The 11th CEReS International Symposium on Remote Sensing, 13-14 Dec. 2005

Ice Breakup Dates on 18 Eurasian Lakes Estimated by MODIS Data fromfrom 2001 to 2005
Takashi Nonaka (PASCO Corporation),
Tsuneo Matsunaga (National Institute for Environmental Studies),
Akira Hoyano (Tokyo Institute of Technology)

Background

Relationships between ice breakup dates and local climate(E.g., Lake Mendota, Lake Monona, WI, USA)・Snow and ice is widely taken up as one of the important indices showing climate change.
“Ice breakup date of the lake and river has become early about fewer weeks overthe past century.” (IPCC, Intergovernmental Panel on Climate Change, 2001 )

130

100

70

1900

Year

1950

-6.0

50
-8.0
1980 1985 1990 1995 2000 2005

2000

Year

Saroma-ko Lagoon

10.0
5.0
0.0
-5.0
50

④

2000
2001
2002
G
D
F

100

Date

150

10.0
5.0

100

Saroma-ko Lagoon

Ice Breakup Date

Date

150

Requirement of satellite sensor for estimating ice breakup date・observing everyday (short revisiting time)・having near infrared band as well as thermal infrared band
→ MODIS AVHRR etc. is suitable
Data accuracy is very good, and various products are prepared.
→ MODIS is especially suitable

LaunchBand
Spatial resolution
Observation frequency
Features

MODIS

AVHRR

ASTER

1999
32
1 km
EverydayHigh Accuracy
Various products

1978
5
1.1 kmEveryday

1999
14
90 m
Every 16 days

Lake Baikal

120
110
100
90
80

120
110
100
90
80
70
60

ST

2001 2002 2003 2004 2005

2001 2002 2003 2004 2005

Water

160
150
140
130
120

Water Temperature (BratskBaikalVilyuisk
Zeya
Khanka

150
140
130
120
110
2001 2002 2003 2004 2005

Year

Year

Year

Year

Area 1

Area 2

Area 3

Area4

Area2
1 2

Lake

Latitude,Longitude

Elevati-on (m)

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
Khubusgol
BratskBaikalVilyuisk
Zeya
Khanka58.4 N, 27.4 E
60.5 N, 31.2 E
61.5 N, 35.2 E
58.0 N, 38.5 E
48.2 N, 43.1 E
50.2 N, 45.4 E
54.5 N, 49.4 E
58.3 N, 56.1 E
45.4 N, 76.2 E
54.5 N, 49.4 E
48.0 N, 83.5 E
54.5 N, 91.4 E
51.0 N, 100.2 E
54.4 N, 102.1 E
53.4 N, 106.4 E
62.5 N, 111.1 E
54.2 N, 128.1 E
44.5 N, 132.2 E

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

Area1

10

12

14

8

4
6

15

17

16

7
5

C)

No

3

18 study lakes
on the Eurasiancontinent.

・Ice breakup dates in 2002 and 2004 are relatively earlier for some of the lakes (especially area 1 and 2).
・Clear trend of the time series of the ice breakup date is not observed for lakes at high elevation (area 3) .
・Ice breakup dates in 2005 is earlier than in 2004 for some of the lakes in area 3 and 4.

o

2.0 ℃
(freshwater)
0.5 ℃ (seawater)

160

100

2001 2002 2003 2004 2005

20

0
0.8 1.2 1.6 2.0 2.4 2.8

170

110

30

Ref

180
Chany
ZaisanKrasnoyarsk
Khubsugul

Ave= 2.0 oC
Std= 0.3 oC

40

10Accurate breakup temperature
estimation is important.

100

+3
0
-1

50

Cloud

170

Ice Breakup Date130

Ice Breakup Date

Ice Breakup Date

140

Tsimlyansk
Volgograd
Kuibyshev
Kama
BalkhashDifference

119
132
118

10 days changes of the ice breakup
dates (for some lakes)

180

130

Observation

122
132
117

60

1 ℃ change of breakup temperature

Ice Breakup Date

140
Peipsi
LadogaOnega
Rybinsk

Estimation

Snow

・Ice breakup dates of the center of each lake were estimated using MODIS Level 3 SST data.
・Breakup temperature of all lakes was set to 2.0 ℃ of freshwater.
150

+1
-6
-1

Accuracy is better than 3 days.

Results

160

Difference

102
105
97

2000
2001
2002

200

Breakup Temperature

Usable satellite data

Observation

103
99
96

Lake Baikal
0.0
-5.0
50

200

Estimation
2000
2001
2002

2000
2001
2002
D
F

Frequency

Time series data

Ice on the Rokannonmiike, atMiyazaki prefecture.

15.0

Water Temperature (oC)Intersection of Threshold and Equation

Date

-4.0

70

15.0

Water Temperature Trend
Regression Equation③

Ice Breakup Date

-2.0

80

Validation

WT = at + bt + c ③④

90

first day when a specific area of lake surface is completely free of ice

2①
Threshold

0.0

100

Ice Breakup Date:

Lake Surface Temperature
Calculation
②

②

2.0

110

Ice breakup date has become early, and mean air
temperature in spring has become an increase.

①

SurfaceTemperature

4.0

120

60

Water Temperature (oC)

Breakup Temperature(Threshold) Estimation

Second Part

80

50

In this study, the ice breakup dates on 18 Eurasian lakes from 2001 to 2005 are estimatedand the relationships between ice breakup dates and local climate will be discussed.

First Part

90

60

We developed the method to estimate the ice breakup dates in previous studies.

MethodIce Breakup Date

Ice Breakup Date・Remote sensing is especially useful to estimate the ice breakup dates for the lakes withno available in-situ data.

110

Air Temperature (oC)・However, few in-situ data of the ice breakup date exists on Eurasian continent.
(For example, Lake Baikal)

Ice Breakup Date
Air Temperature

130
Mendota
Monona

120

9

11
Area3

13
Area4

18

Time series of the ice breakup dates are related with the trend of mean air temperature ??
・Annual changes of the ice breakup date are mostly
can be explained from the mean air temperature.
Relationships between ice breakup dates and local climate

・1 day change of the ice breakup date represents the
0.2 ℃ changes of the mean air temperature fromMarch to May

・Ice breakup date data were estimated by the suggested method.
・Air temperature data at the center of each lake were derived from NCEP/NCAR data set, and averaged from March to May.

2.0
0.0
2001 2002 2003 2004 2005

Year

Mean air temperature in area 1

6.0

Chany
ZaisanKrasnoyarsk

4.0
2.0
0.0
-2.0

2001 2002 2003 2004 2005

Year

Mean air temperature in area 3

10.0
8.0

180
Area1
Area2
Area3
Area4

6.0
4.0
2.0
0.0
-2.0
-4.0

2001 2002 2003 2004 2005

Year

Mean air temperature for each area

Ice Breakup Date

4.0

8.0

Air Temperature (oC)

6.0

-2.0

12.0

10.0
Peipsi
LadogaOnega
Rybinsk

8.0

Air Temperature (oC)

Air Temperature (oC)

10.0

2001
2002
2003
2004
2005
G

160
140
120
100
80

Ice breakup dates on 18 Eurasian lakes from 2001 to 2005
were estimated using MODIS data.
The annual changes of the ice breakup date are different
by the location, elevation, and local climate.
Ice breakup dates are strongly related with air temperature,and 1 day change represents 0.2 ℃ changes.

R=0.9360-10.0 -5.0

Summary0.0

5.0

10.0 15.0

Air Temperature (oC)

Ice breakup date and mean
air temperature

Future study
Long term of the ice breakup dates will be discussed, andthe trend of the climate changes will be derived.

Effects of the 2004 Indian Ocean tsunami in Thailand
based on ASTER images
Fumio YAMAZAKI1, Ken’ichi KOUCHI2, Masashi MATSUOKA3[1] Professor, Department of Urban Environment Systems, Faculty of Engineering, Chiba University, Japan.
[2] Graduate Student, University of Tokyo, Japan. [3] Team Leader, EDM, NIED, Japan.

Introduction
High-resolution satellite (QuickBird, IKONOS) images were employed and GPS-synchronized photos/videos weretaken in the damage survey after the 26 December 2004 Indian Ocean Tsunami in the south Thailand. Terra-ASTER
images were also used to identify tsunami inundation areas comparing the pre-event and post-event images.

Tsunami Damage Survey in Thailand with GPS and Satellite Images
Ko Phra Thongb
Ban Nam Khem
Phang-Nga
Ban Bang SakcKhao Lak

(b)

(a)

(c)

Krabi
PhuketKamalaPatong
Karon

e

f

(d)

(e)

(f)

Ko Phi Phi

100

B

G

R

NIR

Reflectance (%)

80i
IKONOS

h

(g)

(h)

(i)

60

40
grass 49
yellow58
dead 59

20

0
300

400

500

600

700

800

900

1000

1100

Wavelength (nm)A reconnaissance survey was carried out by a team consisting of researchers from Thailand, Japan and USA. The
objective of the survey is to gather geo-referenced tsunami inundation and damage information with the enhanced useof satellite images and GPS. Spectral reflectance of various surface materials were also obtained as ground truth data.

Tsunami Inundation Mapping Using ASTER Images and SRTM
ASTER
050208SRTM
15m mesh

Slope (Unit: deg.)
Kernel: 18pixel

SRTMと地形特性

Ridge・Channel
Kernel: 18pixel

2002/11/15

Decision tree of landform
classification using ASTER
images and SRTM 90m DEM

2004/12/31

False color ASTER images of Khao
Lak overlying on SRTM 90m DEM
NDVI

NDSI

NDWI

ASTER images were used to identify tsunami inundation areas using
the changes in the normalized difference vegetation index (NDVI),
soil index (NDSI), and water index (NDWI). It is found that tsunami
caused the decrease of NDVI and the increase of NDSI and NDWI.
References
Yamazaki, F., Matsuoka, M., Warnitchai, P., Polngam, S., and Ghosh, S. (2005)
Tsunami Reconnaissance Survey in Thailand Using Satellite Images and GPS,
Asian Journal of Geoinformatics, Vol. 5, No. 2, pp. 53-61.
Kouchi, K., Yamazaki, F., Matsuoka, M., (2006) Tsunami Damage Detection
Using Moderate-resolution Satellite Imagery, Asian Conference on Earthquake
Engineering, Manila, Philippines.

