Using freely-available data and open-source software, we developed a remote sensing methodology to identify mining areas and assess recent mining expansion in Myanmar. Our country-wide analysis used Landsat 8 satellite data from a select number of mining areas to create a raster layer of potential mining areas. We used this layer to guide a systematic scan of freely-available fine-resolution imagery, such as Google Earth, in order to digitize likely mining areas. During this process, each mining area was assigned a ranking indicating our certainty in correct identification of the mining land use. Finally, we identified areas of recent mining expansion based on the change in albedo, or brightness, between Landsat images from 2002 and 2015. We identified 90,041 ha of potential mining areas in Myanmar, of which 58% (52,312 ha) was assigned high certainty, 29% (26,251 ha) medium certainty, and 13% (11,478 ha) low certainty. Of the high-certainty mining areas, 62% of bare ground was disturbed (had a large increase in albedo) since 2002. This four-month project provides the first publicly-available database of mining areas in Myanmar, and it demonstrates an approach for large-scale assessment of mining extent and expansion based on freely-available data.

We investigated the use of multi-spectral Landsat OLI imagery for delineating mangrove, lowland evergreen, upland evergreen and mixed deciduous forest types in Myanmar’s Tanintharyi Region and estimated the extent of degraded forest for each unique forest type. We mapped a total of 16 natural and human land use classes using both a Random Forest algorithm and a multivariate Gaussian model while considering scenarios with all natural forest classes grouped into a single intact or degraded category. Overall, classification accuracy increased for the multivariate Gaussian model with the partitioning of intact and degraded forest into separate forest cover classes but slightly decreased based on the Random Forest classifier. Natural forest cover was estimated to be 80.7% of total area in Tanintharyi. The most prevalent forest types are upland evergreen forest (42.3% of area) and lowland evergreen forest (21.6%). However, while just 27.1% of upland evergreen forest was classified as degraded (on the basis of canopy cover <80%), 66.0% of mangrove forest and 47.5% of the region’s biologically-rich lowland evergreen forest were classified as degraded. This information on the current status of Tanintharyi’s unique forest ecosystems and patterns of human land use is critical to effective conservation strategies and land-use planning.

Myanmar is considered as a stronghold for wild elephants, though past estimates varied widely from 3,000 to 10,000. Results of a 2004 expert workshop showed estimates between 1,430 and 2,065. Building on surveys from 1990 to 1992, we conducted new expert interviews in townships throughout the range, with questions focusing on numbers of elephants living in townships and threats to and from elephants. We used general linear models to analyze characteristics of townships with and without elephants and to understand factors influencing changes in elephant presence. Our results show a major decrease in the geographic distribution of wild elephants in Myanmar between 1992 and 2006 with deforestation being the major driver. While forest cover is important for elephant presence, continuity with other elephant populations had a stonger influence on elephant persistence between surveys. Fragmentation of elephant populations is also a major driver of decline. Increases in forest cover increased elephant presence, while increases in edge and human population had the reverse effect. Deforestation and fragmentation lead to increased human–elephant conflict in some areas, sometimes concluding with the capture of elephants for timber operations and further draining wild populations. A national elephant action plan that includes monitoring of elephant status and threats is urgently needed and critical for Myanmar’s wild elephants, particularly as more than 50 years of political isolation are rapidly ending and pressure on the country’s natural resources is increasing exponentially.