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

作　　者：余金格 罗绍龙 钱常明 舒清态 王书伟 胥丽 席磊 宋涵玥 Yu Jinge;Luo Shaolong;Qian Changming;Shu Qingtai;Wang Shuwei;Xu Li;Xi Lei;Song Hanyue(College of Forestry,Southwest Forestry University,Kunming Yunnan 650233,China;Damogu Town Forestry Station,Qujing Yunnan 655607,China;Institute of Ecological Conservation and Restoration,Chinese Academy of Forestry,Beijing 100091,China;College of Forestry,Fujian Agriculture and Forestry University,Fuzhou Fujian 350002,China)

机构地区：[1]西南林业大学林学院,云南昆明650233 [2]云南省陆良县大莫古镇林业站,云南曲靖655607 [3]中国林业科学研究院生态保护与修复研究所,北京100091 [4]福建农林大学林学院,福建福州350002

出　　处：《西南林业大学学报(自然科学)》2025年第1期146-155,共10页Journal of Southwest Forestry University:Natural Sciences

基　　金：国家重点研发计划课题(2023YFD2201205)资助;云南省农业联合专项重点项目(202301BD070001-002)资助。

摘　　要：以ICESat–2/ATLAS数据为数据源,结合54块实测样地,构建机器学习模型并对光斑足迹的地上生物量进行预测,采用Moran's I和半变异函数对反演的森林AGB空间自相关和异质性进行研究。结果表明:梯度提升回归树(GBRT)模型具有较好的预测精度(R^(2)=0.90,RMSE=11.08 t/hm^(2));香格里拉市森林生物量的最佳拟合半变异函数模型为指数模型(C_(0)=0.12,C_(0)+C=0.87,A_(0)=10 200 m);与普通克里格相比,序贯高斯条件模拟得到的AGB空间分布图具有较好的一致性(r=0.59^(**),d=0.70)。AGB的空间分异能够被地形因子解释,在解释力方面,海拔最大,坡向次之,坡度最小;基于星载激光雷达ICESat–2/ATLAS数据的森林AGB反演精度较高(P_(p)=81.43%),为地统计分析提供了可靠的数据源。因此,基于星载激光雷达与地统计学相结合的方法,能较好地实现森林AGB的空间异质性分析。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(R^(2)=0.90,RMSE=11.08 t/hm^(2)).The best-fitting semi-variogram function model of forest biomass was exponential model in Shangri-La(C_(0)=0.12,C_(0)+C=0.87,A_(0)=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(P_(p)=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 

分 类 号：S757[农业科学—森林经理学] S771.8[农业科学—林学]