japan*2

Center of Environmental Remote Sensing, Chiba University, Yayoi 1-33, Inage, Chiba, 263-8522, Japan
E-mail: ahmad@graduate.chiba-u.jp

Abstract
The objectives of this research are to detect the changes of the areas of the Dead Sea (DS) surface water and evaporation ponds
during the past 31 years, and to emphasize the effect of DS area changes on the surrounding groundwater body. A subset ofeach of the Landsat (MSS), Landsat (TM), and ASTER (VNIR), acquired in 1973, 1987 and 2004, respectively, were used inthis study. By comparing only the DS area with the evaporation pond area in the last 31 years and with the aid of the GIS tools
to represent the changes spatially, the evaporation pond area has increased from 32.5 km2 in 1973 to 231.3 km2 in 1987, andthen reached to 244.6 km2 in 2004. On the other hand, the DS area has declined from 922 km2 in 1973 to 671.7 km2 in 1987,
and then to 641.3 km2 in 2004. DS is hydraulically connected with the lower aquifer system of the surrounding groundwaterbasins. Thus, any drops in the DS water level will have a direct effect on the surrounding groundwater body, letting the
groundwater to flow toward the DS due to its position at the lowest point on earth, therefore, causing for the shared countries toloose every year millions of cubic meters of water from its groundwater storage, which may create a serious environmental
problem in the DS region.
Keywords: Dead Sea, Evaporation Pond, Change Detection, GIS, Groundwater
1.

Introduction
Remote sensing provided valuable information that has a wide range of applications in water resources management. The

monitoring of land use/cover changes over time has been widely used within the scientific communities. Land use/coverchange detection using remote sensing and geographic information system (GIS) technologies were approved to be powerfultools for evaluating the environmental conditions andhuman activities in different ecosystems.
For more than 40 years Dead Sea (DS) (Figure 1)
was facing an environmental problem due to thecontinuous drop of its sea level. During this period theamount of water which was used to flow into the DS wasestimated to be 1980 million cubic meter per year
(MCM/year), but due to the projects done by sharedcountries (Israel, Jordan, and Syria) in the DS catchment’s
area during the last 4 decades this amount was reduced to

Figure 1. Location map of the study areaaround 617 MCM/year. The largest amount of water
resources consumption has been done by Israeli government by preventing about 500 MCM/year of lake Tiberias from flowing
into the Jordan river which was considered the major source of feeding the DS, beside hundreds of MCM/year, which were
diverted or dammed by the same country, coming from west side wadis near Jordan river and DS’s west coast.

The 617 MCM/year mentioned earlier was still without taking into consideration the new dams, which were constructed in
Jordan during the last 4 years such as Mujeb dam and Wala dam with capacities 35 MCM/year and 9 MCM/year, respectively.
The 44 MCM/year of storage quantity of these new dams should be subtracted from 617 MCM/ year mentioned above to be 573
MCM/year.
DS was consisted of two parts the deep northern part with maximum depth 728m bsl and the shallow southern part with anaverage depth does not exceed 10m. A drop of more than 1000 MCM/year of water inflow into the DS was able to let thesouthern part of the DS to become dry, which was converted totally after that into a huge evaporation ponds used for industrial
work to extract the valuable minerals such as potassium, magnesium, calcium, and bromine. This process is still making
unbalancing of the DS’s water resources by utilizing every year an average net amount of water around 300 MCM/year, whichmeans that the net amount of water reaches the DS now is equal 273 MCM/year.
Landsat Multi-Spectral Scanner (MSS), acquired in January 1973, Landsat Thematic Mapper (TM), acquired in August
1987, and ASTER visible and near infrared radiometer (VNIR) image, acquired in September 2004, were employed forevaluating the land use/cover change in the DS region.
There are two main objectives of this study, the first one was to study the DS area change detection, and the second objective
was to emphasize the effects of the changes of DS area on the surrounding groundwater body especially from Jordanian side.
2.

Methodology:

2.1

Geometric Correction:
A subset of each of the Landsat Multi-Spectral Scanner (MSS), with 57 m spatial resolution, acquired in January 1973,

Landsat Thematic Mapper (TM), with 28.5 m spatial resolution, acquired in August 1987, and ASTER visible and near infrared
radiometer (VNIR) image, with 15 m spatial resolution acquired in September 2004 were used for evaluating land use/coverchange detection of the DS region. The digital images were geometrically rectified to each other to facilitate their comparison,
which was georeferenced to UTM map projection (Zone 36), and WGS84 ellipsoid. The resultant root mean square error(RMSE) was less than 0.5 pixels, indicating an excellent registration. The nearest neighbor resampling method was used to avoid
altering the original pixel values of the image data.
2.2

Image Classification:
To map changes that had occurred between the three dates, Landsat MSS, TM, and ASTER images were individually used

as input for supervised classification using
maximum likelihood classifier. A modified
version of Sato-Tateishi Land Cover
Guideline (ST-LCG) (Sato and Tateishi,
2002)

was adopted and used as aclassification scheme design for this study.
In total, five land use/cover classes were
included in the scheme: urban, vegetation,water, evaporation pond, and bare land.
The spatial distributions of all five classes
were extracted from each of the land use/cover maps of 1973, 1987 and 2004 (Figure 2).

3.

Results and Discussion:

3.1

Change Detection:
For this study, the post-classification comparison change detection approach was employed. Among the five land

use/cover classes (Table 2); there are two major classes of interest in DS region: water and evaporation pond. Water classrepresents the DS water and a very small part of the Jordan River water, while the evaporation pond class represents all theevaporation ponds used by Potash companies in both sides, Jordan and Israel, for minerals extraction purpose.
Table (2) Land use/cover change for the studied area as extracted from the digital images
1973
Class

Class Area

1987
Class (%)

(Km2)

Class Area
(Km2)

2004
Class (%)

Class Area
(Km2)Class (%)

Urban

8.8

0.3%

16.8

0.6%

40.4

1.5%

Vegetation

49.3

1.9%

40.0

1.5%

68.8

2.6%

Water

922.3

35.7%

678.9

25.3%

644.4

24.0%

Evaporation Pond

34.8

1.3%

233.7

8.7%

245.6

9.2%

Bare Land

1567.860.7%

1714.363.9%

1680.762.7%

By comparing only the DS area, where a significant change has occurred (Table 3), the results show that the DS area has
a continuing decline. In 1973, there were 922 km2 of the water-covered area declined to 671.7 km2 by 1987, and then to 641.3
km2 by 2004, thus representing a decrease of 30.1% in water-covered area between 1973 and 2004. On the other hand, theevaporation pond class has increased from 34.8 km2 (or 1.3%) in 1973 to 233.7 km2 (or 8.7%) in 1987, and then reached to245.6 km2 (or 9.2%) in 2004, thus representing an increment in the evaporation pond class is more than 7 times in land area.
Table (3) a comparison between the areas of the Dead Sea and Evaporation Pan as extracted from Land use/covermaps 1973, 1987, and 2004
Dead Sea area (Km2)2

Evaporation Pond area (Km )

1973

1987

2004

922.0

671.7

641.3

34.8

233.7

245.6

The main reasons of DS declination, as mentioned in (section 1), was due to the reduction in the amount of water
resources which was used to flow into the DS, since most of these water was diverted or dammed by the three shared countries(Jordan, Syria and Israel), and due to theusage of the DS water itself by Potash
companies (at both sides of Jordan and Israel)
for the extraction of its minerals.
For both classes of interest (water andevaporation pond) and by using the digital
data of the three periods, with the aid of the
GIS tools, the changes which have occurredin the DS were represented spatially during

the periods (1973-1987), (1987-2004) and(1973-2004) as shown in (Figure 3). During these
periods, it was clear that the changes have occurredin the southern and western parts of the DS region(Fig. 3B) due to the shallow depths in theseregions.
In 1973 the evaporation pond was occupied
only the end of the southern basin of the DS in the
Israeli side, but during the period (1973–1987)
when the southern part of the DS became dry,
Israel expanded its industry and increased the number of evaporation ponds towards the north direction. In addition to that Jordan
constructed the potash company and evaporation ponds at the end of 1970s, which means, the southern basin of the DS had been
totally converted into an industrial area as shown in (Figure 4A). During the period from (1987–2004) both countries increased
the number of evaporation ponds, Israel towards the southern direction and Jordan towards the northern direction of theevaporation ponds reaching by this its maximum area 244.6 km2 as shown in (Figure 4B).
3.2 The effect of Land use/cover changes on the groundwater:
The land use/cover change detection showed how the DS was shrinking due to the decreasing in its water level, resulted fromthe reduction in the amount of surface water, which was used to feed the DS, beside with the utilizing of the DS water formineral extraction. This is not only the apparent problem of the DS, but there is also another major problem due to the effectsof this phenomenon on the surrounding groundwater
body. Because the DS is hydraulically connected withlower aquifer system, which is composed of
Kurnub/Zarqa

aquiferand Ram/Disiaquifer

especially from Jordanian side, the continuous
decrease in the DS water level is causing an increasein the hydraulic head between the DS and thegroundwater, which consequently, leading tocontinuous flow of groundwater toward the DS since
the latter is the lowest point (Figure 5). Thus, flowingof groundwater from the surrounding lower aquifer
toward the hyper-saline water of the DS is causing forthe shared countries to loose every year millions of

Figure 5. The continuous decrease of the DS water level is leading to increase thegroundwater flow toward DS.

cubic meters of fresh water from its groundwater storage (Salameh and El-Naser, 2000, and JICA, 2001). For that reason morecare should be taken into consideration to not let the DS water level decrease more, otherwise, the groundwater which isconsidered a major water supply for Jordan will be lost due to the intensive use of the DS water for industrial work, which maylead in the future to increase the water crisis in this region.
References:

1.

JICA, 2001, The Study on Water Resources Management in the Hashemite Kingdom of Jordan, final report.

2.

Salameh, E. and El-Naser, H. (2000): Changes in the Dead Sea level and their impacts on the surrounding groundwater
bodies, Acta hydrochim. hydrobiol., 28, pp 24–33.

3.

Sato, H. and Tateishi, R. (2002): Proposal for global land cover guideline legend based on FAO’s LCCS, Asian Journalof Geoinformatics, vol. 3, No.2, December 2002, pp 35-46.

Experimental study on the effect of Cheong-gye stream restoration on
urban environment(Long-path measurement of atmospheric pollutant species with an obstruction flashlight)
Yohei Shiraki, Ippei Harada, Hiroaki Kuze
(Center for Environmental Remote Sensing, Chiba University )Toshiaki Ichinose (National Institute for Environmental Studies)
Yingjiu Bai (Tohoku University of Community Service and Science)
1. Introduction
The expressway was dismantled in Seoul in July, 2003, and the municipal river (Cheong-gye stream) was restored in
October, 2005. In this study, it is intended that the atmospheric pollutant data and the air temperature data are
accumulated in the proof of the relaxation effect of the heat island phenomenon in Cheongecheon.
In the monitoring of atmospheric pollutant species such as NO2, SO2 and O3, conventional point measurements atground sampling sites lead to concentrations for local environments. It is also valuable to obtain additional informationof regional concentrations measured over a certain distance, e.g., several hundred meters to several kilometers.
Differential optical absorption spectroscopy (DOAS) in the visible and near-UV region is more suitable to monitor
horizontally averaged concentrations of pollutants (Edner et al., 1993).
In the conventional long-path DOAS method，a continuously emitting light source is employed，and the source (oroccasionally a retroreflector) is placed at a certain distance from the observation site. Then the combination of a
monochromator and a detector such as a photomultiplier, photodiode arrays, or a charge-coupled device (CCD) servesto spectrally analyze the transmitted light. In the present paper the Center for Environmental Remote Sensing，Chiba
University，reports a novel DOAS spectrometer based on a white flashlight source and a compact CCD spectrometer(Yoshii et al., 2003). The motivation of this study is to utilize white flashlights (obstruction lights) installed on tallconstructions such as towers, bridges, and stacks for the DOAS measurement. Such white flashlights are widely used
for the safety of aviation traffic in many countries. In Japan, it is mandatory for tall constructions (higher than 60m) to
operate highly illuminant (more than 2×106cd) white flashlights during the daytime that are detectable in every
direction from several kilometers away. Normally, xenon lamps are used for this purpose, giving a strobe emission
every 1.5s. Compared with the conventional DOAS measurements, the advantage of the present scheme is evident; if
one can find an appropriate obstruction light, as is the case in cities or industrial areas, the DOAS measurement can be
carried out without bothering to prepare light sources. After transmission in the atmosphere, the spectral analysis iseasily attained by use of a commercially available CCD spectrometer, which enables us to measure the spectrum of thepulsed signal.
Additional advantages associated with the present pulsed DOAS are as follows. First, the background from the sky
radiation is easily subtracted because the spectra with and without the strobe flash are distinguished straightforwardly
from the difference in the observed intensity. Second, simultaneous observation of several trace gas species is feasible if
the relevant spectral features fall within the considered wavelength interval of the lamp and the CCD. Third, when thespectral intensity of the flashlight is known at the strobe site, the transmitted spectra bring about the information on theaerosol extinction along the optical path.
The purpose of this study is twofold. First, we compare the long-path result with the point data simultaneously
measured at ground-based monitoring stations. Second, we analyze quantitative evaluation of the effect of Cheong-gye
stream restoration on air quality.
2. Study area and Experiment
2.1 Study area
Seoul city is located in the northwest of Korea. The area of the city is 605.52km2, and its population about
10,000,000 inhabitants. The topography of the study area is steep range of hills mountain land. In short, ups and downsof land are steep. The study area is around restored a cheong-gye stream in 1 October, 2005. The cheong-gye stream
flowing from west to east in the city center of Seoul was an inner-city river with the length of 10.92 km joining to the
Han-gang river.

Meteorological observation point

Experimental setup

Obstruction flashlight

Obstruction flashlight
Pollution density observation point

Han-gangCheong-gye stream

Cheong-gye stream
Seoul station

river
Study area

Tongdaemun
studium

Fig. 1 Study area (Seoul city)
2.2 Experimental method(a ) Measurement of atmospheric pollution
Fig. 2 shows a schematic of the experimental setup. An astronomical telescope (Meade, DS-115), with an aperture
diameter of 115 mm and a focal length of 910 mm, is employed to focus the image of a point light source located at afar distance. The image is formed near the eyepiece location (the eyepiece itself is removed from the telescope) where
the entrance slit (1 mm high and 5 µm wide) of a CCD spectrometer (Ocean Optics, USB2000) is placed. The CCD
consists of 2048 elements and is sensitive in a wavelength range of 200-800 nm, resulting in an average resolution of
0.3 nm/pixel. This CCD spectrometer is composed of a fixed grating and a linear CCD array with a mechanically stable,
crossed Czerny-Turner design. No moving parts are incorporated, resulting in high reliability and compactness (89 mm
wide × 63 mm deep × 34 mm high). The CCD gate duration is set at 300 ms in the experiment. Between successive gate
periods, there exists a time lag of 7 ms, in which each spectral data is sent to a personal computer (PC) through theuniversal serial bus. The data acquisition can be attained successfully even when no trigger (synchronous with the
flashlight) is applied to the CCD spectrometer, though this relatively long gate time as compared with the flashlight
duration (about 0.5ms) causes somewhat increased amount of the background skylight. For the measurement in thecenter of Seoul, we made use of a xenon strobe install at the top of Doosan tower as a light source. The lamp height isabout 160m above the ground level. According to the regulation, the light intensity is diminished at dusk and dawn, andduring the nighttime blinking red lights replaces the flashlights. Thus, the DOAS measurement is limited to the daytime,around 7 a.m. to 6 a.m. during the summer and autumn. In the west direction 2.0km from the source, a DOAS system
was installed in a Cheong-gye stream at Cheongae-3ga (Fig .1).

Fig. 2 Experimental setup for measuring NO2(b) Meteorological observation
In September 24th 2005, an observation data of air temperature at 19:30-20:30 was used in Cheongecheon. The goalof the observation was to know change in air temperature at sunset. All the observation points are on the paving ofasphalt.

3. Result
3.1 Analisis of the DOAS spectrum
The quantitative analisis of trace compounds is based on Lambert Beer’s law (e.g., Fuqi, 2005).

I1 = I 0 ⋅ e -αLC

(1)

Here I1 and I 0 denote the light intensity at a specific wavelength with and without absorption, respectively. In
addition, α,L and C denote the absorption coefficient, the length of open path, and the mixing ratio of trace gas,
respectively. Since the broad band absorption has little spectral structure, the DOAS technique only considers thenarrow band structure. Therefore, for the derivation of concentrations, the differential cross sections ( ∆α ) over several
wavelength channels are taken into account. The cross sections for the wavelengths (gases) of interest has been
pre-recorded by the manufacturer and stored in the analyzer’s memory. The strength of absorption for each trace gas
however, varies as a function of wavelength. Thus, to optimize the detectibility of each gas for the DOAS system,
wavelength region of the strongest absorption was selected and used for each gas such as: 265.7-304.4 (O3),280.7-319.3 (SO2), and 406.2-444.2 nm (NO2).
An example of this approach is shown in Fig. 3. that shows the two data spectrums compared in the analysis. In thiscase, we employ the DOAS spectrum on 28 September 2005 (9:35-9:40) as the clear-day spectrum. Then a spectrum
observed on 29 September 2005 (16:00-17:00) is divided by the clear-day spectrum. The intensity of observed spectrum
was very small, therefore the intensity of observed during 16:00 to 17:00 was performed integration. The difference inthe bias level between the two spectra is ascribed mainly to the difference in the aerosol optical thickness.
Fig. 4 shows monthly average of NO2 during January, 2001 to October, 2005. However, the air quality standards of
NO2 in Seoul is 0.05 ppm (the value of annual average); the value of NO2 in Chongae 4 ga is more than 0.05 ppm during
July 2003 to June 2005. The influence of construction is stronger than the influence of the autoexhaust. However, it isworth observing atmospheric pollutant species, since the value of NO2 in Chongae 4 ga is low after restored acheong-gye stream (October 2005).

2500

Dongdaemun

1000

Seoul station

Chongae 4ga

0.09
0.08

800

29 Sep., 2005
500

200

0

0
400

410

420
430
Wavelength (nm)

440

450

Fig. 3 Observed spectrum (28 September, 2005

0.05
0.04
0.03
0.02
0.01
0
ry
Ap 0 1
ri l
J 01
O uly
ct
o b 01J a e rnu 01
ar
yAp 0 2
ri l
J 02
O uly
ct
ob 02
J a e rnu 02
ar
yAp 0 3
ri l
J 03
O uly
ct
ob 03
J a e rnu 03
ar
yAp 0 4
ri l
J 04
O uly
ct
ob 04
J a e rnu 04
aryAp 0 5
ri l
J 05
O uly
ct
ob 05
er
-0
5

400

NO 2 (ppm)

1000

0.06

nu
a

600

28 Sep., 2005

0.07

Ja

1500

Intensity (Observed spctrum)

Intensity (Reference spctrum)2000

Fig.4 Monthly average of NO2 around the road.

16:00-17:00 JST) and reference spectrum(29 September, 2005 9:35-9:40 JST).

3.2 Relation of building form and air temperature
Fig. 5 (North-South) and Fig. 6 (East-West) show the change in air temperature obtained according to the airtemperature measurements. At first the decreases in the air temperatures are seen in Cheongecheon according to Fig. 5.
In general, there is relaxation effect of air temperature in the river, and away from the river, the air temperature tends tobe high. Next, the air temperature of point A is the lowest according to Fig. 6. It is thought that the density inthe building influences the air temperature.

Fig. 5 Daily variation of air temperature

Fig. 6 Daily variation of air temperature(Left:2004, Right:2005) (North-South)(2005) (East-West)A(28%)

B(54%)C (46%)

Fig. 7 Pattern diagrams of building (Density in thebuilding of 120m in radius from observation point).
Radiant quantities R from the ground surface when there is an obstacle are shown by the next formula.

R =σT 4 -γσT14 - (1 -γ )ΓσTα4･･･ (2)

Here, σ: Stefan constant, T: Ground surface temperature, T 1: Ground surface temperature of obstacle, Ta: Air
temperature, Γ: The emission absorption quantity by the atmosphere and the emission absorption quantity by theplanckian radiator ratio, γ: The spatial ratio which the obstacle occupies.
In addition, 0 < γ < 1 and 0 < Γ < 1 is. At the early morning of the city, it is thought T 1 > Ta and T > Ta. If the valueof R decreases, cooling rate of the ground level becomes small. As a result, ground surface temperature of the city
becomes high in comparison with suburb. In addition, when altitude of the building is high, in order to reflect multiplex
with the wall surface, the structure of the city is likely to be heated (e.g.Kawamura, 1964). In this study, it thought about
the relation of the density of the building and the air temperature.
As a result, there is a proportional trend between the air temperature and the density in the building, in other words
when the air temperature is high the density of the building is also high, and vice versa. It is necessary to investigate an
accurate architectural form in order to examine the relation of a detailed architectural form with the air temperature in
the future.
The atmospheric pollutant species observation and the meteorological observation in Seoul will be studied, and therelaxation effect of the heat island phenomenon of the municipal river will be clarified. Thus, it is possible to contributeto the creation of the municipal river and the promotion of maintenance in Asian cities.
References
KAWAMURA, T. (1964): Some prospect concerning component of city temperature in Kumagaya City, Geogr. Rev.
Japan, 37, 560-564.
Edner, H., Ragnarson, P., Spannare, S., and Svanberg, S. (1993): Differential optical absorption spectroscopy (DOAS)
system for urban atmospheric pollution monitoring, Applied, Optics, 32, 327-333.
Yoshii, Y., Kuze, H., Takeuchi, N. (2003): Long-path measurement of atmospheric NO2 with an obstruction flashlightand a charge coupled device spectrometer, Applied Optics, 42, 4362-4368.
Fuqi, S., Kuze, H., Yoshii, Y., Nemoto, M., Takeuchi, N., Kimura, T., Umekawa, T., Yoshida, T., Hioki, T., Tsutsui, T.,
and Kawasaki, M. (2005): Measurement of regional distribution of atmospheric NO2 and aerosol particles withflashlight long-path optical monitoring, Atmospheric Environment, 39, 4959-4968.

Study the effect of green covering on the land value of Tokyo
Metropolis using geographic information system (GIS)
Ippei Harada1 and Akihiko Kondoh2
1

Graduate school of Science and Technology, Chiba University (E-mail:ippei@graduate.chiba-u.jp)

2Center for Environmental Remote Sensing, Chiba University (E-mail:kondoh@faculity.chiba-u.jp)

Abstract
The city environmental element was extracted using Geographical Information System (GIS). The influence ofthe city environmental element was correlated with the formation of land values and analyzed using multiple
linear regression analysis. Multiple linear regression analysis was used to analyze the factor of the green
environmental impact on the formation of land value. It was not be able to explain the influence of the vegetation
impact on the land value of the area through the meso-scale in 70km range from the center of Tokyo, but thisinfluence was clarified through the area of a local scale within a radius of 4.4 km range from the city of Tokyo
metropolis as considered one of the elements that the vegetation could influence the formation of land.

