Consistent Discriminant Correlation Analysis

Consistency and Accuracy of Four High-Resolution LULC Datasets—Indochina Peninsula Case Study

Open and high-temporal- and spatial-resolution global land use/land cover (LULC) mapping data form the foundation of global change research and cross-scale land management planning. However, the consistency and reliability of the use of multisource LULC datasets in specific regions need to be quantitatively assessed. In this study, we selected the Indochina Peninsula as the research area; considered four datasets: LSV10, GLC_FCS30, ESRI10, and Globeland30; and analyzed them from four dimensions: the similarity of composition type, the degree of category confusion, spatial consistency, and data accuracy. The results show that: (1) the land composition descriptions of the different datasets are consistent. The study area is dominated by forest and cropland, supplemented by grassland, shrubland, and other land types. (2) The correlation coefficient between datasets is between 0.905 and 0.972; the spatial consistency of datasets is good; and the high-consistency area accounts for 77.87% of the total. (3) The overall accuracy of LSV10 is the highest (83.25%), and that of GLC_FCS30 is the lowest (72.27%). The accuracy of cropland, forest, water area, and built-up land is generally high (above 85%); the accuracy of grassland, shrubland, and bare land is low (below 60%). Therefore, researchers must conduct validation for specific regions and specific land types before using the above datasets. Our findings provide a basis for selecting LULC datasets in related research on the Indochina Peninsula and a reference method for assessing the reliability of multisource LULC datasets in other regions.