TECHNICAL SUMMARY

I EMIntegrated Environmental Monitoring

APEIS-IEM Web sites :http://www-basin.nies.go.jp/english/
project/iem/index.html

June 2004

APEIS-IEM

Technical Summary

1. What is APEIS-IEM?
Rapid population growth and economic development in the Asia–Pacific region has resulted in serious local, national,
and regional environmental problems such as floods, droughts, forest fires, dust storms, air, water and soil pollution,desertification, salinization, water resource depletion, and soil loss. Such problems are serious constraints tosustainable development in the region. In order to cope with these problems, an integrated environmental monitoring
system (IEM) is indispensable. The purpose of the APEIS-IEM project is to accurately describe the present conditions
and trends of ecosystem services, as well as the pressures and impacts upon them, for policymaking to promote
regional sustainability (Fig. 1).

・Desert・Forest・Agricultural land
・Rivers & lakes

・Semiarid area
・Wetland・Grassland
・OceanEcosystem Services

Mitigation

Ecosystems

・Water・Food・Biodiversity
・Carbon sequestration
・Flood control

Feedback

Mitigation

ns
its
tio
c
ef
a
n
r
te
Be
In
ry
a
l
cil
An

Impacts

ImpactMitigation

Pressure・ Climate Change
・ Land use/cover change
・ Dam construction/water transfer
・ Pollutant/nutrient loads

Stresses

Adaptation+ Flood control+ Water resources+ Food production+ Economic development- Eutrophication- Soil degradation- Biodiversity- Pollutant loads

Feedback

Policy・Land use management: Forestation / Return cultivated land to forestry / soil preservation /Return reclaimed farmland to lakes / Relocate towns
・Water resources management: Flood control / Waste water treatment / Reuse of treatedwater / water saving
・Innovative technologies: Zero emission / Reducing-recycle-reuse waste and water

Figure 1. Interactive relationships among policies and ecosystem services

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APEIS-IEM has developed an IEM system that can be used to detect, monitor, and assess environmental disasters
and degradation, and their impacts in the Asia–Pacific region. The system provides validated remote sensing data andimages from MODIS (Moderate Resolution Imaging Spectrometer), and derived ecological indices, such as water
deficit index, dust storm index, land surface temperature (LST), and net primary productivity (NPP). APEIS-IEM has
also developed an integrated model to assess the state of and changes in ecological goods and services, such as
freshwater resources, carbon and nitrogen cycles, and food production. With this model, the trade-off between
ecosystem services and effectiveness of policies for sustainability can be demonstrated (Fig. 2).

MODIS Station Data
Flux tower
Ecological stations

Carbon cycling
model
Nitrogen cycling
model

RainfallPollutant load
inventory model

Ice & snow
ForestEvapotranspiration
Soil moisture
Databases
・Socio-economic
・Geographical・Soil & nutrient

RunoffDam

Evaporation
Grass

Urban
Lake

Groundwater

Sea

Water & heatcirculation model

Food production &consumption model

Integrated model

Figure 2. Integrated environmental monitoring system for the Asia–Pacific Region
The approaches of APEIS-IEM consist of the following components:•

To establish the APEIS-MODIS network, a network of satellite data receiving stations and analyticalsystems for MODIS data that covers the Asia–Pacific region•

To establish the APEIS-FLUX network, a ground-truth observation system to validate satellite remotesensing data for various ecosystem types•

To develop a data-processing software system to derive environmental indices that can be used tomonitor environmental disasters and degradation•

To develop an integrated model to simulate territorial ecological processes, water resources, andagricultural production at a watershed scale in the Asia–Pacific region22. Current Progress and Contributions of APEIS-IEM
2.1 Enhancement of Monitoring System
The integrated monitoring network system established by APEIS-IEM now covers most of the Asia–Pacific region, andcontinuously provides real-time data of both remote-sensing and ground-based measurements in 2003 (Fig. 3). The
MODIS images are publicly released from the APEIS-IEM website, and the original data are planed to be released on
the Internet. (Web site: http://www-basin.nies.go.jp/english/project/iem/index.html )FukangUrumqi

Beijing

Yucheng

FukangHaibei Yucheng

Tsukuba

Taoyuan
Qianyanzhou

Taoyuan

HaibeiSingapore

Flux Network- 5 Eco-stations

Qianyanzhou

Australia

MODIS Network- 4 Receiving stations- 4 Data analyzing centers
Figure 3. The APEIS integrated monitoring system was established in 2002 under the auspices of the National
Institute for Environmental Studies (NIES) in Japan and the Institute for Geographical Sciences and Natural Resources
Research (IGSNRR), the Chinese Academy of Science in China, and was expanded with additional participation by the
National University of Singapore (NUS) and the Commonwealth Scientific and Industrial Research Organization(CSIRO) in Australia