15/11/2002

31/12/2004

15/11/2002

31/12/2004

15/11/2002

31/12/2004

Abstract

After an illegal dumping site was found on the border between Iwate andAomori 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 collectedusing this system, including satellite data, on-site, infrared camera images, andwater quality data, was analyzed to establish the level of on-site contaminationand state of recovery. Some SPOT images were compared to establish the onsite 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 the alkalinity of the water.

Key Words: Industrial waste, Remote sensing, Vegetation analysis, Water
quality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 as environmental contamination. An
environmental monitoring system was established to collect and analyze the environmental
data on-site. This system is very useful for establishing the level of contamination and state ofrecovery on and around the site.
Thesatellite
data
such
as
multispectral
SPOT
image
was
analyzed for environmental analyses in
the wide area, and the data from onsite sensors including water quality
data and weather data was analyzed indetail.

Aerial photograph of illegal dumping site (April 25, 2005)

The waste is taken away in trucks,
but this will take several years tocomplete. 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.

Environmental Monitoring System
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 a building on our campus. Moreover, SPOT images,
DEM data, and Quickbird images were analyzed by the ENVI software.
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 every threeminutes. The items measured were pH, electric conductivity, flow rate, air temperature, andprecipitation.

Vegetation Analysis using NDVIThe NDVI was used to transform multispectral data into a single image band representing
vegetation distribution. 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. Written mathematically, the formula is: NDVI = (NIR - RED)/ (NIR +RED).
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 narrowpaths in Figure (b), in contrast to the natural montane vegetation shown in Figure (a).
Furthermore, these figures show a reduction in vegetation, and we can determine those areasthat had changed in that period.

(a) Jul.20, 1992(b) Jun.17, 2004

Changes in NDVI data on the dumping siteTopographic Analysis(a) DEM data of 15m spatial resolution(c) Three-dimensional visualization(b) Quickbird data of 2.4 m spatial resolution

The DEM data of 15 m spatial resolution was
combined with a Quickbird image of 2.4 m
spatial resolution to obtain high spatial
resolution. The output image is threedimensional visualization of the illegal dumping
site and its surrounding areas.

Water Quality Analysis
Five water quality analyzers have been 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. The pH and electric conductivity of onsite 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).

Table 1 Changes in water quality (pH)2004.92004.10

2005.92005.10

Point 3 (Kumahara River)

7.34

7.15

6.93

6.48

Point 4 (entrance of thewater purifying plant)

7.92

7.97

7.34

7.58

Point 5 (exit of the water
purifying plant)

7.41

7.27

7.18

6.85

34,428 tons of waste (26,668 tons of solid waste and 7,760 tons of
fluid waste) was removed. 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. We found that thealkalinity of the water was reduced by the water purifying plant — themean 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).

Conclusions
A remote sensing system was established to monitor and analyze theenvironmental data at the illegal dumping site on the border between Iwate andAomori Prefectures. In this paper, the data collected using this system, including
satellite data, images from on-site infrared cameras, and water quality data, wasanalyzed to establish 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 in vegetation at the dumping site. We found signs ofhuman activity such as narrow paths and a substantial reduction in on-site
vegetation when the waste was dumped. This system will help identify andconfirm other illegal dumping sites.
Changes in water quality are important in assessing contamination and wereanalyzed, along with rainfall, as the waste was removed. The removal of waste
and other on-site work reduced the alkalinity of the water. In the future, newsatellite data and ground data will be sequentially collected by this system. This
will be compared with current data to understand and forecast environmental
changes.

Processing and interpretation of JERS-1 Synthetic Aperture Radar (SAR)
image of Cepu and its surrounding areas, Central Java Province, Indonesia
1
1
2
3
W.W. PARNADI , S. RUSLI , K. WIKANTIKA , J. TETUKO S.S
1Department of Geophysical Engineering

Institute of Technology Bandung

2

Department of Geodetic Engineering

Institute of Technology Bandung

Introduction
Interpretation of subsurface condition from Radar image is of great interest, covering surface as wellsubsurface features with resolution much higher than resolution offered by radar technique. Lithological as wellas geological informations extracted from the image could be of higher value. Conventionally, geologicalfeatures are derived visually from Radar images. Patterns associated with geological structures/features could
be detected. Attempts to interpret lithological as well as structural geological features deeper than 1 - 10 meters
are of course challenging. We are now in the early stage of our attempts to obtain techniques that makedeeper interpretation possible. In this research, we tried to derived geological structures and other featuresvisually 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 use of geological map isintended to get better understanding of geological features and structures that could be recognized in the
Radar image. We applied conventional image processing flows on the data to improve data quality that lead tomore interpretability of the data.

Fig. 1: Cepu Study Area located in Central Java Province,
Java Isle, Indonesia

3

Center for Environmental Remote Sensing,
Chiba University, Japan

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. 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. Blue lines denote fold structures and yellow line denotes shear
fault.

Fig. 2: Geological Structure Map of study area showing
anticline folds and their orientations

Fig 5: processed JERS-1image after image focusing (left) and an example
of applied parameter for image focusing (right)

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

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. It should be noted that little 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 getbetter results in the determination of structures orientation.

Fig. 4: Distribution of control points in the JERS-1 SAR image(left) and their reference points in the ASTER image (right).

Fig. 6: Interpreted folds (denoted by blue lines) and fault(yellow line) in the study area

Data and Data Processing
JERS-1 as well as ASTER images for this research were available from Remote
Sensing Research Center - Panditho Panji Foundation and ERSDAC. Combining
with data from field observation, we processed the JERS-1 image using following
data processing flows: radiometric correction (speckle reduction) with low-passfiltering, geometric correction (image rectification) with least-square polynomial
regression, image focusing with linear contrasting and high-pass filtering. Allprocessing steps were conducted using ER Mapper 6.4 software. Figure 3 shows
raw image of study area. 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 JERS1 SAR image and their reference points in the ASTER image is shown in the figure 4.
Figure 5 shows processed JERS-1 image after image focusing. Image histogram
after application of high-pass filtering is shown in the right side of the figure. .
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 andData 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(in Indonesian language): BSc. Thesis, ITB, Bandung.
[5] 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.
[6] Tetuko S. S., J, Tateishi, R. And Takeuchi, N., 2003, Estimation of Burnt Coal Seam Thickness inCentral Borneo using JERS-1 SAR Data: Int. J. Remote Sensing, 24, No. 4.

Fig. 7: Image fraction showing more detailed area in theadjacent of interpreted fold no. 1 and shear fault.
Arrow signs denote symmetrical axes.

Conclusion
Geological as well as lithological interpretation of subsurface condition from Radar image is of great interest.
Conventionally, geological features are derived visually from the images. Patterns associated with geologicalstructures/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 derived
lithological and geological features from Radar image conducted in the Department of Geophysical Engineering ofInstitute of Technology Bandung, Indonesia. In this stage, we tried to derived geological structures and otherfeatures visually from JERS-1 SAR data from Cepu Area, 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.
Eleven anticlinal folds and one faulted anticlinal fold were detectable. These folded 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 lineamentsintersecting folds. A little difference in structure orientation interpretation from image-derived structures and thegeological structure map could be resulted from the error in the process for geometric correction, especially forstructures located outside ASTER image coverage acting as geometrical correction reference. Even for structuresinside the ASTER image coverage, Digital Elevation Modeling (DEM) technique should be incorporated in theprocessing for better results in the determination of structures orientation.

Acknowledgement
We thank Remote Sensing Research Center - Pandhito Panji Foundation and ERSDAC ofJapan for providing the JERS-1 SAR and ASTER image data.

#15

Comprehensive evaluation of Leaf Area Index estimated by several method― LAI-2000, SunScan, Fish-eye, and littertrap ―E-mailE-mail :: zoorin@agr.
zoorin@agr.
shizuoka.ac.jp
shizuoka.ac.jp

Midori Kurata1 ，G.A.Sanchez-Azofeifa2 ，Wang Quan1 , Yoshitaka Kakubari1

1 Faculty of Agriculture, Shizuoka University , 2 Earth & Atmospheric Sciences University of Alberta,Canada
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. It is necessary to rely first on ground-based LAI estimates if remotely sensed vegetation indices need cross-calibration. At present, there are several
techniques for estimating LAI. The aim of this study is to establish a practical technique for LAI estimation suitable to mountaineous beech forest stands.
Mt.Naeba (2145.3 m)

2) Material & Method

Mt. Kaguramine(2029 m)Study site ･ The northern slope of the Naeba Mountain (36°51’N, 138°41’N), located in southern Niigata Prefecture in Japan

1500m･ dominant species is beech (Fagus Crenata)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 (%)

old-beech
1500ｋ
1500y
1300y
900X3
1500
1500
1300
900
300
300
300
150
17.6
13.8
11.5
28.9
22
20
20
22
450
229
250
383
30.4
21.3
31.4
41.6
71
72
86
59
○
○
○
○
○; destructive sampling were made in 1970s

700n2
700
200
36.9
30
361
52.4
82

600k
600
250
30.2
27
433
45.6
74

550ｋ
550
260
37.1
34
246
36.3
86
○

1200y
1200
250
20.0
20
539
35.3
33.0

mix-wood
900y
900X4
900
900
250
150
16.6
15.8
31
21
535
829
44.8
30.8
41.0
28.1
○

