No abstract

We examined agricultural land use changes and their impacts on the local livelihoods of upstream farmers after the Paunglaung hydropower dam construction in Myanmar. Landsat and GeoEye images, acquired in 2011 and 2018, were used to detect agricultural land use changes. A marked decrease in agricultural land (5.77% of farmland and 9.64% of swidden) was observed, whereas orchards and plantations were introduced as new farming practices. Using the household survey, the income strategies were categorized by different wealth groups, and the socioeconomic condition was compared among the groups. We also compared the annual household income before and after dam construction. A significant change in income strategies after the dam construction were observed in low‐income households, but not in medium and high‐income households. Local people engaged in diversified livelihood strategies after the dam construction, mainly in relocated villages. Besides, farmland lost due to dam construction caused the significant insufficiency of rice self‐consumption and consequently resulted in greater dependency on the market of rice and their products. This study highlighted land use changes due to dam construction and its impacts on the livelihood of local farmers, contributing to planning the land use and livelihood strategies for affected farmers to ensure their livelihood security.

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.