2.2 Establishment of APEIS-FLUX Network
The MODIS validation sites form a network that not only provides validation data for MODIS products, but also
provides valuable data for research on fluxes of water, heat, and carbon in the Asia–Pacific region. APEIS-FLUX, as
this network has been named, appears in academic journals, newsletters, and documents of the Millennium Ecosystem
Assessment. APEIS-FLUX is a real-time monitoring system comprising five sites in different ecological systems in
China: Haibei (grassland), Yucheng (crop land), Taoyuan (paddy field), Qianyanzhou (forest), and Fukang (saline
desert). Data can be accessed through a mobile modem at any time and place, allowing analysis of observed data at
any time. This design allows comparison of a given vegetation type across different locations, and of multiple

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vegetation types within a given geographic area. The dataset contains eddy-correlation flux measurements ofsensible heat, latent heat, and CO2; micrometeorological measurements of air temperature, wind speed, and winddirection; measurements of total solar radiation, net radiation, and photosynthetically active radiation and soil watercontent, groundwater level, and salinity.
The major task of APEIS-FLUX is to measure the differences in fluxes of water, heat, and carbon in different
ecosystems. By comparing five different ecosystems (forest, grassland, desert, crop land, and paddy field), an
ecosystem model can be developed and calibrated to predict and evaluate the response of ecosystems to different
forces (Fig. 4).

Fukang Station / Saline Desert, 2003
1200.040

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H, LE (Wm )Taoyuan Station / Paddy Field, 2003
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H, LE (Wm )Yucheng Station / Crop Land, 2003
1200

Figure 4. Observations of sensible heat flux (H), latent heat flux (LE), and CO2 flux in 2003 at Fukang (desert),
Yucheng (crop land), and Taoyuan (paddy field) at 30-min intervals in 2003. At the Fukang desert site, the sensible
heat flux was larger than the latent heat flux, and CO2 flux had a very small annual change. However, at the Taoyuan
paddy field site, the latent heat flux was much bigger than the sensible flux, and the CO2 flux was large during thewhole year compared with other sites. At the Yucheng site, there was a high evaporation rate and high CO2 fixation at4the beginning of June, and from the middle of July to the end of September, the major growing season of corn. It isclear that the dynamics of the water, heat, and carbon fluxes reflect the local plant growth patterns
The second task of APEIS-FLUX is to develop a methodology and models to estimate the spatial distributions of water,
heat, and carbon fluxes from MODIS data. Data such as MOD09 for surface reflectance, MOD11 for land surface
temperature and emissivity, MOD12 for land cover, MOD13 for NDVI (Normalized Difference Vegetation Index),
MOD15 for LAI (Leaf Area Index) and FPAR (Fraction of Photosynthetically Active Radiation) and MOD17 for NPP,
developed by the data-processing system, can be used to develop models to estimate the spatial variations in water,
heat, and carbon fluxes in terrestrial ecosystems. With the help of Global Positioning System (GPS) and global
information system (GIS) software, the temporal dynamics of MODIS-generated variables can be validated against
APEIS-FLUX observations in different ecosystems.
The third challenge of APEIS-FLUX is to develop ecosystem models for understanding the mechanisms and processesof the water, heat, and carbon cycles in terrestrial ecosystems, both in natural ecosystems (e.g., forests, grasslands,
and deserts) and in managed agricultural ecosystems (e.g., crop land and paddy fields). These models will rely mainlyon MODIS data as input variables and are expected to accurately simulate the natural processes of water, heat, andcarbon cycles in terrestrial ecosystems and the effects of human disturbance.
2.3 Improvement of the Data Processing System
A network of data-analyzing centers at NIES in Japan, IGSNRR in China, NUS in Singapore, and CSIRO in Australia
was formed in 2003 under the umbrella of APEIS-IEM. These centers store both a wide variety of satellite data andvarious ground-based measurements. The data processing system for deriving the higher-order MODIS products was
also improved (Fig. 5). Fig.6 shows an example of 16-day NDVI composite with a resolution of 500m in China.

