TECHNICAL SUMMARY

IE M
Integrated Environmental Monitoring

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

June 2005

APEIS-IEM Technical Summary
1. What is APEIS-IEM?
Rapid population growth and economic development in the Asia-Pacific region have resulted inserious local, national, and regional environmental problems such as floods; droughts; forest fires;dust storms; air, water and soil pollution; desertification; salinization; water resource depletion; andsoil loss. Such problems are serious constraints to sustainable development in the region. In order
to cope with these problems, a system of integrated environmental monitoring is indispensable.
Since 2001, the APEIS-IEM sub-project has developed a system of integrated environmental
monitoring (IEM) that can be used to detect, monitor, and assess environmental disasters anddegradation and their impacts in the Asia-Pacific region (Figure 1). By using IEM, we try toaccurately describe the present conditions and trends of ecosystem services, as well as thepressures and impacts upon them, to contribute to policy making for sustainable development(SD).

Figure 1 APEIS Integrated Environmental Monitoring (IEM) network in the Asia-Pacific Region

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IEM provides ground-validated remote sensing data and images from MODIS (ModerateResolution Imaging Spectrometer), and derived ecological indices such as water deficit index, dust
storm index, land surface temperature, incidence of forest fires, and land productivity indices, aswell as CO2 sink areas. The APEIS-IEM sub-project has also developed an integrated model toassess the state of and changes in ecological goods and services, such as land use or coverchanges, fresh water resources, carbon and nitrogen cycles, and food production. With this model,
the trade-off between ecosystem services and the effectiveness of policies for sustainability can bedemonstrated (Figure 2).

Figure 2 IEM tries to describe the present conditions and trends of ecosystem services and todemonstrate the trade-off between ecosystem services and the effectiveness of policies forsustainability

22. Current Progress and Contributions of APEIS-IEM
The integrated monitoring network system established by IEM covers most of the Asia-Pacificregion, and continuously provided real-time remote-sensing and ground measurement data
throughout 2004. The IEM network system is composed of two sub-systems: the APEIS-MODIS
network and the APEIS-FLUX network. The APEIS-MODIS network includes four MODIS data
receiving stations and MODIS data analyzing centers. Many environmental indices, such as
MOD09 for land surface reflectance, MOD11 for land surface temperature, MOD12 for land cover,
MOD13 for vegetation index, MOD15 for leaf area index, and MOD17 for net primary production,
are continuously developed by the MODIS data-processing system. APEIS-FLUX, a real-timemonitoring system covering five sites in different ecological systems in China, continues to provide
real-time ground observation data, such as eddy-correlation flux measurements of sensible heat,latent heat, and CO2 fluxes; micrometeorological measurements of air temperature, wind speed,and wind direction; measurements of total solar radiation, net radiation, and photosynthesis; andsoil water content, underground water level, and salinity. The major task of APEIS-FLUX is tomonitor water, heat, and carbon fluxes in different ecosystems. A secondary task is to develop amethodology to estimate regional water, heat, and carbon fluxes by using MODIS data. Anotherchallenge is to understand the mechanisms and processes of the water, heat, and carbon cycles interrestrial ecosystems, both in natural ecosystems, such as forest, grassland, and desert, and inmanaged agricultural ecosystems, such as irrigated agricultural fields and paddy fields, through thedevelopment and application of ecosystem models. The model has been developed to accurately
simulate the natural processes of water, heat, and carbon cycles in terrestrial ecosystems and theeffects of human disturbance. The data are released on the Internet to the world and are
downloadable from the APEIS-IEM homepage at http://www-basin.nies.go.jp/english/project/iem/.
The data accumulated by the IEM network have been used to evaluate ecological goods andservices, such as land use or cover changes, flood control function, carbon fixation, and groundwater utilization for food production.
2.1 Accurate Land Cover Classification
A new approach to classifying land cover from MODIS time-series data has been to use aself-organizing feature map (SOM) neural network. The classification with the SOM neural network
has been obviously improved compared with that of the commonly used maximum likelihoodclassification (MLC) method (Figure 3). Using this method in future to classify crops will greatly
contribute to correct estimations of food production.

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Figure 3 Land cover classification by the SOM neural network and the MLC method, both using
MODIS time-series data

2.2 Assessment of Flood Control Functions and Flood Areas
The dynamic variation in surface water area and water storage in Dongting Lake, China, wasestimated by using the IEM database. The estimated values for water level and storage were foundto correspond well with the measured values. Furthermore, by coupling this information withelevation data and the MODIS vegetation coverage data into a watershed environmental
management model, we could evaluate the loss of water and soil holding capacity of the terrestrial
ecosystem and evaluate the shrinking of the buffering capacity of lakes. Finally, the areas of theChangjiang (Yangtze River) Basin where possible flooding could occur were mapped (Figure 4).

