Integrating Remotely Sensed Data With an Ecosystem Model to Estimate Net Primary Productivity in East Asia
Wenjing Zhao, Masayuki Tamura
Social and Environmental System Division,
National Institute for Environmental Studies
16-2 Onogawa, Tsukuba, Ibaraki 305-0053, Japan
Tel: +81-298-50-2589 Fax: +81-298-58-2645
E-mail: zhao.wenjing@nies.go.jp, m-tamura@nies.go.jp
Keywords: Remotely Sensed Data, Ecosystem Process Model, Net Primary Productivity
Abstract:
This paper describes a method of integrating remotely sensed data with an ecosystem model to estimate NPP in East Asia. Principles of forest biogeochemical cycles (FOREST-BGC) are used for simulating biological processes affecting NPP, such as photosynthesis, respiration, and transpiration. Input requirements for the ecosystem process model are: (1) land cover types and leaf area index from remote sensing data; (2) daily meteorological data such as maximum and minimum air temperature, incoming short-wave radiation, precipitation, and humidity; and (3) water holding capacity of soil. By incorporating the above data sources concerning all major environmental variables affecting plant growth and development, a map showing the distribution of annual NPP in East Asia in 1998 has been produced.
1. Introduction
Net primary Productivity (NPP) is a key component of the terrestrial carbon cycle, and defined as the net amount of new carbon absorbed by plants per unit space and time (Liu et al., 1999). Estimation of NPP accurately at regional or global scales is very important in studies of global climate. Liu et al. (1997) developed a Boreal Ecosystem Productivity Simulator (BEPS) based on the Forest BioGeochemical Cycles (FOREST-BGC) model (Running et al. 1988). Although the BEPS model has improved the original FOREST-BGC model for a regional scale by adding remote sensing inputs, robustness of the BEPS model should be verified for other area (or other land cover types) such as East Asia region, because it was just developed and validated for boreal ecosystem. Therefore, the primary objective of this study are: (1) to develop a model for estimating NPP in East Asia base on BEPS model, (2) to show the NPP distribution in East Asia region.
2. Study Area
The study region of East Asia encompasses a 10
8 km
2 (11040 pixels×9000 lines) area bounded by 66°N-9°S latitude and 78°-170°E longitude (Figure 1). The climatic zone of this area ranges from subfrigid zone in the north to tropical zone in the south. Land cover types includes boreal forest, grassland, crops, needle forest, broadleaf forest, and tropical rainforest.
3. Model
The model is based on BEPS model (Liu et al., 1997), which consists of three parts: remotely sensed and meteorological inputs, ecosystem process model, and NPP output (Figure 2). The BEPS model uses principles of the Forest BioGeochemical Cycles (FOREST-BGC) model (Running et al., 1988) for quantifying the biophysical processes governing ecosystems productivity. The BEPS model modified original model in the following aspects: (1) implement of a more advanced photosynthesis model with a new temporal and spatial scaling scheme (Chen et al., 1999); (2) inclusion of an advanced canopy radiation model to describe the specific boreal canopy architecture; and (3) adjustments of biophysical and biochemical
Figure 1. Location of study area bounded by 66°N-9°S latitude and 78°-170°E longitude.
parameters for the main boreal land cover types. The BEPS model are then modified when we use it in East Asia because its limitation of specific region for boreal ecosystem. They are: (1) more flexible land cover types map (Myneni et al., 1997) for global application is adapted; (2) more robust algorithm of estimating Leaf Area Index (LAI) from remotely sensed data (Myneni et al., 1997) for global application is adapted; (3) adjustments of biophysical and biochemical parameters according to the land cover types in East Asia. The remotely sensed inputs are land cover types and LAI, and the meteorological inputs are temperature, precipitation, humidity, and radiation. The temporal interval is daily for meteorological data, 10 days for LAI, and annual for land cover. The model computes daily NPP pixel by pixel (1km
2) assuming vegetation and environment conditions were uniform within each pixel, and then accumulates daily NPP to annual NPP.
