森林工程2025，Vol.41Issue(5)：912-921,10.DOI:10.7525/j.issn.1006-8023.2025.05.005
基于中空间分辨率遥感影像的县域尺度森林覆盖变化检测
Forest Cover Change Detection at County Scale Based on Medium Spatial Resolution Remote Sensing Images
摘要
Abstract
Accurately grasping the spatial distribution of forest cover is crucial for the protection,restoration and sustain-able use of forest ecosystems.However,it is no longer possible to efficiently and accurately obtain the changes in com-plex forest cover at the county scale by relying on low spatial resolution remote sensing images combined with traditional computer classification models.Therefore,this study took the complex forests in Tangyuan County,Jiamusi,Heilongji-ang Province as the research object,used the medium spatial resolution satellite remote sensing images of Sentinel-1 and Sentinel-2,and constructed a machine learning model optimized by particle swarm optimization(PSO)to detect the changes in forest cover at the county scale.The K-fold cross validation was used to evaluate the accuracy of the forest cover detection results.The results showed that the support vector machine and random forest machine learning models optimized by particle swarm algorithm had improved the accuracy of forest cover change detection compared with their own models without parameter optimization.The support vector machine model increased by 6.52％,and the random for-est model increased by 4.65％.Compared with the current mainstream ESA COVER WORD land cover product,the ran-dom forest model optimized by particle swarm algorithm had the highest accuracy,with an overall accuracy of 0.92.The optimized random forest model was also more precise in detecting forest cover changes.By classifying medium spatial resolution remote sensing images through the random forest model of the particle swarm optimization algorithm,we can quickly and accurately grasp the spatial distribution of forest cover at the county scale,and provide data and technical support for the protection,restoration and sustainable utilization of forest ecosystems.关键词
森林覆盖/遥感变化检测/粒子群优化算法/GEE/机器学习Key words
Forest cover/remote sensing change detection/particle swarm optimization/GEE/machine learning分类
农业科技引用本文复制引用
史大义,毛学刚..基于中空间分辨率遥感影像的县域尺度森林覆盖变化检测[J].森林工程,2025,41(5):912-921,10.基金项目
国家重点研发计划课题(2023YFD2201704) （2023YFD2201704）
国家自然科学基金(32371863). （32371863）
