부산대학교 도서관

Myanmar has experienced rapid socio-economic developments in recent decades, which have a greater impact on land cover change. Accurate long time series land cover datasets for Myanmar can be of great help in environmental protection and natural resource management. However, there are relatively few existing studies on long time series land cover datasets in Myanmar, and the acquisition of training samples within different time series is a big challenge. Therefore, this study used Google Earth Engine and Landsat imagery to produce a land cover dataset for every two years from 1990 to 2020 using a training sample migration framework. First, the differences in index change, spectral value change, and spectral shape change were used to determine whether the sample points had changed between the base year and the previous year, and then a small number of samples were manually selected. Second, the spectral features, index information, and texture information of the remote sensing images and the object-oriented segmentation method were used to obtain object-oriented multidimensional features. Finally, the random forest method was employed to train the samples of the previous year to obtain the land cover data of the previous year. The results of the study show that the average overall precision of the land cover classification results for Myanmar for 1990–2020 is 0.83 and Kappa is 0.79. In addition, the land cover classification results for Myanmar of 1990–2020 are significantly better than those of Globeland30-2020, FROM-GLC, and Dynamic World land cover, and comparing with these products showed good agreement.