1100y
1100
250
11.8
19
625
53.8
38.0
○

700ｎ
700
200
16.0
31
425
30.9
31.4
○

young-beech
900X1
900X5
900
900
70
85
23.1
17.3
21
21
1033
1400
51.1
46.5
94
74

550m
Long Term Ecological Monitoring Studies Sites since 1970

3) Estimating LAI by direct methods
4) Estimating LAI by indirect methods measuring radiation based on gap fraction andcalculating LAI from the Beer-Lambert extinction law
litter collection
allometric aquation In seven sites, destructive samplings were
5-10 littermade for parameterising allometric at each altitude (IBP, 1970) LAI-2000 :The Plant Canopy Analyser (LI-COR Inc. , USA)measure both incoming
measure the diffuse radiation below 490nm
traps were set
radiation andIn this study, with view caps of 22.5°and 90°
randomlylog F = h log ( D2H ) + K
SunScan : Canopy Analysis System (Delta-T Devices Ltd, UK) transmitted through theat 8sites･･･ ( for old-beech and mix-wood )canopyLAILAI-2000
measure the PAR above canopy (with BFS) and below canopy
respectively
F = 0.006 × ( Dπ ) 2+ 0.4656 ( Dπ )(with probe)･･･ ( for young-beech )Fish-eye
F: total leaf area of each tree, D: diameter at breast height,
Hemispherical photograghs taken by using digital cameras with fishey converter
H: tree height,→calculated by a specific software Hemiview (Delta-T Devi ces Ltd, UK).
h and K: fitted coefficients of the equation at different altitudes.
( gap fraction and canopy openness)

3) Results and Discussion
8
litter collection

1) Estimated LAI by direct methods.･litter collection: One of the most reliable methods the LAI
estimation●For the old-beech forest, the relationship was almost 1:1● The destructive sampling was made for sites with no litter traps,
from which the estimated values then used as ground truth

allometric equation

7

LAI2000 (90°)
sunscan6

fisheye

FishFish-eye

54

In this study, LAI from litter collection or allometric equation was
treated as true values,

3

SunScan2

the leaf fall fromthe understories( 1: 1 )

7

1

6

3
old-beech
mix-wood
young-beech

2
1
0
0

old-wood

young-wood

LAI-2000 was suitable for
LAI estimation in mountainbeech forests.
And broad view angle isrequired for measuring. In
addition, it usually
underestimates LAI and thus
needs conection before it canbe used.

Fisheye estimated LAIe is saturated around LAIe =2. Classification of images involves using digital
image process to distinguish canopy opening from foliage, which is achieved by determining athreshold intensity value.

LAI estimated by direct method8( 1: 1)7( 1:1)( 1: 1)( 1:1)
R2 = 0.09

6

R2 = 0.52

52

R = 0.22

4
3

R2 = 0.7121

Although it is very portable,
Fish-eye measuring requires
more technique.

2

R = 0.57

R2 = 0.92

old-beech
mix-wood
young-beech

old-beech
mix-wood
young-beech

R2 = 0.59

0
0

1
2
3
4
5
6
7
80
LAI estimated by LAI2000 (22.5°)

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

80

1Fig. 3 LAI
Estimated bylitter collection,
allometricequatiion, LAI2000, SunScan
and Fish-eye.

4) Conclusion

1
2
3
4
5
6
7
8 SunScan estimated LAIe of old-beech has the linear relationship to direct LAI, although in youngLAI estimated by allometric equation
beech LAIe was underestimated.

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

X4

mix-woodAmong these indirect methods,
LAI-2000 has the strongest correlation with the direct estimated LAI.
with 90°view-cup → Wide field-of-view is much desired for the heterogeneous radiation
environment as below canopy.
Although it was underestimated →It needs connection before it can be used.

700n

900y

1100y

1200y

900X5

900X1

550k

600k

X3

the less destructive
samplings

R2 = 0.95

700n2

4

1300y

5

1500y

0
1500k

LAI estimated by litter collection

8

2
3
4
5
6
7
LAI estimated by SunScan

80

1

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

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

8

SunScan is hard to use in theforest stands if the cable isconnected to BFS. However,
SunScn is resistant to weatherchanges and can keep
measuring under versatile
weather conditions.

1. One of the most significant challenges for improved the national food security program is availability of timely, up-to-date, and accurate data for planners and decision makers. Such information needs to beavailable to concerned government official, regulators, donors, NGO and other interested organizations in a comprehensive, consistent, regular, and easy to understand format.
2. In Indonesia, there are many institutions concerned in food security data, which each institution has own analytical tools and methods for determining the food security parameters; therefore, we often find adissimilar published data for a same physic variable 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 isawfully difficult to access the real time food security information.
3. This research attempts to (1) develop a food security database system included an analytics instrument to improve the quality of paddy field data using remote sensing and GIS technologies, (2) calculate the foodbalance of each region using DSS model, and (3) design the food security information system.
4. All kind of spatial data either image or vector as well tabular data have been integrated in a database system, and then transformed into food security information to enhance the knowledge based as a basis for
decision making are discussed in this paper

Remote Sensing

Theodolite Technology

Global Positioning System

0-8%

8 – 15%3-D of Java island using DEM data

15 – 45%(A) RS VS Theodolite(B) RS VS Theodolite and GPS VS Theodolite
No

< 8%

luas citra vs theodolite 0-8%
8.000
6.000

RS

GPS

RS

GPS

0.95980.96670.99070.9874y = 0.9307x + 0.0999
R2 = 0.9925

4.000
3.000

1.

1.000
0.000
0.000

2.000

4.000 luas theo 6.000

8.000

<8

2.

8 - 163.

16 - 450.94191.05880.96580.93074.

> 45

0.97130.92420.6116

0.7710

3.500

2.500

citra

2.000
1.500
1.000
0.500
0.000
0.500

1.000

1.500

1.10720.7891

2.000

2.500

3.000

8.000
7.000
6.000
5.000
4.000
3.000
2.000
1.000
0.000
0.000

>45%

2.000

y = 1.0588x
R2 = 0.9307

y = 1.1243x - 0.199
R2 = 0.9355
1.000

theodolite

2.000

3.000

4.000

0.8106luas theo vs citra >45%

16 -45%y = 1.0956x
R 2 = 0.7891

3.000

1.0956

theo vs citra 16-45%

8-15%

citra vs theo 8-15%

4.000

0.000

Corelation Coeficeint(R 2 )a constanluas citra

luas citra

5.000

2.000

citra

Slope (%)

y = 0.9598x
R 2 = 0.9907

7.000

> 45%

5.000

6.000

7.000

y = 0.9713x
R2 = 0.6116

1.500
1.000

y = 0.9766x - 0.0049
R2 = 0.6116

0.500
0.000
0.000

theo

0.500

1.000

1.500

luas theo

2.000

N o

N
O

D e saLocation

Distric

Slope
(%)

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

D u k u h K a ry a
K a ry a M u ly a
K a ra n g J a y a
P a g a d u n g a n
C ira n g g o n
C a m p a 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 rta M u k ti
K a ra n g L a y u n g
B a b a k a n S a ri I
S a m p a y
W 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 rg a A sih

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

K a b u p a te n

L e re 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

L u a sGPS
G P S (H a )(ha)

6 .9 6 0
2 .4 5 6
1 .8 8 0
2 .5 0 3
2 .6 0 3
0 .5 1 0
2 .3 1 2
1 .7 8 7
2 .0 2 4
1 .5 2 3
2 .2 7 2
1 .4 0 4
0 .8 3 0
1 .3 4 1
2 .3 0 8
1 .2 1 6
0 .2 8 1
0 .5 9 3
0 .4 2 5
0 .5 1 6

L u a sTheodolite
T h o d o lite(ha)
(H a )
7 .2 9 5
2 .8 6 5
1 .7 3 5
2 .2 4 7
2 .5 6 5
0 .5 2 0
2 .1 2 5
1 .7 4 5
2 .0 4 5
1 .6 2 5
2 .0 5 5
1 .3 9 5
0 .8 9 5
1 .2 5 5
2 .9 4 5
1 .1 2 5
0 .3 7 5
0 .6 4 5
0 .4 7 5
0 .6 8 5

L u a sRS
C itr a(ha)
(H a )
6 .9 7 2
2 .4 7 7
2 .0 1 8
2 .2 9 3
2 .5 6 9
0 .6 4 2
2 .2 0 2
2 .1 1 0
2 .2 0 2
1 .2 6 0
3 .0 6 0
1 .2 6 0
0 .9 0 0
1 .2 6 0
2 .1 6 0
1 .2 6 0
0 .2 7 0
0 .4 5 0
0 .4 5 0
0 .7 2 0

P ro d u k si
Paddy yieldP a d i(kg)
2001
3 9 3 .7 4
1 4 3 .8 5
1 0 1 .4 9
1 2 7 .4 1
1 6 6 .6 6
3 8 .0 6
1 3 0 .5 5
1 2 5 .0 9
1 3 0 .5 5
7 4 .7 0
1 8 1 .4 1
7 4 .7 0
5 3 .3 6
7 4 .7 0
1 2 8 .0 6
7 4 .7 0
1 6 .0 1
2 6 .6 8
2 6 .6 8
4 2 .6 9

RS = remote sensing: GPS = global positioning 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

Tabular data: Production (kg) Paddy field area (ha) Harvest area (ha)
 Rainfall (mm)
 Etc.

Remote sensing data
 Land use
 Etc< 0 = Deficits, addition of rice levying> 0 = Surplus, next month stock or distribute to other region= 0, sufficient

Recommendation

Supporting data Administration
 Accessibility
 River
 Etc.

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 beendeveloped for Central Java province was carried out in three main phases: i. e.
(a) identify and develop required variable in the food security 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 food security 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 the institution as well as minimizing the cost.

