Remote Sensing and GIS

Technology is starting to fill in the blanks in the scientific record for the Hindu Kush Himalayas, whose size and remoteness has served in the past as a major obstacle to data collection. 

Remote sensing technologies are providing new data sets and spatially referenced information to help scientists, development planners and policymakers make informed decisions. From forecasts about crop yields, impending droughts and natural disasters to observations of forest growth patterns and the dynamics of snow and water, the range of topics that benefit from being analysed and modelled visually is vast.  

ICIMOD is internationally regarded as a regional resource centre for geo-information and earth observation applications with a mountain focus. Over the past two decades, we have developed and shared remote sensing and geographic information systems to assist decision-making at many levels. Within our own areas of focus, such as trans-boundary landscape conservation and river basin management, geospatial science is used to expand knowledge of scientific and socio-ecological challenges and ensure that programmes are informed by the latest and most extensive data.

Stories

Datasets

High levels of water-induced erosion in the transboundary Himalayan river basins are contributing to substantial changes in basin hydrology and inundation. Basin-wide information on erosion dynamics is needed for conservation planning, but field-based studies are limited. This study used remote sensing (RS) data and a geographic information system (GIS) to estimate the spatial distribution of soil erosion across the entire Koshi basin. The revised universal soil loss equation (RUSLE) was used in an ArcGIS environment with rainfall erosivity, soil erodibility, slope length and steepness, cover-management, and support practice factors as primary parameters. The estimated annual erosion from the basin was around 42 million tonnes.


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High levels of water-induced erosion in the transboundary Himalayan river basins are contributing to substantial changes in basin hydrology and inundation. Basin-wide information on erosion dynamics is needed for conservation planning, but field-based studies are limited. This study used remote sensing (RS) data and a geographic information system (GIS) to estimate the spatial distribution of soil erosion across the entire Koshi basin. The revised universal soil loss equation (RUSLE) was used in an ArcGIS environment with rainfall erosivity, soil erodibility, slope length and steepness, cover-management, and support practice factors as primary parameters. The estimated annual erosion from the basin was around 42 million tonnes.


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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.


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Digital polygon data of Glaciers of Nepal in 2000. This dataset is created using Landsat MSS, TM imageries of 2000. 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.


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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.


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Digital polygon data of Glaciers of Nepal in 2010. This dataset is created using Landsat TM, 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.


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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.


View Metadata

Digital polygon data of Glaciers of Bhutan in 2000. This dataset is created using Landsat TM and ETM+, imageries of 2000. 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.


View Metadata

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.


View Metadata

Digital polygon data of Glaciers of Nepal in 1990. This dataset is created using Landsat MSS, TM 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.


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