1. Introduction
1.1 BackgroundThe urbanization around the center of Tokyo was expanded due to the increasing population of Tokyo region(Tokyo, Chiba prefecture, Saitama prefecture, Kanagawa prefecture). The housing establishment and city traffic
have developed the suburbs which were before an agricultural land and mountainous area. The rapid expansionof Tokyo from the end of 1980s till the beginning of 1990s due to the impact of the economic bubble haveincreased the land and stock values, thus increased the problem of urban environment, and it is difficult toevaluate the effect of the expansion of Tokyo metropolis on the city environment.
A land price in a city is a comprehensive value which is controlled by many environmental and other related
functions, local economical structure and their close interrelation. Moreover, it is an important factor to which
the land value has specified the spatial pattern of various elements which constitute time and spatial change of aninternal structure of city (Wakita, 1976).
Geographic Information System (GIS) and remote sensing technologies provide potential opportunities forquantifying and monitoring urban environments. For instance, medium resolution remote sensing data (e,g,Landsat Thematic Mapper) have been widely utilized in mapping urban land use and land cover throughclassification algorithms (Harris & Ventura, 1995 and Treitz et al., 1992).

1.2 Purpose
The natural conditions of land specifications, such as convenience, comfortable nature, economical efficiency
inhabitation or a production activity may determine the land value. The purpose of this research verifies whether
a city consists of some various elements which the geographical feature and the green environment, are beingreflected in a land value, in order to evaluate the expansion of Tokyo metropolis during the bubble economic

term from the center of Tokyo to its suburbs.

2. DATA AND STUDY AREA
2.1 Data(a) 10m Grid Land use of Metropolitan Area (in 1984 and 1994)(b) Land classification map of 1:500000 scale (Chiba prefecture, Saitama prefecture, Tokyo prefecture,
Kanagawa prefecture)(c) Official announcement of land price in 1985 and 1997.
(d) 2 scenes of Landsat TM data acquired during daytime August 3rd, 1985 and July 19th, 1997（Path：107 /Row：35）

2.2 Study Area
Tokyo is the center of major activities and developments of Japan. The impacts of the changes took place inTokyo was large even outside its periphery and that has resulted in the land value increase in the nearby
prefectures also. These prefectures include parts of Tokyo metropolis, Chiba prefecture, Saitama Prefecture, andKanagawa Prefecture.

Table 1. Analyzed city

Figure 1. Tokyo region

Analyzed city
LatitudeKumagaya
36°06.8′
Kuki
36°05.0′
Hatoyama
35°58.2′
Urawa
35°52.4′
Koshigaya
35°53.4′
Tokorozawa
35°46.2′
Ome
35°47.2′
Nerima35°44.0′
Hachioji
35°39.8′
Fuchu
35°40.9′
Tokyo(Otemachi) 35°41.2′
Shinkiba
35°38.0′
Abiko
35°52.5′
Funabashi
35°43.7′
Chiba
35°36.0′
Kisarazu
35°22.5′
Ebina
35°26.0′
Yokohama
35°26.2′

Longtitude Altitude(m)139°23.0′30
139°38.4′12139°15.4′
44139°35.4′8139°47.6′5139°25.0′
119139°19.0′
155
139°40.2′
38
139°19.2′
123139°29.2′
58
139°45.9′7139°50.5′6140°02.0′20
139°59.8′24140°06.4′4139°55.3′
5139°23.2′18139°39.4′
39

3. The analysis method
3.1 Land use/cover change in Tokyo metropolis
The extent of Tokyo metropolis is about 100 km and there are many cities existed also on the fringe of Tokyo.
It is necessary to understand the land use change in Tokyo metropolitan area, in order to examine the influence of
this change on the environment. 1984 and 1994 were selected since during this period the economic bubble has
took place and there was serious change in the land use of Tokyo metropolitan area.

3.2 Extracted city environmental elements using GIS(a) Relation between land value and spatial distance from Tokyo station
Wakita (1976) described that the negative correlation between a spatial distance and the land value is an index
of the accessibility to the center of Tokyo metropolitan.

(b) Relation between land value and landform
Topography is a major natural factor that affects the valuation of residential lands because land surface
properties are closely related to hazard vulnerability and amenities for human living. Supposed disaster damages
especially those due to floods are evaluated as negative profits, resulting in the decline of the land value. The
influence of topography on residential land value has been evaluated by deriving land value functions of which
major explanatory variables are topographic and geographic attributes of each residential land. As a result, theland value of lowland was evaluated at about 85 % of that of upland (Mizutani, 2000).
The land price data were classified into four classes based on geographical features (lowland, upland, hilly
land, reclaimed land). Using the land classification map of 1:50000 scale with the aid of GIS, a new factor of city
environment was extracted

3.3 Relation between land value and green environment
Two Landsat TM data were used for the present study. Of these one was acquired before bubble economy
period (August 3rd, 1985), and the other was after the bubble economy (July 19th, 1997). The vegetation indiceswere extracted based on the following equation (1).
NDVI = (NIR (TM band 4)－Red (TM band 3)) / (NIR (TM band 4－Red (TM band 3))(1)

The area where the value of NDVI was larger than 0.1 was assumed to be a vegetated region, therefore, thevalue (0.1) of NDVI was used generally as a threshold of the vegetation region and non-vegetation region (e.g.
Kondoh, 2004). The ratio of green covering around the analytical point (within a radius of 4.4 km from theanalytical point) was calculated. The average area of an administrative district in Tokyo metropolitan area
becomes about 60 km2, and corresponds to the area of the concentric circle of 4.4km in the radius that centers on
the analyzed city.

3.4 Multiple linear regression analysis
The influence of the city environmental element was correlated with the formation of land values and it was
analyzed using multiple linear regression analysis.
Regression equation that evaluates the effect of city environment onto the land value is indicated in equation(2):
P(z)=f・(Z1+Z2+Z3+,…,+Zn)(2)

Where, Z1 is the spatial distance from Tokyo station，Z2 is the spatial distance up to near station, Z3 is thebuilding structure (reinforced concrete, steel framework, timbered), Z4 is the with or without of town gas, Z5 isthe with or without Sewerage, Z6 is the with or without Water supply, Z7 is the land area, Z8 is the building
coverage ratio, Z9 is the building capacity ratio, Z10 is the land form (hilly land, upland, lowland, reclaimedland) and Z11 is the green covering ratio.

Multiple linear regression analysis was used to analyze the factor of the green environmental impact on theformation of land value in 70 km range from the center of Tokyo (meso-scale) and in 4.4 km range from the cityof Tokyo metropolis (local-scale).
Official announcement of land price data of the local scale was classified into four areas:
1.

Nerima, Tokyo (Otemachi) and Shinkiba in Tokyo city area located within a radius of 20 km from Tokyo
station.

2.

Funabashi, Urawa, Koshigaya, Fuchu and Yokohama around Tokyo city area located within concentric
circles of 20～30 km from Tokyo station.

3.

Abiko, Chiba, Kisarazu and Tokorozawa in suburban 1 area located within concentric circles of 30～40 km
from Tokyo station.

4.

Kumagaya, Hatoyama, Kuki, Hachioji, Ome and Ebina in suburban 2 area located within concentric circlesof 40～70 km from Tokyo station.

4. Results
4.1 Land use/cover change in Tokyo metropolis
The outskirts of Tokyo (Otemachi) which is the city center of Tokyo have a small change of land use. However,
it is clear that change of land use is large in the area around Koshigaya and Urawa of Saitama Prefecture from
northwest to northeast within a 40 km of the city center of Tokyo.
Land uses of mountain area and paddy field have decreased since the residential area and community facilities
have increased from 1984 to 1994. Urbanization has progressed in the suburbs and it has grasped spatially thatthe city is expanded in a suburb from the center of Tokyo. However, the land use area of park and green space
increased not only in the center of Tokyo but also in the suburbs.

Kanagawa
Tokyoin 1984

31.4

in 199428.9

in 1984

Chiba

in 1984

SaitamaTable 2. The percentage of changes of land use from 1984 to 1994
45.1

in 1994

41.7

2.3
2.7
4.0
4.6

53.7

in 199455.1

in 1994

51.0

0%

20%

16.4

9.0

30.3

8.9

18.8

11.1

36.6

19.2

10.7

22.4

10.4

25.0

11.0

3.0 11.9
2.0 12.3
2.6 12.8

40%

28.0

34.6

2.5 11.0

49.1

in 1984

15.6

60%

18.7

11.8

21.2

12.4

80%

100%

Mountain area
Paddy field
Agricultural land
Park
Green space
Reclaimed landRoad
Community
facilities
Residential landCommercial land
Industrial land
OthersFig 2. Land use change of Tokyo metropolitan during the period 1984-1994.

4.2 Multiple linear regression analysis
Table 3. Determining factors of land values by regression analysis of the meso-scaleMultiple linear regression analisis (Land value in 1984 and in 1997)
in 1985
in 1997
Spatial distance from Tokyo station
Building capacity ratio
Sewarage
Spatial distance from Tokyo station
Spatial distance up to near station
SewarageLand form（hillyland>upland>lowland>reclaimed land）
Spatial distance up to near station
Town gas
Building structure（reinforced concrete>steel framework>timbered）
Building coverage ratio
Land form（hillyland>upland>lowland>reclaimed land）
Building structure（reinforced concrete>steel framework>timbered）
Building coverage ratio
Building capacity ratio
Town gas
Green covering ratio
Green covering ratio
0.81
0.92
Multiple correlation coefficient
64.5
85.0
Contribution ratio (%)
Year
Explaining variable 1
Explaining variable 2
Explaining variable 3
Explaining variable 4
Explaining variable 5
Explaining variable 6
Explaining variable 7
Explaining variable 8
Explaining variable 9

Table 4. Determining factors of land values by regression analysis of the local-scaleMultiple linear regression analisis (Land value in 1984 and in 1997)
in 1985 (Tokyo city)in 1997 (Tokyo city)
Spatial distance from Tokyo station
Building capacity ratio
Explaining variable 1
Green covering ratio
Spatial distance up to near station
Explaining variable 2
Building capacity ratio
Building structure
Explaining variable 3
Building coverage ratio
Green covering ratio
Explaining variable 4
Spatial distance up to near station
Land form
Explaining variable 5
Land area
Land area
Explaining variable 6
Year

Explaining variable 7
Explaining variable 8
Explaining variable 9
Multiple correlation
coefficient
Contribution ratio (%)

-

-

-

-

-

-

0.647
28.3

0.894
79.5

Multiple linear regression analisis (Land value in 1984 and in 1997)
in 1985 (suburban 1)
in 1997 (suburban 1)
Sewarage
Sewarage
Explaining variable 1
Spatialdistanceup
tonear
station
Building
capacity ratio
Explaining variable 2
Land form
Spatial distance up to near station
Explaining variable 3
Building structure
Green covering ratio
Explaining variable 4
Building capacity ratio
Spatial distance from Tokyo station
Explaining variable 5
Green covering ratio
Building structure
Explaining variable 6
Building coverage ratio
Explaining variable 7
Land form
Explaining variable 8
Year

Explaining variable 9
Explaining variable 10
Multiple correlation
coefficient
Contribution ratio (%)

-

-

0.861
71.4

0.865
73.6

Multiple linear regression analisis (Land value in 1984 and in 1997)
in 1985 (around Tokyo city)in 1997 (around Tokyo city)
Sewarage
Building capacity ratio
Explaining variable 1
Spatial distance up to near station
Spatial distance up to near station
Explaining variable 2
Green covering ratio
Green covering ratio
Explaining variable 3
Town gas
Sewarage
Explaining variable 4
Land form
Explaining variable 5
Building structure
Explaining variable 6
Building coverage ratio
Explaining variable 7
Town gas
Explaining variable 8
Spatial distance from Tokyo station
Explaining variable 9
Year

Multiple correlation
coefficient
Contribution ratio (%)

0.708
48.6

0.883
77.4

Multiple linear regression analisis (Land value in 1984 and in 1997)
in 1985 (suburban 2)
in 1997 (suburban 2)
Green covering ratio
Building capacity ratio
Explaining variable 1
SewarageGreen covering ratio
Explaining variable 2
Spatial distance up to near station
Sewarage
Explaining variable 3
Land area
Building structure
Explaining variable 4
Building coverage ratio
Spatial distance up to near station
Explaining variable 5
Building structure
Spatial distance from Tokyo station
Explaining variable 6
Spatial distance from Tokyo station
Town gas
Explaining variable 7
Town gas
Land area
Explaining variable 8
Building coverage ratio
Explaining variable 9
Land form
Explaining variable 10
Year

Multiple correlation
coefficient
Contribution ratio (%)

0.842
67.3

0.868
74.2

5. Conclusions
Multiple linear regression analysis was used to analyze the effect of the green environmental impact on theformation of land value. It was not be able to explain the influence of the vegetation impact on the land value ofthe area through the meso-scale in 70km range from the center of Tokyo, but this influence was clarified throughthe area of local scale in 4.4km range from the city of Tokyo metropolis as considered one of the elements thatthe vegetation could influence the formation of land value.

References
Harris, P.M. and Ventura, S.J. (1995): The integration of geographic data with remotely sensed imagery to
improve classification in an urban area, Photogrammetric engineering and remote sensing, 61, 8993-8998.
Kondoh (2004): Vegetation and land cover change detection by global remote sensing and its causal analyses,
J.Japan soc. hydrol. & water resour, 17(5), 459-467.
Mizutani, T. (2000): Statistical analysis of the influence of topographic factors on residential land value,
Transactions, Japanese geomorphological union, 22(1), 43-58.
Treitz, P.M., Howarth, P.J. and Gong, P. (1992): Application of satellite and GIS technologies for land cover andland use mapping at rural-urban fringe, Photogrammetric engineering and remote sensing, 58, 4439-4448.
Wakita, T. (1976): Land price formation in a Metropolis, Taimeido, 164p.

The relationship between PAL NDVI and land use changes
in semi-arid regions, China
Hajime OSADA1* and Akihiko KONDOH2
1

Graduate School of Science and Technology, Chiba University,
2

Center for Environmental Remote Sensing, Chiba University*E-mail: osada_hajime@graduate.chiba-u.jp

Abstract
The land-cover or -use over the semi-arid regions, such as the Northeastern China, was rapidly changedin the last twenty years. To detect the environmental changes such as land use, the long term monitoringby the space-borne sensors is useful as a big heritage. The objective of this study is to detect theenvironmental changes of last twenty years by satellites and maps based on the local survey. We focusedon the changes in the Northeastern China. A time series of the Pathfinder AVHRR Land data (PAL) wasused for this study. Land use maps based on local survey, conducted 1980’s and 1990’s, were also used as
evidences. Results ware summarized as follows: (1) NDVI was on the rising or roughly no changes inmost areas . (2) High positive tendency of NDVI could express the land use change to cultivated land
from grassland in this area. (3) The change pattern of NDVI is different depending on the pattern of land
-cover or -use change. Satellites could detect the land use changes in local scale.
1.

Introduction
The land-cover or -use over the semi-arid regions,such as the Northeastern China, was rapidly changed in
the last twenty years. For example, the cultivated landsof Inner Mongolia autonomous region decreased by
15.4% from 1978 to 1987, while they increased by
22.1% from 1987 to 1996 (Yang and Li, 2000). Thisregion has been desertification, induced by unsuitableland use, such as overcultivation, overgrazing of
livestock, and excessive gathering of fuel wood (Zhaand Gao, 1997). To explore suitable land use
management or assessment, it is essential to monitor theenvironmental changes continuously in time.
To detect the environmental changes, the long termmonitoring observed by the space-borne sensors is oneof the important issues. Since operation of NOAA
series satellites started in July 1981, the techniques todetect the globalchangesofvegetation

andFigure 1 Research area and the distribution of linear trend of NDVImax. The area framed in by thickline is extracted as the area showing characteristic trend of rising.

land cover have been developed. There are a lot of studies about the relationships between global changes
of NDVI and climate factor, such as temperature, precipitation, and radiation (e.g. Nemani et al., 2003, Yu

et al., 2003, Kondoh et al., 2002). To detectenvironmental changes, Kondoh (2004) adopted 5
indicators

obtained

by

AVHRR

data,

(a)

as

categorizing surface condition. He showed that thesignal of environmental changes could be extracted
by using the techniques in local scale. However,there are few studies to detect the environmental
change by human activity in local scale. The
objective

of

this

study

is

to

detectthe(b)environmental changes of last twenty years bysatellites and maps based on the local survey insemi-arid regions in china.
2.

Study area and method

2.1 Study area
Study area is located in the semi-arid regions,
northeast part of China (110~125°E, 40~55°N)

Figure 2 Land use map of 1980s (a) and later 1990s (b).

(Figure 1). Steppe grassland is extensive in thisarea. The average annual precipitation is about
200 ~ 500 mm.
2.2 Pathfinder AVHRR Land Data
For this study, the 10-day composite
Pathfinder Advance Very High Resolution Land
data set (hereafter, PAL) was used. The period
examined is from 1982 to 1999. NDVI(Normalized Difference Vegetation Index) was
calculated from channel 1 and 2. Surface
temperature calculated from channel 4 and 5 using

Figure 3 Composition of land use types, 1980s and late 1990s.

sprit window method. The indicators for detecting
environmental change are annual maximum value of NDVI (NDVImax), annual integrated value of
NDVI (iNDVI), annual maximum temperature (Tmax), and annual slope of trajectory on Ts (surface
temperature)-NDVI space (TRJ) (Nemani and Running, 1997). They were admitted the availability for
detecting environmental changes (Kondoh , 2004).
2.3 Land use maps
Land use map of China, 1:1000000 and 1km mesh Land-use map of China were used for evidences ofland-cover or change. The period covered of the former is from 1981 to 1987, and it was used as the dataof 1980s. The period covered of the latter is from 1996 to 2000, and it was used as the data of later 1990s.
Both maps are based on local survey. The names of the types in the two maps were different. They wereclassified into six types, such as cultivated land, forest, grassland, unused land (e.g. sandy land, sandy
desert, and saline-alkali land), city-industrial area-habitat, and water.

2.4 Method
To extract the area through characteristic change, the linear
trend of NDVImax of PAL is obtained from 1982 to 1999. In
the area extracted by above mentioned, land-use or coverchange based on the 2 land use maps was compared with thechanging trend of each indicator.
3.

Results and discussions

3.1 Long term change analysis in large scale
Figure 1 shows the linear trend of NDVImax in the wholestudy area. It shows that rising or roughly no change in mostareas. Its rising may mean that annual production ofvegetation in most areas. The characteristic signal thatinvestigates increasing production of vegetation, wasextracted in southeastern area of Inner Mongolia.
3.2 Land use change
Figure 2 shows the Comparison between the 1980s and thelater 1990s. It indicates that the land use has been changed
significantly in this area. In southeastern part in the area,large unused land changed to grassland. In south part of thearea, grassland changed to cultivated land. Figure 3 shows
the composition of the area of land use types in 1980s andlater 1990s. The cultivated land increased by 10.0 %, andgrassland decreased by 14.4 %. The change must indicatethat large area of unused land turned into grassland.
3.3 Comparison of PAL data and land-cover changes
Figure 4 shows temporal changes of indicators in the area
where changed
grassland from

Figure 4 Temporal changes of indicators at the area where the land-cover changed (a): NDVImax. (b):unused land, andiNDVI. (c): Tmax. (d): TRJ

cultivated land from grassland. NDVImax is on the rise in both areas. The slope of linear regression,
where changed cultivated land from grassland, is higher than where changed grassland from unused land.
In the area changed cultivated land from grassland, iNDVI is also on the rising. But in the area changed
grassland from unused land, iNDVI isn’t on the rise on the after 1990, when it reaches at the maximal
value in 1990. The change pattern of Tmax is similar in the 2 areas. The differences between the twoareas is 5.2 ℃ in 1996 at the maximum value. It indicates that the differences of land cover produced thedifference of temperature.
Figure 5 shows Difference of the average value of indicators between 1982-1987 and 1996-1999. The
signal of the change to cultivated land from grassland on south part could be extract by NDVImax,iNDVI, and TRJ. NDVImax corresponds best with the changes of land-cover in 4 indicators. In the areachanged from unused land to grassland, the coincidence of a distribution with land-cover or -use changewas not obtained. In the Tmax , non-random spatial patterns are obtained. But the distributions don’t
correspond to the change patterns of land-cover or -use changes.

Figure 5 Difference of the average value of the indicators between 1982-1987 and 1996-1999. (a):NDVImax. (b): iNDVI. (c): Tmax. (d): TRJ. Positive value means increasing.
4. Conclusion
In this research, the area implied characteristic change was extracted by using PAL. The relationshipbetween the indicators by using PAL and land-cover or -use changes, was examined in the extracted area.
It is found that the change of land-use or cover can explain the some extracted signal of characteristicchange by using PAL in local scale. The intensity of the environmental change signal is different
depending on the pattern of land -cover or -use change. To detect the environmental changes in semi-arid
regions, it is difficult to explain the land cover changes by using only the variation of NDVI. In the future,
it is necessary to examine the relationship among the patterns of more detailed land covering changes,
remote sensing data, and several causes of changes, such as human activity and climate-driven.

References
Zya, Y. and Gao, J. (1997), Characteristics of desertification and its rehabilitation in China, Journal of
Arid Environments, 37, 419-432.
Yu, F., Price, K. P., Ellis, J., and Shi, P. (2003), Response of seasonal vegetation development to climatic
variations in eastern central Asia, Remote Sensing of Environment, 87, 42-54.
Kondoh, A., Tateishi, K., Runtunuwu, E., and Park, J. (2002), Relating vegetation activity to climatic
variation and atmospheric CO2 content, J.Japan Soc. Hydrol. & Water Resour., 15 (2), 128-138.
Kondoh, A., (2004), Vegetation and land cover change detection by global remote sensing and its causal
analysis, J.Japan Soc. Hydrol. & Water Resour., 17 (5), 459-467.
Nemani, R.R. and Running, S. W., (1997), Land cover characterization using red, near-IR, andthermal-IR data from NOAA/AVHRR, Ecological Applications, 7, 79-90.
Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C., Tucker, C. J., Myneni, R. B.,
and Running, S. W., (2003), Climate-driven increases in global terrestrial net primary production
from 1982 to 1999, Science, 300, 1560-1563.
Yang, H. and Li, X., (2000), Cultivated land and food supply in China, Land use policy, 17, 73-88.

