No abstract

Community forestry, which is how local communities are involved in forest conservation and utilization activities, is an important forestry program in developing tropical countries. We evaluated the importance of geographical factors and community characteristics in the deforestation of community forests between 2000 and 2019 in the buffer zone of Inlay Lake Biosphere Reserve, Myanmar, using a mixed-effects logistic regression model. Distance to the nearest village, slope, and distance to the community forestry boundary were the most important variables explaining deforestation in community forests. Forests closer to human settlements and with gentle slopes faced higher risks of deforestation, presumably because such forests are more accessible. In addition, forests located far from the boundaries of community forests were more vulnerable to deforestation. Community characteristics were less important compared with geographical factors. Leadership was the most important variable among community characteristics, although not statistically significant. We conclude that deforestation depends more on forest accessibility. This indicates that the locations at which new community forests are established should receive increased consideration.

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.