APEIS-FLUX
Network-2(C O2, mol m )NDVI
0.0 0.2 0.4 0.6 0.8 1.0

12/26

12/11

APEIS-MODIS
NetworkLE
H
CO2

Meteorological
Data

Soil Data

LAI

1.Respiration
2.Temperature
3.Water content
4.Organic matter

Digitalelevation
model

LST，℃

NDVI

Remotesensing model

Ecological model
Validation

11/26

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Water, Heat and CO2 Fluxes at Yucheng Station, 2003
1200

Meteorological models
1. Radiation transfer
2. Energy balance
3. Eddy covariance
4. Vapor pressure deficit

Land-surface processmodel

0 10 20 30 40 50 60

FPAR
0.0 0.2 0.4 0.6 0.8 1.0

Water, Carbon, NPP, Crop Production
Crop-growth model
LAI

Planting, Harvesting, Irrigation, Fertilizer Application

Figure 5. Flowchart of APEIS data processing

5

0

2

4

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8 10

Figure 6. Map of 16-day NDVI composite with a resolution of 500m in China, 08-23, July-2002
2.4 Providing Validated Satellite-Derived Indices
Although numerous satellite-derived indices in the Asia–Pacific region have already been produced by other projects
and organizations, most have yet to be calibrated or validated by ground-truth data and they might contain significant
uncertainties. APEIS-IEM has established five validation sites in a variety of ecosystems in China (grassland, crop land,
paddy field, forest, and semi-arid area) at which ground-truth data—including information related to radiation,
meteorology, soil, and vegetation—is continuously measured. Using these consistent and quality-assured datasets,
APEIS-IEM can produce accurate and reliable information specific to the region (Fig. 7).
LAI IEM

LAI MODLAI Obs

31-Jan31-Mar 30-Apr 30-May

6
5
LAI

4
3
2
1
0
1-Jan

1-Mar

Month (2002)
Figure 7. Comparison between APEIS-IEM product of MOD15 (LAI IEM) and that of NASA product of MOD15（LAI
MOD）in 2002, which shows LAI MOD has poor agreement with observations (LAI Obs). One of the reasons isthat misclassification of land lover leads to errors in NASA product.

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2.5 Contributions to Scientific and Technological Progress
Scientifically, APEIS-IEM contributes to mapping of land-cover types, identifying land-cover change, estimating thewater level and volume of lakes, measuring land-surface attributes as inputs to ecosystem models, providing scenarios
for realizing sustainable development through model simulations, and simulating the response of agricultural
production to climate change.
(1) Mapping land cover types
During the 1980s, pioneering research was conducted to map vegetation at continental scales, primarily with dataacquired by the U.S. National Oceanographic and Atmospheric Administration’s (NOAA) meteorological satellite, theAdvanced Very High Resolution Radiometer (AVHRR). In the 1990s, AVHRR data were used to map land coverglobally at increasingly higher spatial resolutions: the first global land cover classification was at a resolution of 1°× 1°(approximately 110 × 110 km); this was followed by classification at 8 km × 8 km resolution and finally at 1 km × 1 km
resolution. A suite of recently launched sensors, including MODIS, SPOT Vegetation, and GLI, with their improved
spectral, spatial, and radiometric resolutions, provide. An initial result of the APEIS-IEM project is the development of aset of maps of land-cover type in the Asia–Pacific region at 1 km × 1 km resolution by using MODIS satellite datasets(Fig. 8).

Evergreen Needleleaf Forest
Evergreen Broadleaf Forest
Deciduous Needleleaf Forest
Deciduous Broadleaf Forest
Mixed Forest
Shrublands
Grasslands
Croplands
Urban and Built-Up Areas
Snow and Ice
Barren or Sparsely Vegetated
Water Body
Other

Figure 8. Land-cover types in 2002 derived from MODIS data
(2) Identifying land-cover change
One of the most significant contributions to be gained from satellite data is the identification and monitoring ofland-cover change. Data acquired by Landsat and SPOT have been the primary sources for identifying land-coverchange in particular locations. As one of the partners of IEM, the IGSNRR of the Chinese Academy of Sciencedeveloped a series of maps of land-use change in China. In addition, they used the Land-Use Dynamic Degree model
to calculate the degrees of land use change in China. The result shows that the change degree of the eastern part ofChina is larger than that of the western part. In the eastern part, the change degree is highest in the southeastern
coastal area, the lower reaches of the Yangtze River plain, the northern part of the North China Plain, and theNortheastern China Plain, but the change degree is very low in the middle and southern parts of the North China Plain.
In the western part of China, the degree of the middle and eastern parts of Inner Mongolia is very high, and that in thewestern part of Xinjiang, the Sichuan Basin, and the Yunnan Plateau is also relatively high. However, those of the7