The characteristics of water resources in XinJiang Uyghur
Autonomous Region, China, using GIS and remote sensing
Dilnur Aji 1)， Akihiko Kondoh 2)
1)

Graduate School of Science and Technology，Chiba University

2)

Center for Environmental Remote Sensing，Chiba University

Email: dilnuraji@graduate.chiba-u.jp and kondoh@faculty.chiba-u.jp

Abstract
Lakes and rivers are the most important water resources in XinJiang Uyghur Autonomous Region，China，
since there are little precipitation and melted-snow water from high mountains，which are limited in summer
season． This research describes the characteristics of water resources through analyzing the water areachanges of main closed lakes using multi-temporal satellite data，and mapping the meteorological observationsof the main rivers outflow for the past 50 years．Land cover change of Lake Ebnur region and its vicinity from1972 to 2003 were analyzed by comparing the land cover classification images．As a result，some remarkablechanges of water resources in XinJiang were produced．It is supposed that from 1950 until the second half of1980，the changes of the water resources in XinJiang have been affected by human activities．On the contrary，
from the second half of 1980 until present，the water resources have been affected by climatically fluctuationstrongly．The detected land use /cover change of Lake Ebnur region from 1972 to 2003 shows that water area ofthe lake were governed by these changes．
Key words: water resources，satellite image，GIS，lake area，river outflow

1.Introduction
Satellite remote sensing and geographical information system (GIS) are useful techniques for monitoring theenvironmental changes at local，regional，and global scales．
In XinJiang Uyghur Autonomous Region，China, since the initiation of open policy in 1978，and especially inrecent years，because of rapid economic development，water resources have drastically changed．Many
researchers have engaged in environmental change studies in Xinjiang，and it have been thought that there is alack of water resources since the population rate increases aswell as the water resources decrease，beside with the climaticconditions becomes dry with time in XinJiang．Looking at
the outflow of the rivers，the area of the lakes，and the land
cover change around the Lake Ebnur region，we can see thatthe long time range is not always connected with thedecreasing of water resources in XinJiang．In this research，
the characteristics of water resources in XinJiang werediscussed specifically ． Multi-temporal satellite images
covering some main closed lakes and meteorologicalFig.1 Lakes and rivers distribution in XinJiang

observations of main rivers were used to analyze the changes of water resources．Water area changes of eachlake were carried out by threshold method using the normalized difference vegetation index (NDVI)．

Meteorological observations were used for mapping the distribution of rivers outflow changes．Land coverchange around the Lake Ebnur region from 1972 to 2003 was analyzed through comparing the land coverclassification images．Climatically fluctuation was studied using CRU TS2．0 data set，and World Climate Data．
Finally，the main factors governing water resources were analyzed from both human and natural dimensions byadding the socio-economic statistical data．

2.Object area
XinJiang Uyghur Autonomous Region is located in Northwest of China between the range of
34°25′N~49°10′N， and 73°40′E~96°23′E，including 4 main closed lakes，which chosen for this study such as
Lake Wulungu，Lake Bostan，and Lake Ayding，Lake Ebnur，and 26 rivers (Fig1)，which have sustained
agriculture production of XinJiang as the important water resources．The 4 closed lakes cultivated by each river，
which flow into each lake from high mountains．Large delta used for agricultural land is formed in each rivermouth.

3.Used data and Methodology
3.1 Data
In this study，multi-spectral image data，multi-temporal meteorological observations of main rivers outflow
for the past 50 years ， andmulti-annual

socio-economic

statistical data are used as an input
data

for

evaluatingthecharacteristics of water resourcesin XinJiang ， and land use land
cover change for Lake Ebnur region．
For analyzing the climaticallychange for study area，We extracted
the precipitation and temperature
data of XinJiang during the period
1951~2000 from CRU TS2．0 data
set ， There are five variables
supplied in CRU TS2．0 data set，such as cloud cover ， diurnal
temperature rang，precipitation，andtemperature，vapour pressure．The
data is supplied on a 0.5-degree
grid ， covering the global land
surface and supplied at a monthly

Fig.2 The flow of the analysis for water resources changes using timeseries satellite data，climatic data and other auxiliary sources

time-step for 1901~2100．World Climate Data was used supplementing for after 1982，when the observational
data cab be obtained．

3.2 Geometric Correction
A subset of each of the Landsat MSS digital images(1972，1977)，Landsat TM digital image(1990)，and
Landsat ETM+ digital image(2003)，were used for evaluating water area change detection of Ebnur lake region．
Only the TM and ETM+ images were used for another three lake regions．The digital images weregeometrically calibrated to each other to facilitate their comparison for Ebnur lake region．The 1990 Landsat
TM image，which was supplied by Earth Satellite Corporation，had already been rectified and georeferenced to
UTM map projection (Zone 44)，and WGS1984 ellipsoid．Then，this image was therefore employed as areference scene to which the 1972 Landsat MSS scene，the 1977 Landsat MSS scene， and the 2003 Landsat
ETM+ scene of Ebnur lake region were registered．Using image to image registration，the other 3 images (MSSof 1972，MSS of 1977，and ETM+ of 2003) were matched to the TM image with the total root mean squareerror (RMSE) of less than half-pixels．The nearest neighbor resampling method was used to avoid altering theoriginal pixel values of the image data．The flow of data analysis is shown in Fig2．

3.3 Generation of NDVI image
Normalized difference vegetation index (NDVI) images was generated using visible and near-infrared bandsof each satellite data as shown in the following equation:
NDVI=［（CH2－CH1）／（CH2+CH1）］×a+b(1)

Where，CH2 is band 4 for both TM and ETM+ images，and CH1 is band 3 for the both images．For MSS
images，band 7 was used as CH2，and band 5 as CH1．Land cover types of water，vegetation and another werediscriminated with NDVI images by threshold method．Then，the water area was calculated of each lake region
by multiplying the number of pixels and the area of each pixel together of each item．a and b are coefficients，and each of them was determined as 100．

3.4 Satellite image classification
To map changes of Ebnur lake region that had occurred between the four dates，Landsat MSS (1972，1977)，
TM (1990) and ETM+ (2003) images were individually used as input for supervised classification purpose．
Maximum likelihood classifier was employed to detect the land cover types．Land use map，which was
published by the Chinese science publisher in 1990 with 1/1000000 scale，was used as an information data forclassification．

3.5 Grid data differencing
To investigate the climatically changes in the second half of 1980s in XinJiang，the climate data during theperiod 1951~2000 was extracted from CRU TS2．0 data set．This study was focused on the changes inprecipitation and temperature in two periods in 1951~1990 and 1991~2000．To map the changes ofprecipitation and temperature between the two periods，grid data differencing was adopted for comparison andwas performed on the generated dates of both periods．The differencing of precipitation and temperature were
calculated as follows:(1991~2000)-(1951~1990)(2)

4. Results and Discussions:
4.1 Water area of lakes
For this study， the changes in water area of each lake based on satellite data were extracted as shown in
Fig3．The results show that the water area of each lake has a continuing growth from the end of 1980s．The area
of

lake

Wulungu

wasincreased to about 899km2 in
2002

from

862km2in

1989(Fig3-A)．
The water area of Lake
Bostan was increased fromabout 980．0 km2 in 1990 toabout 1230 ． 2 km2 in
2000(Fig3-B)．
According to the previous
investigation， the water areaof Lake Ayding was about
152 km2 in 1949，which was
shrunk to about 22 km2 in
1958 ． The result of thisstudy shows that this lake
was totally disappeared in
1990， and there was only
about 34 km2 of saline areaaround the lake． Then， theFig.3 Change detection of water area of each lake

situation was changed for the（A:Wulungu， B:Bostan， C:Ayding， D:Ebnur）

same lake since its water area turned to increase about 16．7 km2 in 1999(Fig3-C)．
By studying the Lake Ebnur area， it was found that the water area was 528．2 km2 in 1972， which
decreased to 524．8 km2 in 1977， On the contrary，water area for the same lake has increased to 547．
6 km2 in 1990， and then reached to 1029．2 km2in 2003(Fig3-D)．

4.2 Outflow changes of main rivers
By comparing the outflow changes of chosen
main rivers in this study， it was found that thegrowth rate of these rivers outflow were –1．6%，
5．9%， and 7．1%， in each period during
1956~1979 ， 1956~1986 ， and 1987~2000 ，

respectively． It was shown that the water resources have a continuing increase in XinJiang after the second
half of 1980． There were 65% of decreased rivers and 35% of increased rivers between the period of
1956~1986． Most of the decreasing processes were happened in north XinJiang， and most of the increasing
processes were happened in south XinJiang in that period． On the contrary， there was a significant change，
which has occurred during the period of 1987~2000， when the outflow of the 77% rivers have doubled， andthe slight of outflow of the 23% rivers have decline． Remarkable increase was in north XinJiang and a slightdecrease was in south XinJiang during the period of 1987~2000 as shown Fig4．

4.3 Change detection of land cover in Lake Ebnur region
There are four scenes of MSS (1972， 1977)，TM (1990) and ETM+ (2003) images that include the Lake
Ebnur region， and they were used for the post-classification change detection as shown in Fig5．Cultivated，
and water ， were the major land
use/cover classes of interest in thisstudy．Based on Fig5，cultivated area
has a continuing increase ， which
increased from 54．2 km2 in 1972 to
127．3 km2 in 1977，to 267．2 km2 in
1990，and then to 734．4 km2 in 2003
for the studied area of Lake Ebnur
region ． The water class has also
showed a significant change in thisarea．In 1972，there were 528．2 km2
of water area (or 12%) ， which
declined to 524．8 km2 (or 11．9%) by

Fig.5 Land use/cover map of Lake Ebnur region from1977(Table1)．On the contrary，theLandsat MSS/ TM /ETM+ dates

water area has increased in 1990 from
2

Table.1 Calculated area of land use/cover in Lake Ebnur region

2547．6 km (or 12．4%) to 1029．2 km(or 23．4%) in 2003，which means，
the water area changes of Lake Ebnur
were governed by cultivated changes
from 1972 to 1990．After 1990，waterarea has doubled without regard toeven the growth of cultivated area.

4.4 Driving factors of the water resources
The changes of water resources was including the rivers outflow and lakes area that were governed bysocio-economic and natural factors，such as population and the climatic change，etc．Fig6 shows that thetemporal changes of population and cultivated area in each northern XinJiang and southern XinJiang．Each

of

these

changewasaccorded

with

the

fluctuationofriversoutflow from 1950s to theend of 1980s

as shown

Fig4．
The

changeinthesituation of precipitationand temperature as shownin Fig8 indicated that theincreasetendencywashappened after the end of
1980s．Based on the Fig7，
it was considered that theincreasing of precipitation
directly causes lakes area
expansionandriversoutflow rising in the end of 1980s，the rising of temperature may make the melted-snow water， which flow
into the rivers and lakes from high mountains．
The characteristics of water resources in XinJiang for the second half of the 1980s had some remarkablechanges．It was presumed that the causes of water resources changes were not only due to the effect of humanactivities but also due to the effect of climatic fluctuation．
It is supposed that from 1950s until the second half of
1980s，the changes of the water resources in XinJiang
have been affected by human activities strongly．On thecontrary，from the second half of 1980s until present，thewater resources have been affected by climaticfluctuation strongly．The detected land use /cover change
of Lake Ebnur region from 1972 to 2003 shows thatwater area of the lake were governed by these changes．
Fig.8 Variation of precipitation and temperaturein XinJiang
References
Hietel，E．，Waldhardt，A．
，2004：Analyzing land-cover changes in relation to environmental variables in Hesse，Germany，Landscape Ecology 19，pp．473–489．
Nakayama,Y.,Tanaka,S.,Endo,K.and Suga,Y．(1993)： A change of Aral Sea’s water area by satellite data，Proceedings of IGARSS，1，pp．194-196．
Micklin，P．，1998：Desiccation of the Aral Sea，Soviet Union，Science，241，pp.1170-1176．

Information design for
agricultural Plant Planning and Satellite based Remote Sensing Data Visualization
Tomoyuki SHIGETA, Takeshi SUNAGA,
Yutaka MUGISHIMA, Noriyuki YOYASU, Hironobu RYOU, Daigo YAMAZAKI,
Chiharu HONGO*,
Kazunari YOKOYAMA**
Information Design Department, Tama Art University*Center for Environmental Remote Sensing (CEReS), Chiba University**National Agricultural Research Center for Hokkaido Region

Abstract
This paper shows design and cooperative design process for agricultural plant planning system basedon information Design point of view. The system allows farmers planning kinds and yields ofvegetables for the coming year. Developing functionalities for plant planning, visualization of Satellite
based Remote Sensing (SRS) data, interactions for operating the system and graphical user interface
are concerned in the design. The design project consists of farmers and multidisciplinary researchers
those are agricultural biologist, remote sensing researcher and information designers. The objective ofthe design project has been vegetable farming in Tokachi area of Hokkaido, Japan.
1. Introduction
The vegetable farmers deal with thirty hectare
field on average and fifteen to twenty crops in
Tokachi. For the planning at this moment of oursurvey, hand drawing maps of the field and handwriting names and quantities of crops that heplanted in each year have been used as figure 1.
And notes of manures and fertilizers are existed.
The design we proposed is an assistance for theplanning as interactive system for the farmers.
Basic functions of the system are browsing,planning and recording concerning to vegetable
crops, several values of harvests, and records ofinsects and disease.

Fig. 1. A hand drawing map of fields, names and quantities ofcrops planned by farmer for 2005 (YM, 2005)

2. Planning Support System for Cropping and Fertilizing
2.1. Basic functions of the system
Following functions are designed in the
Planning Support System. Those are (1)cropping for the next year, (2) fertilizing for the
next year, (3) recording agricultural information
concerning to this year that is listed in table 1,and (4) browsing past information aboutplanning and recording in preceding years.

Fig. 2. A Screenshot of the Planning Support System for
Cropping and FertilizingA main screen of the system is shown in figure 2.
Left side of the screen is an area for planning
crops on a plan of field where the farmer
considers and right side is an area for browsing
past information on same plans. Horizontal listsof the left area shows potential kinds of crops forthe planning. The right area shows harvested
kinds of crops with which the farmer deals.

Table. 1. Variables of agricultural information for the planninga. Plan of Field for Croppingb. Plan of Field for Fertilizingc. Yields and productions
d. Starch-concentration (potato)e. Sugar-concentration (beet)f. Protein (wheat)g. Nitrogen-content (soil)h. Insects and diseases
i. Profit and loss

2.2. SRS data for the system
Variables of estimation for the system are followings. Those are calculated from SRS data.
j. Estimated yield of each cropk. Estimated starch-concentration of potato on each fieldl. Estimated sugar-concentration of beet on each fieldm. Estimated protein of wheat on each fieldn. Estimated nitrogen-content of soil on each field
2.3 Social system configuration for the planning support system
The planning support system is used by a number of farmhouses under management by a certain
organization of the region. The organization contracts with SRS data provider. Figure 3 shows theproviding system.

Fig. 3. Providing system for the planning support system(1) SRS data of the region(2) Providing values of the calculated data of eachfarmhouse field for the system on farmer’s PC

3. Information Design Study on Visualizations for variables from SRS data
3.1. Visualizations
Values of the Variables are calculated from SRS data by functions constructed by SRS researcher(HONGO. 2004). Information designers visualize the variables. Purpose of the visualization is making
legibility of the values for the farmers who deals with the values of crops, fertilizers, and his/her tasks
on the fields.
Our study of the visualization has been based on SRS research for a couple of fields of potato that isgrown by farmer YM in Tokachi as a case. Following visualizations are derived from the potato data.

Fig. 4. Legible scatter-gram of Estimated yields of potato by
SRS data
A-1. Scatter-gram of Estimated yields from SRS values
A-2. Scatter-gram of Estimated yields from SRS values(different color scale from A-1)
A-3. Overlapped visual expression depicting Preceding
Crops

Fig. 6. 3D Visual Expression depicting Preceding Crops andCrops of this Year on specific Area of the fields
C-1. Check boxes of variables to visualize;o : Yearp : Field in a farmhouseq : Kind of crop, Yield, Sales of the crop, Value of
harvest on the cropr : Kind of manure, Quantity of the manures : Kind of fertilizer, Quantity of the fertilizert : Value of soil
C-2. Visualization of this year (Left), 3D Visual Expression(right)C-3. Visualization of preceding year (Left), 3D Visual
Expression (right)

Fig.5. 3D Visual Expression depicting Yields,
Starch-concentration and Preceding Crops
B-1. Visual Expression depicting Preceding Crops andEstimated yields (Height represents Estimated yield,
Color is a kind of the crop)
B-2. Visual Expression depicting Estimated starchconcentration and Estimated yields (Height represents
Estimated yield, Color is Estimated starch concentration)
B-3. 3D Physical model of B-1
B-4. Legend;a : Estimated starch concentrationb : Preceding Crops
c : Estimated yields

Fig. 7. Simulative Expression for the
Influenced-by-the-preceding-crop Model
D-1.Simulative Expression for wheat with
Influenced-by-the-preceding-cropu : Preceding cropv : Filter of “wheat” for preceding cropw : Estimated yield of wheat
D-2. Graph of differentiations ofInfluenced-by-the-preceding-crop to crop of this year
D-3. Influenced-by-the-preceding-crop Model(OUMURA et al, 1991)

3.2. Evaluation for the visualizations
Evaluation for the visualizations has been received from farmer YK and other in a few times ofmeeting. Opinions from the hearing and discussion for some visualizations are presented as figure 8.

Fig. 8. Presentation of one discussion

Opinions of Farmers for the visualizations are followings.
(1) “The visualization is similar with farmer’s image. And it demonstrates state of his field much
more clear than usual image through his working. ” This opinion is for the legible scatter-gram ofestimated yields of potato by SRS data (Fig. 4.).
(2) “It may possible to simulate relationship between Estimated starch-concentration and Yield ofpotato on the field. ” This opinion is for the 3D visual expression depicting yields,
starch-concentration and preceding crops (Fig.5.).
(3) “It is useful that system represents estimations and facts of each field.” This opinion is for the 3D
visual expression depicting preceding crops and crops of this year on specific area of the fields(Fig. 6.).
(4) “The system allows new comers to learn their work and business on agriculture concerning tocrops, fertilizers, distributions and so on.” This opinion is for the simulative expression for the
influenced-by-the-preceding-crop model (Fig. 7.).

4. Future work and Acknowledgements
Through the design development of the plant planning support system, it is figured out that SRS data
allows a various understanding of relations between estimated values and harvested facts on theirfarming. Future work of our design studies is developing cyclic model of crops and soil. The cyclic
model is able to describe as follows.
(i) Crops and fertilizers in preceding year have been comprised in values of soils.
↓(ii) The values of soils constraint plant planning of cropping and fertilizing in the next year.
↓(iii) Crops and fertilizers makes the yields of this year. And those are comprised in values of soils.
Finally, we thank partner farmers for their contribution to the design development.

Reference(1) (Okumura et al, 1991) Masatoshi OKUMURA, Syuji HIGASHIDA, “Current Status and Problemsof Research on Soil-Microorganisms in Tokachi district”, Hokunou, vol.58, No.1, PP 6-15
(2) (Hongo, 2004) Chiharu HONGO, “The 10th CEReS International Symposium on Remote
Sensing”

ANALYSIS OF SURFACE TEMPERATURE IN URBAN GREENSPACES BY USING LANDSAT TM AND ETM+ DATA
Tsuyoshi HONJO1 and Hiroshi UEDA2 and Yui NAGATANI3 and Eunmi LIM4 and Kiyoshi UMEKI5
1
Faculty of Horticulture, Chiba University 2,3,4,5 Chiba University Graduate School of Science and Technology
E-mail: honjo@faculty.chiba-u.jp
Abstract
In this study, we studied relations between surface temperature of urban green areas and factors of each green
areas shown as Tab.1. We analyzed Landsat TM and ETM+ data (Path: 107;Row: 35) and the area which
includes metropolitan Tokyo was chosen as analyzed area. Analysis of the relation of surface temperature andfactors of green areas were done by regression analysis of each factors and combination of principle component
analysis and multiple regressionss and neural network. As a result, surface temperature was obviously influenced
by three major components deduced by principle component analysis. The first component represented the effectof factors related to the sizes and the shapes of green areas, the second component represented the effect ofcoverage of vegetation and the third component represented the water. The effect of factors related to the sizes
and the shapes of green areas and thar related the water was significant for analyzing the decrease of surface
temperature. We tried to compare the method estimating surface temperature by multiple regressionss with thatby neural network. It is implied the mothod by neural network is more effective.
1. INTRODUCTION
It is well known that the surface temperature of urban green area is lower than surrounding urban area. The
decrease of surface temperature of bigger green area is higher than smaller green area. But there have been few
studies on the effect of many factors LANDSAT ETM+ data. In this study, we analyzed relations between surface
temperature of urban green areas and factors related to the shape of green area. It was recongnized that the effectof shape factors of green area was important by the following analysis.
2. METHOD
2.1 Analyzed area
For the analysis, Landsat TM and ETM+ data of Kanto area in Japan (Path:107,Row:35) taken 24th July 1987 ,7th September 1997, and 4th June 2001 was used. Analyzed area was 1120 pixels x 1120 pixels including Tokyo
metropolitan area. This area is proper for 33.6km2 as shown in Fig.1.
2.2 Classification and analyzed green area
Classification of the surface cover was done by minimum distance classification method. The analyzed area wasclassified with five kinds of covers, i. e. water, tree, grass, bare, urban as shown in Fig.1. In this study, green area
was defined as sum of the tree and grass covers. The green areas, which are more than 2 ha in Tokyo, werechosen for the analysis. The number of the green areas was 116 as shown in Fig.2.
2.3 Calculation and analysis of surface temperature
Surface Temperature, T was calculated based on following equation,
Vc = (9.299 × 10 −3 × T − 3.2) × T + 268.05 ,where Vc is CCT value of Band 6.
For each 116 green areas, twelve factors shown in Tab.1 and surface temperature were calculated. Then, relation
between the averaged surface temperature of each green area and averages of above twelve factors was analyzed
by simple regression, combination of principle component analysis, multiple regressions analysis. It was also
analyzed by neural network The result of neural network was compared to that of multiple regressions.The
neural network with a back propagation algorithm was used as a method of nonlinear multiple regressions which
has advantages over conventional linear regression. As shown in Fig.3, The network consists of 3 layers whichhave 12 units in the input layer, 1 unit in the output layer and 12 units in an intermediate layer. advantages overconventional linear regression. As shown in Fig.3, The network consists of 3 layers which have 12 units in the
input layer, 1 unit in the output layer and 12 units in an intermediate layer.

0

1

2

3

4

5

6

7

8

analyzed pixel
pixel of green or water surface
pixel of urban area

water

tree

bare

grass

urban

0

10

Fig.1 Result of classification.

20(km)

Fig.2 116 green spaces.

Tab.1 Various of green area.
Variables
Total area of greenspace(ha)Tree area of greenspace(ha)Grass area of greenspace(ha)-1Ratio of perimeter to area(m )Continuity Index(/8)
Minimum distance from center to water(km)Minimum distance from center to coastline(km)Ratio of tree within 1km range(%)
Ratio of grass within 1km range(%)
Ratio of water within 1km range(%)
Ratio of tree within 1km range except itself(%)
Ratio of grass within 1km range except itself(%)

Fig.3 Neural network.
28

20000
18000

1

27

16000

2

14000

26

12000

3

4

5

25

10000
8000

24

6000

6

7

8

analyzed pixel
pixel of green or water surface
pixel of urban area

Fig.4 Definition of continuity
index.

