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Abstract
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.
Highlights
IntroductionThe floristically-distinct forest types that occur in the region vary in their species assemblages, vulnerability to habitat conversion or degradation, conservation value, and representation within protected area networks [3,4]
The complex geological and bioclimatic history of Southeast Asia has resulted in an exceptionally rich biodiversity [1] and some of the highest concentrations of endemic species in the world [2].The floristically-distinct forest types that occur in the region vary in their species assemblages, vulnerability to habitat conversion or degradation, conservation value, and representation within protected area networks [3,4]
For land cover classifications with just two natural forest classes, validation based on withheld training data resulted in kappa coefficients of 0.70 and 0.78 for the GMLC and Random Forest classifications, respectively
Summary
The floristically-distinct forest types that occur in the region vary in their species assemblages, vulnerability to habitat conversion or degradation, conservation value, and representation within protected area networks [3,4]. Forests that are accessible and occur in areas with high human population densities are especially vulnerable to degradation and deforestation. The region’s remaining forest cover predominantly occurs at high elevations or in areas that are difficult to access due to steep terrain [5]. Due to the unique threats faced by different forest types, conservation strategies and risk assessments in the region should be based on an accurate understanding of current forest distributions and knowledge of where forest loss and degradation are taking place
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