摘要
准确掌握森林覆盖空间分布对于森林生态系统保护、恢复和可持续利用至关重要。但高效、精准地获取县域尺度复杂森林覆盖变化依靠低空间分辨率遥感影像结合传统计算机分类模型已经无法满足。以黑龙江省佳木斯汤原县复杂森林为研究对象,采用哨兵一号、二号(Sentinel-1、Sentinel-2)中空间分辨率卫星遥感影像,构建基于粒子群优化算法(particle swarm optimization,PSO)优化的机器学习模型,检测县域尺度森林覆盖变化,应用K-折交叉验证对检测森林覆盖结果进行精度评价。研究结果表明,基于粒子群算法优化的支持向量机和随机森林2个机器学习模型与未经参数优化的自身模型相比,森林覆盖变化检测精度均得到提高,支持向量机模型提高6.52%,随机森林模型提高4.65%。与目前主流ESA World Cover土地覆盖产品相比,基于粒子群算法优化的随机森林模型精度最高,总体精度达到0.92。优化后的随机森林模型对森林覆盖变化检测也更加精细。通过粒子群优化算法的随机森林模型对中空间分辨率遥感影像进行分类,可以快速、准确地掌握县域尺度森林覆盖空间分布情况,为森林生态系统保护、恢复和可持续利用提供数据和技术支撑。
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.
作者
史大义
毛学刚
SHI Dayi;MAO Xuegang(College of Forestry,Northeast Forestry University,Harbin 150040,China)
出处
《森林工程》
北大核心
2025年第5期912-921,共10页
Forest Engineering
基金
国家重点研发计划课题(2023YFD2201704)
国家自然科学基金(32371863)。
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
Forest cover
remote sensing change detection
particle swarm optimization
GEE
machine learning