4000

pixels2000

M ean Tem p.(C )

23

0

22
0

1

2

3
4
5
C ontinuity index

6

7

8

Fig.5 Result of the relation between surface
temperature and the number of pixels in each index

S urface M ean T em p.(C )0pixels

NO.
①
②
③
④
⑤
⑥
⑦
⑧
⑨
⑩
⑪
⑫

Tab.2 Result of single regression analysis on the relation between surface
temperature and each variable.
D ependent V ariable: S urface T em p. (deg. C )
2001
1997
1987
R eg. C oef. C onstant C orr. C oef. R eg. C oef. C onstant C orr. C oef. R eg. C oef. C onstant C orr. C oef.
a
b
r
a
b
r
a
b
r
-0.02425.60-0.36**-0.01324.82-0.27**-0.02128.51-0.49**-0.02625.54-0.34**-0.01224.74-0.21**-0.02128.42-0.42**-0.04825.39-0.22*-0.03924.80-0.26**-0.05928.39-0.39**
112.9422.48
0.45**
63.32623.05
0.32**
77.76426.17
0.47**-0.64528.30-0.49**-0.38526.42-0.34**-0.31529.54-0.41**
0.487
24.79
0.26**
0.358
24.16
0.35**
0.336
27.67
0.50**
0.115
24.18
0.42**
0.030
24.21
0.20*
0.084
27.41
0.45**-0.06125.60-0.26**-0.01324.67-0.08-0.05328.54-0.36**-0.08425.36-0.16-0.05724.75-0.16-0.10828.39-0.34**-0.05925.48-0.39**-0.06424.88-0.52**-0.07528.43-0.63**
0.221
24.89
0.18
0.159
24.31
0.20*
-0.19128.56-0.29**
0.056
25.17
0.05
0.041
24.54
0.05
-0.09528.23-0.15* P <0.05 ** P <0.01

V aluables
①
②
③
④
⑤
⑥
⑦
⑧
⑨
⑩
⑪
⑫

Tab.3 Result of Principle Component Analysis.
Variables / Principle Component
①
②
③
④
⑤
⑥
⑦
⑧
⑨
⑩
⑪
⑫

E igen V ector of First C om ponent

E igen value
P roportion(% )C um ulative(% )

1
0.439
0.394
0.329
-0.4030.387-0.118
0.041
0.345
0.294
0.049
0.029
0.09
4.322
38
38

2001
2
-0.122-0.2530.312
0.082-0.053-0.405-0.206-0.3150.424
0.438-0.137
0.344
2.082
17.2
53.2

3
-0.142-0.1950.081-0.026
0.042
0.211
0.536
-0.1070.309-0.363
0.393
0.455
1.896
15.8
69

1
0.445
0.407
0.291
-0.3920.379-0.158
0.022
0.362
0.276
0.024
0.076
0.122
4.176
34.8
34.8

1997
2
0.128
0.242
-0.227-0.0880.076-0.098-0.3790.179-0.4820.113-0.284-0.591
2.107
17.6
52.4

3
0.021
-0.13
0.385
0.133
-0.117-0.376-0.325-0.245
0.27
0.572
-0.31
0.003
2.056
17.1
69.5

1
0.445
0.406
0.298
-0.351
0.335-0.192-0.081
0.38
0.263
0.141
0.14
0.127
4.047
33.7
33.7

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7

2001
1997
1987

1

2

3

4

5

6
7
8
N O . of V ariables

9

Fig.6 Eigen Vector of First Component with 3-year.

10

11

12

1987
2
0.153
0.284
-0.345-0.1770.151
0.18-0.1030.24-0.512-0.161-0.244-0.524
2.364
19.7
53.4

3
0.016
-0.0230.13-0.248
0.289
0.372
0.558
-0.1510.161-0.476
-0.3
0.151
1.998
16.7
70.1

Tab.4 result of multiple regressions.
D ependent V ariable: S urface T em p. (deg. C )2001
V ariables(C om ponent
12
3-0.16*
0.43**-0.31**
P art. R eg. C oef.
33.64
4.52
29.81
F value
25.21
C onstant
0.37**
C oef. of D et.

1-0.18**
16.12

1997
2
-0.15*
5.54
24.59
0.30**

3-0.32**
25.87

1-0.32**
112.22

1987
2
3
0.07
0.25**
3.56
34.86
28.16
0.57*** P <0.05 ** P <0.01

3. RESULTS AND DISCUSSION
3.1 Surface temperature and continuity index
Continuity index is Numbers of pixels of green area or water surface per 8 pixels, which surrounds a pixel ofgreen area as shown in Fig.4. The continuity index has values from 0 to 8. The value of 8 means that all thesurrounding pixels is green area. The value of 0 means that the pixel is sole green area. Relation between surface
temperature and the number of pixels in each index is shown in Fig. 5. As the value of index is larger and lager,surface mean temperature becomes lower and lower. The total number of each index keeps rising until it gets thevalue of 5, and that of 6 and 7 gets lower. But that of 8 is the largest number.
3.2 Simple regression
The result of simple regression analysis was shown in Tab.2. Total area of green space, tree area of green space,grass area of green space, continuity index and ratio of water within 1km have minus correlation with surface
temperature. On the contrary, ratio of perimeter to area, minimum distance from center to water, minimum
distance from center to coastline has plus correlation with surface temperature.
3.3 Principle component analysis
The result of principle component analysis was shown in Tab.3. The meaning of each principle components wasconsidered as follows. The first component represented the sizes and the shapes of green areas, the second
component represented the effect of coverage of vegetation and the third component represented the water. Eigen
vector of first component with 3-yaer is shown in Fig.6. Result of each year was shown a similar tendency.
Result of second and third component was also shown same outcomes.
3.4 Multiple regressions and neural network
By using the three principle components as independent variable and surface temperature as dependent variable,
multiple regressions analysis was taken and the result was shown in Tab.4. The 3-year coefficient ofdetermination was 0.37, 0.30, and 0.57 respectively. The effect of factors related to the sizes and the shapes ofgreen areas and that related the water was significant for analyzing the decrease of surface temperature. Result ofmultiple regressions and neural network in 2001 is shown in Fig.7. The scatter of the data of neural network wasless than that of multiple regressions. The result of the other year was almost same difference.

Reference
Honjo, T. and Takakura T., 1986, Analysis oftemperature distribution of urban green spaces using
remote sensing data, Journal of Japanese institute ofLandscape Architecture, 49, 299-304.
Sawada, D, Honjo, T, Maruta, Y and Kimura, K., 1986,
Analysis of surface temperature in urban green spaces
by using LANDSAT TM DATA, Journal ofEnvironmental Information Science, 16, 393-398

28
MR
NN
Calculated Surface Temp.(C)4. Conclusion
From above analysis, in the first instance, the effect ofcontinuity index was considered to be important for thedecrease of surface temperature. Secondly, Eigen vector
with 3-yaer was shown a similar tendency. Thirdly, The
effect of factors related to the sizes and the shapes ofgreen areas and thar related the water was significant foranalyzing the decrease of surface temperature. Lastly,
result of estimation by neural network was shownsuperiority to that multiple regressionss.

26

24

22
2

R MR = 0.3777
2

R NN = 0.8278
20
20

22

24

26

28

Measured Surface Temp.(C)Fig.7 result of multiple regressions and neuralnetwork(2001).

Urban monitoring using former Japanese Army maps and remote sensing- The 100 years of urban change of Jakarta city J. Tetuko S. S.1, I. Indreswari S.2, and R. Tateishi1
1 Center for Environmental Remote Sensing, Chiba University, 1-33, Yayoi, Inage, Chiba 263-8522
Japan, Tel +81(0)43 290 3840 Fax +81(0)43 290 3840, Email jtetukoss@faculty.chiba-u.jp
2 Department of Architecture, School of Graduate Study, Institute of Technology Bandung, Jalan
Ganesha 10, Bandung 40132 Indonesia, Tel +62(0)22 250 1214 Fax +62(0)22-250 8059 Emailinnes@bdg.centrin.net.id
INTRODUCTION : A BRIEF HISTORY OF JAKARTA CITY
From prehistoric time to Muslim and Hindu-Javanese kingdoms, the Jakarta area (now thecapital of Republic of Indonesia) was a small village called Sunda Kalapa in twelfth century (Simon
2003) which appears to have been a harbour for a Hindu-Javanese kingdom called Padjajaran, thecapital of which was near the present mountain resort of Bogor, south of Jakarta. A port on the
Ciliwung river (see Figure 1) emerged as an important part of Indonesian trade. The importance of
Sunda Kalapa was similarly affected as the port of Malacca on the west coast of Malaya that wasconquered by the Portuguese in 1511. The Sunda Kalapa was renamed to Jayakarta (Victorious andProsperous) by the sultanate of Banten
Then this area was started to develop by building of Dutch East India Company (VOC) fort onwest bank of the River Ciliwung in 1619 (Susan 1989). Then this area was familiar by calling‘Batavia’ and about ten thousands people were living in this small city. Traders from India, China,
England, Holland and other islands of the archipelago are recorded continuing to visit the port for
spices trading.
Total population of Jakarta (inside the wall or fort Batavia) in 1673 was recorded 27,068 people.
By the end of the eighteenth century, the VOC was bankrupt that affected the total population would
be 35,000 peoples in 1730. This economic situation was worse; hence the population of city had
dropped to 12,131, with 160,986 living in the environs, a large area extending south to themountains (Bogor area or former Buitenzorg city). In 1815, although the power of VOC declined,
the population increased slowly to be 47,000. The city was sprawling by the installing of modern
public transport, therefore the population increased to be 70,000 in 1850, and 116,000 in 1900. The
city was strung out over 10 to 12 km from north to south. By the 1930, the population of the city of
Batavia had grown to 435,000 where the immigration caused it to expand. Most of the road network
had been asphalted and public services (electricity and telephone) were established in 1940. In 1942,
Japanese occupied the archipelago and divided it into regions, and changing the capital’s name to
Jakarta that was treated as the capital of one such region, Java. 1942 to 1949 periods is the struggle
period of Indonesian for Independence of Indonesia from Dutch, and Jakarta assumed as the capitalof an independent Indonesian nation-state in December 1949. Van des Plas reported the population
was 844,000 in September 1945. After the independent and Jakarta was decided as the capital of
Republic of Indonesia, the urbanization made increasing the population that recorded 1,050,000 inIndonesia

Sumatera island
JakartaJava island
Indian Ocean
6o04’ S
north
Jakarta

Cisadanewest Jakarta

Ciliwung

centralJakartaAngke

Bekasieast
Jakartasouth
Jakarta

6o22’ S
107o00’ E

106o40’ E

Figure 1. Study site : Jakarta city, Indonesia and its environment

population [millions]

10
8

Legend64
20

Table 1 Population of Jakarta city

2004

2000

1995

1990

1980

1971

1961

1952

1948

1930

1900

1815

year

:

Statistics (BPS):

Susan 1989

photographs

old maps

VOC 1887

former JapaneseArmy maps 1927

Ground survey

digital scanning

Joint MappingIndonesia-US 1950

satellite imageries

Landsat MSS(19760621)

KH-7 / Gambit(19670526)

Geometric correction

GIS processing

Landsat TM(19890503)

Urban area coverage

Figure 2 Flowchart of analysis
1948, almost double the figure for 1930. President Soekarno’s visions had little relevance to thedominant fact of Jakartan life in the period, official figures show the the population was increasing
drastically 1,782,000, 2,973,000 and 3,813,000 in 1952, 1961 and 1965, respectively. Base on thecensus report of Indonesian Govermental Statistics (BPS 2005), the population in 1971, 1980, 1990,
1995, 2000 and 2004 are 4,579,303; 6,503,449; 8,259,266; 9,112,652; 8,389,443; and 9,792,000,
respectively. The population in 2000 decreased comparing to 1995, it is assumed the impact ofAsian crisis in 1997. The population in 2004 was increasing again by the economy recovery for thecrisis. The population trend of Jakarta city from 1815 to 2004 can be seen in Table 1.
The Statistics shows that the urban area coverage of Jakarta 93,7% in 1980, and 100% after
1990s (see Table 2), where total area is 661 km2. The data shows the lack of information of urban
area coverage before 1980. Therefore, in this research, old maps and satellite images were
employed to obtain the urban area coverage. The detail analysis will be explained next.
STUDY SITE
Figure 1 shows the study site, Jakarta city (capital of Republic of Indonesia) that located ino
106 40’E - 107o00’E, 6o04’S - 6o22’ S and covering about 661 km2. The area around the mouth of
the Ciliwung river in west Java, the site of present-day Jakarta, has known human settlement fromprehistoric times. Built up from the silt washed down from the volcanic mountain range to the south,an alluvial plain spreads out in a fan shape traversed by several rivers: Cisadane, Angle, Ciliwung,Bekasi and Citarum.
ANALYSIS
The urban area change of Jakarta city is investigated by using old maps and satellite images.
The employed old maps are VOC (1887), former Japanese Army map (1927), and Joint MappingIndonesia - US 1950 maps. Especially, the former Japanese Army map is composed or mosaickedby 11 maps (Gaihozu 1927) as shown in Figure 3. Jakarta city boundary in this Figure shows thepresent boundary of Jakarta. Then the satellite images are KH-7 / Gambit (26 may 1967), Landsat:
Jakarta cityboundary

Figure 3 Mosaic maps of the former Japanese Army

1887

1927

1950

19670526

19760621

19890503

Figure 4. Urban area change of Jakarta city in time seriespercentage of urban area [%]

100

80

Legend60:

Statistics (BPS):

Estimated results

40

20

0
2004

2000

1995

1990

1980

1976

1967

1950

1936

1887

year

Table 2 Urban area of Jakarta in time series
MSS (21 June 1976) and Landsat TM (3 May 1989).
As shown in Figure 2, firstly the old maps are scanned. Secondly, the maps were geometriccorrected before digitizing process (visually) to obtain the urban area class. The satellite images arealso geometric corrected, then supervised classification process was employed to acquire the urban
area class. The topographic maps (Bakosurtanal 2001) with 1:25,000 scale were used in thegeometric correction.Then the urban area class only was delineated to obtain the urban area
distribution more clearly. Base on the digitizing or delineation process, the coverage of urban city ofJakarta in each date could be acquired as shown in Figure 4. This Figure shows (visually) that theurban area was increasing drastically after 1945 or the independent year of Republic of Indonesia.
Figure 4 shows that the urban area coverage is 8%, 13%, 21%, 32% and 64% in 1887, 1927, 1950,
1967 and 1976 respectively. Base on Landsat TM data (3 May 1989), the coverage in 1990s is
almost 90% or matches well with the statistics data. Table 1 and Table 2, or the population andurban area change of Jakarta city respectively show the strong relationship. These tables mean theincreased population caused the sprawling of urban area.
CONCLUSIONS
Like many big cities in developing countries, Jakarta city has almost 250 years history andsuffers from major urbanization problems. The population has sharply risen after 1960s, and baseon the old maps and satellite images extracting the urban area, this area covers whole of city (661
km2) in 40 years after the independent and the declaring of Jakarta as the capital of Republic ofIndonesia. The result shows that the old maps (1887 - 1950) include former Japanese Army maps
(Gaihozu), and satellite images (1967 - 1989) combination can be employed to monitor the city
sprawling and its problems.
In the near future, the authors will employ these data and Geographical Information System(GIS) to retrieve the city spatial information and its change. The information of urban area,
vegetation, digital elevation model (DEM), annotation, transportation network and hydrologic

network will be retrieved from the former Japanese Army map to obtain the topographic
information of 1900s. The high resolution of satellite images also will be employed to monitor thearea around Jakarta city or known as buffer zone of Jakarta (Bekasi, Bogor, Tangerang, and Banten)
called Jakarta Megapolitan area.
ACKNOWLEDGEMENT
Thank to the Museum of Natural History, Tohoku University to the Gaihozu; Pandhito PanjiFoundation - Remote Sensing Research Center for old maps; and University of Maryland forLandsat datas.
REFERENCES
Bakosurtanal, 2001, Jakarta 1209-441, Tangerang 1209-432, Cakung 1209-442, Pasar Minggu
1209-423, Pondok Gede 1209-424, Teluk Naga 1209-434, Ancol 1209-443, Tanjung Priok
1209-444
BPS, 2005, Indonesian Statistics Report.
Gaihozu, 1927, Blad 36/XXXVIIA (oud No.17A) Maoek, 36/XXXVIIC (oud No.17C) Tangerang,
36/XXXVIIIA (oud No.18A) Paroeng Pandjang, 36/XXXVIIB (oud No.17B) Batavia,
36/XXXVIID (oud No.17D) Kebajoran, 36/XXXVIIIB (oud No.18B) Paroeng, 37/XXXVIIA(oud No.23A) Tandjoeng Priok, 37/XXXVIIC (oud No.23C) Meester Cornelis, 37/XXXVIIIA(oud No.24A) Depok, 37/XXXVIIB (oud No.23B) Moeara Bekasi, 37/XXXVIID (oudNo.23D) Bekasi, 24B Tjibaroesa
Simon Winchester, 2003, Krakatoa - the day the world exploded: August 27, 1883, Sterling Lord
Literistic
Susan Abeyasekere, 1989, Jakarta : A History, Revised edition. Oxford University Press.

APEX, a tool for the simulation, calibration and validation ofEarth observation sensors
Jens Niekea,b, Yoshiaki Hondab, Hiroshi Murakamic , Daniel Schläpfera , Nobuo Takeuchib, Klaus I. IttenaaRemote Sensing Laboratories (RSL), Winterthurerstr. 190, University Zurich, CH-8057 (CH)bCenter for Environmental Remote Sensing (CEReS), University of Chiba, 1-33 Yayoi, Chiba, 2638522 (JP)cEarth Observation Research and application Center (EORC), Japan Aerospace Exploration Agency(JAXA), 1-8-10, Harumi,Tokyo 104-6023 (JP)Abstract:
In the next decades, JAXA plan to launch a series of GCOM satellites. Onboard the GCOM-C satellite the SGLI
sensor will enable global observation of the environment and human activities. The successful calibration andvalidation of space sensors (such as SGLI) is crucially important and is depending on many factors, such as rigid
ground-truth campaigns, the validation of the product algorithms. Also the transfer of ADEOS-2 GLI algorithms to
the SGLI project needs a special validation effort. The airborne imaging spectrometer APEX is able to support
these activities since it was especially designed for the calibration, validation (CalVal) and simulation of spacesensors. In this paper the binning pattern are discussed to cover the spectral bands of the former GLI sensor in away, it promises best results.
1. Introduction
The SGLI sensor series onboard the GCOM-C satellite will enable continuous observation of climate change andhuman interaction over 13 years from the year 2011. To cover the time range of 13 years, three series of satellites
are planned to be launched every four years assuming a lifetime of each of the satellites of around five years andconsidering one year overlap for cross calibration between satellites. The APEX sensor is an airborne imaging
spectrometer to be flown on various platforms, such as the DO-228, HALO. Within the APEX project a new
exchange node of scientific ideas, methods and results, the APEX Science Center (ASC) is currently set-up, whichallows the exchange of scientific knowledge in the science community using the open source code development.
The APEX instrument has during operations two spectral modes, i.e., a spatial mode (about 300 spectral bands)
and a spectral mode (about 500 spectral bands). While for the SWIR channel all 199 can be read-out
simultaneously, the APEX VNIR channel has 312 spectral bands, where only 114 bands can be read out in spatialmode, i.e., 312 bands have to be binned in a way that is optimal for various applications, while offering
appropriate Signal–to-Noise Ratio (SNR) performance. In this study, binning patterns for the various spacesensors1 simulation (up/down-scaling), such as GLI/SGLI, MODIS or MERIS, are proposed. For the crosscalibration also the edges of the Relative Spectral Response functions (RSR) of the space sensor bands have to betaken into considerations using weighted software binning methods.
2. Satellite Sensor SGLIGCOM-C is a JAXA mission on environmental remote sensing, enabling global change monitoring over a long
duration of 13 years. The SGLI sensor is the core sensor of this mission dedicated to environmental remote sensing
in the spectral range from 0.38 to 12 micron with a spatial resolution of 250 m to 1 km2,3. In the following weassume similarity of SGLI and GLI4 spectral bands.
3. APEX – the airborne simulator
APEX5,6 is an dispersive pushbroom imaging spectrometer operating in the spectral range between 380 - 2500 nm.
The spectral resolution will be better than 10 nm in the SWIR and < 5 nm in the VNIR range of the solar reflected
range of the spectrum. The total FOV will be ± 14 deg, recording 1000 pixels across track with about 300 spectral
bands simultaneously. A large variety of characterization measurements will be performed in the scope of the
APEX project, e.g., on-board characterization, frequent laboratory characterization, and vicarious calibration. The
retrieved calibration parameters will allow a data calibration in the APEX Processing and Archiving Facility (PAF).
The data calibration includes the calculation of the required, time-dependent calibration coefficients from thecalibration parameters and, subsequently, the radiometric, spectral and geometric calibration of the raw data.
4. Approach
To analyse the binning pattern for SGLI/GLI an approach was selected having the following steps:
1. For the calculation of all 312 peak-normalized APEX VNIR-channel RSR, the Spectral Sampling Interval (SSI),$  W



: G

G

6

W

E

    

the Center Wavelength (CW) and the ratio between SSI and the spectral band width (Dlambda) was used.
2. Calculation of APEX VNIR 312 RSR assuming Gaussian distribution using the above values.
3. All Gaussian RSR are plotted vs. the wavelength.
For the comparison with space sensor RSR, the following narrow bands (Dlambda ~10nm) were used: GLI (band 1– 19), MERIS (band 1 – 15) and MODIS (band 8 – 19).
Note, for the simulation of broad-band RSR for sensors, such as ETM+ and SPOT, the 10-nm bands can be binned
afterwards in the PAF.
3. Visualization of all APEX, MERIS and GLI RSR in 13 regions of interest.
4. Visual decision process selecting the appropriate APEX bands for each of the GLI, MERIS and MODIS bands.
Note, this selection is still preliminary and has to be re-evaluated during future scaling and CalVal activities.
5. Calculation of the in-band irradiance values for all space sensor RSR using Thuillier-2003 data (E0)7.
6. Calculation of the in-band irradiance values of binned APEX RSR representing 15 MERIS and 19 GLI bands.
7. Solar irradiance data and the in-band irradiance of 15 MERIS bands and 19 GLI bands are plotted in absolute scaleand relatively as APEX (E0) / E0 (Meris, GLI) (see Figure 3).

$

: G

G

6

E

Figure 1: In the region of MERIS band 1 and GLI band 1,2,3 all APEX RSR are plotted vs wavelength.