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(CEReS, Chiba University )Toshiaki Ichinose(Center for Global Environmental Research,National Institute for Environmental Studies)
Yingjiu Bai(Tohoku University of Community Service and Science)
Study Area(SEOUL)

PurposeThe expressway was dismantled in Seoul in July, 2003,
and the municipal river (Cheong-gye stream) was restoredin October, 2005.

A

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 Cheongegecheon.

B

C

SEOUL
StationMeteorological observation point
Obstruction flashlight
Pollution density observation point
Cheong-gye stream

Research method

Result of observation Air temperature

1) Long-path measurement of atmospheric pollutant species
with an obstruction flashlight2) Observation of air temperature

south
Cheongegecheon
north

32.00

south
Cheongegecheon
north

28.00

point A
point B
point C

25.0

24.5
27.50
31.00

30.00

26.50

26.00

23.5

23.0

22.5

22.0

25.50

28.00
21:00

22:00

20:00

23:00

21:00

22:00

20:30:00
20:27:00
20:24:00
20:21:00
20:18:00
20:15:00
20:12:00
20:09:00
20:06:00
20:03:00
20:00:00
19:57:00
19:54:00
19:51:00
19:48:00
19:45:00
19:42:00
19:39:00
19:36:00
19:33:00
19:30:00

20:00

23:00

time

time

observed timeFig2. daily variation of air temperature(Left:2004, Right:2005)

Fig3. daily variation of air temperature(North-South)(East-West)

Future work
The atmospheric pollutant species observation and themeteorological observation in Seoul will be studied, and therelaxation effect of the heat island phenomenon of themunicipal river will be clarified.

Fig1. Experimental setup for measuring NO2.

Seoul station

Thus, it is possible to contribute to the creation of themunicipal river and the promotion of maintenance in
Asian cities.
Chongae 4ga

As for the rise of NO2 in Chongae
4ga,2004, The influence of construction
is stronger than the influence of theautoexhaust.

0 .0 90 .0 80 .0 7
NO 2 (ppm)

27.00

29.00

2)simultaneous observation of several trace gas species
is feasible if the relevant spectral features fall within theconsidered wavelength interval of the lamp and theCCD.
3)when the spectral intensity of
2km
the flashlight is known at the
strobe site, the transmitted
spectra give an information on
the aerosol extinction along the
Skyscraper
optical path.

Dongdaemun

Air Temperature

Air Temperature1)The background from the sky radiation is easily
subtracted because the spectra of strobe flash are
distinguished straightforwardly from the difference ofthe observed intensity.

Air Temperature

24.0

Observation technique of atmospheric pollutant species

0 .0 60 .0 50 .0 40 .0 30 .0 20 .0 10
Jan 01

Apr 01J u l01

Oct01

Jan 02

Apr 02

Ju l02O c t02

Jan 03Apr03

Ju l03Oct03

Jan 04

Apr 04

Ju l04Fig3. Secular distortion of NO2 measured around road

Cheong-gye stream (August 2004)

Cheong-gye stream (September 2005)

O c t04

J an 05

Apr 05

Ju l05

Oct05

It is worth observing atmosphericpollutant species .

Pollution density observation point

obstruction flashlightStudy the effect of green covering on the land value of Tokyo Metropolisusing geographic information system (GIS)
Ippei Harada (Graduate school of Science and Technology, Chiba University)
Akihiko Kondoh (Center for Environmental Remote Sensing, Chiba University )Table 1.The percentage of changes of land
use from 1984 to 1994.

Background

Land use

Chiba

Mountain district
Paddy field
Agricultural land
Park
Green space
Reclaimed landRoad
Community facilities
Industrial land
Residential landCommercial land

SaitamaTokyo

Kanagawa-2.58
The urbanization around the center of Tokyo was expanded due to theincreasing population of the Tokyo region (Tokyo, Chiba prefecture, Saitama
0.46
0.48
0.50
0.35
prefecture, Kanagawa prefecture). The housing establishment and city traffic
1.53
0.84
1.08
0.98
have developed the suburbs which were before an agricultural land andmountain area. The rapid expansion of Tokyo region from the end of 1980s till
1.95
2.39
1.82
1.74
the beginning of 1990s was due to the impact of the economic bubble, which
consiquence, increased the land and stock values, thus increased the problem of Artificial land use was increased in Tokyo
region. Natural land use was decreased inurban environment.

Land value is a quantified economic parameter which shows the utility valueof land. It is a comprehensive value which reflects the environmental condition
and many other functions of the city.

Study Area
The extent of Tokyo metropolis isabout 100 km and there are many
cities existed also on the fringe ofTokyo. Tokyo is the center of major
activities and developments of Japan.
The impacts of the urbanization was
not limited in Tokyo area but it was
extended also outside Tokyo region to
include the nearby prefectures, whichthen increased their land prices.

-3.26

-4.23

-2.31

Tokyo region. However, park and green
space were increased.

The center of Tokyo → Land use change
is small
The suburbs → Land use change is large

Multiple linear regression analysis
Land valuep(z) =f＊(Z1+Z2+,・・・,Zn)

Fig 4. Land-use change ofTokyo Metropolitan during theperiod (1984-1994).

Extracted using GIS
Tokyo
Z1: Spatial distance from Tokyo station，Z2:Spatial distance up to near station → convinience
Metropolis Z3:Building structure（reinforced concrete, steel framework, timbered） → safety
Z4:With or without of Town gas, Z5: With or without Sewerage, Z6: With or without Water
supply → economical efficiency
Z7:Land area, Z8:Building coverage ratio, Z9:Building capacity ratio, Z10:Land form（hillyland,
upland, lowland, reclaimed land）, Z11: Green covering ratio → amenity
Saitamaprefecture

Table 2. Determining factors of land values by regression analisis of the meso-scale

Data

Multiple 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
Explaining variable 2
Explaining variable 3
Spatial distance up to near station
Sewarage
Explaining variable 4
Land form（hillyland>upland>lowland>reclaimed land）
Spatial distance up to near station
Explaining variable 5
Town gas
Building structure（reinforced concrete>steel framework>timbered）
Explaining variable 6
Building coverage ratio
Land form（hillyland>upland>lowland>reclaimed land）
Building structure（reinforced concrete>steel framework>timbered）
Building coverage ratio
Explaining variable 7
Building capacity ratio
Town gas
Explaining variable 8
Explaining variable 9
Green covering ratio
Green covering ratio
Multiple correlation coefficient
0.647
0.794
Contribution ratio (%)
64.4
63.0
Year

・10m Grid Land Use:Metropolitan Area (in1984 and 1994) ．
・2 scenes of Landsat TM data acquired
during daytime August 3,1985 and July
19,1997 (Path：107 / Row：35) ．
・Official announcement of land price in 1985
and 1997．
・Land classification map at 1:500000 scale(Chiba, Saitama, Tokyo and Kanagawa
prefecture → Tokyo region) ．

Explaining variable 1

Tokyo
prefecture
Tokyo
Bay Chiba
prefecture

Kanagawa
prefecture

Fig 1. Tokyo region NDVI= (NIR(TMband4)－Red(TMband3)) /(NIR(TMband4)＋Red(TMband3))

Research method

Vegetation region： 0.1<NDVITokyo city (in 20km range from the center of Tokyo) :Nerima, Tokyo(Otemachi), Shinkiba
Around Tokyo city (in 20～30km range from the center of Tokyo) :Funabashi, Urawa, Koshigaya, Fuchu, Yokohama
Suburban 1 (in 30～40km range from the center of Tokyo) :Abiko, Chiba, Kisarazu, Tokorozawa
Suburban 2 (in 40～50km range from the center of Tokyo) :Kumagaya, Hatoyama, Kuki, Hachiouji, Ome, Ebina

The area of local scale in 4.4km
1) The city environmental element was extracted using Geographicalrange from the city
Information System (GIS).
Table 3. Determining factors of land values by regression analisis of the rocal-scale
2) The influence of the city environmental element was correlated with theMultiple linear regression analisis (Land value in 1985)
formation of land values and analyzed using multiple linear regression
Tokyo city
around Tokyo citty
Suburban 1
Suburban 2
Spatial distance from Tokyo station
Sewarage
SewarageGreen covering ratio
analysis.
Green covering ratio
Spatial distance up to near station Spatial distance up to near station
Sewarage
Explaining variable 1
Explaining variable 2

Green covering ratio
Town gas

Explaining variable 6

Building capacity ratio
Building coverage ratio
Spatial distance up to near station
Land area

Explaining variable 7--

Explaining variable 8

-

-

-

Multiple correlation coefficient

0.647
28.3

0.708
48.6

0.861
71.4

Explaining variable 3

Land use/cover change during the bubble economic term

Explaining variable 4
Explaining variable 5

Contribution ratio (%)

50km

50km

Explaining variable 1
Explaining variable 2
Explaining variable 3
Explaining variable 4

40km

40km

Explaining variable 5
Explaining variable 6
Explaining variable 7
Explaining variable 8
Explaining variable 9
Explaining variable 10
Multiple correlation coefficient
Contribution ratio (%)-

Land form
Building structure
Building capacity ratio
Green covering ratio-

Spatial distance up to near station
Land area
Building coverage ratio
Building structure
Spatial distance from Tokyo station
Town gas
0.842
67.3

Multiple linear regression analisis (Land value in 1997)
Tokyo city
around Tokyo citty
Suburban 1
Suburban 2
Building capacity ratio
Building capacity ratio
Sewarage
Building capacity ratio
Spatial distance up to near station Spatial distance up to near station
Building capacity ratio
Green covering ratio
Building structure
Green covering ratio
Spatial distance up to near station
SewarageGreen covering ratio
SewarageGreen covering ratio
Building structure
Land form
Land form
Spatial distance from Tokyo station Spatial distance up to near station
Land area
Building structure
Building structure
Spatial distance from Tokyo station
Building coverage ratio
Building coverage ratio
Town gas
Town gas
Land form
Land area
Spatial distance from Tokyo station
Building coverage ratio
Land form
0.894
0.883
0.861
0.842
79.5
77.4
71.4
67.3

Conclusions
10km

10km

20km

30km
40km

20km
30km
40km

Fig 2. Land-use of Tokyo Metropolitan Fig 3. Land-use in the Tokyo Metropolitan
in 1984.
in 1994.

