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基于多源遥感数据的生态保护修复项目区监测方法评述
引用本文:陈元鹏,任佳,王力.基于多源遥感数据的生态保护修复项目区监测方法评述[J].生态学报,2019,39(23):8789-8797.
作者姓名:陈元鹏  任佳  王力
作者单位:自然资源部国土整治中心, 北京 100035,自然资源部国土整治中心, 北京 100035,中国科学院遥感与数字地球研究所, 北京 100094
基金项目:国家自然科学基金项目(41771207)
摘    要:回顾了山水林田湖草生态保护修复项目的实施背景,针对生态保护修复项目监测监管范围广、技术难等问题,强调了基于多源遥感数据开展项目遥感监测的重要性与必要性。从监测指标拟定、遥感地物信息提取、多源遥感数据融合、动态变化检测等方面评述了基于多源遥感数据的生态保护修复项目区监测方法,包括基于中高空间分辨率遥感数据的地物信息提取、融合机器学习的非线性混合像元分析、基于混合像元分析的时空融合等。在总结技术和工作推进方面的优势、局限基础上,提出要结合实际工作,持续优化国土空间生态保护修复监测指标;充分挖掘遥感数据解析的相关算法潜力,提升地物信息提取和混合像元分析的精度;加强时空融合算法与变化检测方法的研究探索,加强相关方法的实践应用;以“山水林田湖草生态保护修复工程试点”项目为平台,建立稳定的国土空间生态保护修复遥感监测运行机制,加强科技创新,形成技术标准,指导工作开展。

关 键 词:生态保护修复项目  多源遥感数据  地物信息提取  时空融合  变化检测
收稿时间:2019/5/30 0:00:00
修稿时间:2019/9/16 0:00:00

Review on monitoring method of ecological conservation and restoration project area based on multi-source remote sensing data
CHEN Yuanpeng,REN Jia and WANG Li.Review on monitoring method of ecological conservation and restoration project area based on multi-source remote sensing data[J].Acta Ecologica Sinica,2019,39(23):8789-8797.
Authors:CHEN Yuanpeng  REN Jia and WANG Li
Institution:Land Consolidation and Rehabilitation Center, Ministry of Natural Resources, Beijing 100035, China,Land Consolidation and Rehabilitation Center, Ministry of Natural Resources, Beijing 100035, China and The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Abstract:This paper briefly reviewed the implementation background of ecological protection and restoration project of mountains-rivers-forests-farmlands-lakes-grasslands. In view of the wide range of monitoring and technical difficultiesof ecological protection and restoration projects, the importance and necessity of remote sensing monitoring based on multi-source remote sensing data were emphasized. This paper summed up the main methods, applications and research progress from aspects of the monitoring index series establishment, remote sensing information extraction, multi-source remote sensing data fusion and dynamic change detection. It covered feature extraction based on sub-meter or meter-level high spatial resolution optical remote sensing data, non-linear mixed pixel analysis based on machine learning, the quantitative inversion of ecological parameters, and the spatio-temporal fusion based on mixed pixel analysis. It is proposed that the monitoring index of ecological protection and restoration of land space should be continuously optimized in combination with the actual work. The algorithm potential of remote sensing data analysis should be fully explored to improve the precision of ground feature information extraction and mixed pixel analysis. The research and exploration of space-time fusion algorithm and change detection method should be improved to strengthen the practical application of the related methods. A stable remote sensing monitoring mechanism for ecological protection and restoration of land space should be established based on the platform of ecological protection and restoration project of mountains-rivers-forests-farmlands-lakes-grasslands. Scientific and technological innovation should be strengthened to form technical standards to guide the related work.
Keywords:ecological conservation and restoration project  multi-source remote sensing data  ground feature information extraction  spatio-temporal fusion  change detection
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