Figure 2: In the region of MERIS band 2,3 and GLI band 4,5,6, all APEX RSR are plotted vs wavelength.
5. Results
The results of the RSR calculations and the visualization of both, APEX bands and space sensor bands, and finallythe evaluation of the binned pattern are outlined.
In Figure 3, the selection of the APEX binning pattern were evaluated. For the evaluation the in-band irradiance of

the 15 MERIS, 19 GLI bands and the corresponding APEX binned bands were calculated using the following
equation:max

E

Thuillier2003(  )RSRband ( )d



in _ band _ irradiance = E 0Band = min

max

 RSR

band

(  )d

min

EL

L

91,5 

GI 

: G

G

6

W

E

    

When plotting the ratio, E0(APEX)/E0(MERIS,GLI), ideally, the values should be around 1. All MERIS (andmost of the GLI) in-band irradiance values are within the ±1% error bar. GLI bands 1,4,7 are underestimated by
APEX binned bands and GLI band 14,15 are overestimated, however all bands are within the ±2% error range. The
RSR of the outliner GLI band 1 (-7%) starts at 375 nm, thus the first APEX binning band is not able to cover theentire spectral range of this band (see Figure 1).

Figure 3: Solar irradiance data and the in-band irradiance of 15 MERIS bands and 19 GLI bands are plotted inabsolute scale and relatively.
6. Conclusion
In this paper various binning patterns for APEX are proposed. This binning pattern will allow the simulation(up/down-scaling) of space sensors including the narrow spectral bands (~10nm) of GLI (and MODIS, MERIS).
The results of the selection process are still preliminary, since these findings must be re-evaluated during future
scaling and CalVal activities. The patterns are also validated for the retrieval algorithms of vegetation, water, soilrelated parameter products.
Acknowledgement
Part of the work in this paper has been carried out under ESA/ESTEC contract no. 15440/01/NL/Sfe. The authors
would like to express their appreciation to the JSPS, SNF and the Universities of Zurich and Chiba, enabling thevisit of JN at CEReS.
References
1

Nieke, Schwarzer, Neumann, Zimmermann; Imaging Spaceborne and Airborne Sensor Systems in the Beginningof the Next Century; SPIE Vol. 3221 (1997)
2
Kimura, Tange, Tanaka, Itoh, Matsuyama, Okamura, Kasahara, Honda; Concept study of multispectral imager
for next generation; SPIE, Vol. 5570 (2004)3
Honda, Y.; SGLI for global change monitoring; Geoscience and Remote Sensing Symposium, IEEE 2001
proceedings, Vol. 6 (2001)
4
Nakajima T.Y., T. Nakajima, M. Nakajima, Fukushima, Kuji, Uchiyama, Kishino; Optimization of theAdvanced Earth Observing Satellite II Global Imager channels by use of radiative transfer, calculations; Appl.
Opt., 37, 3149-3163 (1998)
5
Itten, Schaepman, De Vos, Hermans, Schlaepfer, Droz; APEX – Airborne PRISM Experiment: A new concept
for an airborne imaging spectrometer, Proc. ERIM, Vol. 1 (1997)
6
Nieke, Itten, Debruyn, et al., The imaging spectrometer APEX: from concept to realization, eproceedings
EARSeL (2005)
7
Thuillier et al., Solar Physics 214: 1–22 (2003)

A New Approach Using Various Remote Sensing Data For Vegetation
Parameter
Retrieval As Input To Ecosystem Models
Silvia Huber, Jürg T. Schopfer, Mathias Kneubühler,
Jens Nieke, and Klaus Itten
Remote Sensing Laboratories (RSL),
Dept. of Geography, University of Zurich,
Winterthurerstrasse 190, 8057 Zurich (Switzerland).
Email:shuber@geo.unizh.ch
Abstract
The objective of the following outline is to propose a method of combining various remote sensing data and to point out
the potential of diverse remote sensing data as an input to local, regional or global ecosystem models. The combinationof different data sources including several observation geometries and an ensured comparability of the retrieved data
products demand for a uniform baseline of normalized ground reflectance data (geometrically, atmospherically, and
BRDF corrected), which also provides the possibility of comparison between different sensors and observationgeometries for calibration/validation purposes. Possible disadvantages of a spatial and spectral information loss have to
be assessed, being a result of the necessary resampling and spectral convolution.
1.

Introduction

Reflectance measurements are usually performed at a local scale and on a specific vegetation surface and therefore
exhibit a limited significance for larger areas. During a field campaign of RSL in July 2004, a large dataset ofspaceborne (MERIS, MODIS), airborne (HYMAP), and ground spectral information was acquired at the same time andof the same area. By combining this spectral information obtained by different sensors with different characteristics, thegeneration of a value added product is possible. The objective here is to propose an approach of how multi-source
remote sensing data can be used as an input to local, regional or global ecosystem models. Therefore it is necessary to
generate a uniform baseline of normalized ground reflectance. Various ground information is necessary either forcorrection or validation purposes. A BRDF correction is done by using spectro-directional field measurements, whichwere performed with the RSL field goniometer system FIGOS [1] or by using modeled HDRF data. Further reflectanceinformation of different vegetation surfaces is gathered by an ASD field spectroradiometer. Validation of the retrieved
model output parameters is assured by using various measured structural and biochemical vegetation parameters.
The Vordemwald study area (E 47°16’ N 07° 53’) is located in the canton of Aargau, Switzerland and covers an areaof approximately 50 km2 of mixed forest, cultivated fields and grassland. The field campaign was conducted by RSL incollaboration with the Swiss Federal Institute of Forest, Snow and Landscape Research (WSL).
2.

Data

Field measurements were performed in the second half of July 2004 at six different sites in the forest of the
Vordemwald study area. The forest consists of two main coniferous species (Picea abies and Abies alba) and fivedeciduous species (Fagus sylvatica, Acer pseudoplatanus, Fraxinus excelsior, Quercus robur, and Quercus rubra). At
each plot the trees were georeferenced, diameter at breast height (DBH), tree height, crown base, and crown dimension
were measured. Three top of canopy branches were excised by a tree climber from each sampled tree. The plantmaterial from the treetop is important in order to compare remotely sensed data with ground truth information. A
handful of leaf samples were packed from each tree, stored in a cool environment, and transported to the lab for avariety of analyses such as chlorophyll, specific leaf area (SLA), leaf water content (LWC), and C:N ratio.
Hemispherical photographs were taken to determine Leaf Area Index (LAI) and Gap fraction [Figure 1]. The photos
were acquired with a digital camera and a hemispherical lens converter. Additionally, a gas exchange experiment was
conducted in the field to measure the leaf net CO2 uptake as well as the stomatal conductance.
The leaf optical properties (leaf reflectance and transmittance) of broadleaf plant types representative for the studysite were measured. Spectral properties of single leaves are essential to understand the interaction of the incident
radiation with the vegetation. The optical properties provide the critical link between remote sensing data on the canopy
scale and the physiological energy and mass transfer processes on leaf level.

The acquisition of spectral measurements involved an ASD field spectroradiometer coupled to an integrating sphere anda custom made light source for improved illumination. Both adaxial and abaxial sides of the foliage elements wereconsidered for reflectance and transmission measurements.

Fig. 1. Hemispherical photograph for the retrieval of
canopy geometrical structure.

Fig. 2. Anisotropy factor of the observed meadow.

In order to validate and correct the acquired airborne and spaceborne at-sensor radiance with respect to the observation
directions, spectro-directional measurements were performed with the RSL field goniometer system (FIGOS). FIGOS
was therefore placed on a meadow at the intersection area of all three flight lines. The albedo normalized anisotropy
factor ANIF of the observed meadow exhibits a slight bowl shape. The typical reflectance peak for vegetation in thebackscattering region is less dominant than expected [Figure 2]. This effect might occur due to the specific constituentsof the meadow, which included some white clover (Trifolium repens) leading to a higher reflectance at large
observation zenith angles. Simultaneously, the direct and diffuse irradiance was measured with a MFR 7 sunphotometerin order to correct the measured hemispherical directional reflectances for the diffuse part of the incoming radiance [2].
HyMap airborne hyperspectral imagery was recorded on July 29, 2004 during excellent weather conditions. HyMap
is an imaging spectrometer providing spectral coverage in the visible, near, shortwave and middle infrared in 125 bands
with a bandwidth of 10–20 nm at 5 m spatial resolution. Three flight lines were acquired over the test site, two in
direction NS and one in direction WE, in order to obtain spectro-directional information. Additionally to the imaging
spectrometry data, MERIS (Full Resolution Level 1B) and MODIS (Level 1B) data were acquired. MERIS data was
obtained one day before the HyMap and MODIS acquisition but under comparable weather conditions [Figure 3].

Fig. 3. a) MODIS 250 m spatial resolution, false color,b) MERIS 300 m spatial resolution RGB, andc) HyMap NS flight lines with 5 m spatial resolution.

Fig. 4. Flowchart outlining the proposed approach for
two the usage of various remote sensing data as input
to local, regional or global ecosystem process models.

3.

Methods

To obtain ground reflectance, the acquired reflectance data of every sensor has to be atmospherically and geometricallycorrected. The airborne image data is orthorectified based on the parametric geocoding procedure PARGE [3]. The
parametric geocoding methodology considers the terrain geometry and allows for the correction of altitude andflightpath-dependent distortions in airborne imagery. The approach relies on several auxiliary data such as Inertial
Navigation System parameters, Ground Control Points (GCP), and a Digital Elevation Model (DEM) describing theterrain. Orthorectified imagery is a prerequisite for the subsequent radiometric correction. The radiometric correction of
the geocoded and orthorectified imagery is performed using ATCOR4 [4], a software tool to invert the radiative transfer
code MODTRAN4. ATCOR4 provides surface reflectance and temperatures performing a combined
atmospheric/topographic correction where the effects of terrain of the observed surface are accounted for. The proposed
approach for integrating various remote sensing data is given in Figure 4.
Classification: After preprocessing the data, main landcover classes have to be determined. Classification is aprerequisite for the subsequent landcover specific BRDF correction, which is performed using either measured ormodeled HDRF data. Local spectro-directional observations are extrapolated across larger areas to retrieve corrected
information.
Baseline: The atmospherically, geometrically and directionally corrected reflectance data are called baseline or
normalized ground reflectance. This dataset serves as input to various semi-empirical and empirical techniques (e.g.
vegetation indices, position of the inflection point of the red edge), physically based models (e.g. Prospect [5], Sail [6]),and analytical methods (e.g. Principal Component Analysis) for the acquisition of surface variables required byecological models.
Harmonization: Since reflectance data of various sensors with different spectral and geometrical characteristics are
combined, a convolution of the spectral data to the necessary bandwidth must be performed as well as a spatial
upscaling to a common pixel size.
Validation: The retrieved spatial estimates of vegetation parameters are validated against ground truth data.
The verified spatial information can then be implemented into an ecosystem process model (e.g. Biome-BGC [7],PnET [8]). Ecosystem process models are important tools for applying the information provided by remote sensing
products to quantify fluxes of carbon and other elements. Physiologically based process models, applied in a spatiallydistributed mode, can assimilate and effectively integrate a diverse assemblage of environmental data, including
information on soils, climate, and vegetation [9].
4.

Conclusion

The proposed approach shows the potential of combined remote sensing data in various model ouputs. Remote sensing
data can be acquired with various instruments (sensors) and methods, leading to a large and heterogeneous dataset. In
dependence of the research objective, the appropriate sensor or method is chosen in order to minimize disadvantages.
But there lies also a great potential in the combination of multi-source remote sensing data both for correction purposesof sensor artefacts or directional effects and for the generation of an added value product.
The proposed approach reveals the following advantages:- the generation of a remote sensing based input to regional and global models, which is spatially continuous and daily
repeatable,- retrieval of vegetation parameters over large areas, including spatial information on temporal dynamics- validation/calibration of different sensors and observation geometries and- datafusion in order to correct the image for bad pixels or clouds.
Possible disadvantages that strongly depend on the requested scale of the research objective are:- a spatial and spectral information loss occurs due to the resampling and convolution process, which has to be
performed,- possible influence of a limited model performance (e.g. for modeled BRDF).

Acknowledgment
The authors would like to thank the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), DLR, andthe Swiss National Science Foundation.
References[1] Sandmeier S. and K.I. Itten, A Field Goniometer System (FIGOS) for Acquisition of Hyperspectral BRDF Data,
IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, 978–986, 1999.
[2] Schopfer, J.T. et al., Comparison of Field and Laboratory Spectrodirectional Measurements Using an Artificial
Target, Proceedings of 11th SPIE International Symposium on Remote Sensing, Vol. 5570, 626–633, 2004.
[3] Schlaepfer, D. and R. Richter, Geo-atmospheric Processing of Airborne Imaging Spectrometry Data. Part 1:
Parametric Orthorectification, International Journal of Remote Sensing, Vol. 23, 2609–2630, 2002.
[4] Richter, R. and D. Schlaepfer, Geo-atmospheric Processing of Airborne Imaging Spectrometry Data. Part 2:
atmospheric/topographic correction, International Journal of Remote Sensing, Vol. 23, 2613–2649, 2002.
[5] Jacquemoud, S. and F. Baret, PROSPECT: a Model of Leaf Optical Properties Spectra, Remote Sensing ofEnvironment, Vol. 34, 75–91, 1990.
[6] Verhoef, W., Light Scattering by Leaf Layers with Application to Canopy Reflectance Modeling: the SAIL Model,
Remote Sensing of Environment, Vol. 16, 125–141, 1984.
[7] Running, S.W. and E.R. Hunt, Generalization of a Forest Ecosystem Process Model for other Biomes, Biome-BGC,
and an Application for Global Scale Models. Scaling Processes between Leaf and Landscape Levels, Scaling
Physiological Processes: Leaf to Globe, Academic Press, San Diego, 141–158, 1993.
[8] Aber, J.D. and C.A. Federer, A Generalized, Lumped-Parameter Model of Photosynthesis, Evapotranspiration and
Net Primary Production in Temperate and Boreal Forest Ecosystems, Oecologia, Vol. 92, 463-474,1992.
[9] Turner, D.P. et al., Integrating Remote Sensing and Ecosystem Process Models for Landscape- to Regional-Scale
AnalysisoftheCarbonCycle,
BioScience,Vol.
54,
573–584,
2004.

Application of Regression Tree Method for Estimating Percent Tree
Cover of Asia with QuickBird images as training data
Rokhmatuloh1*, H. Al-Bilbisi1, K. Arihara1, T. Kobayashi1, D. Nitto1, B. Erdene1, K. Hirabayashi1, T.A.
Javzandulam1, S.A Lee1, E. Migita1, N. Soliman1, Y. Ouma1, M. Aslam2 and R. Tateishi1
1

Center for Environmental Remote Sensing (CEReS), Chiba University, Japan2
Department of Post-Graduate Studies and Research in Geology,
Government College, Kerala, India*
Email: rahmat@graduate.chiba-u.jp

Abstract
Regression tree is more robust than linear regression method, primarily due to its capabilityof approximating complex non-linear relationships using a set of linear equations. The
coarse resolution MODIS data were used as a predictor variable and as a training data, 22
scenes of very-high resolution QuickBird satellite were employed. Stepwise Linear
Regression (SLR)-selected variables produced good result with prediction error of 5.70
percent and high correlation coefficient of 0.97. The zero percent tree covers wide areas insouth-western parts of Asia, while 100 % tree distributed in south-eastern of Asia. This
study also shows that regression tree method is more robust to overcome cloud problem
existing in raw MODIS data, e.g. in the Equator areas.
Keyword: regression tree, predictor variable, training data, QuickBird, prediction erros

1. Introduction
Carbon dioxide (CO2) in the atmosphere is a greenhouse gas that contributes significantly to
global warming [1]. One possible strategy to reduce the green house gases with great
potential is to use trees to sequester CO2 [2]. The growing trees remove CO2 from theatmosphere through the process of photosynthesis and store the carbon in plant structures[3]. Proportions of tree coverage are intended to be used within carbon models to improve
our understanding of carbon dynamics at regional or global scales [4]. Different percent
tree covers store different amounts of carbon and the changes in tree cover are used in themodel to calculate annual changes of carbon [5][6]. Tree cover is the percentage of theground surface area covered by a vertical projection of the outermost perimeter of thenatural spread in the plants’ folliage; small openings in the crown are included [7].
Previous studies to estimate tree cover percentage have utilized spectral mixture analysis(SMA) [8] and conventional linear regression techniques [9]. The SMA and linear
regression techniques use linear model to approximate the relationship between spectralsignal and canopy cover. However, such relationships are often very complex and highlyvariable, especially over large areas. A separate study of percent tree cover mapping was
performed by the scientists from the University of Maryland (UM) using 500 m MODIS
data [10]. This study applied regression tree method, which was subsequently modified by a
stepwise procedure and bias adjustment. However, for training data, they used Landsat MSS(60 m) and TM (30 m) data where discrimination of tree and non-tree is relatively difficult.
For each training data, each pixel was assigned one of four tree cover strata (0, 25, 50 or
80 %). Therefore, high resolution images, such as IKONOS or QuickBird, are required forbetter discrimination between tree, herbaceous, grass, urban and bare in the mix-land coverareas e.g. in [11][12].
This study describes an effort to develop a method to map percent tree cover of Asia withvery-high resolution of QuickBird images as the training data or predicted variable. The
explanatory or predictor variables were extracted from MODIS data. The percent tree cover
information in annual basis is an important parameter in order to implement the Kyoto
Protocol as committed by the United Nations Framework Convention on Climate Change(UNFCC) [9]. The percent tree cover estimation was accomplished using regression tree
method. The regression-tree method estimates a case’s target value in terms of its attributevalues by constructing a model containing one or more rules, where each rule is aconjunction of conditions associated with a linear expression [13][14]. One advantage of theregression tree is the ability to effectively use proportion or continuous predictor data setswith different measurement scales [13].
2. Study Site and Data Acquisition

Fig. 1 Location of QuickBird scenes as training data

The study site is Asia, ranges from 700 U to 120 S and 250 E to 1800 E and covering about 30
percent of the global land area (Fig. 1). To cover large area with daily acquisition, the coarse
resolution of MODIS/TERRA nadir BRDF-adjusted reflectance 16 day L3 global 1 km SIN
grid product (MOD43B4NBAR) data (MODIS) with seven bands (band 1 - 7) starting fromJanuary 1 to December 31, 2003 were used as input. The MODIS data were acquired from
USGS in 10°x10° of Latitude/Longitude format. This data were then mosaicked andreprojected into Geographic Latitude/Longitude to have the same projection as QuickBird
data. The 16-day MODIS bands data were composite to 32-day (monthly) data using
maximum value (MVC) formula [15]. In addition to these monthly data, NDVI, Enhanced
Vegetation Index (EVI) [16] and Normalized Difference Soil Index (NDSI) [17] were also
extracted from MODIS data. Land Surface Temperature (LST) 8-day composite 1-km withsame period as MODIS 16-day was also included in the explanatory or predictor variables
for estimating percent tree cover [18]. The LST data are required to stratify geographicallybetween dry woodland and moist forest in Asia [19]. This LST data contains two timeobservations: day and night data.
Table 1. QuickBird scenes employed in this study (22 scenes)
Scene
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22

Acquisition
Date(Y/M/D)
2003/06/05
2003/06/05
2003/06/14
2003/09/25
2004/01/28
2004/01/07
2002/12/04
2002/12/01
2002/11/30
2004/02/02
2003/10/14
2002/07/10
2004/08/03
2004/06/27
2002/06/12
2003/09/10
2004/07/13
2002/07/19
2002/08/07
2004/06/27
2003/08/15
2002/08/29

CenterCoordinate(Lat/Long)
35.17/139.86
35.66/140.03
33.93/107.28
25.09/106.25
25.60/95.75
22.52/91.85
22.14/89.16
19.10/84.20
14.97/74.43
21.12/70.92
18.64/42.08
48.56/46.38
62.68/40.90
64.54/55.67
64.94/57.21
61.76/52.30
54.85/57.30
56.57/85.71
55.85/92.83
62.02/102.65
67.36/101.70
66.33/111.60

Off-NadirAngle(degrees)
27
27
14
9
8
2
5
11
9
4
13
4
10
7
5
5
8
10
6
5
9
8

GLC2000`s Land Cover Classes
Tree cover, needle-leaved, evergreen
Artificial surfaces and associated areas
Tree cover, broadleaved, deciduous, closed
Shrub cover, closed-open, evergreen
Tree cover, broadleaved, evergreen
Shrub cover, closed-open, evergreen
Tree cover, regularly flooded, saline water
Tree cover, broadleaved, evergreen
Tree cover, broadleaved, evergreen
Tree cover, broadleaved, deciduous, open
Sparse herbaceous or sparse shrub coverSparse herbaceous or sparse shrub cover
Tree cover, broadleaved, deciduous, closed
Tree cover, needle-leaved, evergreen
Tree cover, needle-leaved, evergreen
Tree cover, needle-leaved, evergreen
Tree cover, broadleaved, deciduous, closed
Tree cover, broadleaved, deciduous, closed
Tree cover, needle-leaved, evergreen
Tree cover, needle-leaved, deciduous
Bare areas
Tree cover, needle-leaved, deciduous

For the training data, 22 scenes of QuickBird data were chosen to represent different land
cover types according to Global Land Cover (GLC) 2000 map of Asia (Fig. 1 and Table 1)with each scene of QuickBird data covers an area of about 5 x 5 km in length and wide.
QuickBird data is satellite imagery, which records high-resolution data in the visible andnear-infrared range. The QuickBird data contains: multispectral data with 2.44 m resolution
and panchromatic data with 0.60 m resolution. The multispectral bands are blue (450 – 520nm), green (520 – 600 nm), red (630 – 690 nm), and near infrared (760 – 900 nm). For thisstudy, the QuickBird pan sharpened 0.61 cm resolution data were used. The list of
QuickBird scenes employed in this study is described in Table 1.
3. Methodology
Modeling an empirical relationship between percent tree cover as a predicted variable and
MODIS 1 km data as a predictor or explanatory variables was accomplished using
regression tree methods. The models were then applied to all pixels in a mapping area toproduce a per-pixel estimation of a percent tree cover over Asia. This method quantifies thespatial distribution of tree cover as a continuous variable from 0 to 100%, and offers arepeatable technique to characterize percent tree cover for global area.
The main approach in this study consists of four steps:
1. Deriving percent tree data from very-high resolution QuickBird images using
unsupervised clustering or supervised classification and visual interpretation,
2. Constructing predictor variables such as surface reflectance, NDVI, EVI, NDSI, andLST derived from MODIS data,
3. Selecting the potentially most useful predictor variables based on Cp statistics, andfinally
4. Extrapolating spatially the developed models from Cubist to the entire 1-km MODIS dataof Asia using National Land Cover Database (NLCD) mapping tool [20]. Fig. 2
illustrates the regression tree method applied in this study.
Percent tree covers, which then will be used as the ground truth data, from QuickBird scenes
were obtained firstly, by clustering or supervised classification method. Then, this result will
be refined by on-screen digitizing method in the areas where tree and non-tree were difficult
to be separated each other. The predictor variables were extracted from monthly-composited
MODIS data that consists of surface reflectances of band 1 to band 7, NDVI, EVI, NDSI
and LST starting from January 1 to December 31, 2003. The selection of possible predictor
variables is often a difficult process and expert knowledge is required. For this purpose,
Stepwise Linear Regression (SLR) method [21] from S-PLUS [22], which is one of theclassification and regression tree software, was accomplished and Cp statistic for eachvariable was examined. The SLR method selects the best subset of predictor variables to be
employed in regression tree model using a stepwise procedure, which repeatedly altering themodel at the previous step by adding or removing a predictor variables [21]. Cp statistic
provides a convenient criterion for determining whether a model is improved by adding anddropping the predictor variables [22]. The Cp statistic specifies which predictor variables aresignificantly relevant to percent tree cover prediction. The Cp statistics is expressed in [21]as:2

Cp = p +(n − p)( s p − σ 2 )(3)σ2where n is the number of observations (number of training data), p is the number of2
coefficients (number of predictor variables plus one), s p is the mean square error (MSE) of
the prediction model, and σ 2 is the minimum MSE among the possible models which is thebest estimate of the true error [21][22]. The best-selected predictor variables were then used
for constructing model in Cubist by analyzing the relationships within the data and createdan appropriate regression tree and rule set.
For evaluating the developed model, a total of 125 cells were separated from the total
ground truth data (526 cells) in order to validate the model. The rules created from thedeveloped model in Cubist were then extrapolated spatially to the entire 1-km MODIS data
to produce a final percent tree cover map of Asia using NLCD mapping tool (see Fig. 2).
The rules were applied according to the pixel values within the corresponding input imagery
of predictor variables.
MODIS 2003 16-daycomposite
Convert projection to
Latitude/Longitude
Per-pixel cloud analysis
Overlay 2003 data with 2002
and/or 2004
Re-composite into 32-day(monthly) using MVC
Extract tree andnon-treefrom QB

Extract predictor variables

Selecting predictor variables
using SLR method
Creating tree model using
CubistPercent tree estimation using
NLCD mapping tool
Percent tree cover map

Fig. 2 Cloud contamination reduction and regression tree methods applied inthis study.

