摘要
植被是湿地生态系统健康状况的"晴雨表",明晰湿地中植被的时空分布,是湿地修复与重建、保护与合理利用的前提和基础。以洪河国家级保护区为研究区,利用全极化C-band Radarsat-2和L-band PALSAR数据,根据极化合成孔径雷达(synthetic aperture radar,SAR)目标分解理论,提取了该保护区不同波长的极化分解参数和特征参量,整合为多源极化SAR数据集,利用多尺度迭代分割算法和Random Forest机器学习算法,构建了研究区中植被的遥感识别模型,实现了对研究区中植被的高精度分类,并对比分析了不同频率SAR数据集在植被识别精度上的差异。研究结果表明,利用整合PALSAR和Radarsat-2极化数据集,获取的植被遥感分类结果的总体分类精度为86.77%,比利用PALSAR极化数据集的分类结果精度提高了15%,但是其与利用Radarsat-2极化数据集的分类结果精度差异不显著;浅水草本沼泽的生产精度达到了90.91%,深水草本沼泽的用户精度为90.63%;C-band PALSAR数据比L-band PALSAR数据更适用于高精度识别洪河国家级自然保护区中的植被。
Vegetation is a'barometer'of health status of wetland ecosystem.Exploration of the spatial-temporal distribution of vegetation is the premise and foundation of wetland ecological restoration and reconstruction,wetland resource protection and rational utilization.This paper extracted different wavelength polarimetric decomposition parameters of Honghe National Nature Reserve based on polarimetric synthetic aperture radar(SAR)target decomposition theory,and integrated C-band Radarsat-2 with L-band PALSAR for multisource polarimetric parameters dataset.Multi-scale iterative segmentation algorithm and Random Forest machine-learning algorithm were utilized to classify land cover types in the reserve.This research further analyzed differences between multi-frequency SAR datasets in the identification accuracy of vegetation types in the reserve.The results showed that the integrating PALSAR and radarsat-2 polarization datasets achieved86.77%overall classification accuracy,which was 15%higher than that using PALSAR polarization images.However,there was no significant difference between classification result using radarsat-2 polarization images and that using combined SAR images.The production precision of shallow-water marshes achieved 90.91%.The user precision of deep-water marshes obtained 90.63%.C-band polarimetric SAR was more suitable than L-band polarimetric SAR for vegetation classification in Honghe National Nature Reserve.
作者
付波霖
李颖
张柏
何宏昌
高二涛
范冬林
杨高
FU Bolin;LI Ying;ZHANG Bai;HE Hongchang;GAO Ertao;FAN Donglin;YANG Gao(College of Geomatics and Geoinformation,Guilin University of Technology,Guilin 541004,Guangxi Autonomous Region,P.R.China;Key Laboratory of Wetland Ecology and Environment,Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,Changchun 130102,Jilin,P.R.China)
出处
《湿地科学》
CSCD
北大核心
2019年第2期199-209,共11页
Wetland Science
基金
国家自然科学基金项目(41801071)
广西自然科学基金项目(2018GXNSFBA281015)
桂林理工大学科研启动基金项目(GUTQ DJJ2017096)资助
关键词
植被
沼泽
SAR
极化目标分解理论
多尺度迭代分割算法
洪河国家级自然保护区
vegetation
marsh
SAR
polarimetric target decomposition theory
multi-scale iterative segmentation algorithm
Honghe National Nature Reserve