Multiple linear regression analysis was used to analyze the effectof 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 fromthe center of Tokyo, but this influence was clarified through the areaof local scale in 4.4km range from the city of Tokyo metropolis asconsidered one of the elements that the vegetation could influence
the formation of land value.

Hajime OSADA (Graduate School of Science and Technology, Chiba University)
Akihiko KONDOH (Center for Environmental Remote Sensing, Chiba University)
Objective
・To discuss the relationship between remote sensing data and land use/cover changes.
・To evaluate whether it is suitable for which indicator expressing the kind ofenvironmental change in semi-arid regions.

The relationship between PAL data and land use/cover change(Local scale)Land use change
Land use map, 1980s

Study area

Study area

・110～125°E, 40～55°N.
・It was mainly located in the semi-arid regions, northeast partof China .
・The Grassland occupies the greatest portion of land cover inthis area.
・The grassland of the semi-arid regions tend to be influenced
by the human-driven factor and the climate-driven factor.

Mongolia
er
Inn

lia
ngo
Mo

A

B

China

1980s

32.3

8.1

Later
1990s

34.9

7.8

40.8

40%

60%

0%

C

Data

Figure 1. Study area

25 layers

Table 2. Fusion of land use types of different two maps

Cultivated land

・Central area ( on the fringe of point A)
Unused land → Grassland
・South area ( on the fringe of point B)
Grassland → Cultivated land
・Southwest area ( on the fringe of point C)
Grassland → Forest

1km mesh Land-use map of China(Later 1990s)

Paddy, Irrigated field, Non-irrigated field, Paddy, Field
GardenForestTimber forest, Economic forest

Forestland, Shrub, Sparse forest, other forest.

Grassland

Pastureland, Meadow, Steppe

High, middle, and low coverage grassland

Unused land

Sandy land, Sandy desert, Gobi, Salinealkali land

Sandy land, Sandy desert, Gobi, Salinealkali land, Bare land

City, Industrial area, Habitat

City, Town, Industrial and mining area

City, Habitat of farmer, Other building site

Water

River, Lake

River, Lake, Dam, Sandy Shore

NDVImax

・ NDVImax is sensitive to land usechanges in the 3 point.
・ iNDVI is less sensitive to land use inpoint A (Unused land → grassland)and point B (grassland → cultivatedland). It can express the change of
grass to forest in point C.
・ Tmax shows similar trend of
temporal change, so it may react toannual climatic variation.
・ TRJ is sensitive to land use change inpoint A and B.

Tmax

0.7

60

0.65

55

0.6

50

0.55

45

0.5

40

0.45

35

0.4
1982

1984

1986

1988

1990

1992

1994

1996

30
1982

1998

1984

Long term change analysis (Large scale)

1988

1990

1992

1994

1996

1998

year

iNDVI

TRJ

11

150

10

130

9

110

8

90

70

7

50

6

5
1982

1984

1986

1988

1990

1992

1994

1996

1998

30
1982

1984

1986

1988

1990

1992

1994

1996

1998

year

year

The temporal change pattern which wasalike at 3 different points was shown.
This was considered to be influenced ofa climate change.

1986

year

NDVI

Number of land use types
52 layers

1996-2000

Land-use map of China, 1:1000000,(1980s)100%

Grass land

Characteristic region

Figure 3. Land use map of 1980 and later 1990s.

NDVI

1km mesh Land-use map of China(Later 1990s)

Land use types

A
B

NDVI

Period covered
1978-1987

80%

ForestOthersComparison of PAL NDVI and land use/cover change

Table 1. Property of land use maps
Name
Land-use map of China, 1:1000000, (1980s)

13.6 3.0

・It was guessed that Almost 10 % of unused
land changed to grass land.

C

The source of land use information is Land use map of China, 1:1000000 and 1km mesh Land-use
map of China. Both maps were made by Chinese Academy of Science, and they have different period
covered (table 1). Since the names of the types in the two maps were different, they were classified
into six types which unified both (table 2).

0.7

Overall trend

・ Spatial resolution: 0.1°
・ Period covered: 1982-1999

Land use map

28.0

Figure 4. Composition of land use types,
1980s and later 1990s.

Land use map, later 1990s

Pathfinder Advanced Very High Resolution Radiometer Land Data Set (PAL)・ Instrument: NOAA/AVHRR
・ Temporal resolution: 10-day

20%

Cultivated land
Unused land

30.8

Figure 5. Temporal changes of indicators at the 3 points on fig 4.
Broken line shows regression line.

NDVImax

Tmax

iNDVI

TRJ

Indicators for environmental change detection
・NDVImax: Annual max value of NDVI(Normalized Difference Vegetation Index).
It means annual max production of vegetation.
・iNDVI: Annual integrated value of NDVI.
It means annual biomass of plants.
・Tmax: Annual surface temperature.
Surface temperature was calculated by using split window method from channel 4 andchannel 5.
・TRJ: Annual slope of trajectory on Ts(surface temperature)-NDVI space.
This shows the reaction which changes with land cover types change. It shows small value
on forest and high value on grassland.

・The trend maps of NDVImax
and iNDVI may show thatannual production ofvegetation in most areas tend
to increase.
・The distribution of negative
trend of TRJ is correspondwith positively indicating
trend of NDVImax and iNDVI.
The extracted are showing
characteristic trend which wascompared with PAL NDVI andland use/cover change on local
scale.
Extract the area showing
characteristic trend

NDVImax

Tmax

Figure 6. Difference of the average value of indicators between 1982-1987 and 1996-1999.
Positive value means increasing and negative value means decreasing.

iNDVI

TRJ

・All indicators except Tmax show the signal of the change to cultivated land fromgrassland on south part.
・On the area changed from unused land to grassland, all indicators didn’t show
characteristic trend.
・Because coincidence of a distribution with land use change was not obtained, this wasconsidered that factors other than the human-driven factor have influenced strongly.

Conclusion

Figure 2.Trend of indicators, 1982-1999

・ In this study, the changes of the land use/cover in the local scale weredetected using PAL NDVI data.
・It was strongly found that the human-driven factor has more effect than the
climate-driven factor.

The characteristics of water resources in XinJiang Uyghur Autonomous Region,China,
using GIS and remote sensing
Dilnur Aji 1，Akihiko Kondoh 2
1Graduate School of Science and Technology，Chiba University
2Center for Environmental Remote Sensing，Chiba University

Introduction

Study area

Objectives

Lakes and rivers are the most importantwater resources in XinJiang，since there arelittle precipitation and melted-snow waterfrom high mountains，which are limited insummer season．The characteristics ofwater resources and the cause of thesechanges was analyzed in this research．

．To detect the water areafluctuation of main lakes．To explore the rivers outflow
change in the last 50 years．To analyze the main factors thatgoverning water resources

Used Data and Methodology

Results:Water area of lakes

DA

Socio-economic factors
250

Precipitation(mm)

14

XJ

12

SXJ

10

NXJ

7
6
5

100

4
10year MA of precipitation fromCRU data

50

10year MA of precipitatin fromWorld Climate Data
10year MA of temperature fromCRU data
10year MA of temperature fromWorld Climate Data

3
2
1
0

1956
1958
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002

0

Temperature differencing

16

9
8

150

4.0

10

8
6
4

cultivated area (10 6 ha)(1991~2000)(1951~1990)

200

Precipitation differencing

D

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, and theslight of outflow of the 23% rivers have decline. Remarkable increase
was in north XinJiang and a slight decrease was in south XinJiang
during this period.

1987~2000

population (million)

Climatic factors

C

Temperature(℃）

Rivers outflow
1956~1986

B

3.5

SXJ

3.0

NXJ

2.5

XJ

2.0
1.5
1.0

2

0.5

0
1944 1950 1956 1962 1968 1974 1980 1986year

0.0
1944 1950 1956 1962 1968 1974 1980 1986year

Validation of ocean color in the western equatorial Pacific Ocean by using in situ
spectroradiometer and ocean color satellite data
Katsutoshi Kozai (Kobe University Faculty of Maritime Sciences)
Kensuke Tanaka (Kobe University Graduate School of Science and Technology)
0.23

Chlorophyll-a concentratio

0.21
0.19
0.17
0.15
0.13
0.11
0.09
0.07
2004
1216
0617
2004
1218
0623
2004
1220
0617
2004
1222
0638
2004
1224
0617
2004
1226
0629
2004
1228
0610
2004
1230
0625
2005
0101
0608
2005
0103
0611
2005
0105
0623
2005
0107
0558

In the western equatorial Pacific Ocean high sea surface
temperature area is well known for the warm pool. This
makes the area highly convective, which in turninfluences the visible and infrared wavelength sensor
signal through clouds and water vapor. In situ spectral
radiometry for sea surface is inevitable for calibrating andvalidating physical and biological parameters such as sea
surface reflectance and chlorophyll-a concentration
without atmospheric corrections of remotely sensed data.
The purpose of the research is to not only validate sea
surface reflectance and chlorophyll-a concentration
derived from MODIS but also investigate the parameters
affecting sea surface reflectance by using a radiativetransfer code known as 6S (version 5.2B).

Date (year,month,day,hour,minute)Fig.1 Study area and the stationary point (circle on theleft figure, Dec.16, 2004~Jan.7, 2005).