4. Results and Analysis
The preliminary result of this study using only surface reflectances variables as predictor.
With SLR-selected variables, the predictor variables were ordered in term of the months
relevance to the percent tree cover estimation based on its Cp statistics. The higher Cp
values compared to their initial Cp models means the more relevant variables for prediction(Table 2). The predictor variables with the Cp values greater than the Cp initial model werethen included in developing model using Cubist. The Cp statistic for all significant
variables are shown in Table 2 and it reveals that the Cp statistics varies depending on thevariables selected for tree cover prediction.
Table 2. Selected predictor variables using SLR method for surface reflectances
Predictor Variable

Months

Surface reflectances

Jan (b3, b5, b7), Feb (b5, b6), Mar (b3, b5, b6, b7),
Apr (b1, b2, b7), May(b1, b2, b4, b5, b6, b7), Jun(b5,b6), Jul(b2, b4, b7), Aug(b2, b4, b5, b6, b7), Sep(b1),
Oct(b1, b3), Nov(b6, b7), Dec (b1, b3, b4, b5, b6, b7)

The percent tree cover map resulted from SLR-selected variables method is shown in Fig. 3.
The values range from 0 to 100 %. The zero percent tree covers wide areas in southwestern parts of Asia, while 100 % tree distributed in south-eastern of Asia. Low
percentage of tree cover distributed also in the northern part of Asia and the central parts ofAsia with the percentage ranges from 0 to 30 %. This figure shows also the percentage oftree covers in areas with frequently covered by cloud in raw MODIS data, e.g. in theFig. 3 Final percent tree cover map for Asia using SLR-selected variables.

Equator areas. With regression tree method such areas were resolved based on surface
reflectance values from other cloud-free months. This advantage is important for frequentglobal forest/tree cover change study.
Validation of the regression tree result was made through validation cells (a total 125 cells)
The validation cells were selected from the ground truth data based on a stratified random
sampling method and also contain percent tree cover values ranging from 0 to 100 % (Table3). The prediction model derived from SLR-selected variables produced a low prediction
error (5.70%) and contributes significantly to depict percent tree cover mapping. Table 3
also demonstrates a high correlation coefficient for this result (0.97). The future of this study
will use all predictor variables as described in the previous parts.

Table 3. Prediction error and correlation coefficient for SLR-selected variables
Predictor Variables

Prediction
Errors (%)

Correlation
Coefficients (R2)

SLR-selected variables

5.70

0.97

5. Conclusions
The very-high resolution QuickBird data is an improvement for depicting a continuous
percent tree cover of training data. From accuracy measurement demonstrated that SLRselected variables produced good result with prediction error of 5.70 % and high correlation
coefficient of 0.97. The zero percent tree covers wide areas in south-western parts of Asia,
while 100 % tree distributed in south-eastern of Asia. Low percentage of tree cover
distributed also in the northern part of Asia and the central parts of Asia with the percentage
ranges from 0 to 30 %. The result shows also the percentage of tree covers in areas withfrequently covered by cloud in raw MODIS data, e.g. in the Equator areas. The regression
tree is an advance as well, in that it can handle the non-linear relationships in the training
data.
Acknowledgments
The MODIS data used in this study were acquired as part of the NASA's Earth Science
Enterprise. The data were processed by the MODIS Adaptive Processing System(MODAPS) and Goddard Distributed Active Archive Center (DAAC), and are archived anddistributed by the Goddard DAAC. NLCD mapping tool software used courtesy of USGS.
This study was carried out as a part of activities by Working Group 4 of Global Mapping
project.

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Alexeyev, and M.K. Hughes, “A large carbon sink in the woody biomass of Northern
forests”, Proc. of the Nat. Acad. Sci., vol. 98, no. 26, pp. 14784-14789, Dec. 2001.
[7] FAO (Food and Agriculture Organization), Forest resources assessment 2000: termand definition. FAO forestry paper 1, FAO, Rome, 1998.
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Int. J. Remote Sens., vol. 10, no. 11, pp. 1805-1812, 1989.
[9] R. DeFries, M. Hansen, J.R.G. Townshend, A.C. Janetos, and T.R. Loveland, “A new
global 1km data set of percent tree cover derived from remote sensing,” Glob. Change
Bio., vol. 6, pp. 247-254, Feb. 2000.
[10] M.C. Hansen, R.S. DeFries, J.R.G. Townshend, M. Carroll, C. Dimiceli, and R.A.
Sohlberg, “Global percent tree cover at a spatial resolution of 500 meters: first resultsof the MODIS vegetation continuous field algorithm,” Earth Inter., vol.7, no. 10, pp.
1-15, Dec. 2003.
[11] Y. Zhang, “Texture-integrated classification of urban treed areas in high-resolution
color-infrared imagery,“ Photogram. Enggineer. Remote Sen., vol. 67, no. 12, pp.
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Derivation of distributions of surface albedo over land areas in Japan
from GMS-5/VISSR images
Koji Horiuchi,1) Hiroyuki Sakai,1) Mitsuo Minomura,1,2) Bannu,1)Hiroaki Kuze,1) Nobuo Takeuchi,1)1)Center for Environmental Remote Sensing, Chiba University,
1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
2)
Earth observation research and application center, Japan Aerospace
Exploration Agency, 1-8-10 Harumi, Chuou-ku, Tokyo 104-6023, Japan
Abstract: Satellite remote-sensing data on the sea and land areas provide the information on boththe surface albedo and aerosol optical thickness (AOT). In this study, the surface albedo
distributions are derived from the satellite data on the monthly composite image with relatively
clear atmosphere, and subsequently, the AOT at 550 nm is extracted for relatively turbid days. The
temporal changes of the albedo and AOT distributions are derived from GMS-5/VISSR visible
images with radiance components calculated by the MODTRAN4 code.
Keywords : GMS-5/VISSR, atmospheric correction, ground albedo, aerosol optical thickness,
MODTRAN4
1. Introduction
Radiances measured by a satellite sensor include the effect of extinctions (i.e. the sum of
scattering and absorption) due to air molecules and atmospheric aerosols. Although ground sensors
provide local values of aerosol extinction, generally ground-based measurements over a wide range
are not practical. If albedo distributions are available a priori, two-dimensional aerosol distributions
can be readily obtained from satellite images. Recently, we have investigated the method ofderiving the ground albedo and aerosol optical thickness (AOT) over the Chiba area fromhigh-resolution, Landsat-5/TM data.1) In the present paper, we employ the GMS-5/VISSR data,
which provide us with hourly data,2) in contrast with the data at fixed local time in the case ofpolar-orbiting satellites such as the Landsat satellite. From the GMS data, the atmosphericallycorrected albedo image (ρclear map) is derived over and around the main islands in Japan underrelatively clear atmospheric conditions. This albedo information, in turn, is used to generate thetwo-dimensional distribution of AOT (τ map) every one hour during the daytime. We use thechannel 1 (wavelength: 0.55-0.99 µm) data of GMS-5/VISSR on August and December 1998.
Images are extracted by choosing 200 pixels×200 pixels around the main land area in Japan.
2. Method
2.1 Generation of the lookup table
The radiation-transfer code, MODTRAN4,3) is employed to calculate the total radiance Ltotal(ρ,ρ,τ550) at the top of the atmosphere for the GMS-5/VISSR visible channel. Here, ρ stands for the
diffuse albedo of the target (with the assumption of Lambertian reflectance), ρ is the averagealbedo around the target pixel, and τ550 the AOT at the wavelength of 550 nm. In the algorithm of1

the code, the wavelength dependence of the extinction(and absorption) is dictated by the aerosol model chosen
in the calculation. Here we assume the maritime modelthroughout the present study. As explained below, thechoice of the aerosol model does not influence theresulting albedo distribution in a significant way, though
the choice is more influential in the AOT retrieval from
relatively turbid images. For each pixel in the VISSR
image, appropriate values of the solar and observationgeometry are given to the code as well.
Figure 1 illustrates the four radiance components
required in the process of atmospheric correction.4)

Fig.1 Radiance components at the topof the atmosphere. These values arecalculated by the MODTRAN4 code.

Among the four components, three components (directreflection Lgd, indirect reflection Lgi, and path-radianceinvolving the surface scattering Lpm) are dependent on the surface albedo values, while the lastcomponent (L0) stands for the path radiance that is dependent only on the atmospheric conditions.
Using these four components, the total radiance (Ltotal) is given as
Ltotal⎛⎜⎝ ρ , ρ ,τ 550 ⎞⎟⎠ = Sgd (τ 550 ) × ρ + Sgi (τ 550 ) × ρ × ρ + Spm (τ 550 ) × ρ + L 0(τ 550 )(1)

In the present analysis, we omit the consideration on the adjacency effect ( ρ = ρ ).
2.2 Derivation of ground albedo
From the condition that the calculated value of the total radiance Ltotal(ρ,τ550) is equal to theobserved radiance Lobs(DN) (here DN stands for the digital number of a pixel in the satellite image),
we can construct a lookup table (LUT) that shows the relationship among ρ,τ550 and DN. An
example of this relationship is shown in Fig. 2. From Fig.2, it is evident that when the value of the
AOT is small (clear atmosphere), the ρ-τ550 relation does not change significantly even when adifferent aerosol model is assumed for the
1.6
calculation of the LUT. For clear atmosphere, in

2

1.4

DN=20
DN=36
DN=52
DN=68
DN=84
DN=100
DN=116
DN=132
DN=148
DN=1641.2
1τ

addition, the value of ρ varies only slightly for asmall change of τ550. Here the ρclear map is derived
under these assumptions, and furthermore, themap is considered as representing the albedo
distribution of the relevant region during the same
season.
Unlike the case of TM images, it is rather
difficult to find a completely “clear” day for the
VISSR images around Japan. Therefore we deal
with the images over a time span of one month,and synthesize a hypothetically clear image by

0.8
0.6
0.4
0.2
0
0

0.2

0.4

0.6

0.8

1

ρ

Fig.2 Relationship between albedo ρ andaerosol optical thickness τ=τ550.

choosing pixels that exhibit the smallest (or second smallest) DN values. It is noted that sometimes,
pixels with the second smallest DN must be chosen, since the rainfall on the day before, forexample, might have caused irrelevantly small value of the surface albedo. We assume in thisatmospheric correction procedure that the AOT can be given as τ550=0.1. The error range associatedwith this assumption will be discussed in Sec. 3.3. Another important aspect about the present
analysis is that the ρclear image needs to be prepared for each local time, since it is found that thealbedo values exhibit non-negligible variations in accordance with the change of the solar position.
2.3 Derivation of aerosol optical thickness
In order to derive the distribution of AOT, we choose images that are mostly free from the effectof clouds, but considerably affected by aerosols. For each pixel, the determination of τ550 isstraightforward, since the LUT calculated as mentioned above can be used to derive τ550 from eachpixel using the corresponding DN (from the satellite data) and ρ (from the ρclear image) values.
3. Results and discussion
3.1 Albedo distribution
Examples of the ρclear maps are shown in Fig. 3. As a whole, as the local time (Japan Standard
Time, JST) changes, the albedo values change as well. Since the viewing angle is kept constant in
the case of the geo-stationary satellite, this change is traced back to the change in the solar zenith
angle. In Fig. 3, some areas over the ocean show high albedo values. These correspond to the pixels
with high DN values even in the case of the monthly composite image, indicating insufficient
removal of the cloud effects.

(a)
Fig.3

(b)

(c)

ρclear maps for (a) 9:00, (b) 11:00, (c) 13:00 JST in December 1998.

3.2 AOT distribution
Multi-temporal τ550 maps on a relatively turbid day (13 December 1998) are shown in Fig. 4,
where the albedo image (monthly composite) used in the retrieval is the one at 10:00 JST. In thiscase, appropriate masks are applied for areas with outside the reasonable range in the ρ-τ550
relationship (e.g. τ550>1) shown in Fig. 2. In Fig. 4, it is found that the temporal variation of AOT islarge, and the resulting τ550 values are in general considered too high over the land regions. In Fig. 5,
the temporal τ550 maps on the same day as in Fig. 4 are shown, but here they are based on the3temporal ρclear maps, each corresponding to the same local time as the turbid image. In Fig. 5, thetemporal change in τ550 is smoother, and reasonable values are obtained over both the ocean andland regions. This example indicates that in order to derive reasonable τ550 maps, it is required to
employ the ρclear map that corresponds to the same local time.

(a)

(b)

(c)

Fig.4 τ550 maps on a relatively turbid day (13 December 1998) calculated with a fixed ρclear map(10:00 JST, December 1998): (a) 9:00, (b) 11:00, and (c) 13:00 JST.

(a)

(b)

(c)

Fig.5 τ550 maps on a relatively turbid day (13 December 1998) calculated with the ρclear map for thesame local time: (a) 9:00, (b) 11:00, and (c) 13:00 JST.
3.3 Accuracy of the albedo map
Comparison of ρclear images from the August and December observations is shown in Fig.6. In
the latter (winter) case, high albedo values are observed in the Hokkaido (northern island) andnorthern areas along the Sea of Japan due to the ground snow. In the former (summer) case, lowalbedo values are observed over the land areas along the seashore. If no irregularities in the data are
responsible for these low albedo values, the most plausible cause would be the local weather.

Fig.6 ρclear map using theminimum DN image: (a) 10:00
JST, December 1998, (b) 10:00
JST, July 1999.

(a)

(b)
4

Figure 7 shows an example of the monthly variation of DN values for a pixel that has shown
very small DN values. It is seen from Fig. 7 that such small values of DN are found just after thedays with higher DN values, suggesting the occurrence of rainfall and subsequent enrichment in thesoil moisture. In this context, the use of the lowest DN is often problematic. The ρclear map
corresponding to the second lowest DN is shown in Fig. 8. Here, recovery of the seashore albedo
values is seen, at the expense of somewhat insufficient removal of the cloud effects.

Fig.7 Monthly change of DN for a fixed pixel (December 1998, in land area).

(a)(b)
Fig.8 ρclear map using the second smallest DN.
(a) 10:00 JST, December 1998, (b) 10:00 JST, July 1999.
The effect of the AOT assumption (τ550=0.1) in the derivation of the ρclear map is examined in
the resulting ρclear maps. At two pixels over the land area each having DN=40 and 80 respectively,
the values of ρ are studied for three values of AOT (τ550=0.08, 0.10, and 0.12). The result isρ=0.0397, 0.0391, and 0.0382 for DN=40 and ρ=0.2121, 0.2118, and 0.2114 for DN=80. Therefore
the error is insignificant, on the order of 2%.
4. Conclusions
We have constructed time-dependent albedo maps form the monthly composite imagery of
GMS-5/VISSR channel 1 (visible and near-infrared) data. These ρclear maps are used to derive thetemporally changing AOT distribution maps (τ550 maps) for a relatively turbid day in the same
season. By matching the local time between the albedo and AOT maps, we have obtained
reasonable AOT distributions over both sea and land surfaces. In some regions, however, theseparation between the sea and land areas is still evident: this can be treated more suitably by, for5example, improving the aerosol model with consideration on the regional change of aerosol opticalproperties. More theoretical validation is needed to check the validity of the temporal changes
found in the ρclear maps, since this apparent BRDF effect6) is significant in spite of the fact that theeffect is brought about mainly by the change in the solar zenith angle.
It has also been turned out that the AOT value assumed for the derivation of the ρclear map does
not affect the result in a significant way. However, care must be taken with respect to theappropriate choice of the dark pixels (low DN values). The long-term retrieval of accurate albedo
and AOT maps will contribute greatly to the understanding of the atmospheric trends around theJapanese archipelago and East Asia. For that purpose, we will continue our effort on further
improvement in the accuracy of the albedo and AOT maps.
References
1) Y. Todate. M. Minomura, K. Asakuma, H. Kuze, N. Takeuchi, Consideration on thederivation method of aerosol information over land areas from Landsat-5/TM data, Proceedingsof 36th Scientific Meeting of Remote Sensing Soc. Jpn., pp.57-58 (2004) (in Japanese).
2) K. Horiuchi, M. Minoura, Y. Todate, H. Kuze, N. Takeuchi, Determination of albedo and AOT
distributions around Japan using the visible data of GMS-5/VISSR,

Proceedings of the 38th Scientific

Meeting of Remote Sensing Soc. Jpn., pp.55-56 (2005) (in Japanese).
3) A. Berk, G.P. Anderson, P.K. Acharya, J.H. Chetwynd, M.L. Hoke, L.S. Berntein, E.P. Shettle, M.W.
Matthew, and S.M. Adler-Gordon: MODTRAN4 Version2 USER’S MANUAL, Air Force ResearchLaboratory.
4) M. Minoura, H. Kuze, N. Takeuchi, Method for atmospheric correction and derivation of AOT
distributions from Landsat-7 ETM+ imagery using the lookup table calculated with MODTRAN4 code,
Proceedings of the 36th Scientific Meeting of Remote Sensing Soc. Jpn. pp.59-60 (2004) (in Japanese)
5) International Satellite Cloud Climatology Project (ISCCP) Web Site (http://isccp.giss.nasa.gov/)
6) P. North, Three-dimensional forest light interaction model using a Monte Carlo method,
IEEE Transactions on Geoscience and Remote Sensing, 34, 946-956 (1996)

6

Cloud characterization in Chiba area from dual-site lidar observation
and NOAA-AVHRR satellite
Gerry Bagtasa1, Cheng Liu1,2, Hiroaki Kuze1, Nobuo Takeuchi1,
Suekazu Naito3, Akihiro Sone4, Hirofumi Kan4
1

Center for Environmental Remote Sensing, Chiba University
2

Science and Technology University of China

3

Chiba Prefectural Environmental Research Center,4

Hamamatsu Photonics K.K.

E-mail address: gerry@graduate.chiba-u.jp
Abstract
Cloud heights and cloud types are characterized from the lidar data observed by two continuously operated
portable automated lidar (PAL) systems and images from the visible and thermal infrared channels of
NOAA16-AVHRR. The PAL systems are located in Chiba and Ichihara city areas, separated by approximately
10 km from each other. Measurements from October 2003 to March 2004 reveal that similar cloud structures areobserved especially when the wind is along the path connecting the two sites. Slight time lags are frequently
observed in the cloud occurrence, and they can be explained from the wind velocity data in the region. Monthly
average of cloud base height (CBH) and cloud cover ratio show good correlation between the two sites.
Cloud-type classification using a threshold technique in split window data of NOAA16-AVHRR gives resultsthat are found to be consistent with the PAL cloud observations.
1.

Introduction
Cloud information such as the cloud type, structure and altitude is of importance for a variety of

meteorological and climatological applications. By intercepting the solar radiation, clouds have a cooling effecton the earth’s surface.1) Knowledge of cloud properties can give us the thermodynamic and hydrodynamic
structure of the atmosphere. The height of an inversion layer can often be related to the cloud appearance.2) In
this study, we compare the ground-based lidar observations with the satellite-derived cloud information over the
Chiba area, continuously unaided operation of PAL allowed for long term cloud monitoring without sacrificing
the high temporal and spatial resolution, also, this study validates the usefulness of the split-window technique inthe cloud classification.
2.

SystemLidar data are obtained from two identical portable automated lidar (PAL)4) systems. One of the PAL systems

is located in Ichihara, at the Chiba Prefectural Environmental Research Center (CERC) (35.52N, 140.07E), while
the CEReS PAL system is on the main campus of Chiba University (35.62 N, 140.12 E). These two sites areabout 10 km apart from each other (Fig. 1). Both are Mie scattering lidar systems capable of measuring
backscattered radiation of up to 15 km in altitude. Specifications of the two PAL systems are given in Table 2.
Both systems are equipped with automatic realignment systems that adjust laser beam directions every 15 min toensure proper lidar alignment.

