Digital dataset of Forest Cover of Central Karakoram National Park (CKNP) area, Pakistan. This dataset is extracted from land cover dataset of 2010.

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.

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.

Digital dataset of Agriculture Cover of Central Karakoram National Park(CKNP) area, Pakistan. This dataset is extracted from land cover dataset of 2010 created from 30m resolution LandSat imagery of 2010.

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.

Digital dataset of Musk Deer habitat area of Central Karakoram National Park (CKNP) area, Pakistan. This dataset is compiled through GPS based field survey conducted in year 2008 based on baseline study volume II, draft report, by Hagler Bailly (2005) and TJ Roberts. Habitat area is identified based on the GPS survey, baseline report and potential habitat area modeling using variables like land cover, slope, DEM etc through ENFA.

Digital polygon data of Status of Glaciers in Ganges Basin during 2005 ± 3 (2002-2008) years. This dataset is created using Landsat ETM+ imageries of respective years. 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. The attribute data were assigned to each glacier using 90m resolution SRTM DEM.

Digital polygon data of Status of Glaciers in Brahmaputra Basin during 2005 ± 3 (2002-2008) years. This dataset is created using Landsat ETM+ imageries of respective years. 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. The attribute data were assigned to each glacier using 90m resolution SRTM DEM.

Large landslide dams are one of the most disastrous natural phenomena in mountainous regions all over the world Such dams are formed most commonly in tectonically active settings where high mountains border narrow and steep valleys and earthquakes occur frequently. Landslide dams are very diverse in terms of their formation, geotechnical characteristics, longevity, stability, and flood hazard. The two major causes of landslide dam formation are precipitation and earthquake. About 50% of dam-forming landslides are brought about by rainstorms and snowmelts, 40% by earthquakes, and 10% by other factors Geometry of valley in relation to geometry and volume of debris and discharge of damming river are some of the factors which are responsible for the development of landslide dams. Schuster et al. (1998) mentioned four groups of governing factors responsible for the spatial distribution of landslide dams. They are i) seismic intensity, ii) slope gradient and topography, iii) lithology and weathering properties, and iv) soil moisture and groundwater content. Landslide dams are generated by various types of mass movements, which range from rock falls and rockslides in steep walled, narrow canyons to earth slumps in flat river lowlands. Managing landslide-dam hazards requires an understanding of the temporal and spatial scales on which such phenomena occur. Many previous works on landslide dams have been mainly descriptive in character, and have produced a multitude of documented case studies and inventories (e.g. Costa and Schuster, 1988; Costa and Schuster, 1991). More recent work is focused on quantitative methods of determining the post-formation development, in particular, the controls on dam longevity.