• Water Mask
  • Landsat

LEGEND


Rivers

River systems in the Greater Mekong Subregion. Attributes include:  name of river, name of basin, name of sub-basin, Strahler number.

 

Domanant Soil Types

Polygons of dominant soil types in the Greater Mekong Subregion, according to FAO classifications (2007)

 

GMS Transboundary Biodiversity Landscapes

Boundary of GMS Transboundary Biodiversity Landscapes a project of the Critical Ecosystem Partnership Fund and WWF Terrestrial Ecoregions.

Distribution of Mangroves (2011)

This dataset shows the distribution of mangrove forests, derived from earth observation satellite imagery for the Greater Mekong Subregion including Thailand, Cambodia, Vietnam and Myanmar.

Freshwater Ecoregions

Freshwater species and habitats are, on average around the world, more imperiled than their terrestrial counterparts. Yet, large-scale conservation planning efforts have rarely targeted freshwater biodiversity. This inattention is due in part to the fact that, compared to better-studied terrestrial taxa, there has been a severe lack of comprehensive, synthesized data on the distributions of freshwater species. Existing worldwide species-level data have covered only the largest river basins or select hotspots, rather than all inland waters. Additionally, these data syntheses have made little attempt to describe biogeographic patterns.

Lower Oder Valley National Park, Brandenburg, Germany. (c) WWF-Canon / Chris MartinFreshwater Ecoregions of the World (FEOW) is a collaborative project providing the first global biogeographic regionalization of the Earth's freshwater biodiversity, and synthesizing biodiversity and threat data for the resulting ecoregions. We define a freshwater ecoregion as a large area encompassing one or more freshwater systems that contains a distinct assemblage of natural freshwater communities and species. The freshwater species, dynamics, and environmental conditions within a given ecoregion are more similar to each other than to those of surrounding ecoregions and together form a conservation unit.

The freshwater ecoregion map serves as a complement to the global terrestrial and marine ecoregion maps and differs from them in that freshwater species (primarily fish) and freshwater processes drove the map delineation. A detailed description of the delineation methodology is available in Abell et al. (2008).

 

Terrestrial Ecoregions

Terrestrial Ecoregions of the World (TEOW) is a biogeographic regionalization of the Earth's terrestrial biodiversity. Our biogeographic units are ecoregions, which are defined as relatively large units of land or water containing a distinct assemblage of natural communities sharing a large majority of species, dynamics, and environmental conditions. There are 867 terrestrial ecoregions, classified into 14 different biomes such as forests, grasslands, or deserts. Ecoregions represent the original distribution of distinct assemblages of species and communities. There are multiple uses for TEOW in our efforts to conserve biodiversity around the world.

Protected areas and heritage sites

The World Database on Protected Areas (WDPA) is the most comprehensive global spatial dataset on marine and terrestrial protected areas available. Protected areas are internationally recognised as a critical means of conserving species and ecosystems. Up to date information on protected areas is essential to enable a wide range of conservation and development activities. Since 1981 UNEP-WCMC, through its Protected Areas Programme, has been compiling this information and making it available to the global community. The WDPA is a joint project of UNEP and IUCN, produced by UNEP-WCMC and the IUCN World Commission on Protected Areas working with governments and collaborating NGOs. 

Tree cover (2000)

A dataset visualising tree cover across the Greater Mekong Subregion at 30 x 30m resolution (2000). For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities. Dataset is encoded as a percentage per output grid cell, in the range 0-100. Sourced from Landsat 7 ETM+. Open Development Mekong has trimmed this data to the area of interest visualised here.

 

Tree cover loss 2000-2013

Production date: 2015

Description: A dataset describing forest loss during the period 2000–2013, defined as a stand-replacement disturbance, or a change from a forest to non-forest state. Encoded as either 1 (loss) or 0 (no loss).

This data layer was updated in January 2015 to extend the tree cover loss analysis to 2013. The 2013 data update included new Landsat 8 data (launched in February 2013) as well as re-processed 2010-2012 data from Landsat TM and ETM+, which increased the amount of change that could be detected, resulting in some changes in calculated tree cover loss for 2011 (global increase of 6%) and 2012 (increase of 22%). Calculated tree cover loss for 2001-2010 remains unchanged. The integrated use of the original 2001-2012 (Version 1.0) data and the updated 2011–2013 data (Version 1.1) should be performed with caution.

The data were generated using multispectral satellite imagery from the Landsat 7 thematic mapper plus (ETM+), and Landsat 7 thematic mapper plus (ETM+), and Landsat 8 Operational Land Imager (OLI) sensors. Over 1 million satellite images were processed and analyzed, including over 600,000 Landsat 7 images for the 2000-2012 interval, and approximately 400,000 Landsat 5,7 and 8 images for the 2010-2013 interval . The clear land surface observations in the satellite images were assembled and a supervised learning algorithm was applied to identify per pixel tree cover loss.

Tree cover loss is defined as “stand replacement disturbance,” or the complete removal of tree cover canopy at the Landsat pixel scale. Tree cover loss may be the result of human activities, including forestry practices such as timber harvesting or deforestation (the conversion of natural forest to other land uses), as well as natural causes such as disease or storm damage. Fire is another widespread cause of tree cover loss, and can be either natural or human-induced.

For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities. “Loss” indicates the removal or mortality of tree canopy cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.

Open Development Mekong has trimmed this data to the area of interest visualised here.

Source: Global Forest Watch / Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “Hansen/UMD/Google/USGS/NASA Tree Cover Loss and Gain Area.” University of Maryland, Google, USGS, and NASA.

Tree cover gain 2000-2012

Production date: 2012

Description: Forest gain during the period 2000–2012, defined as the inverse of loss, or a non-forest to forest change entirely within the study period. Encoded as either 1 (gain) or 0 (no gain). For the purpose of this study, “tree cover” was defined as all vegetation taller than 5 meters in height. “Tree cover” is the biophysical presence of trees and may take the form of natural forests or plantations existing over a range of canopy densities. “Loss” indicates the removal or mortality of tree canopy cover and can be due to a variety of factors, including mechanical harvesting, fire, disease, or storm damage. As such, “loss” does not equate to deforestation.

 This data set measures areas of tree cover gain at 30 × 30 meter resolution, displayed as a 12-year cumulative layer. The data were generated using multispectral satellite imagery from the Landsat 7 thematic mapper plus (ETM+) sensor. Over 600,000 Landsat 7 images were compiled and analyzed using Google Earth Engine, a cloud platform for earth observation and data analysis. The clear land surface observations (30 × 30 meter pixels) in the satellite images were assembled and a supervised learning algorithm was then applied to identify per pixel tree cover gain.

Tree cover gain was defined as the establishment of tree canopy at the Landsat pixel scale in an area that previously had no tree cover. Tree cover gain may indicate a number of potential activities, including natural forest growth or the crop rotation cycle of tree plantations. Open Development Mekong has trimmed this data to the area of interest visualised here.

Source: Global Forest Watch / Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “Hansen/UMD/Google/USGS/NASA Tree Cover Loss and Gain Area.” University of Maryland, Google, USGS, and NASA.

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