文献类型：期刊文献

英文题名：Extraction of rocky desertification information in karst area by using different multispectral sensor data and multiple endmember spectral mixture analysis method

作者：Li, Yifu[1,2] Sun, Bin[1,2] Gao, Zhihai[1,2] Su, Wensen[1,2] Wang, Bengyu[1,2] Yan, Ziyu[1,2] Gao, Ting[1,2]

第一作者：Li, Yifu

通信作者：Sun, B[1];Sun, B[2]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing, Peoples R China;[2]NFGA, Key Lab Forestry Remote Sensing & Informat Syst, Beijing, Peoples R China

年份：2022

卷号：10

外文期刊名：FRONTIERS IN ENVIRONMENTAL SCIENCE

收录：;Scopus(收录号：2-s2.0-85142237037);WOS:【SCI-EXPANDED(收录号:WOS:000890902400001)】；

基金：This research was funded by the National Science and Technology Major Project of China's High Resolution Earth Observation System (Project Number: 21-Y30B02-9001-19/22) and within the ESA-MOST China Dragon five Cooperation (ID:59313).

语种：英文

外文关键词：fractional vegetation cover; fraction bare rock; multiple endmember spectral mixture analysis; karst rocky desertification; sentinel-2; GF-6 WFV; landsat 8 OLI

摘要：As a typical form of land degradation, karst rocky desertification seriously restricts the development of the regional social economy and seriously threatens the living environment of residents. Fractional vegetation cover (f(VC)) and bare rock fraction (f(BR)) are important indicators to identify and evaluate rocky desertification. However, it is a great challenge to obtain f(VC) and f(BR) due to the complex terrain and fragmentation of karst rocky desertification areas. In this study, comparisons between Sentinel-2A Multispectral Instrument (Sentinel-2), Landsat-8 Operational Land Imager (Landsat-8), and GF-6 Wide Field View (GF-6) sensors for retrieving f(VC) and f(BR) are presented. The multiple endmember spectral mixture analysis (MESMA) and measured spectral dates were used to overcome the limitations of Spectral mixture analysis (SMA). Subsequently, f(VC) and f(BR) were validated using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R (2)). The results showed that: 1) Sentinel-2 performed best in estimating f(VC) and obtained the highest accuracy (R (2) = 0.6259, root mean square error = 0.1568, mean absolute error = 0.1215), followed by GF-6 and Landsat 8; in the extraction of the f(BR), the performance was relatively the same, and the implementation of Sentinel-2 was also the best (R2 = 0.4911, root mean square error = 0.0714, mean absolute error = 0.0539), followed by GF-6 and Landsat 8. 2) Sentinel-2 images have higher resolution, the narrowest band range, and the most significant number of bands, which can better extract information about f(VC) and f(BR) in rocky desertification areas. 3) For the three optical sensors, the spatial resolution of the images is more important to extract the information of f(VC) and f(BR) in the rocky desertification areas. 4) In general, the extraction accuracy of f(BR) is not as good as that of f(VC). The complicated ecological and geological environment of decertified areas has more influence on the effect of extraction of the f(BR). 5) The Sentinel-2 achieves high accuracy for both f(VC) and f(BR) under different-level application scenarios. It thus has great potential for application in rocky desertification information extraction.