Fig. 1: Location of 2 PAL systems.
The images from thermal infrared channels (ch.4, 10.3-11.3 µm and ch.5, 11.5-12.5 µm) and visible channel(ch.1, 0.58-0.68 µm) of Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA16 satellite
are used for classifying cloud types. In the split-window technique, threshold values are assumed in the 2-D
histogram whose axes are the brightness temperature of the ch.4 data and the brightness temperature difference(BTD) between the ch.4 and ch.5 data.3) Table 2 shows main characteristics of the NOAA16/AVHRR sensor
Table 1: Portable Automated Lidar System Specification
Ichihara
Chiba Univ.
Configuration
Co-axial
Co-axial
380 slant path
900 Vertical
LD-pumped Q-switch Nd:YAG
Laser
Wavelength
532nm
Repetition rate
1.4 kHz
2.5 kHz
Laser energy
15mJ
divergence
50µradReceiver
Diameter
20cm
Type
Cassegrain
Field of view
0.2 mrad

Table 2: NOAA-16 Specification Orbital
characteristics
Orbit inclination
98.8 deg
Mean altitude (km)851
Equatorcrossing Northbound 13:54A
timeSouthbound 1:54D
Period (min.)102.1
AVHRR characteristics
resolution
1.1 kmSwath width
3000 kmSpectral range / IFoV
Channel 1 (visible)
0.50 - 0.68 µm / 1.39mrad
Channel 4 (infrared) 10.3-11.3 µm / 1.41 mrad

Figure 2 shows the two-dimensional diagram with threshold values used in the present analysis. The classes
are cirrus (Ci), dense cirrus (with emissivity greater than about 0.8), cumulo-nimbus (Cb), and cumulus (Cu,
including stratocumulus). The hatched area above Cu indicates non-classifiable region. Since the thresholdvalues given in ref.3 are mainly for tropical clouds, here we have adopted slightly different values for the cloud
retrieval over the Kanto plain area. This adjustment has been attained by inspecting visible, infrared, and BTD
images. The result shows that (i) cirrus clouds show relatively larger values of BTD, (ii) cirrus clouds have low
reflectivity and are relatively cold, (iii) cumulus clouds exhibit small BTD, and (iv) cumulus clouds haverelatively high reflectivity in visible images and relatively warm in infrared images. By this method, very thincirrus clouds can be detected, even if they cannot be seen clearly in visible images. Cumulus cloud cover is also
retrieved reasonably well.

(K)6Cirrus(BTD)

T 4 - T55
4
3
2

Clear area or Unclassified

Dense Cirrus

1

Cb
220

230

240

250

260

270

280

290 (K)11-µm Brightness Temperature (TB)

Fig. 2
3.

Two-dimensional diagram for cloud-type classification used in this study

Results and discussion
The range-corrected back- scattered signal of the two PAL systems from 23:00 JST of 2 June to 4:00 of 3

June 2004 are shown in Fig. 4(a) and (b) for CEReS and Ichihara sites, respectively. Strong backscattering,shown with brighter shade, from clouds is found in the altitude range of about 5–7 km. Due to the orientation of
the systems, both PAL systems are actually observing about the same cloud at this altitude range, thus we canobserve similar cloud structure in Figs. 4(a) and (b). The observed clouds have downward streaks. This indicatesthe presence of falling cloud particles (heavy ice particles), the observed cloud is indicative of a cirrus type cloud.
Figure 5 shows the cloud type classification map (1:00 JST on 3 June 2004). As indicated in the figure, cloudtypes of dense cirrus and cumulo-nimbus are seen, in addition to unclassified (or cloud free) area. This isconsistent with the PAL observation.
Figure 6 shows the brightness temperature of the same scene as Fig. 3. The measured temperature in the areaof the two PAL sites is about 253.5 K. Assuming that the cloud emissivity is around unity, we can estimate thecloud top height at approximately 6 km from the vertical temperature profile observed by a sonde at Tsukuba.
Although Tsukuba is about 50 km away from Chiba, the high altitude of the clouds leads to the consistency
among the results. However, for optically thin clouds, a previous study5) using inversion of visible and infrared
radiances from such clouds overestimates a typical cirrus cloud by about 15 K, which amounts to cloud top
height underestimation of about 2 km.

Fig. 4PAL.

More results will be presented at the symposium.

(a)(b)
PAL data for 23:00 JST on 2 June 2004 to 4:00 JST on 3 June 2004: (a) CEReS PAL and (b) Ichihara

Dense Ci

Unclassified

CEReS
Ichihara

Cb

Fig. 5 Cloud classification map obtained from satellite
data at 1:00 JST on 3 June 2004. The area of theimage is approximately 80×60 km2.
4.

Fig.6 Brightness temperature map (channel 4)
obtained from satellite data at 1:00 JST on 3 June
2004.

Conclusion
This study has shown that continuous cloud observation can be attained using the PAL system. The

operation of two virtually identical lidar systems separated about 10 km has revealed that local cloud distributionis almost non-varying. We have applied the split-window technique to derive cloud-classification maps fromNOAA-AVHRR images. The method is based on the brightness-temperature data in the infrared bands, mostly
corresponding to the information at cloud top height. Lidar observations, on the other hand, can provide data thatare mostly from the cloud base height. Nevertheless, consistent results in cloud type classification have beenobtained between the cloud observations using the PAL systems and the result of cloud classification using the
split-window data of NOAA16-AVHRR. Moreover, if clouds are not very thick, present results show that lidar
data are useful for the validation of the cloud’s physical structure as observed by satellite.

5.

References

1. R.R. Rogers and M.K. Yau, 1989, A short course in cloud physics, Pergamon, 293pp.
2. Kokhanovsky, A.A. et al., 2004. The determination of a cloud altitude using SCIAMACHY on board
ENVISAT. IEEE Trans. Geosci. Remo. Sen. Letter 1, 211-214
3. Inoue, T., 1987. A Cloud Type Classification With NOAA 7 Split-Window Measurements. JGR 92, 3991-4000
4. Lagrosas, N. et al., 2004. Observation of boundary layer aerosols using a continuously operated, portable lidar
system. Atmospheric Environment 38, 3885-3892.
5. Minnis, P. et al., 1993. Inference of cirrus cloud properties using satellite-observed visible and infrared
radiances. Part II: Verification of theoretical cirrus radiative properties. American Meteorological Society
vol. 50 no. 9, 1305-1322

Growth analysis of potato using a satellite image and GIS
Chiharu Hongo1 and Takuya Michiba2E-mail:hongo@faculty.chiba-u.jp
1
Center for Environmental Remote Sensing, Chiba University
2
Farmer’s Union, Memuro, Hokkaido
Abstract : Site-specific crop management requires the evaluation map of crop conditions, yields and crop
quality. In Hokkaido, evaluation maps of rice and wheat were created and used effectively for precision
farming. To continue with this trend of research, the current study focuses on producing potatoes. Then fordeveloping the map of potato, the analysis of the growth was conducted in the Memuro town, Hokkaido. The
equations to estimate root yields and starch concentration of potatoes were derived from Quick Bird satellite
image acquired on July 18, 2004, and the spatial distribution maps were created through these equations.
The result indicated that root yield and starch concentration were affected by species of preceding crop. The
fields with the highest root yields were detected in the fields where the sugar beets were cultivated in theprevious year, and the average yield was 37.4t/ha.
Keywords : precision farming, root yield, starch concentration

1. Introduction
It has succeeded in evaluating of wheat and sugar beet yields using satellite imagery in Hokkaido. For
instance, these maps are used to decide the harvesting order. But the cost effectiveness is not approved toevaluate the crop yield because satellite data is expensive.
A rotation crop management is performed in Tokachi, so it is possible to extract some kinds of crop
information. This is the effective method to resolve the cost effectiveness issue. In this study, the growth
analysis was executed to potato crop to extract more crop information from a satellite image.
2. Methods
2.1 Study site
The study was conducted in Memuro town, Hokkaido, Japan(Fig.1). The size of test site for analysis is
8km in east and west, and 8km in north and south. A large-scale agricultural management is performed here.
The cultivation area of Memuro town is 19,720ha, and the averaged cultivation area is 28ha per farmer. The
major crops are sugar beets, potatoes, wheat, sweet corns and legumes at planting ratio of 20.9%, 19.8%,
36.0%, 5.6%, and 14.2% respectively(Fig.2). Rotation of crops are executed by combination of major crops
and vegetables at three years or four years interval.
Potatoes were selected in order to analyze yields and quality. The usage of cultivated potatoes are for edible,forstarch materials and for seeds at ratio of 47.2%, 30.0%, 16.1%, and 8.0% respectively. The mayqueen
was selected as analysis crop with the highest planting ratio among nine cultivars of edible potatoes.

Fig.1 Study site

Fig.2 Planting ratio of major crops

2.1 Ground truth data
Farmer’sfields within the test site were used in this study. The investigation points were set in 33 places per
five fields, which are two rows×2m width area. The field survey was performed on July 21 and 22, 2004. The
plant height, hill number, leaf color were measured. Root yields(total amount of M,L,2L3Lsize) and starchconcentration were measured on august 23 to 25.
2.3 satellite data and GIS deta
Quick Bird data was acquired on July 18,2004. The resolution is 2.4m. The GIS data of 2004 were selectedfrom data base of JA Memuro data center to extract potato fields from satellite data. And the GIS data of
2003 was used to evaluate the yields of each kind of preceding crops.
2.4 Procedure
The procedure of image analysis is describedin Figure 3. The satellite data and GIS data
were rectified against 1:25000 digital
topographic maps by nearest neighbor
resampling algorithm using selected groundcontrol points. The shape file of potato field
was created from GIS data and overlaid tosatellite data to extract potato fields. The
position of investigation on satellite data wasdetermined using ground based positioning data.
The
digitalnumber(DN)valuesforinvestigation points were extracted andregression analysis was executed on yields andstarch concentration, then the maps weredeveloped. Finally the GIS data of 2003 wasoverlaid to these maps, and the relationships
between root yields, starch concentration andpreceding crops were evaluated.
Fig.3 The procedure of image analysis
3 Result and discussion
Because harvest parts of the root vegetables like potatoes are hidden in the soil, information of root area is
not contained on the satellite image. Therefore, the DN value extracted from the image is the only
information on the potato with which soil face is covered stem and leaf. It is necessary to find the relation
between the ground part and the underground part to estimate the amount of the yields from a image.
In general, growth rate of stem and leaf increases as the amount of nitrogen fertilizer application increases,
also root weight increases. On the other hand starch concentration in root decreases. There was a positive
relation between root yields and (SPAD x plant height)(Fig.4), on the other hand there was a negative relation
between starch concentration in root and (SPAD x plant height )(Fig.5).

This relation indicates that root yields could be estimated from vegrtative parts. Regression analysis was
executed on the root yield, starch concentration and DN value of satellite data. There was a positive relation
between root yields and (B4-B2)/(B4+B2)(Fig.6). There was a negative relation between starch concentrationin root and (B4-B2)/(B4+B2)(Fig.6).Then maps of yield and starch concentration were developed(Fig.7).The
average of the percentage of relative error was 4.6% against root yields and 9.9% against starch concentration.

Fig.6 Relationships between root yieldconcentration and Band ratio

Fig.8 Sectional map of root yieldFig.7 Relationships between starchand Band ratio

Fig.8 Sectional map of starch concentration

Comparing the yield data of each crop indicated that root yield and starch concentration were affected by
species of preceding crop. The fields with the highest root weight were detected on the fields where the sugar
beets were cultivated in the previous year, and theaverage yield was 37.4t/ha.
The highest starchconcentration value was found on the fields where thered beans were cultivated in the previous year.
It was possible to estimate the root yields and starchconcentration of potato for large area using highresolution satellite data. The analysis of satellite data
produced a better understanding of the relationshipbetween preceding crops and yield. These results can beused for fertilizer application improvement, site specificplanning for harvesting .
Fig.10 Root yield and starchconcentration on each crops

Monitoring ET with Remote Sensing and theManagement of Water Resources on a Basin Scale
Jiemin WANG1,2, Reiji KIMURA1, Wim BASTIAANSSEN3
1

Arid Land Research Center, Tottori University, Tottori, Japan2

Cold and Arid Regions Environmental and Engineering Research Institute, Chinese
Academy of Sciences, Lanzhou, China

3

WaterWatch, Wageningen, The Netherlands

Abstract
The shortage of water resources is serious in Hai River Basin. Managing water resources on thebasis of evapotranspiration (ET), retrieved mainly from satellite remote sensing, is a new approach
introduced in the GEF Hai Project. The most popular remote sensing algorithms in the retrieval of ET
are reviewed, among them the SEBAL (Surface Energy Balance Algorithm for Land) is used for apreliminary analysis. Data from the MODerate resolution Imaging Spectroradiometer (MODIS) andLandsat TM/ ETM+ have been used to evaluate daily, seasonal and annual ET per county and per land
use category. ET distributions and other related results for the Hai Basin, 2002 and 2003, are presented.
Further studies will be continued in next five years, with an independent validation program to checkand improve the ET retrieval algorithm. It is aimed to obtain a real saving of water resources throughan ET reduction strategy, having more water resources remaining in the basin for production andenvironmental use, including that more freshwater discharge into the Bohai Sea.
1. Introduction – Water shortages in Hai River Basin
The shortage of water resources is serious in Hai River Basin, which is one of the seven largest
river basins in China, with 10% of the country’s population, including mega cities such as Beijing andTianjin; Hai River Basin is also one of the most important agricultural and industrial regions in China.
However, the water availability in the basin is only 305 m3 per capita, which is 14% of national averageand 4% of the world average. River channels and lakes on North China Plain are almost all dried up,
because of the surface water over exploitation. Agricultural irrigation relies mainly on groundwater
pumping; Net reduction of ground water is about 9billion m3 per year. The over exploitation of groundwater causes water table declining continuously in last
30 years. Based on water balance analysis, in average,

Precipitation Evapotranspiration
181.59175.27PETannual evapotranspiration (ET) of the basin exceeds
about 4% of precipitation in recent decades. A
quantitativedescription

of

the

water

balancecomponents in Hai Basin for 1998, when the annual

Haihe Basin318⋅103 km2

Discharged to
Bohai Sea 4.28

precipitation was about the multi-year average, isshown in Fig. 1. Water shortage also induces serious
environment

deterioration.

Inadditiontothedegradation of surface water quality, ecologicalenvironmental deterioration in the Bohai Sea, including
mass mortalities of aquaculture species, is also striking
because so less water discharged to the sea and heavy

Transferred fromYellow River 5.1
Excess Exploitationof Groundwater 5.5

Fig. 1 Water balance components foran average year (1998, unit: 109 m3)

land-based pollutants.
2. A new approach in basin-scale water management
As shown in Fig. 1, evapotranspiration (ET) and precipitation (P) are two major components forwater balance in the Hai Basin. ET larger than P and supplemented by other water sources, mainly
groundwater pumping, makes the severe non-sustainability. In a water-short area such as Hai Basin, it
is important now to manage water resources in terms of the amount of water consumption, i.e. ET,
which can be better monitored quantitatively as the rapid development of remote sensing in recent
years. Actually, only the reduction of comprehensive ET, especially regional non-beneficial ET, canresolve gradually the shortage of water resources in the Hai River Basin. A GEF (Global Environment
Facility) Hai River Basin Integrated Water and Environment Management Project is being carried out
since 2004 (Olson, 2004). A new approach which uses the unit of evapotranspiration, monitored mainly
by satellite remote sensing, is adopted in the water resources planning and managements. Remotesensing and ET management systems are to be built in the Basin. Better quantitative data on the spatial
distribution and temporal variation of ET, for different land use and cropping periods in the entire Basin,
will be supplied to water managers and consumers, and be used as the basis in the evaluation of water
use efficiency and the formulation of water rights such as the allocation of irrigation (groundwater
pumping) quota, etc. Reducing evapotranspiration that does not contribute to the plant growth could beachieved by, for example, reducing waterlogged areas, irrigation when evaporation being the lowest(such as at night), using moisture-retaining mulches, replacing open channels and ditches by pipes,
fine-tuning deficient irrigation, and using water stress-resistant varieties, etc. A goal of real watersaving and the recovery of water ecology in Hai River Basin should be reached finally.
3. Methods in the evaluation of regional evapotranspiration
Evapotranspiration has been observed for centuries; however, a better regional understanding isonly possible by using satellite remote sensing. A simpler way is based on improved empirical or
statistical models, such as the extended Priestley-Taylor method (Jiang & S.Ialam, 1999), thecomplementary relationship model, etc. A combined method have been used in the algorithm of
MODIS evaporation product, MOD16, (Nishida et al., 2003) which calculates potential evaporation by
Penman equation, calculates the ‘wet surface’ ET by Priestley-Taylor method (α=1.26), and calculate
actual ET by Penman-Monteith method, then, using complementary relationship to calculate the‘evaporation fraction’ for vegetated surface (EFveg),

EFveg =

λ EvegRn − G0=α∆∆ + γ (1 + 0.5rs / ra )(1)

Where Rn is net radiation, G0 the soil heat flux at ground surface, λ the latent heat of evaporation, ∆ isthe slope of the saturation vapor pressure temperature relationship, γ is psychrometric constant, and rs
and ra are the (bulk) surface and aerodynamic resistances. By using an estimation of vegetation
coverage, also based on an evaluation of bare surface evaporation, evaporation fraction for a composite
surface can be obtained accordingly.
Since the beginning of 1990’s, the most popular remote sensing algorithms in the retrieval of ET
are based on surface energy balance, such as the SEBAL (Surface Energy Balance Algorithm for Land)(Bastiaanssen et al. 1998) and SEBS (Surface Energy balance System) (Su, 2002), both developed in
The Netherlands. Actual evaporation (Ea) is evaluated as the residual when net radiation, soil heat flux,
and sensible heat flux (H) are retrieved with satellite data combined with some surface observations,λ Ea = Rn − G0 − H ,(2)where λ is latent heat for evaporation. Both SEBAL and SEBS have better physical background. The
major difference is that the latter tries to use meso-scale atmospheric model to predict meteorologicalfields near the surface. SEBAL has been used operationally in many countries since 1990’s, and is now
selected as the main algorithm in the GEF Hai Basin Project.
4. SEBAL procedures
SEBAL uses spectral radiances recorded by satellite-based sensors, mainly the MODIS (daily
observation with surface resolution 250 m to 1 km) and Landsat TM/ETM+ (every 16 days, resolution
30 m). Combined with ordinary meteorological data, it retrieves at first the surface albedo, temperature,and vegetation index for each image pixel. Then, net radiation Rn is calculated by surface radiation
balance,

Rn = (1 − r0 ) Rs↓ + RL↓ − RL↑ + (1 − ε 0 ) RL↓(3)

Where Rs↓ is solar irradiance, RL↓ and RL↑ are long wave radiance emitted by atmosphere and land
surface respectively, r0 and ε0 are surface albedo and emissivity respectively; all are calculated bystandard algorithms and/or land surface parameterization schemes. Soil heat flux G0 is evaluated by anempirical relation with net radiation and a few other surface parameters; it is a comparatively small
component; however, the refinement in empirical formula is still a critical part in SEBAL application.
Sensible heat flux H is calculated, as the ‘bulk’ profile method in micrometeorology, by using thetemperature difference between air and surface and the surface aerodynamic resistance to heat flow,

H = ρC p

Ts − Tara(4)

Where Ts is surface temperature retrieved by remote sensing. Ta is air temperature at specific height(such as 2 m above ground surface). ρ and Cp are air density and air specific heat at constant pressure
respectively. ra is the surface resistance to heat transfer; considered the effect of atmospheric stability, it
is calculated by,ra =ln( z / z0 ) −ψ h ( z / L) + ψ h ( z0 / L)
ku*

(5)

Where Ψh is the Monin-Obukhov stability function (L is Obukhov length). z0 is surface roughness
length, estimated by landuse status. The innovative point of SEBAL is the utilization of two “anchor”
pixels, “hot” and “cold”, in the image to fix boundary conditions for the air-surface temperature
difference, and assumes a linear function between this difference and the radiometric surface
temperature. An iterative way started from neutral stability assumption is used in the calculation of
surface friction velocity (u*), ra, and then, the surface sensible heat flux H.
The instantaneous ET for each pixel is evaluated as the residual of the surface energy balance,
then, daily (24 hr) evapotranspiration can be estimated based on the fact that ‘evaporation fraction’

EF =

λ Ea
Rn − G0(6)

is almost constant in daytime hours. Monthly ET are based on the results of a few days processing in
the month, and using the index Kc = ET/ETr to fill the gaps. Kc (‘crop factor’) is a more consistentratio during daytime hours (Allen et al. 1998).
ETr, the reference ET, is computed fromground-based measurements of solar radiation,wind speed, air temperature, and humidity. For
each land use category and each county of thewhole Hai River Basin, daily, seasonal andannual ET are monitored accordingly.
5. Preliminary results
Preliminary analysis has been done for2002 and 2003 for the whole Hai River Basin.
By using of MODIS (1 km resolution, twice
per month), DEM and land use/cover, androutinemeteorologicalobservations,thedistribution of ET for year 2002 has been
evaluated (Figure 2). 2002 was a very dry year,with a basin-wide annual precipitation 400 mm,
27% less than average; while the ET retrieved
by SEBAL was much larger, about 714 mm.
Spatial distribution showed that ET in somearea, such as near the left bank of Yellow River,

400

600

800

1000

Fig. 2 Annual actual evapotranspiration for2002 interpreted from MODIS measurements in the Hai Basin, with spatial resolution
1 km. The position of the river basin in China
is also shown.

was higher (800-900 mm) because of higher
water availability; while the ET in the

Luancheng County

downstream area of the Hai River, mainlyETwith the local observation at Luancheng

25
20
15
10
5
0

County and Yellow river embankment, which

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Decade no.

shows an acceptable agreement (Figure 3). A
distribution map of the excess of annual actual
overprecipitation

Yellow River Embankment
ET

for

60

different counties in the Hai Basin also showed

50

interesting results and could be a goodreference for water management.
Year 2003 had much more precipitation
about 582 mm (8.8% larger than average),while the ET retrieved was lower, about 556
mm. A distribution map of the basin-wide
evapotranspiration of 2003 is shown in the leftof Figure 4. Detailed analysis has been donefor each land use categories in the Basin (Table1), particularly, for the two major crops in the

ET (mm /d ecade)evapotranspiration

Biom ass production(kg/ha/decade)and biomass production have been compared

30

Bio
1600
1400

Biom ass production(kg/ha/decade)surface water. Monthly variations of actual ET

1000
900
800
700
600
500
400
300
200
100
0

35
ETact (m m/ decade)(400-500 mm) because of the dried up of

Bio

40

south and southwest Tianjin, was much lower

1200
40

1000

30

800
600

20

400
10

200

0

0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Decade no.

Fig. 3. Monthly variations of actual ET andbiomass production of a 1 km * 1 km pixelin Luancheng County (upper) and the
Yellow River embankment (lower)
respectively.

Fig. 4 Annual evapotranspiration (left) and the water depletion (precipitation minus ET)(right) of the Hai River Basin, 2003. Related values for each area (each county, each specificcrop, etc.) can be tabulated accordingly.
river basin, winter wheat (winter to early summer) and maize (summer to autumn). Similar analysis canbe also done for each prefecture, each county, and even smaller political area in the river basin. All ofthese results could be useful in the evaluation of water use efficiency and allocation of irrigation quota
for different regions, so as to reduce the basin-wide ET, at same time, to maintain (if not increase) the
agriculture production.
For 2004, an analysis has only been done for Beijing area so far, by using of Lansat TM data,
which gave a much finer distribution. A comparison for a small area with field flux observations by
eddy-correlation method showed very encouraged result (see Figure 5) (Mao, 2005).
6. Validation
It is always important to validate
the remote sensing retrieved ETagainst local surface measurements,
particularly in an operational use suchas in Hai River Basin. This appeared
not to be straight-forward due todiscrepancies in scale. There have
been

no

corresponding

surfaceobservations in the Hai Basin in 2002
and 2003 for proper validation.