4Figure 4 Evaluation of flood control capacity, such as the water storage and areas possibly prone toflooding by using the database of the APEIS-IEM network
2.3 Evaluation of Groundwater Used by Agricultural Production
Sustainable water use is in crisis in the North China Plain, owing to rapid groundwater drawdown.
Estimating the water requirements for agriculture, the biggest user of groundwater, is helpful inunderstanding groundwater decline and the sustainable use of water resources. In 2004, theAPEIS-IEM sub-project aimed to study the trends between change in groundwater levels andsimulated agricultural water use in the North China Plain. The Decision Support System forAgro-technology Transfer (DSSAT) model was used to estimate groundwater use for crops. The
results for Gaocheng City, the site selected for a point-level study, showed that groundwater decline
was sensitive to simulated crop water requirements and irrigation requirements. There wereobvious relationships between groundwater depletion and agricultural water use on a monthly,
seasonal, and yearly level (Figure 5). According to regression analysis, a 100-mm waterrequirement by cultivated land (mainly wheat and maize) resulted in an approximately 640-mm
decline in groundwater. This relationship might be useful in controlling the groundwater decline
through controlling crop water use or through using water saving technologies.

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Water requirement

Water requirement (mm)

0-0.2
-0.4
-0.6
-0.8
-1
-1.2
-1.4
-1.6
-1.8
-2

350
300
250
200
150
100
500

Groundwater level (m)GW change
400

88 89 90 91 92 93 94 95 96 97 98 99 00 01
Year
Figure 5 Changes in groundwater level in relation to simulated agricultural water requirements bywinter wheat in March–June in 1988–2001. An inverse trend between the two factors is clear andsignificant.
Since 1987, the annual average decline in groundwater level has been 620 mm/yr. Considering that
there is still a considerable amount of upstream groundwater recharge, much larger than theamount of water deficit, sustainable water use is still possible in the region if water-saving
technologies are applied widely. Moreover, our study shows that through scientific irrigation
management, approximately 76 mm of evapotranspiration water, which is sufficient to result in a
420 mm drawdown of groundwater, can be saved through irrigation planning without much
reduction in the yield of winter wheat.

2.4 Evaluation of Land Surface Evapotranspiration and Water Deficit Index
Evaluation of evapotranspiration from the land surface and the water deficit index is essential inorder to fully understand climate dynamics and ecosystem productivity, because they are closely
related to energy transfer and material recycling processes between the Earth’s surface and theatmosphere. The water deficit index is also widely used in water resource management, droughtmonitoring, catchment management, and wildfire assessment. The APEIS-IEM sub-project hasdeveloped a method using MODIS remote sensing datasets to estimate both theevapotranspiration and water deficit index of winter wheat fields in the North China Plain (Figure 6).
The estimated values showed a relatively good consistency with the on-ground observations aftersome biophysical and meteorological parameters were adjusted.

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Jun. 10, 2003

May 25, 2003

Figure 6 Water deficit index (EF) and evapotranspiration (ET) of winter wheat fields in the North
China Plain estimated with MODIS remote sensing datasets

2.5 Monitoring of Plant Growing Process
The leaf area index (LAI) is the best index to monitor the growth of plants. With the help of theGlobal Positioning System (GPS) and geographical information systems (GIS), the temporal
dynamics of MODIS-generated LAI can be compared across different ecosystems. We improved
the MODIS LAI product using field-sampled data collected at APEIS-FLUX sites in China, and theimproved spatial distribution of LAI in China, with a 1-km resolution, was produced (Figure 7). As
the maps show, the lowest LAI occurred in January and the highest was in August, which suggeststhat the MODIS LAI product is reasonable throughout the phenological cycle of plants. Suchdatasets are very useful for assessment of land productivity, carbon sinks and sources, andagricultural and forestry production.

7Figure 7 Monitoring of plant growth by leaf area index (2002)

2.6 Evaluation of Land Productivity and Carbon Fixation
Many ecosystem models have been developed for simulating water and carbon exchanges
between the atmosphere and terrestrial ecosystems. One, the BIOME-BGC model, simulates thestorage and fluxes of water, carbon, and nitrogen within the vegetation, litter, and soil components
of a terrestrial ecosystem. The model permits sophisticated simulations of natural ecosystems suchas forests, grasslands, and shrublands. However, it has rarely been applied to agriculturalecosystems. We modified the model by using APEIS-FLUX data, and then computed the carbon
fixation in the Asia-Pacific region through a scaling-up approach using remote sensing data (Figure8). Finally, we developed high-resolution maps of carbon fixation by vegetation from 2001 to 2003
in the Asia-Pacific region (Figures 9 and 10).

