Consistency Analysis of Multi-Source Remote Sensing Land Cover Products in Arid Regions—A Case Study of Xinjiang

• Published in: Land (Basel) Vol. 12; no. 12; p. 2178
• Main Authors: Liu, Shen, Xu, Zhonglin, Guo, Yuchuan, Yu, Tingting, Xu, Fujin, Wang, Yao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2023
• Subjects: Accuracy; accuracy assessment; Agricultural land; arid region; Arid regions; Arid zones; case studies; China; Classification; Climate change; Comparative analysis; Consistency; consistency analysis; Correlation coefficient; Correlation coefficients; cropland; ecosystems; Environmental changes; forests; Grasslands; high resolution; ice; Land area; Land cover; Land use; land use and land cover maps; landscapes; Mountains; Performance indices; Regions; Remote sensing; Research centers; Satellites; Snow; Central Asia; China; Africa
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land12122178

Arid regions are considered to be among the most ecologically fragile and highly sensitive to environmental change globally, and land use and land cover conditions in the region directly influence large-scale ecosystem processes. Currently, thanks to diverse remote sensing platforms, geographers have developed an array of land cover products. However, there are differences between these products due to variations in spatio-temporal resolutions. In this context, assessing the accuracy and consistency of different land cover products is crucial for rationalizing the selection of land cover products to study global or regional environmental changes. In this study, Xinjiang Uygur Autonomous Region (XUAR) is taken as the study area, and the consistency and performance (type area deviation, spatial consistency, accuracy assessment, and other indexes) of the five land cover products (GlobeLand30, FROM_GLC30, CLCD, GLC_FCS30, and ESRI) were compared and analyzed. The results of the study show that (1) the GlobeLand30 product has the highest overall accuracy in the study area, with an overall accuracy of 84.06%, followed by ESA with 75.57%, while CLCD has the lowest overall accuracy of 70.05%. (2) The consistency between GlobeLand30 and CLCD (area correlation coefficient of 0.99) was higher than that among the other products. (3) Among the five products, the highest consistency was found for water bodies and permanent snow and ice, followed by bare land. In contrast, the consistency of these five products for grassland and forest was relatively low. (4) The full-consistency area accounts for 49.01% of the total study area. They were mainly distributed in areas with relatively homogeneous land cover types, such as the north and south of the Tianshan Mountains, which are dominated by bare land and cropland. In contrast, areas of inconsistency make up only 0.03% and are mostly found in heterogeneous areas, like the transitional zones with mixed land cover types in the Altai Mountains and Tianshan Mountains, or in areas with complex terrain. In terms of meeting practical user needs, GlobeLand30 offers the best comprehensive performance. GLC_FCS30 is more suitable for studies related to forests, while FROM_GLC30 and ESRI demonstrate greater advantages in identifying permanent ice and snow, whereas the performance of CLCD is generally average.