西南林业大学学报2025，Vol.45Issue(1)：146-155,10.DOI:10.11929/j.swfu.202311061
基于ICESat-2/ATLAS与地统计学的森林生物量空间异质性分析
Spatial Heterogeneity Analysis of Forest Biomass Based on Spaceborne LiDAR ICESat-2/ATLAS and Geostatistics
摘要
Abstract
Using ICESat-2/ATLAS data as data source,combined with 54 measured plots,a machine learn-ing model was built and the AGB of the spot footprint was predicted.Moran's I and semi-variogram function were used to study the spatial autocorrelation and heterogeneity of inverse forest AGB.The results showed that the Gradient Boost Regression Tree(GBRT)model had a great prediction accuracy(R2=0.90,RMSE=11.08 t/hm2).The best-fitting semi-variogram function model of forest biomass was exponential model in Shangri-La(C0=0.12,C0+C=0.87,A0=10200 m).Compared with ordinary Kriging,the spatial distribution of AGB obtained by the Se-quential Gaussian Conditional Simulation had better consistency(r=0.59**,d=0.70).The spatial differentiation of AGB could be explained by topographic factors.In terms of explanatory power,elevation was the largest,slope was the second,and slope was the least.The inversion accuracy of forest AGB based on spaceborne LiDAR ICESat-2/ATLAS data was high(Pp=81.43%),which provided a reliable data source for geostatistical analysis.Therefore,the method based on spaceborne LiDAR and geostatistics can greatly analyze the spatial heterogeneity of forest AGB.关键词
地上生物量/空间异质性/机器学习/半变异函数/ICESat-2/地理探测器Key words
aboveground biomass/spatial heterogeneity/machine learning/semi-variogram function/ICESat-2/geopraphic detector分类
农业科技引用本文复制引用
余金格,罗绍龙,钱常明,舒清态,王书伟,胥丽,席磊,宋涵玥..基于ICESat-2/ATLAS与地统计学的森林生物量空间异质性分析[J].西南林业大学学报,2025,45(1):146-155,10.基金项目
国家重点研发计划课题(2023YFD2201205)资助 （2023YFD2201205）
云南省农业联合专项重点项目(202301BD070001-002)资助. （202301BD070001-002）
