长白山区4种针叶林有效叶面积指数遥感精细反演及空间分布规律OA北大核心CSTPCD

[Objective]The aim of this study was to study develop the rapid,accurate and macroscopic methods for obtaining effective leaf area index(LAIe)of different forest types,and to explore their spatial distribution,so as to provide new ideas for the development of medium and small-scale forest LAIe remote sensing products,and offer scientific and reliable technological means for precision forestry monitoring and simulation of forest ecosystem carbon and water cycles.[Method]Using Changbai Mountain as the research area,this study extracts the spatial distribution of four coniferous forest types(Larix olgensis,Pinus sylvestris var.mongolica,Pinus koraiensis and Picea koraiensis)based on Sentinel-2A multispectral images through a three-dimensional convolutional neural network.It adopts 2 schemes,differentiated forest types and full sample,to analyze the correlation between field-measured LAIe and 7 vegetation indices[enhanced vegetation index(EVI),inverted red-edge chlorophyll index(IRECI),modified simple ratio(MSR),normalized difference water index(NDWI),normalized difference vegetation index(NDVI),soil adjusted vegetation index(SAVI),simple ratio(SR)].The optimal vegetation index corresponding to each tree species was used to construct regression models for LAIe and vegetation index of differentiated forest type and full sample,and the accuracy performance of differentiated forest type model,full sample model and PROSAIL model in LAIe inversion was compared based on validation sample data.Subsequently,spatial pattern and change rule of LAIe of 4 tree species were analyzed by combining geographical factors.[Result]7 vegetation indices in all sample groups were significantly correlated with relative LAIe values(P<0.01),except for EVI and LAIe of Pinus koraiensis,SR and LAI of Picea koraiensis,the correlation coefficients were all greater than 0.6,while the correlation between LAIe and different vegetation indexes was significantly different among groups.LAIe of Pinus koraiensis,Larix olgensis and Pinus sylvestris var.mongholica had the highest correlation with IRECI,while LAIe of Picea koraiensis and Pinus sylvestris var.mongholica had the highest correlation with EVI and MSR,respectively.Compared with the full-sample model,the R2 value of the 4 different forest types model increased by more than 12.7%,and RMSE decreased by 34.5%.The LAIe of the 4 forest types in the study area ranged from 0.37 to 5.86,and the average LAIe from high to low was Pinus koraiensis,Larix olgensis,Pinus sylvestris var.mongholica.and Picea koraiensis.Pinus koraiensis was the most sensitive to changes in altitude,slope and aspect,followed by Picea koraiensis and Pinus sylvestris var.mongholica,while Larix olgensis was the least affected.[Conclusion]There were significant differences in the correlation between LAIe and remote sensing vegetation index among different forest types.The inversion accuracy of LAIe can be improved by constructing specific regression models for different forest types.The accuracy of the fitted linear model after distinguishing forest types was higher than that of the PROSAIL model and the full-sample model,but in the area with high LAIe value performed unstable compared with the PROSAIL model.LAIe of the 4 tree species varied greatly in reflecting the changes of geographical factors.This study can provide a scientific reference for the selection of LAI remote sensing inversion models for coniferous forests in small and medium scales under tree species differentiation.