Tanintharyi Region Land Cover (Improved)

Zipped Shapefile. Geotiff download link: http://bit.ly/2ToMIjL

This dataset is the result of a land cover analysis for Myanmar's Tanintharyi Region based on March, 2016 Landsat 8 OLI imagery. The primary purpose of the study was to map natural forest for each of four ecological forest types (Mangrove, Mixed Deciduous, Lowland Evergreen, Upland Evergreen). A number of other land use/land cover types are also included in the dataset, including human settlement areas, rice paddyfields, and agroforestry plantations. This dataset is a REVISED version of the land cover map generated according to the methodology outlined in the corresponding manuscript (see below for citation). This version has been manually edited to fill cloud holes using 2015 data and to fix a number of obvious mis-classifications, particularly for oil palm and settlement areas. [Citation: Connette, G., P. Oswald, M. Songer, and P. Leimgruber. 2016. Mapping distinct forest types improves overall forest identification based on multi-spectral Landsat imagery. Remote Sensing 8: 882.] [Spatial reference: WGS84 UTM47N]

GeoJSON file. Geotiff download link: http://bit.ly/2ToMIjL

This dataset is the result of a land cover analysis for Myanmar's Tanintharyi Region based on March, 2016 Landsat 8 OLI imagery. The primary purpose of the study was to map natural forest for each of four ecological forest types (Mangrove, Mixed Deciduous, Lowland Evergreen, Upland Evergreen). A number of other land use/land cover types are also included in the dataset, including human settlement areas, rice paddyfields, and agroforestry plantations. This dataset is a REVISED version of the land cover map generated according to the methodology outlined in the corresponding manuscript (see below for citation). This version has been manually edited to fill cloud holes using 2015 data and to fix a number of obvious mis-classifications, particularly for oil palm and settlement areas. [Citation: Connette, G., P. Oswald, M. Songer, and P. Leimgruber. 2016. Mapping distinct forest types improves overall forest identification based on multi-spectral Landsat imagery. Remote Sensing 8: 882.] [Spatial reference: WGS84 UTM47N]