Tibetan Plateau and the western part of Inner Mongolia are very low. Furthermore, the IGSNRR studied thespatial–temporal dynamic change of land use in China from 1990 to 2000, based on analyses of the basic
characteristics of change of major land-use types. They found that over the research period, areas of cropland, towns,
rural residences, and water bodies increased in both eastern and western parts of China; the rate at which areas ofcropland increased was higher in the western part. The areas of woodland and grassland decreased in both eastern
and western parts of China over the past ten years; the rate of decrease was higher in the eastern part. The area of
unused land in the eastern part of China decreased over the research period; however, the area of unused land in thewestern part of China increased over the same period (Fig. 9).

(103 hm2)
1500.00
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Paddy
Dry farming
ForestShrub
Woods
Others
Dense
Moderate
SparseRivers
Lakes
Reservoir
Permanent
Beach and
Bottomland
City builtRural
other builtSand
Gobi
SalinaWetlandBare Soil
Bare RockOther
sea area

0.00-1500.00

Figure 9. Land-use changes from 1990 to 2000 in China. Areas of cropland, towns, rural residences, and water
bodies increased in both eastern and western parts; the area of cropland increased at a higher rate in the western part.
Areas of woodland and grassland decreased in both eastern and western parts; the rate of decrease was higher in theeastern part. The area of unused land in the eastern part decreased; however, that in the western part increased.

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To predict both quantitative and qualitative changes in the production and runoff of pollutant loads brought about by
artificial changes in the environment, APEIS-IEM is developing a comprehensive method to estimate changes inpollutant load runoff due to land-use/land-cover change in the Changjiang basin (Fig. 10).

1990

2000

1990

2000

8000002( km )

600000400000200000

ar
ea

la
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at
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gr
as
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ar
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ar
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le

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Figure 10. Land-use change from 1990 to 2000 in the Changjiang basin, China. (1) Areas of arable land and forest
have decreased as shown in Figure 8. On the other hand, areas of grassland, water bodies, built-up areas, and unused
land have increased. (2) The decrease in area of arable land is obvious, among which most has been converted tobuilt-up areas, water bodies, or forests. (3) Areas of built-up regions greatly increased. Most were converted fromarable land and forest. (4) Of the many kinds of land-use change, the main types in the Changjiang basin areconversion of arable land to built-up area, forest to grassland, grassland to forest, forest to arable land, arable land towater body, arable land to forest, and grassland to arable land.
(3) Estimating the water level and volume of lakes
In 2002, APEIS-IEM used Terra/MODIS satellite data to estimate the dynamic variations in surface water area andwater storage of Dongting Lake. Firstly, the whole of Dongting Lake was divided into three parts according togeographical characteristics (WDL: West Dongting Lake; SDL: South Dongting Lake; EDL: East Dongting Lake).
Secondly, surface water area was determined from spectral differences between water and terrestrial areas by using
satellite (Terra/MODIS) NDVI data. Thirdly, the surface water area was overlaid onto the lake bottom DEM data (gridsize of 50 m × 50 m) to calculate water level. Finally, the water storage in each water column was obtained bymultiplying water depth (difference between water surface elevation and lake-bottom elevation) by grid area. The total
water storage of Dongting Lake was calculated by adding the values for all water columns for the whole lake. The
estimated water level and storage corresponded well with actual measurements in the three parts of Dongting Lake
(Fig. 11), which implies that the method is an effective approach to estimating water level and water storage of large
lakes.

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EDL
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5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5
5 5 5 5 5 5 5 5 5Month (2002)
Figure 11. Comparison of water volumes in three lakes of Dongting Lake in 2002 calculated from measured data andfrom estimated data based on MODIS satellite images. ●: measured from data at each station; ○: estimation basedon MODIS image analysis(4) Measuring land-surface attributes as inputs to ecosystem models
Satellite data and ecosystem models provide spatially comprehensive estimates of parameters such as
evapotranspiration, primary productivity, fraction of solar radiation absorbed by photosynthetic activity (FPAR), leaf
area index (LAI), and percentage of solar radiation reflected by the surface (albedo) (Fig. 5). These parameters are
related to several ecosystem services such as water resources, food, and carbon fixation. Satellite-derived parametersprovide an important means for linking changes in ecosystem conditions with implications for their services. IEM’s
data-processing system has derived higher-order environmental indices from MODIS data that can be used as inputs
to a range of models for assessing climate change, ecological conditions, and agricultural production.

