自然资源遥感2025，Vol.37Issue(2)：204-211,8.DOI:10.6046/zrzyyg.2023321
基于图谱耦合的高寒湿地土地类型识别与分类
Identification and classification of land types of alpine wetlands based on spectral coupling
摘要
Abstract
Alpine wetlands,a critical part of the natural ecosystem in the Qinghai-Tibet Plateau,serve as extremely significant water conservation and climate regulation areas in China.Accurately extracting land cover information of alpine wetlands is crucial for local ecological security monitoring and protection.This study performed object-oriented classification of the data from the Zoige wetland,including the Zhuhai-1 hyperspectral remote sensing image,Sentinel-2A remote sensing image,and Landsat-8 OLI image,integrated with spectral,textural,and topographic features.The results show that the overall data classification accuracy of the three images exceeded 85％,with a Kappa coefficient above 68％.The optimal classification result was observed in the Zhuhai-1 hyperspectral remote sensing image.The three images showed generally consistent data classification results,with marsh wetlands being the dominant land type.They indicated roughly the same distribution of riverine and lacustrine wetlands and slightly varying distributions of alpine grasslands,with minor area differences.Additionally,they displayed minimally different distributions of desertified land and almost the same hydrographic net distribution except for slightly different tributary distributions.This study fully explores the combinations of spectral features favorable for image classification,improving the identification accuracy of remote sensing images and providing technical support for the conservation of alpine wetlands.关键词
高寒湿地/遥感影像分类/图谱耦合/特征选择/若尔盖Key words
alpine wetland/remote sensing image classification/spectral coupling/feature selection/Zoige分类
信息技术与安全科学引用本文复制引用
聂诗音,刘严松,李会玲,薛凯伦,沈杜衡,何博宇..基于图谱耦合的高寒湿地土地类型识别与分类[J].自然资源遥感,2025,37(2):204-211,8.基金项目
国家自然科学基金项目"低山丘陵区土壤元素空间运移与沉积对景观格局与过程的响应规律研究"(编号:41971226)、四川省自然资源厅基金项目"大渡河区域金矿成矿带地质找矿关键科学技术难题研究与示范"(编号:KJ-2016-07)、甘肃省教育厅高校教师创新基金项目"铅锌矿稀散元素含量的高光谱反演研究"(编号:2023A-253)和四川省教育厅基金项目"基于元素迁移的矿区重金属污染快速填图方法研究与示范"(编号:18ZB0065)共同资助. （编号:41971226）
