摘要
地上生物量是反映草地生态系统功能和质量的重要指标之一,对于草地生态系统的经营管理至关重要。本研究结合长时序遥感影像和地面调查数据,筛选表征地上生物量的重要指标,采用随机森林算法构建了锡林郭勒盟草地地上生物量遥感反演模型,并将其应用于锡林郭勒盟2000—2023年逐年草地地上生物量估算,运用Mann-Kendall检验法和Theil-Sen斜率估计方法对近24年来锡林郭勒盟草地地上生物量时空变化趋势进行遥感诊断。结果表明:(1)利用随机森林算法构建的地上生物量反演模型具有较高的精度,最优模型的决定系数R2为0.75;(2)锡林郭勒盟草地地上生物量呈现空间异质性,自西向东逐渐增加,与草地类型的分布相对应;(3)近24年来,锡林郭勒盟草地地上生物量波动较小,草地生态系统保持相对稳定。本研究可为林草管理部门进行大尺度区域草地地上生物量调查提供科学支撑。
Aboveground biomass(AGB)is a key indicator of grassland ecosystem function and quality,and is crucial for effective management.We combined a long-term time series of remote-sensing imagery with ground-survey data to develop a random-forest inversion model for grassland AGB in the Xilin Gol League.The best-performing model was applied to estimate annual AGB from 2000 to 2023,and the Mann-Kendall test and Sen′s slope were used to evaluate spatiotemporal trends over this period.The result show that:(1)The model achieved high accuracy,yielding a coefficient of determination(R 2)of 0.75,indicating robust predictive skill for regional-scale AGB estimation.(2)Estimated AGB exhibited pronounced spatial heterogeneity which gradually increased from west to east,that is in consistent with the distribution of grassland types.(3)From 2000 to 2023,interannual variation in AGB was small,indicating a relatively stable grassland ecosystem at the regional scale.The multi-decadal record provides a quantitative baseline for evaluating restoration outcomes and grazing management effectiveness.These findings show that random-forest-based remote sensing supports accurate,large-area AGB monitoring and provides practical evidence for regional management.
作者
高晓彤
徐敏
田海静
王少华
李京博
曹春香
GAO Xiao-tong;XU Min;TIAN Hai-jing;WANG Shao-hua;LI Jing-bo;CAO Chun-xiang(State Key Laboratory of Remote Sensing and Digital Earth,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100101,China;College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100094,China;Academy of Forestry Inventory and Planning,State Forestry and Grassland Administration,Beijing 100013,China)
出处
《生态与农村环境学报》
北大核心
2025年第12期1543-1552,共10页
Journal of Ecology and Rural Environment
基金
国家重点研发计划(2021YFB3901104)。
关键词
遥感诊断
草地地上生物量
反演模型
随机森林
时空变化
remote sensing diagnosis
grassland above-ground biomass
inversion model
random forest
spatiotemporal variation