(5) Creating scenarios for realizing sustainable development
APEIS-IEM data can help create scenarios for realizing sustainable development. As one of the most important driversof both ecological change and economic development, changes in land use and land cover have been dynamicallymonitored by APEIS-IEM, as mentioned above. Furthermore, changes in NPP due to land-use/land-cover changes in
China have been estimated by using ecological modeling. This modeling shows an increase in NPP from the 1980s to
the 1990s in most parts of China, but a decrease in the Loess Plateau and western part of northeastern China (Fig. 12).
Meanwhile, provisioning services, measured by food production, also increased.

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NPP Change (gC/m2)

Figure 12. Changes in NPP due to land-use/land-cover changes from the 1980s to the 1990s in China

Water resource management is one of the most important issues that we are currently facing. APEIS-IEM developed
an integrated watershed management model that simulates ecological functions, such as water, heat, carbon andnitrogen cycles, and sediment transport, as well as agricultural production. We used the model to simulate sedimentloads under different land-use change scenarios over the whole of the Jialingjiang catchment (160 000 km2, located in
the upper reaches of the Changjiang basin), to evaluate how sediment loads from the catchment would be affected.
One of the main flood-prevention policies adopted by the Chinese Government is conversion of farmland to forest on
steeply sloped areas. Scenarios were given to conversing farmland on the sloped areas of ≧25°, ≧20°, ≧15°and ≧10°to forest, respectively (Fig.13). Simulations to these scenarios showed that the volume of
sediment erosion in this catchment will obviously decrease according to the recovery of forest area on sloped areas(Fig.14).

11≧25°（0.6％ of total area）≧20°（1.5％）≧15°（3.2％）≧10°（6.3％）

Figure 13. Distribution of the areas to convert farmlands to forest land on the slope of ≧25°, ≧20°, ≧15°and≧10°respectively in the Jialingjiang Catchment, China.

Sediment loads

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River bank erosion

Annual total sediment loads(109 kg)

Annual total sedimentproduction (109 kg)

Sediment production

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Original ≦25°≦20° ≦15°≦10°

-4.9%

-9.5%

-17%

-22%

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Figure 14. Effects of policy of returning farmland to forest on prevention of both sediment production and sediment
loads

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(6) Simulating the response of agricultural production to climate change

In order to test the response of wheat and corn (maize) growth under different climate conditions, we simulated theeffect of climate change on growth under 3 climatic scenarios (using meteorological data from 1987 to 2001): 3 °C
increase in mean daily temperature, double CO2 concentration, and both combined. The basic simulation shows:1.

A 3 °C increase will shorten the growing periods of both crops: The 15-year simulation showed that if present
cultivars are used, the average growth period might be 16 days shorter for winter wheat and 18 days shorter
for corn averagely (Fig. 15). The decrease in growth period of both crops would be good for cultivation in
North China Plain, which has an extremely limited growing season at present.

Growt h period for corn
P resent

Growth period for wheat
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days

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1999

Figure 15. Effect of temperature rise on growth period of corn and winter wheat (T3: 3 °C increase in daily mean
temperature)2.

Increased concentration of CO2 will benefit the yield of both corn and wheat, while the effect of temperature is
likely to be negative. Under the higher temperature and double CO2 concentration, the yield of corn could
decrease by 12%, influenced possibly by the shortened growing period at the higher temperature. However,
the yield of winter wheat should benefit greatly from an increase in CO2 concentration. Under the higher
temperature and double CO2 concentration, the yield of winter wheat could increase by 16% (Fig. 16).

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Normal
10000

T3

T3CO2

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CO2

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1988 1991 1994 1997 2000

Figure 16. Effect of different climatic scenarios on yields of winter wheat (right) and corn (left) (T3: 3 °C increase inmean daily temperature; CO2: double CO2 concentration; T3CO2: combination of 3 °C increase and double CO2
concentration)
3.

Under conditions of double CO2 concentration, water use by corn growth might greatly decrease. On theother hand, increased temperature will increase crop water use, in both corn and winter wheat. Under 3 °C
higher temperature and double CO2 concentration, water use by corn could decrease by over 14%, whilewater use by winter wheat could rise by 3%, showing the trade-off between the positive effect that increased
CO2 has in decreasing crop water use and the much stronger negative effect caused by increased
temperature (Fig. 17).
Normal

T3

T3CO2

CO2

wheat

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Figure 17. Effect of climatic change on cropwater use (ET: evapotranspiration) by winterwheat and corn (Normal: crop water use
under the present climatic conditions; T3:
3 °C increase in mean daily temperature;
CO2: double CO2 concentration; T3CO2:
combination of 3 °C increase and double CO2
concentration).