MODIS-derived chlorophyllconcentration (μg/L)

Fig.4 Variation of in situ chlorophyll-aconcentration.
0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
0

0.02 0.04 0.06 0.08

0.1 0.12 0.14 0.16 0.18 0.2

In situ chlorophyll-a concentration (μg/L)

Fig.2 R/V MIRAI and onboard spectroradiometer
GER1500.

Fig.5 Comparison of MODIS-derived chlorophylla and in situ chlorophyll-a concentration.

8
7

Summary

Reflectance

6(1) The maximum spectral reflectances areobserved between 370~380nm and the minimum
spectral reflectances are observed over 700nm.

543(2) MODIS-derived chlorophyll-a concentration
was underestimated comparing with in situ
chlorophyll-a concentration.

2
1
0
350

450

550

650

750

850

950

1050

Wavelength(nm)Fig.3 Spectral reflectance curves (red: before Dec.25,
blue: after Dec.26, 2004)

Dec.164h20m（UT）

Dec.203h55min（UT）

Dec.21
4h35min（UT）

Dec.254h10h（UT）

Chiba University

Urban Change Monitoring using Former
Japanese Army Maps and Remote Sensing
J. Tetuko S.S1)., I. Indreswari S.2), and R. Tateishi1)
1) Center for Environmental Remote Sensing, Chiba University2) Department of Architecture, Institute of Technology Bandung

Abstract

Institute of Technology
Bandung

Study site

In this research, urban change area of Jakarta city, Indonesia isderived by employing old maps of VOC, former Japanese Army andIndonesian Army – US Army Joint Mapping maps, and satellite
imageries as KH-7/Gambit, Landsat MSS and Landsat TM. The
result of this research can enrich the lack of urban area spreadingof Jakarta city before 1980s. The result shows the Jakarta area
(661 km2) became urban area completely in 1990s with population
about 8.5 millions.

Indonesia

6o04’ S
Sumatera island
northJakartaJakartawest
JakartaJava island

centralJakartaeast
Jakarta

Indian Ocean
south
Jakarta

Fig. 1 Study site :map of Jakarta city, Indonesia

Methodsatellite
photographs

old maps

6o22’ S
107o00’ E

106o40’ E

satellite imageries

Sources
VOC 1887

Ground survey

former JapaneseArmy maps 1927

Joint MappingIndonesia - US 1950

digital scanning

KH-7/Gambit(19670526)

Geometric correction

Landsat MSS(19760621)

GIS processing

Fig. 2 Research flow-chart

Landsat TM(19890503)

Urban area coverage

Maps
1. VOC 1887 [1]2. Joint Mapping Indonesia Army – US Army 1950 [1]3. Former Japanese Army maps 1927 [2]Satellite images
1. KH-7 / Gambit 19670526
2. Landsat MSS 19760621 [3]3. Landsat TM 19890503 [3]estimation results

BPS [4]

percentage of urban area [%]

100

1887

1927

80

60

40

20

0
1887

1927

1950

1967

1976

1980

1990

1995

2000

2004

year

Fig. 4 Percentage of urban area versus year
BPS [4]

Susan [5]195019670526

population [millions]

10
8
6
4
2
0
1815 1900 1930 1948 1952 1961 1971 1980 1990 1995 2000 2004

year

Fig. 5 Population versus year

19760621

19890503

Fig. 3 Urban change of Jakarta city from 1887 to 1989

References
1. Pandhito Panji Foundation Archive
2. Former Japanese Army maps : Possession of the Museum of
Natural History, Faculty of Science, Tohoku University, Japan
3. Global Land Cover Facility, University of Maryland, United States
4. BPS : Statistics Indonesia
5. Susan Abeyasekere, Jakarta : A History, revised edition, Oxford
University Press, 1990.

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.

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 andthermal infrared channels of NOAA16-AVHRR. The PAL systems are located in Chiba and Ichihara city areas, separated by approximately 10 km from each other. Measurementsfrom October 2003 to March 2004 reveal that similar cloud structures are observed 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 results that are found to beconsistent with the PAL cloud observations
(K)

6
5

Introduction

Results - 8 March 2004

Cirrus

T 4 - T5 4

Cloud information such as the cloud type, structure and altitude is ofimportance for a variety of meteorological and climatological applications.
By intercepting the solar radiation, clouds have a cooling effect on theearth’s surface.
Knowledge of cloud properties can give us thethermodynamic and hydrodynamic structure of the atmosphere. The heightof an inversion layer can often be related to the cloud appearance.
In this study, we compare the ground-based lidar observations with thesatellite-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 in the cloud classification.

(BTD)

3
2

Clear area or Unclassified

Dense Cirrus

1

Cu

Cb
220

230

240

250

260

270

280

290 (K)1111-µm Brightness Temperature (TB)

- Split-Window Measurements is based on a threshold technique inthe two-dimensional histogram.
- The vertical axis is the brightness temperature difference (BTD)
between the split-window data
- The horizontal axis is the brightness temperature of 11-μm channel
data

CEReS PAL

Ichihara PAL

Clear data: noon of 8 March

Results – 5 November 2003

CEReS PAL

Visible channel of
NOAA16-AVHRR
CEReS PAL

Lidar data are obtained from two identical portable automated lidar (PAL)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 are about 10 km apart from each other(refer to figure above). Both are Mie scattering lidar systems capable ofmeasuring backscattered radiation of up to 15 km in altitude. Specifications of
the two PAL systems are given in Table 1. Both systems are equipped with
automatic realignment systems that adjust laser beam directions every 15 min
to ensure proper lidar alignment.

Table 1: Portable Automated Lidar System Specification

Laser

Ichihara

Chiba Univ.

Co-axial
380 slantpath

Co-axial
900 Vertical

LD-pumped Q-switch
Nd:YAG

Wavelength
Repetition rate

532nm
1.4 kHz

2.5 kHz

Table 2: NOAA-16 Specification Orbital characteristics

Orbit inclination

98.8 deg

Mean altitude (km)851

Equator crossing
timeNorthbound 13:54ASouthbound 1:54D

Period (min.)102.1

AVHRR characteristics

Laser energy

15mJ

resolution

divergence

50µradSwath width

1.1 km3000 kmSpectral range / IFoV

Receiver
Diameter

20cm

Type

Cassegrain

Field of view

0.2 mrad- Satellite data on 8 March 2004 at 1200H JST- clear sky all throughout Chiba area
- Consistent with PAL data

- Data on 2300H of 4Nov to 0400H of 5 Nov 2003
- Clouds seen at around 2 km for both site
- data indicative of low-altitude cumulus clouds

Ichihara PAL

Configuration

Ichihara PAL

Channel 1 (visible)

0.50 - 0.68 µm /1.39mradChannel 4 (infrared)

10.3-11.3 µm /1.41 mrad

The next figure shows the two-dimensional diagram whose axes are thebrightness temperature of the ch.4 data and the brightness temperature
difference (BTD) between the ch.4 and ch.5 data 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.
Here we have adopted slightly different threshold values (as compared toliteratures) for the cloud retrieval over the Kanto plain area. This adjustment
has been attained by inspecting visible, infrared, and BTD images. The resultshows 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 have relatively high reflectivity invisible images and relatively warm in infrared images. By this method, verythin cirrus clouds can be detected, even if they cannot be seen clearly invisible images. Cumulus cloud cover is also retrieved reasonably well.

LegendGreen – dense cirrus
Yellow – cumulonimbus
Blue – cirrus
White – cumulus
Black – blank/unclassified

Results - 3 June 2004

Cloud type graph- Satellite data on 5 November 2003 at 0100H JST-Mostly cirrus (blue) with patches of cumulus (white)- Consistent with PAL data

CEReS PAL

Ichihara PAL- Data from 2300H of 2 June to 0300H of 3 June 2004
- Clouds seen at 6 to 8 km for both site
- Slant streaks indicate falling particles inside cloud(e.g. heavy ice particles)- data indicative of mid-altitude cirrus clouds

Results – 26 August 2004

LegendGreen – dense cirrus
Yellow – cumulonimbus
Blue – cirrus
White – cumulus
Black – blank/unclassified

CEReS PAL

Ichihara PAL

Cloud type graph- Data on 26 August 2004 from 0000H to 0500H- Clouds seen at around 2 km for both site
- “shadows” formed above clouds suggesting high optical
thickness- data indicative of low-altitude cumulus clouds
Pal data displayed with altitude up
to 15 km shows geometrically thickclouds around 9 km, high altitude
indicates cirrus clouds are overlying
the lower cumulus clouds

3 June 2004 0100H JST- Satellite data on 3 June 2004 at 0100H JST- Predominantly dense cirrus (green) and cumulonimbus (yellow)- Consistent with PAL data

Conclusion
Cloud classification using NOAA16-AVHRR
using split-window technique

LegendGreen – dense cirrus
Yellow – cumulonimbus
Blue – cirrus
White – cumulus
Black – blank/unclassified

Cloud type graph- Satellite data on 26 August 2004 at 0200H JST-Mostly cirrus (blue) with patches of cumulus (white)- Consistent with PAL data

2 PAL systems located 10 km from eachother validate satellite data
Local vertical temperature profile needed
for better cloud top height estimation
When satellite data has both cumulus andcirrus clouds, lidar data show only the
low lying cumulus

Comparison of monitoring applicability between
Crop Production Index and conventional methods
using satellites

3. ModelingPSN =the stomatal opening，a and
By Daijiro KANEKO
Matsue National College of Technology,
Department of Civil and Environmental Engineering,
14-4, Nishiikuma-chou, Matsue, 690-8518 Japan.
E-mail: kand@ce.matsue-ct.ac.jp

1. Backgrounda ・ APAR・ f Syn ( T c ・
) β・
eLAI
s
b + APAR

Bad harvest(4)

where PSN is the photosynthesis rate，APAR is the absorbed photosynthetically active radiation, βs isb are Michaelis-Menten constants,

is the effective leaf area index, andThe authors' former paper

16),

Tc

is the canopy temperature, eLAI

Crop indicesfster is the sterility response function for the air temperature.

