A Field-Data-Aided Comparison of Three 10 m Land Cover Products in Southeast Asia


引用 6|浏览6
To study global and regional environment protection and sustainable development and also to optimize mapping methods, it is of great significance to compare three existing 10 m resolution global land cover products in terms of accuracy: FROM-GLC10, the ESRI 2020 land cover product (ESRI2020), and the European Space Agency world cover 2020 product (ESA2020). However, most previous validations lack field collection points in large regions, especially in Southeast Asia, which has a cloudy and rainy climate, creating many difficulties in land cover mapping. In 2018 and 2019, we conducted a 56-day field investigation in Southeast Asia and collected 3326 points from different places. By combining these points and 14,808 other manual densification points in a stratified random sampling, we assessed the accuracy of the three land cover products in Southeast Asia. We also compared the impacts of the different classification standards, the different sample methods, and the different spatial distributions of the sample points. The results show that in Southeast Asia, (1) the mean overall accuracies of the FROM-GLC10, ESRI2020, and ESA2020 products are 75.43%, 79.99%, and 81.11%, respectively; (2) all three products perform well in croplands, forests, and built-up areas; ESRI2020 and ESA2020 perform well in water, but only ESA2020 performs well in grasslands; and (3) all three products perform badly in shrublands, wetlands, or bare land, as both the PA and the UA are lower than 50%. We recommend ESA2020 as the first choice for Southeast Asia's land cover because of its high overall accuracy. FROM-GLC10 also has an advantage over the other two in some classes, such as croplands and water in the UA aspect and the built-up area in the PA aspect. Extracting the individual classes from the three products according to the research goals would be the best practice.
10 m land cover products,FROM-GLC10,ESRI 2020 land cover,world cover 2020,land use,cover,field data,accuracy assessment,earth observation,sentinel-2
AI 理解论文
Chat Paper