Digital polygon data of Glaciers of Nepal in 1980. This dataset is created using Landsat MSS, imageries of 1980. 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.

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.

Digital polygon data of Glaciers of Bhutan in 1980. This dataset is created using Landsat MSS imageries of 1980. 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 land cover data for the years 1990 have been derived from Landsat TM 30m images using object based image analysis. The study estimated that 45.15% of Nepal is covered by forests and 25.41% by agriculture.

VDC level indicators for hazard, vulnerability and risk due to flood are aggregated to district level to prepare this dataset

Hazard, vulnerability and risk due to flood are calculated at Village Development Committee (VDC) level of Nepal. Hazard index has been calculated as an arithmetic sum of flood frequency, height above nearest drainage (HAND), river density and forest density. Vulnerability index is the function of population as an Exposure, Sensitivity (sex ratio, dependency ratio and child women ratio) and Resilience (road density, population in abroad, literacy, transport facility, etc). Then risk is the sum of hazard and vulnerability index.

The past few decades have seen high levels of deforestation and forest degradation in the Hindu Kush Himalayan (HKH) region. In this context, under SERVIR Himalaya programme, ICIMOD has developed forest cover monitoring system to identify forest change hotspot areas which need critical forest management attention in four pilot areas. This forest loss data of Bandarban, Bangladesh between 2010 and 2013 has been developed using LandSat 8 data in a semi-automated manner as part of the forest cover monitoring system.

The past few decades have seen high levels of deforestation and forest degradation in the Hindu Kush Himalayan (HKH) region. In this context, under SERVIR Himalaya programme, ICIMOD has developed forest cover monitoring system to identify forest change hotspot areas which need critical forest management attention in four pilot areas. This forest cover data of Bandarban, Bangladesh between 2013 and 2014 has been developed using LandSat 8 data in a semi-automated manner as part of the forest cover monitoring system.

The past few decades have seen high levels of deforestation and forest degradation in the Hindu Kush Himalayan (HKH) region. In this context, under SERVIR Himalaya programme, ICIMOD has developed forest cover monitoring system to identify forest change hotspot areas which need critical forest management attention in four pilot areas. This forest cover data of Paro, Bhutan for 2013 has been developed using LandSat 8 data in a semi-automated manner as part of the forest cover monitoring system.

The past few decades have seen high levels of deforestation and forest degradation in the Hindu Kush Himalayan (HKH) region. In this context, under SERVIR Himalaya programme, ICIMOD has developed forest cover monitoring system to identify forest change hotspot areas which need critical forest management attention in four pilot areas. This forest cover data of Paro, Bhutan between 2010 and 2013 has been developed using LandSat 8 data in a semi-automated manner as part of the forest cover monitoring system.