GDD
iNDVI

presents sensitivity analysis curves for the Michaelis-Menten-type

Short of Solar radiation

known as the Sigmoidal-Logistic type function :The demand for crop production will rise as a result of population
growthSterility1f Syn (Tc ) =  1 + exp {k syn (Tc − T hv )}

(5)

Stomata control

NDWI,SIWSI
BMDI

response function versus solar radiation and the temperature response of the photosynthesis rate as well

Good harvesting

Low air temperature
drought

in China and India.
where Thv

1) Rice is an excellent grain food, which supports the gigantic population in
China and India.
2) The amount of rice export is small compared with the corn and the wheat.

is the temperature parameter at half of the maximum photosynthesis rate, and ｋsyn

The temperature response functions for low-temperature sterility and high-temperature injury are definedby the following equation, referring to the curves obtained by

3) Problems with the rice production could therefore have a catastrophic effecton countries that import rice and other crops.
4) Global warming makes both heavy rain and drought more likely, increasing
fluctuations in the pattern of precipitation.

2. Objectives

is theslope parameter.

(6a)

FHster (Tc ) = 1 − exp [k Hster (Tc − THster )](6b)where，kLster is the low temperature sterility constant,
the high temperature injury constant, THster

Ristriction ofwater resources

Water stress

Vong and Murata 21):FLster (Tc ) = 1 − exp[k Lster (TLster − Tc )] ,TLster is the low sterility limit temperature,ｋHster isFig. 3 Main causes of bad harvest and relating factors for monitoring
rice production in the era of water resources restriction.

is the high injury limit temperature, and Tc is the plant leaf

temperature.

This paper compares the applicability of the Crop Production
Index CPI with conventional methods and proves the ability of the
CPI to predict poor crop production using rice yield statistics,
contrasting it against conventional methods such as cumulative
Growing degree day GDD, integrated NDVI, and photosynthesis rate
PSN.

Finally, the response function of the compounded temperature sterility effects due to both low and high

4. Data used in the modelingtemperatures in grain 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 plantstage defines the photosynthesis-based crop production index CPI for rice having the following forms:

Emergent Import by Japan

220$

450

420$

During crop plant stage 1, of growth:275$

Export Price ($/t)

Rice
400

Wheat

Thailand

350

Maize
300
250
200

USA

150
100
50

USA

0

1985

1990

1995

As a result ofJapanese imports of
2.88 Mt of rice
following Japan’s
worst harvest in 1993,
the price in Thailandrose from $220/t to$420/t in just six
months. It then fell
drastically to $275
once Japan ceased
these imports. This
example shows thepotential for havoc in
Asian rice markets if aharvest is poor.

t

CPI U = ∫ PSN U ⋅ dt

(7)

ts

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

CPI U = FSter (Tc ) ⋅ ∫ PSN U ⋅ dttst

FSter = ∫ f Ster (Tc ) ⋅ dt

(7a)

(7b)

tf

1) Meteorological Data
The ground air temperature data, which are supplied by theJapanese Meteorological Agency from the AutomatedMeteorological Data Acquisition System (AMeDAS) point at tensites, distributed in the Japanese agricultural plains, have large
acreages suitable for satellite monitoring of the paddy fields.
2) Crop statistics
The Japanese Ministry of Agriculture, Forestry, and Fisheries
provides grain statistical information, which includes crop situationindex for the paddy rice at ten sites for monitoring and validation
district. This crop situation index is the ratio of crop production in
the year in question to the mean annual production for the ten mostrecent years.
3) NDVI
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 Tateishi(2001).

2000

At the crop plant stage 3 of harvesting:

Year
tr

CPI U = FSter (Tc ) ⋅ ∫ PSN U ⋅ dt

Fig. 1 Sudden rise and fall in trade price of rice comparedts

to cheaper prices of other grains in main trading countries.

tr

FSter = ∫ f Ster (Tc ) ⋅ dt

(7c)

Paddy: Land cover classification
(7d)

0.0

tf

Modeling of photosynthetic crop production index

NDVI is liable to differ each year.
dependent on the individual sites.
Vegetation biomass

Hakodate

Ohogata

The plant coverage ratio per data pixel of remote sensing is

Karikawa

Furukawa

Kurashiki

NDVI unit

Kohoriyama

Global Solar radiation

World Weather Data

Crop characteristics
Crop situation index

Effective temperature
Daily Radiation
Growing Degree Day GDD

Water stress
s

t

r

e

s

s

Ref. spectrum
NDWI, SIWSI

Satellite data

Paddy
test site

The photosynthesis rate is similarly normalized to give the ‘PSN Unit’ upon dividing by the normal

Matsue City

Hachikai

MODIS, ASTER
GMS

Modeling of CPI

1.0

NDVI

Kuki

(8)

NOAA

Yield, Crop calendar
Sterility temperature

0.5

Shizukuishi

Maki

AMeDAS
Air temperature

M. N.
College

IwanaiWe therefore define the standardized NDVI, called the NDVI Unit, by

dividing by the annual average yield as follows:

NDVI, eLAI

MeteorologicalObservatories

Shinshinotsu

It is necessary to normalize the NDVI so as to remove the effect of planting area (plant coverage ratio)on the photosynthesis rate at different paddy sites. Even if the crop yield in a year was the norm, theKurumephotosynthesis rate averaged annually, as follows:
Crop production index CPI
Cumulative GDDIntegrated NDVI iNDVIIntegrated Photosynthesis iPSN

(9)Sterility functions

Integrated photosynthesis

Fig. 2 Flowchart of research on crop production monitoring anddevelopment of photosynthesis type of crop production
index using satellite remote sensing and world weather data.

The EPIC (Erosion-Productivity Impact Calculator) uses the same idea to normalize the effect of
accumulation of Growing Degree Day, by defining the Heat Unit Index.

Fig. 4 Distribution of NDVI and monitoringsites in Japan for validation of Crop Production
Index CPI.

Fig. 5 Distribution of Paddy
fields (red areas) in Matsue Cityand paddy test site.

120

Crop Situation Index

Shinshinotsu
Iwanai
Hakodate
Karikawa
Ohogata
Shizukuishi
Furukawa
Kohoriyama
Maki
Kuki

Figure 8 shows the performance of the integrated photosynthesis rate iPSN used
as a crop yield index. The crop situation index decreases linearly with iPSN butshows 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 or iNDVI since it is linear with respect tocrop yield; however, the iPSN values show considerable scatter arising fromdependence on regional conditions.

0
0

50

100

150

200

Integrated NDVI

120

2000

3000

4000

400
300
200

Cumulative GDD

100
0
0

50

100

150

200

Integrated NDVI

80

Shinshinotsu
Iwanai
Hakodate
Karikawa
Ohogata
Shizukuishi
Furukawa
Kohoriyama
Maki
Kuki

60
40
20

400
300
200

0

Relations between the integrated NDVI and Cropsituation index and between yield and iNDVI.

0

0.5

1

Crop Production Index
120

Crop Situation Index

500

Fig.7

Shinshinotsu
Iw anaiH akodateK arikaw a
O hogata
Shizukuishi
Furukawa
K ohoriyam a
M aki
K uki

100

100

80
60
40

1.5

CPIU

Fig. 9 Relation between the crop production index CPIU
and Crop situation index at ten monitoring points.

20(4) Photosynthesis type of crop production index CPI

0
0

0.2

0.4

0

0.6

0.8

1

1.2

1.4

Figure 9 shows the relation between crop situation index CSI and the CPIU,
to verify applicability to rice yields at 10 sites in Japan. Abnormal weather
with low temperature and much cloud causes low temperature sterility andlate ripening of rice. The CPIU then rapidly falls to zero, since the limiting
problem is not photosynthesis but inadequate flowering and late ripening in
the worst case of the crop situation index below 50 in 1993.

Integrated PSN

0

1000

Fig.6

2000

3000

Cumulative GDD

4000

Comparison of monitoring applicability between Crop
Production Index and conventional methods. These figures
show relations between the cumulative growing degree day GDD
and Crop situation index at ten monitoring points and between
yield and cumulative GDD.

700

Yield （Kg/10a)Yield （Kg/10a)

Shinshinotsu
Iwanai
Hakodate
Karikawa
Ohogata
Shizukuishi
Furukawa
Kohoriyama
Maki
Kuki

jury

100

700
600

High T. In

esis

ili
ty

500

synth

er

Shinshinotsu
Iwanai
Hakodate
Karikawa
Ohogata
Shizukuishi
Furukawa
Kohoriyama
Maki
Kuki

Photo

St

600

T.

700

0
1000

40
20

20

0

60

w

40

80

Lo

60

Shinshinotsu
Iwanai
Hakodate
Karikawa
Ohogata
Shizukuishi
Furukawa
Kohoriyama
Maki
Kuki

Crop Situation Index

80

Yield （Kg/10a)

Crop Situation Index

120
100

(3) Integrated PSN (iPSN)
100

600

Shinshinotsu
IwanaiH akodateK arikawaO hogata
Shizukuishi
F urukaw a
K ohoriyam a
M aki
K uki

500
400
300
200
100

6. Conclusions

00

5. Results of crop production indices(1) Growing index GDD
Figure 6 shows relations between the crop situation index CSI
and the cumulative growing degree day (GDD), and between theyield 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 fromheading to flowering of the grain. This sterility effect on thepollination is not linear in temperature, but cuts in rapidly below
a threshold of about 18 degrees centigrade.

