Swidden agriculture is a common land use found in the mountainous regions, especially in Southeast Asia. In Myanmar, the swidden agriculture has been practicing as an important livelihood strategy of millions of people, mainly by the ethnic groups. However, the extent of swidden agriculture in Myanmar is still in question. Therefore, we attempted to detect swidden patches and estimate the swidden extent in Myanmar using free available Landsat images on Google Earth Engine in combination with a decision tree-based plot detection method. We applied the commonly used indices such as dNBR, RdNBR, and dNDVI, statistically tested their threshold values to select the most appropriate combination of the indices and thresholds for the detection of swidden, and assessed the accuracy of each set of index and thresholds using ground truth data and visual interpretation of sample points outside the test site. The results showed that dNBR together with RdNBR, slope and elevation demonstrated higher accuracy (84.25%) compared to an all-index combination (dNBR, RdNBR, dNDVI, slope, and elevation). Using the best-fit pair, we estimated the extent of swidden at national level. The resulting map showed that the total extent of swidden in Myanmar was about 0.1 million ha in 2016, which is much smaller than other previously reported figures. Also, swidden patches were mostly observed in Shan State, followed by Chin State. In this way, this study primarily estimated the total extent of swidden area in Myanmar at national level and proved that the use of a decision tree-based detection method with appropriate vegetation indices and thresholds is highly applicable to the estimation of swidden extent on a regional basis. Also, as Myanmar is the largest country in mainland Southeast Asia in area with a great majority of the population living in rural areas, and many in the mountains, its land resources are of great relevance to the people’s livelihoods and thereby the nation’s progress. Therefore, this study will contribute to sustainable land management planning on both regional and national scale.
