Land cover data of Greater Chittagong, Bangladesh for 2010. This dataset is created using the LandSat 30 meter spatial resolution satellite image of 2000.

This Layer shows five major water basins (Norther Basin, Hari Rod-Lower, South Eastern Basin, Northern Basin, Helmand Basin and Nondranage areas in Southern part of Afghanistan.

Glacier data of Afghanistan were prepared on the basis of Landsat imageries from 1990. The glacier outlines were derived semi-automatically using object-based image classification (OBIC) separately for clean-ice and debris-covered glaciers and further manual editing for quality assurance. The attributes of glacier data were derived from SRTM DEM. This dataset was jointly prepared by the Ministry of Energy and Water (MEW), Government of Afghanistan, and ICIMOD under the SERVIR-HKH Initiative.

Land cover data of Hindu Kush Himalayan region of Myanmar for 2010. This dataset is created using the LandSat 30 meter spatial resolution satellite image of 2010 and includes land cover information for Chin, Kachin, Rakhine and Shan states of Myanmar.

Albedo layer gives information about the impacts of albedo on forest ecosystems in Chitwan Annapurna Landscape.

Digital polygon data of Glaciers of Bhutan in 1990. This dataset is created using Landsat MSS, imageries of 1990. The glacier outlines was derived semi-automatically using object-based image classification (OBIC ) method separately for clean ice and debris cover and further editing and validation was done carefully by draping over the high resolution images from Google Earth.

Bangladesh is one of the most flood-affected countries in the world. In the last few decades, flood frequency, intensity, duration, and devastation have increased in Bangladesh. Identifying flood-damaged areas is highly essential for an effective flood response. This study aimed at developing an operational methodology for rapid flood inundation and potential flood damaged area mapping to support a quick and effective event response. Sentinel-1 images from March, April, June, and August 2017 were used to generate inundation extents of the corresponding months. The 2017 pre-flood land cover maps were prepared using Landsat-8 images to identify major land cover on the ground before flooding. The overall accuracy of flood inundation mapping was 96.44% and the accuracy of the land cover map was 87.51%. The total flood inundated area corresponded to 2.01%, 4.53%, and 7.01% for the months April, June, and August 2017, respectively. Based on the Landsat-8 derived land cover information, the study determined that cropland damaged by floods was 1.51% in April, 3.46% in June, 5.30% in August, located mostly in the Sylhet and Rangpur divisions. Finally, flood inundation maps were distributed to the broader user community to aid in hazard response. The data and methodology of the study can be replicated for every year to map flooding in Bangladesh.

This dataset assesses land degradation in Bhutan in 2020 based on SDG Indicator 15.3.1 by analyzing changes in land cover, land productivity, and soil organic carbon stocks. The 1OAO principle is applied in the computation method where changes in the sub-indicators are classified as improving, declining and stable. A land unit is considered degraded if any sub-indicator shows a negative or remains stable when previously degraded.

Digital polygon data of Glaciers of Bhutan in 2010. This dataset is created using Landsat TM and ETM+, imageries of 2010. The glacier outlines was derived semi-automatically using object-based image classification (OBIC ) method separately for clean ice and debris cover and further editing and validation was done carefully by draping over the high resolution images from Google Earth.

Airfield layer gives information about the major airport sites in Kailash Sacred Landscape.