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 dataset provides potential suitable area for cultivating barley in the Karnali Province, Nepal. It is one of the datasets produced through a land suitability analysis conducted for high value agricultural commodities in the province. The suitability analysis was based on the FAO's' land suitability framework, which evaluates the suitability of land for cultivation of specific crop using climatic, topographic and soil characteristics. The analysis was conducted by ICIMOD under the Himalayan Resilience Enabling Action Programme (HI-REAP) project to support data-driven decision making and promote sustainable and climate-resilient agriculture in Karnali Province.

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.

The dataset present the crop sown area for rice crop during 2010 - 2015.

Bangladesh is one of the most flood affected country in the world. The frequency, intensity and duration of floods has been increased during last few decades. Due to increased population settlements in floodplains and irregular development damage of infrastructure, crop and property has increased creating severe impact on lives and livelihood. Understanding the severity and identification of extent and types of flood damage is highly important to plan effective response. The aim of this study was to develop appropriate methodology to determine extent of flood and damaged areas in near real time basis to support operational response. We have used Sentinel-1 synthetic aperture radar (SAR) images to generate flood extend data for the year 2017.

This dataset assesses land degradation in Nepal in 2015 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.

Nepal is one of the most flood affected country in the world. The frequency, intensity and duration of floods has been increased during last few decades. Due to increased population settlements in floodplains and irregular development damage of infrastructure, crop and property has increased creating severe impact on lives and livelihood. Understanding the severity and identification of extent and types of flood damage is highly important to plan effective response. The aim of this study was to develop appropriate methodology to determine extent of flood and damaged areas in near real time basis to support operational response. We have used Sentinel-1 synthetic aperture radar (SAR) images to generate flood extend data for the year 2017.

Exposure layer gives information about the climate change exposure of forest ecosystems in Chitwan Annapurna Landscape.

Digital raster dataset of mountain regions in Bangladesh. This dataset is prepared based on SRTM 30m resolution DEM showing different classes of mountains in the country defined by the Kapos et al. (2000): Class 1: elevation > 4500m; Class 2: elevation 3500–4500m; Class 3: elevation 2500-3500m; Class 4: elevation 1500–2500m and slope >= 2deg; Class 5: elevation 1000–1500m and slope >= 5deg or local elevation range (7km radius) > 300m; Class 6: elevation 300–1000m and local elevation range (7km radius) > 300m;