3. Capacity Building and Contributions to Policymaking
The Second APEIS Capacity Building Workshop on Integrated Environmental Monitoring of the Asia–Pacific Region
was held in Sydney, Australia, on 27 and 28 November 2003 (Fig. 18). Participants from the Philippines, Malaysia,
Australia, Pakistan, Nepal, Japan, Singapore, and China attended. The first day of the workshop presented theimportance of capacity building in the APEIS-MODIS Network and the APEIS-FLUX network, as well as the regional
integrative models and their applications to the Asia–Pacific region. In the first session, several advantages of theintegrative model based on MODIS data were presented and discussed. The CSIRO Atmospheric Research Project,
the Global Carbon Project, and the APEIS-IEM Project each reported the current situations and problems encounteredwith the model. In the second session, for the purpose of making most efficient use of the MODIS datasets, it was
discussed how MODIS data could be validated and calibrated through APEIS-FLUX research activities. On the secondday, presentations focused on land-use/land-cover change, the carbon cycle, and MODIS validation in different
countries. In the afternoon sessions, topics concerning APEIS-IEM collaboration and integrated capacity building werediscussed.
Participants pointed out that APEIS-IEM can help in developing and validating integrative models for sustainable
development of the Asia–Pacific region. The models are constrained by multiple factors, which can be produced by the
MODIS data processing system and by flux-tower measurements. Participants suggested that the integrated models
need to include not only natural processes, such as atmospheric advection, but also the human dimension, such as
population movement and material transportation. It was also suggested that APEIS-IEM should foster collaboration
with NASA, particularly with the Boston group, to validate MODIS products. Finally, the workshop concluded that theparticipants:•

Need to share data processing code, MODIS direct broadcast code, and validation strategies•

Need to compare MODIS products developed by different groups•

Need to expand the MODIS network to other countries, such as Russia•

Need to foster data exchange between MODIS stations•

Need to process AVHRR data (e.g., as a record of past land-use change—especially for phenology, crop
harvesting, burning history, etc.—and rapid land-cover change)•

To collaborate toward developing maps of the ecological conditions in the Asia–Pacific region, such as fireand land-use change. Other future developments could include maps of water resources, biomass, flood
variation, and wetlands, and methods to detect environmental changes and disasters•

To exchange information on how to improve the performance of tower sites, especially those with marked
topographic variation; and will promote inter-comparison among tower sites to gain insights into theecological controls of energy, water, and carbon cycles•

To emphasize both flux measurements and the study of ecological processes with the APEIS-FLUX
network; and will develop methodologies of scaling up and scaling down flux data to validate MODIS
products•

To encourage collaboration among the regional flux tower networks (e.g., OZ-FLUX, CHINA-FLUX,APEIS-FLUX)•

To expand the application of the IEM network to south and central Asia and to Russia

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Figure 18. Participants of the second APEIS-IEM capacity building workshop in Australia. Major suggestions:
Strengthen the cooperation and data exchange among stations; Improve the quality of tower site data, especially inregard to the effects of topographic variation and horizontal advection; improve scaling methodologies that enable flux
data to be used to validate MODIS products and integrated models
APEIS-IEM also published a special issue of the Journal of Geographical Sciences (Vol. 59, No. 1, 2004), and the
APEIS-FLUX sites have become important training centers for the Chinese Ecological Research Network, and havebeen visited by many scientists, students and policy-makers (Fig. 19).

Figure 19. APEIS-FLUX sites have become important training centers for the Chinese Ecological Research Network;
the sites are visited by many scientists, students, and policy-makers

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Finally, the activity of APEIS-IEM is closely linked with the Millennium Ecosystem Assessment (MA) through theparticipation in the China MA, one of the sub-global assessment projects. The outcomes of APEIS-IEM are contributing
to Chapter 7 in the sub-global assessment as a core scientific method for assessing conditions and trends of
ecosystem services.

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TECHNICAL SUMMARY

I EMIntegrated Environmental Monitoring

APEIS-IEM Web sites :http://www-basin.nies.go.jp/english/
project/iem/index.html

June 2004