Simulation of Temporal and Spatial Distribution of Natural Vegetation Light Utilization Efficiency Based on 3s

Abstract

Light utilization efficiency ( LUE ) directly influences the distribution of energy and rate of photosynthesis in all layers of vegetation. LUE is very valuable in deciding the integrated limits of environment to photosynthesis and plant growth allocation of aboveground, and is an important index in weighing functions of system. In China, the studies on LUE focus usually on crops, rarely on natural vegetations, and mostly calculate mean LUE over the country. The studies on LUE of natural vegetations in some regions are limited to one or two types of vegetation. Thus, it is very difficult to reflect the total conditions of all vegetations over these regions in different periods. In the study, leaf area index ( LAI ) that greatly influences LUE of vegetation was received from remote sensing images. The ecosystem productivity process model at landscape scale (EPPML) that described carbon cycle and water cycle of system was built by computer program (Visual C++), and seasonal dynamics and spatial distributions of total solar radiation, net primary productivity ( NPP ) and LUE in Changbai Mountain Nature Reserve were simulated. Geographical Information System (GIS) was used to process, analyze and display spatial data. Thus, we could extend and convert the studies on physiological ecology of plants from small scale to a larger scale. EPPML uses the principles of Century, BIOM_BGC, Forest_BGC and BEPS for quantifying the biophysical processes governing ecosystem productivity, but the original model is modified to better represent Changbai Mountain region. A numerical scheme is developed to integrate different data types: remote sensing data (TM), gridded vegetation, soil and topographic maps at 30_m resolution in Albers projection; daily meteorological data in Changbai Mountain station in 1995, including precipitation, maximal temperature, minimal temperature, mean temperature, solar zenith angle at noon, air pressure and wind speed; diameter data from field measurement and national forest survey; data from literatures for inputs to EPPML and validation of EPPML. Vegetation index is derived from remote sensing data for estimating daily LAI and biomass at landscape scale. The information about vegetation type, soil type, elevation, slope and aspect can be derived from vegetation, soil and topographic maps. EPPML uses the biochemical model for photosynthesis of leaves developed by Farquhar et al. (1980) to simulate the rate of photosynthesis. NPP is the organic matter eliminating respiration from gross photosynthetic productivity ( GPP ). In addition, EPPML uses the sub_module MT_Clim in Forest_BGC to calculate total solar radiation. In EPPML, the spatial scale is 30 m and temporal scale is daily and yearly. The whole simulating process is easily understood and realized. EPPML is run and values are cumulated in each pixel. The major outputs include seasonal dynamics and spatial distributions of some carbon cycle and water cycle variables including NPP and LUE . The results indicated that the seasonal variation of LUE of vegetations in Changbai Mountain was similar to that of NPP with peak value in July (2.9%). The LUE in spring, summer, autumn and winter averaged 0.551%, 2.680%, 0.551% and 0.047% respectively. The annual LUE of all vegetation types averaged 1.075%, varying from -3.272% to 3.556%. The maximal annual LUE appeared in mixed broad_leaved and korean pine forests (1.653%), minimum in alpine grasses (0.146%), others being Changbai larch forest (1.227%), spruce_fir forest (1.019%), meadow (0.983%), broad_leaved forest (0.728%), shrub (0.478%), alpine tundra (0.442%) and Betula ermanii forest (0.298%). Though the LUE of mixed broad_leaved and korean pine forests were very high, it still had great increasing potential. In conclusion, EPPML could well and truly simulate NPP and total solar radiation of main vegetations at landscape scale in Changbai Mountain Nature Reserve. Therefore, it could well reflect the seasonal dynamic a