S

N

Fig. 5 Comparisons of sensible and latent heat
fluxes retrieved from Landsat TM/ETM+ withobservations by eddy-correlation method at
Xiaotang Shan station near Beijing.

Nevertheless, some attempts havebeen done by referencing the results of a few agricultural stations in the basin and rather traditional
results (see also Fig. 3). An independent validation program has been started in the GEF Hai Basin
Project, with some specific stations built in southern and northern parts of the river basin, that havedirect flux measurements with eddy-correlation method and the LAS (Large Aperture Scintillometer),in addition to standard observations of radiation components and automatic weather stations. These
data have been used for the validation of the ET retrieved in 2004 for Beijing area (Fig. 5).

7. Concluding remarks
Table 1. Landuse categories of the Hai Basin,
and the annual ET for each category in 2003.
Landuse nameArea (km2) Area (%) ET (mm) ET (%) ET(km2*mm)
plain irrigated field
142549 44.6929
569 47.601
81110381
mid-cover (20-25%) grassland
33573 10.526
500 9.8514
16786500
shrublands
28201 8.84177
539 8.9206
15200339
forest coverage >30%
23807 7.46413
524 7.3211
12474868
low-cover (5-20%) grassland
21468 6.73079
460 5.7955
9875280
forest coverage 10-30%
16788 5.26349
513 5.0542
8612244hill non-irrigated field
16345 5.1246
428 4.1055
6995660
mountain non-irrigated field
9358 2.93398
475 2.6087
4445050high-cover (50%) grassland
6635 2.08025
396 1.542
2627460
plain peddy
4311 1.35161
607 1.5357
2616777
reservoir & pond
3920 1.22903
701 1.6127
2747920
urban & town land
3716 1.16507
582 1.2692
2162712other forest
2493 0.78162
579 0.8471
1443447
countryside residential area
1372 0.43016
593 0.4775
813596bottom land
1094
0.343
545 0.3499
596230saline-alkali land
954 0.2991
683 0.3824
651582industrial & communication land
609 0.19094
623 0.2227
379407river & channel
535 0.16774
603 0.1893
322605
marshes
437 0.13701
389 0.0998
169993sandy land
369 0.11569
361 0.0782
133209exposed rock
182 0.05706
455 0.0486
82810
tidal flat
117 0.03668
753 0.0517
88101
bare ground49 0.01536
481 0.0138
23569
lake
30 0.00941
477 0.0084
14310
mountain paddy
20 0.00627
522 0.0061
10440
slope non-irrigated land
9 0.00282
525 0.0028
4725
other land
7 0.00219
688 0.0028
4816
hill paddy
4 0.00125
569 0.0013
2276

As shown in the preliminary works donein recent two years, it is feasible to have abetter understanding of the basin-wide
evapotranspirationbyschemes.

MODIS

Daily

resolution 1km,

remotesensingdata,

are good

with

in

ET

monitoring for a basin scale and long-term
analysis, while the high resolution datasuch as Landsat TM and ASTER, withresolution 15-30 m, are good in detailed
analysis in demonstration counties. Further
studies will be continued for the Hai River
Basin over next five years, including an
independent validation sub-project whichisessentialinimprovingtheET

monitoring reliability as well as improving
the retrieving algorithm. The major task is the establishment of a real water saving program through an
ET reduction strategy, with more water resources remaining in the basin for production andenvironmental use, including more freshwater discharges into the Bohai Sea. ET reduction is land use
and crop type dependent. Other management factors also need to be included.
Acknowledgements
Many people participated in the tremendous works of the preliminary stage of the GEF Hai Basin
Project. Various data collection particularly in situ flux observations done by Prof. Liu Shaomin and his
group at Beijing Normal University are highly acknowledged.
References
Allen R G, Pereira L S, Raes D, smith M.,1998. Crop Evapotranspiration. FAO Irrigation and Drainage
Paper 24, Rome.
Bastiaanssen, W.G.M., Menenti, M., Feddes R.A. and Holtslag, A.A.M., 1998. The Surface Energy
Balance Algorithm for Land (SEBAL): Part 1 formulation. J. of Hydr. 212-213: 198-212
Jiang L. & S.Ialam: A methodology for estimation of surface evapotranspiration over large areas using
remote sensing observations [J]. Geophysical Research Letter 26(17), 1999, 2773-2776
Mao Defa, 2005. Evaluation of regional ET with high resolution satellite data. Master Thesis of Beijing
Normal University (In Chinese)
Nishida,K, R.R.Nemani, S.W.Running, J.M.Glassy, 2003: An operational remote sensing algorithm ofland surface evaporation. Journal of Geophysics Research, 108(D9), 4270, doi:10.1029/
2002JD002062
Olson, D. C., 2004. Planning scarce water resources using ET quota: The GEF Hai Basin Integrated
Water and Environment Project in China. ‘Cases Database’ in FAO-Netherlands International
Conference on Water for Food and Ecosystems, The Hague, The Netherlands.
Su, Z., 2002. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes [J].
Hydrol. Earth Syst. Sci. 6(1), 2002, 85–99.

- 1 -

Comparison of monitoring applicability between Crop Production Indexand conventional methods using satellites
Daijiro KANEKO1,
1

Department of Civil Engineering, Matsue National College of Technology, 14-4, Nishiikuma-chou, Matsue,
690-8518 Japan, Professor, Phone & Fax: +81-852-36-5266,E-mail: kand@ce.matsue-ct.ac.jp

ABSTRACTThis paper compares the applicability of the Crop Production Index CPI with conventional methods, taking into accountthe acquisition of the necessary data by remote sensing. The author has developed the CPI index as a remote sensing
method for monitoring grain production in the early stages of crop growth in Japan and Asia. A photosynthesis based crop
production index CPI takes into consideration the solar radiation, the effective air temperature, and NDVI as a factor
representing vegetation biomass. The CPI index incorporates temperature influences such as the effect of temperature on
photosynthesis by grain plant leaves, low-temperature effects of sterility, cool summer damage due to delayed growth, andhigh-temperature injury. These latter factors are significant at around the heading period of crops. The CPI index for rice
was validated at ten monitoring sites in the central and northern half of Japan. This paper proves the ability of the CPI to
predict poor crop production using rice yield statistics, contrasting it against conventional methods such as the cumulative
Growing degree day GDD, integrated NDVI, and photosynthesis rate.
Keywords: monitoring, crop production, rice, photosynthesis, NDVI

1. INTRODUCTION
This research aims to monitor grain production by developing a photosynthesized type of crop production index, whichdisplays functional dependence on solar radiation, temperature effects, stomatal opening, and vegetation biomass. It is
important to oversee the quantity of grain in production in Japan and Asia at an early stage in the present era of increasing
Asian population and water-resources restrictions. Continuous predictive monitoring of crop production in East Asia
would allow orderly management of food security issues including Japan, which is one of the world’s major grain
importers. Conventional methods have generally been based on the cumulative value of the effective air temperature(growing degree day, GDD) by Idso 1), Bollero et al. 2). or integrated NDVI proposed by Rasmussen3). However, none ofthese methods correctly expresses the grain production by estimating the time-integrated photosynthesis rate. Many
researchers have presented conventional papers on crop simulation, including Williams et al.4). Priya et al.5) and Perez etal.6), and remote sensing data have been incorporated in the models by Maas 7) and Wiegand 8). However, those models
simulate the growth process of crops, making them highly complicated, as Monteith 9), and Sinclair and Seligman 10) havepointed out. Several methods use seasonal changes of the normalized difference vegetation index NDVI derived fromsatellite observation. The method in this paper takes the amount of growth and biomass as known, using remotely sensed
data NDVI, and estimates the instantaneous photosynthesis velocity easily and accurately. The photosynthesis-based crop
production index CPI proposed in this paper can predict the rice yield under conditions of low temperature sterility,
sunshine shortage, and high temperature injury. This paper validates the CPI index at ten monitoring sites in Japan andverifies its particular ability to predict poor grain production, in contrast to conventional indices.

- 2 -

2. METHOD OF CROP PRODUCTION INDEX CPI
2.1 Conventional methods
Many conventional crop studies have correlated the grain quantity in production with the growth index of Growing
Degree Day GDD, or with water stress indices such as stress degree day1),2).

GDD =Tmax + Tmin
− Tb
2

(1)

where，Tmax is the maximum daily air temperature, Tmin is the minimum daily air temperature, and Tb is a threshold
temperature for the crop, below which physical activity is inhibited and equal to 10 ℃.
In conventional research using remote sensing, the vegetation index NDVI 11),12),13),14), concerning the vegetation
biomass, is related to the crop production. Rasmussen3) defined the integrated NDVI (iNDVI) and related it to the grain
yield.
t2

Yield = a ⋅ ∫ NDVI (t )dt + b
t1(2)

where, a and b are regression coefficients, and t1 and t2 are the day number of seeding and harvesting.
In grain production forecasting using the latest remote sensing, daily values of the photosynthetically active radiation PAR
and the vegetation biomass (NDVI) are taken into the model. Frthermore, Rasmussen (1998) gave the net primary
production NPP using satellite data according to the following formula:t

NPP = ε ∫ ( aNDVI + b ) ⋅ PAR ⋅ dt
0(3)

where ε is the efficiency coefficient,，ｔ is the time，and PAR is the photosynthetically active radiation.
This NPP is a photosynthesis-type model. However, this formula does not allow for such important factors as
temperature influences on photosynthesis, temperature sterility and stomatal opening of crops. The present research
improves modeling based on the photosynthesis type of crop production index so as to incorporate the effects of global
solar radiation, temperature, stomatal opening, and vegetation biomass. Although the areas of crop study and remotesensing have generated much research, especially on the production of wheat and corn, it is all restricted to considerationof the water stress or formulas based on vegetation indices. To give a more accurate value of the grain quantity inproduction, the crop production index should take the form of the photosynthesis velocity so as to express the growth ofcrop vegetation and filling of grain, both of which relate directly to the quantity produced.
2.2 Proposed method
Rasmussen 15) proposed a net primary production (NPP) model by taking into account the daily photosynthetically
active radiation PAR and the amount of vegetation biomass (NDVI), so as to estimate the NPP from satellite data. The
equation of NPP is of photosynthetic type, but sterility due to low- and high-temperature injury and stomatal opening due
to shortage of water resources or inadequate irrigation are not accounted for. It is reasonable to suppose that crop
production can be estimated based on a daily photosynthesis rate. Generally, temperature has two effects on the quantityof grain production: its normal influence on the rate of photosynthesis, and the effect of extremes of temperature on
sterility during the stages of heading, flowering, and filling. To model these two effects, three response functions, tophotosynthesis, low-temperature sterility and high-temperature injury are employed in the period before and after heading.
This research expands the form of NPP by Rasmussen to consider air temperature and stomatal opening, as well as thesolar radiation and the amount of vegetation biomass already considered. The photosynthesis velocity is defined by

- 3 -

equation (1) as follows16), 17), 18),19),20):

a ・ APAR(4)・ f Syn (T c ・
) β・
eLAI
s
b + APAR
where PSN is the photosynthesis rate，APAR is the absorbed photosynthetically active radiation, βs is the stomatal opening，
a and b are Michaelis-Menten constants, Tc is the canopy temperature, eLAI is the effective leaf area index, and fster isthe sterility response function for the air temperature.
The authors' former paper16),presents sensitivity analysis curves for the Michaelis-Menten-type response function versus
solar radiation and the temperature response of the photosynthesis rate as well known as the Sigmoidal-Logistic typefunction :
PSN =

⎤
⎡
1
f Syn (Tc ) = ⎢⎥⎢⎣1 + exp {k syn (Tc − Thv )}⎥⎦

(5)

where Thv is the temperature parameter at half of the maximum photosynthesis rate, and ｋsyn is the slope parameter.
The temperature response functions for low-temperature sterility and high-temperature injury are defined by the following
equation, referring to the curves obtained by Vong and Murata 21):FLster (Tc ) = 1 − exp[k Lster (TLster − Tc )] ,(6a)

FHster (Tc ) = 1 − exp[k Hster (Tc − THster )](6b)where，kLster is the low temperature sterility constant, TLster is the low sterility limit temperature,ｋHster is the hightemperature injury constant, THster is the high injury limit temperature, and Tc is the plant leaf temperature.
Finally, the response function of the compounded temperature sterility effects due to both low and high temperatures ingrain production is expressed by the following equation:

F Ster (T c ) = {1 − exp[ k Lster (T Lster − T c )]}⋅ {1 − exp[ k Hster (T c − T Hster )]}(6c)

Next, integration of the photosynthesis rate over an interval from seeding ts to the time t of crop plant stage defines thephotosynthesis-based crop production index CPI for rice having the following forms:
During crop plant stage 1, of growth:t

CPI U = ∫ PSN U ⋅ dt
ts

(7)

During crop plant stage 2, of booting, heading, flowering to ripening:t

CPIU = FSter (Tc ) ⋅ ∫ PSNU ⋅ dttst

FSter = ∫ f Ster (Tc ) ⋅ dt
tf

(7a)
(7b)

At the crop plant stage 3 of harvesting:tr

CPI U = FSter (Tc ) ⋅ ∫ PSN U ⋅ dt
ts

(7c)

- 4 -tr

FSter = ∫ f Ster (Tc ) ⋅ dt

(7d)

tf

It is necessary to normalize the NDVI so as to remove the effect of planting area (plant coverage ratio) on thephotosynthesis rate at different paddy sites. Even if the crop yield in a year was the norm, the NDVI is liable to differ eachyear. The plant coverage ratio per data pixel of remote sensing is dependent on the individual sites. We therefore define
the standardized NDVI, called the NDVI Unit, by dividing by the annual average yield as follows:

NDVI U ,i =NDVI iiNDVI H 100(8)

The photosynthesis rate is similarly normalized to give the ‘PSN Unit’ upon dividing by the normal photosynthesis rate
averaged annually, as follows:
t

PSN U

∫ PSN ⋅ dt
=
ts

(9)

iPSN 100

The EPIC (Erosion-Productivity Impact Calculator) uses the same idea to normalize the effect of accumulation ofGrowing Degree Day, by defining the Heat Unit Index.
The quantity of grain production in the growth stages of heading and filling is influenced by a crop physiologicalmechanism called low-temperature sterility, and by high temperature injury, in addition to cumulative photosynthesis up
to heading. To transform the CPI index into the appropriate photosynthesis type of grain production index, thephotosynthesis rate PSN of equation (1) must be multiplied by the temperature sterility function Fstr of equation (3c)involving the heading term to be expressed via equation (5b and 6b), which is of time-integrated form to account for theeffect of temperature on flowering, pollination, and ripening.

0.0

3. DATA USED IN THE
MODELING
The present research uses domestic
meteorological data to verify the CPI
index. The ground air temperature data,
which are supplied by the Japanese
Meteorological Agency from theAutomated
Meteorological
Data
Acquisition System (AMeDAS) point at
ten sites, distributed in the Japanese
agricultural plains, have large acreages
suitable for satellite monitoring of the
paddy fields.

Shinshinotsu
Iwanai
Hakodate

Ohogata
Karikawa

0.5

Shizukuishi

Maki

Furukawa

Kurashiki

Kohoriyama

1.0

NDVI

Kuki
Hachikai
KurumeFigure 1:

Distribution of NDVI and monitoringu sites in Japan forvaridation of Crop Production Index CPI.

- 5 -The Japanese Ministry of Agriculture, Forestry, and Fisheries provides grain statistical information, which includes
crop situation index for the paddy rice at ten sites for monitoring and validation district. This crop situation index is theratio of crop production in the year in question to the mean annual production for the ten most recent years. The Society ofAgricultural Meteorology in Japan has published a special report, which summarizes the relation between themeteorological conditions and the poor harvest in 1993. The satellite NDVI data used in the CPI index is the 4-minutemesh set of vegetation index data derived from NOAA Advance Very High Resolution Radiometer (AVHRR) by
Tateishi22).

4. COMPARISON of the CPI and CONVENTIONAL METHODS

Crop Situation Index

140
120
100

Shinshinotsu
Iwanai80

Hakodate
Karikawa

60

Ohogata
Shizukuishi

40

Furukawa
Kohoriyama
Maki

20

Kuki

0
0

0.5

1

1.5

2

Crop Production Index CPIU
Figure 2: Relation between the crop production index CPIU and Crop situation index at ten monitoring points.

Figure 2 shows the relation between crop situation index CSI and the CPIU, to verify applicability to rice yields at 10
sites in Japan. The photosynthesis rate of the CPIU index decreases as a result of the inadequate solar radiation or
accumulation of air temperature. The CPI Unit becomes a little less than 1, implying poor production compared to thenormal harvest averaged annually. The CPIU can predict a trend of poor production, expressed by the crop situation index
decreasing linearly to below 100. The many values of the CPI index close to 1 imply the usual behavior of thephotosynthesis rate governing rice yields in most years. However, abnormal weather with low temperature and muchcloud, which happens about every 10 years, causes low temperature sterility and late ripening of rice. The CPIU thenrapidly falls to zero, since the limiting problem is not photosynthesis but inadequate flowering and late ripening. The
sterility function curve, which has a steep gradient for low air temperatures, is able to capture the very bad harvest in theworst case of the crop situation index below 50 in 1993. The third mechanism is high temperature injury, expressed byequation (6b). When the air temperature is excessive, the CSI decreases slightly with further increase in air temperature as- 6 -

700

Shinshinotsu
Iwanai
Hakodate
Karikawa
Ohogata
Shizukuishi
Furukawa
Kohoriyama
Maki
Kuki

100
80
60
40

Yield （Kg/10a)

Crop Situation Index

120

Shinshinotsu
Iwanai
Hakodate
Karikawa
Ohogata
Shizukuishi
Furukawa
Kohoriyama
Maki
Kuki

600
500
400
300
200

20

100

0

0

0

1000

2000

3000

Cumulative GDD

4000

0

1000

2000

3000

Cumulative GDD

4000

Figure 3: Comparison of monitoring applicability for Crop Production Index and conventional methods. The figures show
relations between the cumulative growing degree day GDD and Crop Situation Index at ten monitoring points,
and between yield and cumulative GDD.

seen on the right in Figure 2, though it is still greater than 100 in the right side range of x axis, that is greater than CPIU=1
in the Figure2. According to the mechanisms included in the CPIU index, CPIU can predict rice production taking into
account photosynthesis, low temperature sterility, and high temperature injury, depending on meteorological conditions.
Figure 3 shows relations between the crop situation index CSI and the cumulative growing degree day (GDD), andbetween the yield and cumulative GDD. The cumulative GDD has a linear relationship to the yield but shows no ability to
distinguish bad production due to low temperature sterility from normal rice yields in other years. The air temperature has
two effects, on growth and ripening by photosynthesis and on pollination from heading to flowering of the grain. This
sterility effect on pollination is not linear in temperature, but cuts in rapidly below a threshold of about 18 degrees
Figure 4 shows relations between the crop situation index and the integrated NDVI (iNDVI), and between yield and theiNDVI. The integrated NDVI is not able to predict either the crop situation index or the yield, and in particular is unable topredict a bad harvest due to low temperature sterility. The iNDVI values depend strongly on regional characteristics suchas soils, type of rice and mixcel effects, involving other plants (vegetables, trees, etc.).

700

100

Shinshinotsu
Iwanai
Hakodate
Karikawa
Ohogata
Shizukuishi
Furukawa
Kohoriyama
Maki
Kuki

80
60
40
20
0

Yield （Kg/10a)

Crop Situation Index

120

600

Shinshinotsu
Iwanai
Hakodate
Karikawa
Ohogata
Shizukuishi
Furukawa
Kohoriyama
Maki
Kuki

500
400
300
200
100
0

0

50

100

Integrated NDVI

Figure4:

150

200

0

50

100

150

Integrated NDVI

Relations between the integrated NDVI and Crop Situation Index, and between yield and iNDVI.

200

- 7 -

Figure5: Applicability to crop production of integrated PSN based on remotely sensed data. The figures show relations between the
Crop Situation Index and integrated PSN, and crop yield and iPSN.

700

Shinshinotsu
Iwanai
Hakodate
Karikawa
Ohogata
Shizukuishi
Furukawa
Kohoriyama
Maki
Kuki

100
80
60
40

Yield （Kg/10a)

Crop Situation Index

120

600

Shinshinotsu
Iwanai
Hakodate
Karikawa
Ohogata
Shizukuishi
Furukawa
Kohoriyama
Maki
Kuki

500
400
300
200

20
100

0
0

0.2

0.4

0.6

0.8

1

Integrated PSN

1.2

1.4

0
0

0.2

0.4

0.6

0.8

1

Integrated PSN

1.2

1.4

Figure5: Applicability to crop production of integrated PSN based on remotely sensed data. The figures show relations between the
Crop Situation Index and integrated PSN, and crop yield and iPSN.

Figure 5 shows the performance of the integrated photosynthesis rate iPSN used as a crop yield index. The crop situationindex decreases linearly with iPSN but shows no ability to predict low temperature sterility, because sterility is notdependent on photosynthesis but is related to flowering and pollination. The iPSN performs much better than the iGDD oriNDVI since it is linear with respect to crop yield; however, the iPSN values show considerable scatter arising fromdependence on regional conditions.
Consequently, only CPIu is able to predict a bad harvest due to sterility effects, by making the CPIu values decrease
sharply to zero based on the eigen-functional relationship between CPIu and the crop situation index CSI, as well as theyield.

5. CONCLUSIONSThis paper compares the applicability of the Crop Production Index CPI and conventional indices, based on remotelysensed data. The aim is to develop a remote sensing method suitable for monitoring rice production from the early stagesof crop growth right up to harvesting in Japan and Asia. The present paper proposed a photosynthesis-based crop
production index CPI that takes into consideration the solar radiation, the effective air temperature, and NDVI as a factor
representing vegetation biomass. The CPI index incorporates the mechanism of temperature influences such as the effectof temperature on photosynthesis by grain plant leaves, low-temperature effects of sterility, cool summer damage due todelayed growth, and high-temperature injury. These latter factors are significant at around the heading period of crops.
The CPI index for rice was validated at ten monitoring sites in the central and northern half of Japan using the rice crop- 8 -

situation index. The validation exercise clearly proves the superior ability of the present index to predict poor production
using rice yield statistics in comparison to conventional methods such as cumulated growing degree day GDD, integrated
NDVI, and photosynthesis rate. The method is based on routine observation data, allowing automated monitoring of crop
production at arbitrary sites without any special observations.

ACKNOWLEDGMENTS
This study was funded by Grants-in-Aid for Scientific Research. We wish to express thanks to the Japan Society for the
Promotion of Science, which belongs to Ministry of Education, Culture, Sports, Science and Technology.

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