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 km 2 ; 39% of total area), as well as total forest conversion into agricultural plantations (3214 km 2). Gross forest loss (5133 km 2) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5471 km 2) and rubber (4025 km 2) 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.
Political transitions often trigger substantial environmental changes. In particular, deforestation can result from the complex interplay among the components of a system-actors, institutions, and existing policies-adapting to new opportunities. A dynamic conceptual map of system components is particularly
Mangroves are one of the world's most threatened ecosystems, and Myanmar is regarded as the current mangrove deforestation hotspot globally. Here, we use multi-sensor satellite data and Intensity Analysis to quantify and explain patterns of net and gross mangrove cover change (loss, gain, persistence) for the 1996-2016 period across all of Myanmar. Net national mangrove cover declined by 52% over 20 years, with annual net loss rates of 3.60%-3.87%. Gross mangrove deforestation was more profound: 63% of the 1996 mangrove extent had been temporarily or permanently converted by 2016. Rice, oil palm, and rubber expansion accounted for most conversion; however, our analysis revealed targeted systematic transitions of mangroves to water (presumably aquaculture) and built-up areas indicated emerging threats for mangroves from those land uses. Restoration programmes facilitated mangrove gains and represent a critical area for investment alongside protection. This study demonstrates the importance of multi-sensor satellite data for national-level mangrove change assessments, along with gross land cover transition analyses to assess landscape dynamics as well as prioritise threats and interventions in an effort to develop holistic strategies that aim to conserve important habitats.
Southeast Asian forests are dominated by the tree family Dipterocarpaceae, whose abundance and diversity are key to maintaining the structure and function of tropical forests. Like most biodiversity, dipterocarps are threatened by deforestation and climate change, so it is crucial to understand the potential impacts of these threats on current and future dipterocarp distributions. We developed species distribution models (SDMs) for 19 species of dipterocarps in the Philippines, which were projected onto current and two 2070 representative concentration pathway (RCP) climate scenarios, RCP 4.5 and 8.5. Current land cover was incorporated as a post-hoc correction to restrict projections onto intact habitats. Land cover correction alone reduced current species distributions by a median 67%, and within protected areas by 37%. After land cover correction, climate change reduced distributions by a median 16% (RCP 4.5) and 27% (RCP 8.5) at the national level, with similar losses in protected areas. There was a detectable upward elevation shift of species distributions, consisting of suitable habitat losses below 300 m and gains above 600 m. Species-rich stable areas of continued habitat suitability (i.e., climate macrorefugia) fell largely outside current delineations of protected areas, indicating a need to improve protected area planning. This study highlights how SDMs can provide projections that can inform protected area planning in the tropics.
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