文献类型：期刊文献

英文题名：Desertification Monitoring in Northern China by Combining a Novel 3-D Desertification Index With a Gaussian Mixture Model

作者：Dou, Yaqing[1] Zhang, Meng[1] Zhang, Huaiqing[2] Liu, Yang[2]

第一作者：Dou, Yaqing

通信作者：Zhang, M[1]

机构：[1]Cent South Univ Forestry & Technol, Hunan Prov Key Lab Forestry Remote Sensing Based B, Changsha 410004, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

年份：2025

卷号：63

外文期刊名：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

收录：;EI(收录号：20253118907260);Scopus(收录号：2-s2.0-105012040330);WOS:【SCI-EXPANDED(收录号:WOS:001534542200049)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2023YFF1303701 and in part by the National Natural Science Foundation of China under Grant 41901385.

语种：英文

外文关键词：3-D desertification index (TDDI); desertification; Euclidean distance; Gaussian mixture model (GMM); moderate resolution imaging spectroradiometer (MODIS); 3-D desertification index (TDDI); desertification; Euclidean distance; Gaussian mixture model (GMM); moderate resolution imaging spectroradiometer (MODIS)

摘要：As desertification is one of the most severe ecological and environmental issues worldwide, monitoring desertification and studying its evolution patterns are highly important for its governance and prevention. In this study, a novel desertification monitoring method is developed that combines the 3-D desertification index (TDDI) and Gaussian mixture model (GMM). The results of applying this method to desertification monitoring, which is based on historical Google Earth images, in northern China from 2000 to 2020 indicate that the accuracy of desertification classification using the TDDI and GMM algorithms exceeds 82%. Compared with the national desertification survey statistics, the accuracy of classifying areas with different degrees of desertification exceeds 93.4%. In terms of the stability of the monitoring results under different data source and spatial region conditions, TDDIMODIS shows a strong correlation and high consistency with TDDILandsat and TDDISentinel-2. The overall accuracies are greater than 55%. Additionally, the TDDI comprehensively considers the soil moisture level, vegetation coverage, and surface conditions and reflects the complexity of the desertification process more accurately than the normalized difference vegetation index (NDVI) and desertification difference index (DDI).