林业科学2026，Vol.62Issue(2)：173-185,13.DOI:10.11707/j.1001-7488.LYKX20240765
基于Sentinel-2卫星影像与梯度提升树回归模型的疏林郁闭度精准监测
Accurate Monitoring of Sparse Forest Canopy Closure Based on Sentinel-2 and GBRT Model:a Case Study on the Returning Farmland to Forest Project in the Inner Mongolia
摘要
Abstract
[Objective]The purpose of this study is to integrate high-resolution UAV data with Sentinel-2 satellite imagery and utilize the Gradient Boosting Regression Tree algorithm to achieve accurate monitoring of sparse forest canopy closure in the Returning Farmland to Forest Project areas,thereby providing technical support for effectiveness assessment of the new round of the project.[Method]UAV LiDAR and visible light image data were collected in typical areas of returning farmland to forest.Combined with Sentinel-2 remote sensing images in the growing and non-growing seasons of 2024 and terrain data,a gradient boosting regression tree model was established to estimate the canopy closure of the sparse forest,and its accuracy and discrimination ability were evaluated.[Result]With UAV-based LiDAR point cloud and visible light images of 90 open forest plots of land for returning farmland to forests,the canopy height model(CHM)combined with the threshold segmentation method were used to construct 5 764 sparse forest canopy closure sample points.Based on multi-temporal Sentinel-2 remote sensing image features and topographic information,a gradient lifting regression tree model was established to realize the accurate monitoring of sparse forest canopy closure,and the model coefficient of determination(R2)was 0.731,the root mean squared error(RMSE)was 0.028,and the mean absolute error(MAE)was 0.021.The vegetation indexes and reflectance of non-growing seasons and the elevation were the key factors for estimating sparse forest canopy closure.[Conclusion]The gradient boosting tree regression model constructed by combining high-precision UAV LiDAR data and Sentinel-2 remote sensing images can better predict the sparse forest canopy closure,and has good stability under the influence of different geographic environments and vegetation types,which is of great significance for the effectiveness assessment of the new round of returning farmland to forest projects in Inner Mongolia.关键词
Sentinel-2/无人机/退耕还林/内蒙古/疏林/郁闭度/梯度提升树Key words
Sentinel-2/UAV/returning farmland to forest/Inner Mongolia/sparse vegetation/canopy cover/gradient boosting regression tree分类
农业科技引用本文复制引用
王天璨,格根塔娜,李晓松,月亮高可,沈通,陈超超,智育博,赵立成,姬翠翠..基于Sentinel-2卫星影像与梯度提升树回归模型的疏林郁闭度精准监测[J].林业科学,2026,62(2):173-185,13.基金项目
能力培育项目-MFST-蒙古高原土地退化零增长决策支持系统与示范应用(4221101459) （4221101459）
内蒙古自治区林业与草原工作总站委托项目"内蒙古自治区退耕还林生态成效监测评估与数据库建立(二期)" （二期）
重庆市自然科学基金面上项目(CSTB2023NSCQ-MSX0967) （CSTB2023NSCQ-MSX0967）
空间观测可持续发展国际大科学计划(STS)(313GJHZ2022040BS). （STS）
