Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km²; 39% of total area), as well as total forest conversion into agricultural plantations (3214 km²). Gross forest loss (5133 km²) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5471 km²) and rubber (4025 km²) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes.

Aim: Google Earth Engine (GEE) is a free Web‐based spatial analysis platform that requires only a web browser and an Internet connection to programmatically access and analyse data from its multi‐petabyte catalog of regularly updated satellite imagery (e.g. MODIS, Landsat, Sentinel) and other geospatial datasets. The high computing capacity of GEE can make computationally demanding analyses more accessible to researchers and practitioners, especially those with limited access to advanced computational resources. Here, we present a workflow in GEE to fit species distribution models, offering direct access to a multi‐petabyte catalog of raster products to obtain estimates of habitat suitability.Innovation: We implemented a workflow for species distribution modelling in GEE that includes importing species occurrence data into the GEE platform, selecting and preparing predictor variables, and performing model fitting with spatial or temporal split‐block cross‐validation techniques. We present three case studies that demonstrate: (i) a baseline SDM workflow that produces informative model predictions, (ii) a workflow that accounts for temporal variability in predictor variables to study changes in habitat suitability over time and (iii) a complex and computationally demanding analysis incorporating thousands of satellite images for modelling habitat suitability at high spatial resolution.Main Conclusions: Our SDM workflow allows users to benefit from the high speed and performance of GEE without the need for significant computing infrastructure. This workflow may be especially beneficial to researchers in countries where computing power is limited, as SDMs frequently require the download, storage and processing of large raster datasets. We also discuss key limitations of implementing SDMs in GEE, such as user memory limits and the lack of high‐level functions. We include a step‐by‐step guide for the general model workflow and for each of the case studies presented to facilitate its implementation.

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’s recent transition from military rule towards a more democratic government has largely ended decades of political and economic isolation. Although Myanmar remains heavily forested, increased development in recent years has been accompanied by exceptionally high rates of forest loss. In this study, we document the rapid progression of deforestation in and around the proposed Lenya National Park, which includes some of the largest remaining areas of lowland evergreen rainforest in mainland Southeast Asia. The globally unique forests in this area are rich in biodiversity and remain a critical stronghold for many threatened and endangered species, including large charismatic fauna such as tiger and Asian elephant. We also conducted a rapid assessment survey of the herpetofauna of the proposed national park, which resulted in the discovery of two new species of bent-toed geckos, genus Cyrtodactylus. We describe these new species, C. lenya sp. nov. and C. payarhtanensis sp. nov., which were found in association with karst (i.e., limestone) rock formations within mature lowland wet evergreen forest. The two species were discovered less than 35 km apart and are each known from only a single locality. Because of the isolated nature of the karst formations in the proposed Lenya National Park, these geckos likely have geographical ranges restricted to the proposed protected area and are threatened by approaching deforestation. Although lowland evergreen rainforest has vanished from most of continental Southeast Asia, Myanmar can still take decisive action to preserve one of the most biodiverse places on Earth.