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Figure 8 Evaluation of carbon fixation through a scaling-up approach using remote sensing data,
field measurements, and the integrated ecosystem modelFigure 9 Carbon fixation by vegetation in the Asia-Pacific region as evaluated by the integrated
ecosystem model using MODIS data

9Figure 10 Carbon fixation by vegetation in each country of the Asia-Pacific region
3 Capacity Building and Contributions to Policy Making
The APEIS-IEM sub-project has contributed to the Millennium Ecosystem Assessment (MA),Eco-Asia, CSD12, and Water Forum. In addition, more than 30 peer-reviewed papers have been
published in academic journals, and the APEIS-FLUX sites have become important training centers
for the Chinese Ecological Research Network, and have been continuously visited by many localpeople, scientists, students, and policymakers in China. The activities of APEIS-IEM are closely
linked with the MA through its participation in the China MA, one of the sub-global assessment
projects of MA. The outcomes of APEIS-IEM have contributed to both the China MA report and theSynthesis Report of the MA sub-global assessment as a core scientific method for assessing
conditions and trends of ecosystem services.
The third APEIS Capacity Building Workshop on Integrated Environmental Monitoring of theAsia-Pacific Region was held on December 9–11, 2004, in Singapore (Figure 11). Participants fromVietnam, Australia, Mongolia, China, Singapore, India, Russia, and Japan attended the workshop.
The workshop presented the importance of capacity building in the MODIS Network and theAPEIS-FLUX tower measurements, and included presentations on the regional integrated models
and their applications to the Asia-Pacific region. Other presentations focused on cooperative
studies of forest fires, the carbon cycle, and MODIS validation in different countries of the region.

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Figure 11 The third APEIS-IEM capacity building workshop in Singapore

4 Contributions from the IEM Network
APEIS-IEM is an international cooperative project comprising many different institutes thatfrequently exchange information. Figures 12 to 14 are provided by member institutes of the IEM
network.

Imaging date: 13 Jan 2003
Imaging date: 29 Dec 2004
Figure 12 Tsunami disaster in Aceh, Sumatra, Indonesia (Provided by Centre for Remote Imaging,Sensing and Processing, National University of Singapore)11

Figure 13 Forest fire in Australia (Provided by CSIRO Earth Observation Centre)

Figure 14 Typical Fire Danger Map based on MODIS information on May 28, 2004, in which yellow
shows the most dangerous areas (Provided by Russian Academy of Sciences, V.N. Sukachev
Institute of Forest, Krasnoyarsk, Russia)

12Following are the Internet addresses of the member institutes of the APEIS-IEM network:
Watershed Environments and Management Research Project, National Institute forEnvironmental Studies (NIES), Japan
http://www-basin.nies.go.jp/english/
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy ofSciences (CAS), China
http://www.igsnrr.ac.cn/
Earth Observation Centre, Commonwealth Scientific and Industrial Research Organization(CSIRO), Australia
http://www.eoc.csiro.au/
Centre for Remote Imaging, Sensing and Processing, National University of Singapore,Singapore
http://www.crisp.nus.edu.sg/
Chinese Ecosystem Research Network, CAS, China
http://www.cern.ac.cn/0index/
Xinjiang Institute of Ecology and Geography, CAS, China
http://www.xjb.ac.cn/
Institute of Subtropical Agriculture (ISA), CAS, China
http://www.isa.ac.cn/

5. Contacts:
Project Leader: Dr. Masataka WATANABE
Water and Soil Environment Division
National Institute for Environmental Studies (NIES), Japan 305-8506
http://www.nies.go.jp/aquaterra/index-e.html
Tel: +81-29-850-2791
Fax: +81-29-850-2756
E-Mail: masataka@nies.go.jp
Project Co-leader: Dr. Jiyuan LIU
Institute of Geographical Sciences and Natural Resources Research
Chinese Academy of Sciences
Building 917, Datun Road, Beijing, China 100101
Tel: +86-10-64889281
Fax: +86-10-64851844
E-Mail: liujy@igsnrr.ac.cn
Research Coordinator General: Dr. Qinxue WANG
Watershed Environments and Management Research Project, National Institute for Environmental
Studies (NIES), Japan 305-8506
http://www-cger.nies.go.jp/lugec/wang/
Tel. & Fax: +81-29-850-2128
E-Mail: wangqx@nies.go.jp

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