Analysis of Spatial and Temporal Variation of Vegetation Net Primary Productivity in Saihanba Based on BEPS Model
Leaf area index(LAI)is the key driving data of BEPS model,and it is important to obtain high accuracy LAI for re-gional forest ecosystem carbon cycle,however,the MODIS LAI products used in most current studies lack credibility.To this end,this study constructed a data assimilation system based on LAI dynamic model,PROSAIL radiative transfer model and Hierarchical Bayes-ian Network(HBN)to obtain LAI data with a spatial resolution of 20 m to drive the BEPS model and simulate the vegetation net prima-ry productivity(NPP)of Saihanba Mechanical Forest during 2011-2021,and the spatial and temporal variation of NPP and the influ-encing factors of NPP were analyzed.The results showed that the high-resolution LAI data obtained based on Bayesian assimilation method greatly improved the accuracy of MODIS LAI products;the correlation between the simulated forest NPP obtained from BEPS model driven by assimilated LAI data and the NPP calculated from the sample plots was high(R2=0.77);the mean value of vegeta-tion NPP in Saihanba Mechanical Forest during 2011-2021 was 307.4 g/(m2·a),and the NPP of forest showed a steady growth trend;the simulated NPP of different vegetation types were different,and the simulated NPP of coniferous,deciduous and mixed for-ests were 484.9,402.4,287.9 g/(m2·a);the correlation between vegetation NPP and temperature factor was high,and the bias correlation coefficient was 0.2-0.8,while the correlation between vegetation NPP and precipitation was relatively low in general,with bias correlation coefficients of-0.3-0.4.The influence of precipitation on vegetation NPP was low in this region,and temperature was the dominant factor of NPP variation in this region.In this study,high spatial resolution LAI data were obtained to provide a basis for accurate spatial and temporal simulation of the carbon cycle in forest ecosystems.
Saihanbaleaf area indexBEPS modelvegetation net primary productivity
国家科技期刊平台
NSTL
万方数据
维普
