文献类型：期刊文献

英文题名：Synergistic Coupling of Multi-Source Remote Sensing Data for Sandy Land Detection and Multi-Indicator Integrated Evaluation

作者：Wu, Junjun[1] Li, Yi[2] Zhong, Bo[1] Zhang, Yan[3] Liu, Qinhuo[1] Shi, Xiaoliang[2] Ji, Changyuan[1] Wu, Shanlong[1] Sun, Bin[4] Li, Changlong[5] Yang, Aixia[1]

第一作者：Wu, Junjun

通信作者：Zhong, B[1]

机构：[1]Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100864, Peoples R China;[2]Xian Univ Sci & Technol, Coll Geomat, Xian 710054, Peoples R China;[3]Jiangsu Normal Univ, Coll Geog Mapping & Urban & Rural Planning, Xuzhou 210023, Peoples R China;[4]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[5]Guangzhou Coll Commerce, Sch Informat Technol & Engn, Guangzhou 511363, Peoples R China

年份：2024

卷号：16

期号：22

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20244817447251);Scopus(收录号：2-s2.0-85210242536);WOS:【SCI-EXPANDED(收录号:WOS:001366055100001)】；

基金：This research was funded by the National Key Research and Development Program (2021YFE0117400), and supported by the National Key Research and Development Program (2022YFF1300200), and Natural Science Foundation of Hainan Province (422QN350), and supported by Beijing Science and Technology Plan (Z241100005424006). Thanks to China Land Surveying and Planning Institute for providing the field data.

语种：英文

外文关键词：mixed pixel decomposition; polarization decomposition; image fusion; texture features; random forest

摘要：Accurate and timely extraction and evaluation of sandy land are essential for ecological environmental protection; it is urgent to do the research to support the sustainable development goals (SDGs) of Land Degradation Neutrality. This study used Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 8 OLI multispectral data as the main data sources. Combining the rich spectral information from optical data and the penetrating advantages of radar data, a feature-level fusion method was employed to unveil the intrinsic nature of vegetative cover and accurately identify sandy land. Simultaneously, leveraging the results obtained from training with measured data, a comprehensive desertification assessment model was proposed, which combines multiple indicators to achieve a thorough evaluation of sandy land. The results showed that the method based on feature-level fusion achieved an overall accuracy of 86.31% in sandy land detection in Gansu Province, China. The integrated multi-indicator model C22_C/FVC is the ratio of correlation texture features of VH to vegetation cover based on which sandy land can be classified into three categories. When C22_C/FVC is less than 2.2, the pixel is classified as fixed sandy land. Pixels of semi-fixed sandy land have an indicator value between 2.2 and 5.2. Shifting sandy land has values greater than 5.2. Results showed that shifting sandy land and semi-fixed sandy land are the predominant types in Gansu Province, with 85,100 square kilometers and 87,100 square kilometers, respectively. The acreage of fixed sandy land was the least, 51,800 square kilometers. The method presented in this paper is robust for the detection and evaluation of sandy land from satellite imageries, which can potentially be applied for conducting high-resolution and large-scale detection and evaluation of sandy land.