0.2

0.4

0.6

0.8

1

1.2

1.4

Integrated PSN

Fig.8
Monitoring applicability of integrated PSN to crop
production. These figures show relations between crop situationindex and integrated PSN and crop yield between iPSN.
(2) Integrated NDVI (iNDVI)
Figure 7 shows relations between the crop situation index and theintegrated NDVI (iNDVI), and between yield and the iNDVI. The
integrated NDVI is not able to predict either the crop situation index or
the yield, and in particular is unable to predict a bad harvest due to lowtemperature sterility. The iNDVI values depend strongly on regional
characteristics such as soils, type of rice and mixcel effects, involving
other plants (vegetables, trees, etc.).

1.

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

2Geologic Information Retrieving using Synthetic Aperture Radar
Situation map

North

SumateraislandRoc1

Lampung

Lampung bay
Legundi island

Roc2
Panjang island
Banten

Su
nd
a

st
ra
it

Krakatau
volcanocomplex

6o04’

ancient Krakatau
60,000 years ago

by Josaphat Tetuko Sri Sumantyo, Ph.D

Sertung island
Rakata island

The birth of Krakatau volcano complex

Retrieved Geologic Map of
Krakatau Volcano Complex

N

Chiba University

Roc1

Panaitan island

0

Sertung island

Smd

Java island

RakataPanjang island

15 km

25

Anak Krakatau island

125
150

A

Roc2

Smd

175

Pca1

0

Smd

1

Roc2
Smd

sertung
north

Roc2

Pca1
Lav 1972
Lav 1972/1973
Lav 1963

growing up of Rakata,
Danan and Perbuwatan
volcanoes 17th century

Danan
Perbuwatan

HalD

Lav 1979 E

Hal

Pca1

5

125
100

Lav 1972/1973

75

25

200

50

PanjangHal

12

Sertung

Rakata

Smd

100

Pca2
100 75 50

25

F

75

Roc2
Pca1

Roc2

C

50

Smd

Hal
6o06’

Roc2

Pf 1883100

calderabefore 1883

25

50
75

Pf 1883

Panjang

after gigantic eruption withthe lost of volcano domein 60,000 years ago

RakataHal

Rakata, Danan and
Perbuwatan volcanoes
activities stopped in 1681.

2km

Panjang
Danan
Perbuwatan
Sertung

Map legend
Lav

6o08’

Lavas were formed in 1963, 1972, 1975, 1980

Pca2 Pyroclastic airfall layer of ash, scoreae, lapilli, sand andbasaltic andesit bombs forming a younger active cindercone cindercone inside the old crater in October 1956.

450

Bal

calderaafter 1883

Smd

Hyperstene augite andesite lava flow and silica.

22 2
400
20 5 25075 300350
17 0
5
15
12 0
10 5
0

Pf 1883 Pyroclastic flow, intercalated by airfall deposit, surge
Basaltic lava

Sertung

800
700
650
600 550
500

Smd

Rakata75 0
5

Roc1 Rock distribution

Krakatau volcano complex
growing slowly nowadays Panjang
and always create
spectacular history …

Smd

25

Roc2 Estimated rock distribution

paroxymal explotion of
Danan and Perbuwatan
volcanoes on August 27,
1883

PanjangBal

Roc2

Smd Material deposit of the rock such as silt, sand or block size,
andesites, basaltic andesite, and obsidian fragments
Hal

RakataRakata island

Hal

Pca1 Pyroclastic airfall layer of ash, scoriaeous lapilli, sand andbasaltic andesit bombs exposed in 1982.

Anak krakatau

SertungB

6o10’

Validation maps and code reference :* ESDM Krakatau Volcano Complex map 1986
* Simon Winchester, Krakatoa 2003

Roc2
Pf 1883

105o24’105o22’

Pr
e-h
isto
ric

Lav 1972/1973

105o28’105o26’

Anak Krakatau island
Pca2 Pca1 Pca2

Kr
ak
ata
u

1000m

Panjang island
Hal

750m

Pf 1883
Smd

500m
250m

Sea level

0m

2000m

Kr

u
ata
ak

1000m

Sertung islandHal
Pf 1883

83
18
ePr

Hal

Sea level

ata
ak
Kr

Bal

u

500m

Pf 1883

B

Radar response
NCRS : Normalized Radar Cross Section

-10
-20
-30

Sertung island

Hal

ric
to
his
ePr

at
ak
Kr

au

Anak Krakatau island
Pca1
Lav 1972/1973 Pca2
Pca2

Pf 1883
Sea level

C0
NRCS [dB]

NRCS [dB]

A

Pre
-his
tori
c

Kra
kata
u

1000m

Panjang island
Hal

750m

Pf 1883

500m

Smd

250m
Sea level0m

DRadar response

-10
-20
-30

North

North

Sertung island

Panjang island

Anak Krakatau island

Rakata island

JERS-1 SAR 19980731

Radar response

F

-10
-20
-30

0m

0

E
0

1500m

Rakata island

NRCS [dB]

P

c
tori
his
re-

Sertung island

Panjang island

Anak Krakatau island

Rakata island

Landsat TM 19920518

Historic eruptions and recent activities
Krakatau was well known and became famous in the world because of
the paroxymal eruption on August 27, 1883 and it was considered to agreat event of the eruptive history, which has erupted more than 18 cubic
km of ash column of about 80 km high, and rose tsunami as high as 30 m
along the west coast of Banten and south coast of Lampung. Although atthat times the big cities have not developed as such today, but 295 small
towns were swept by tsunami and killed 36, 417 people (Verbeek,1884),
and the explosion could be heard at Africa, Australia, Philippine etc or
4800 km far away. Krakatau began to be known in the eruptive history
when the gigantic eruption occurred in 416 BC, which caused a tsunami
and formed a caldera (Judd, 1889). Furthermore De Neve (1981) notedsome information that before the second paroxymal occurred in 1883,
several large eruptions of Krakatau arose in the centuries of
3,9,10,11,12,14,16 and 17 which were followed by growing up of Rakata,
Danan and Perbuwatan volcanoes. This volcanic activity stopped in 1681.
After reposing of about 200 years, Krakatau began to indicate its activity
which has preceded by the eruptions of Danan and Perbuwatan
volcanoes. On May 20, 1883 the eruption of Perbuwatan volcano withbasaltic composition, initiated a paroxymal explosion on August 27, 1883
(Neumann, 1951).
Second repose period of Krakatau began in February 1884 to December
1927, when the first phase of eruption on December 29,1927 occurred asa submarine explosions in the center of Krakatau Volcanic Complex. The
visible phenomena of the eruption comprised bublings and fountains ofseawater (Stehn, 1929a). It was observed on January 20, 1929 the craterrim appeared east of the eruption point, consisting of ashes, lapilli, loose
block. It formed an island above the water and was named "AnakKrakatau" (Child of Krakatau). The eruptions continued until February 15,
1929 where the explosion column reached a height of 800 m.
Observations on February 18, indicated that the eruption was no longer
visible and the Anak Krakatau island was shaped like a sickle, opening to
the south west, and which had reached a height of 38 m above sea level.
This event was declared by Stehn as a birthday of Anak Krakatau.
(Article source: Directorate of Volcanology and Geological Hazard
Mitigation, ESDM, Indonesia and Krakatoa by Simon Winchester)Contact person :Josaphat Tetuko Sri Sumantyo, Ph.D
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 3857
Email jtetukoss@faculty.chiba-u.jp
http://www2.cr.chiba-u.jp/lab/jtetukoss/Satellite Observation and Analysis of
Terrestrial Environmental Changes
Development and Publication of Land Cover and Vegetation Datasets at Regional and Global Scales
H. Sasakia, H. Nakajimaa, T. Okatania, Y.Numataa, Y. Yamadaa and R. Tateishib
a: Geographical Survey Institute, b: Chiba University
Summary: With the participation of 155 National Mapping Organizations of all over the world, ISCGM (International
Steering Committee for Global Mapping), whose secretariat is hosted by Geographical Survey Institute of Japan, hasbeen developing “Global Map.” Global Map is global digital geographic framework datafor sustainable development
which describes global environmental status and its changes.
ISCGM places a special emphasis on raster formatted global land cover data and percent tree cover data
development, in the second phase of Global Mapping to be completed by 2007. As one of central activities of ISCGM,
this joint study aims to explore effective acquisition methodology of these raster data by fully utilizing satellite remotesensing technology. Followings are the outline of Global Map and expected outcomes of this study.

What is Global Map ?
Digital geographic information in 1 km resolution (at app. 1: 1 million scale)
To cover the whole land of the globe in consistent specifications under international cooperation
Includes 8 data layers

Transportation

Population Center

Drainage

Boundaries

Land Use

Vegetation

Land Cover

Elevation

Status of Global Mapping Project
155 countries/areas participating
Data available

21 countries released
Data for verification

Global map data

Data developing

Considering joining the project

As of December 2005

Not participating in the project

Australia

Japan

Mongolia

Sri Lanka

Bangladesh

Kazakhstan

MyanmarSwaziland

Botswana

Kenya

Nepal

ThailandBurkina Faso

Kyrgyzstan

PanamaColombia

Lao P. D. R.

Philippines

Iran

MexicoSamoa

This map is for the purpose of reference and the boundaries in this map are not authorized by any organizations.

Expected Outcomes of This Study
Tree Canopy Coverg

Land Cover(Example;Kenya)
LegendEvergreen Needleleaf Forest
Evergreen Broadleaf Forest
Deciduous Needleleaf Forest
Deciduous Broadleaf Forest
Mixed Forest
Closed Shrublands

Ground Truth data

Open Shrublands
Woody Savannas
Savannas
Glasslands
Permanent Wetlands
Croplands
Urban and Built-up
Cropland/Natural Vegetation Mosaic

Snow and Ice
Barren or Sparsely Vegetated

Water Bodies

Secretariat of International Steering Committee for Global Mapping at Geographical Survey Institute, Japan

URL: http://www.iscgm.org/

e-mail: sec@iscgm.org