Remote sensing estimating net primary productivity of temperate deciduous forest in Northeast China using satellite data: approach and preliminary results

Abstract

The application of remote sensing in the study of terrestrial productivity opens up an effective way for simulating terrestrial ecosystem productivity. Observation by space-borne sensors can provide parameters to describe ecosystem processes related to plant,growth. In this paper, a biogeographic process model (forest-BGPG) is established to estimate forest net primary productivity, which caters for mountainous distribution and species' diversity of forest in China. Gross photosynthesis and respiration are evaluated separately in forest-BGPG because of different meteorologic, soil and biological factors exert varies degrees of control on these processes. In forest-BGPG, an alternative satellite algorithm is used to estimate photosynthetically active radiation absorbed by forest canopy (APAR), spatial and seasonal patterns of the maximum efficiency of PAR utilization of forest woodland are simulated by a physiological model at stand level. Taking temperate deciduous forest in northeast China as an example, forest-BGPG is applied to simulate NPP of temperate deciduous forest over study area using MODIS data. The mean NPP value is close to 4.7 MgC ha(-1) for deciduous broadleaved forest, and 4.32 MgC ha(-1) for deciduous coniferous forest in northeast China at the period from November of 2002 to October of 2003. Compared with MODIS NPP products from School of forestry of the university of Montana, both of them are in agreement.