Here we present a dataset of Transient Snowline Altitude (TSLA) measurements for glaciers in High Mountain Asia (HMA) based on Landsat satellite imagery and digital elevation model data. The data were obtained using the MountAiN glacier Transient snowline Retrieval Algorithm (MANTRA), a Google Earth Engine tool to measure the average altitude of the snow-ice boundary. Each MANTRA result consists of reference data (e.g. Landsat scene, date, glacier ID), relevant topographic metrics (glacier area, minimum and maximum elevation of the glacier), results of the surface material classification (areas covered by ice, snow, debris and clouds), summary statistics of the TSLA measurement, and quality metrics (cloud cover close to snow-ice boundary, class coverage). For the dataset presented here, we applied MANTRA to all glaciers in HMA with an area larger than 0.5 km² (ca. 28,500 based on Randolph Glacier Inventory v6 glacier outlines). After filtering and postprocessing, the dataset comprises ca. 9.66 million TSLA measurements with an average of 341 ± 160 measurements per glacier, covering the time span 1985 to 2021. Time series of Transient Snowline Altitude (TSLA) metrics for glaciers in High Mountain Asia, 1986 to 2021.The file is in NetCDF format, with the date of the Landsat measurement (LS_DATE) as index.Individual glacier are identified through Randolph Glacier Inventory v6 IDs (RGI_ID).The recommended metric to use for analyses is the median elevation of the detected TSLA range (TSLrange_median_masl).

This dataset accompanies a manuscript submitted for review to the Journal of Remote Sensing. Lakes in direct contact with glaciers (ice-marginal lakes) are found across alpine and polar landscapes. As dynamic features that experience short-term (i.e., day to year) variations in area and volume, they form an important yet understudied element of the complete hydrologic system of glaciers with which they are in contact. To accelerate the study of ice-marginal lakes over large temporal and spatial extents, we automate the mapping of ice-marginal lakes by implementing a trained minimum-distance classifier of monthly Landsat 8 data products in Google Earth Engine. We produce maps of ice-marginal lakes in south Alaska for the summer months March through August for each year from 2013 through 2019. These maps are manually reviewed for accuracy. By spatially joining all maps, we can identify lakes throughout time, even if they are changing rapidly or dramatically. This dataset includes the spatial join of all lakes and shapefiles of each individual lake identified, grouped by lake. Within these lake shapefiles is illustrated an individual history of lake change; each feature is a delineation of the lake at a specific point in time.

This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, consists of mean monthly velocity maps for selected glacier outlet areas. The maps are generated by tracking visible features between optical image pairs acquired by the Landsat 4 and 5 Thematic Mapper (TM), the Landsat 7 Enhanced Thematic Mapper Plus (ETM+), the Landsat 8 Operational Land Imager (OLI), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER).

See Greenland Ice Mapping Project (GIMP) for related data.

Global Land Ice Measurements from Space (GLIMS) is an international initiative with the goal of repeatedly surveying the world's estimated 200,000 glaciers. The project seeks to create a globally comprehensive inventory of land ice, including measurements of glacier area, geometry, surface velocity, and snow line elevation. To perform these analyses, …

The Digital Geologic-GIS Map of Glacier Bay National Park and Preserve and Vicinity, Alaska is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (glba_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (glba_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (glba_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (glba_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (glba_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (glba_geology_metadata_faq.pdf). Please read the glba_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (glba_geology_metadata.txt or glba_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:1584,000 and United States National Map Accuracy Standards features are within (horizontally) 804.7 meters or 2640 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Global Land Ice Measurements from Space (GLIMS) is an international initiative with the goal of repeatedly surveying the world's estimated 200,000 glaciers. GLIMS uses data collected by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument aboard the Terra satellite and the LANDSAT series of satellites, along with historical observations.

The GLIMS initiative has created a unique glacier inventory, storing information about the extent and rates of change of all the world's mountain glaciers and ice caps. The GLIMS Glacier Database was built up from data contributions from many glaciological institutions, which are managed by Regional Coordinators, who coordinate the production of glacier mapping results for their particular region. The GLIMS Glacier Database provides students, educators, scientists, and the public with reliable glacier data from these analyses. New glacier data are continually being added to the database.

The GLIMS Glacier Viewer was developed to provide the public with easy access to the GLIMS Glacier Database. This Web application allows users to view and query several thematic layers, including glacier outlines, Regional Coordinator institution locations, the World Glacier Inventory, and more. GLIMS data can be downloaded into a number of GIS-compatible formats, including ESRI Shapefiles, MapInfo tables, Geographic Mark-up Language (GML), and Keyhole Mark-up Language (KML) suitable for viewing in Google Earth.

S1 provides a list of imagery used in this study, a supporting map with Landsat Path/Row, and terminus position shapefiles for all study glaciers. S2 combines relevant glacier information from RGI v6.0 with this study’s alphanumeric identifiers, quantified surge characteristics, and allocated surge-type classifications from the study. S3 provides the terminus position time series graphs normalized by glacier length that were used to classify the study glaciers.

These polygon features represent digitization of the glacier margins for the 37 named glaciers of Glacier National Park (GNP) and two glaciers on U.S. Forest Service’s Flathead National Forest land, derived from 2015 satellite imagery. The polygons represent only the main body portion of each glacier as it appeared in 2015 satellite imagery. Disconnected patches are not included as this dataset represents only the main body features of the named glaciers in GNP and environs. Polygons were digitized from WorldView imagery acquired on the following source dates: 20150822, 20150912, 20150915, 20150925 (World View 01 satellite). Initial digitization was completed by Melissa Brett, PSU graduate student. This set of polygons represents revisions based on supplemental imagery (20140825, 20141019, 20160915 - WorldView-01, oblique images in USGS collection, GoogleEarth collection), and local knowledge and interpretation by Dan Fagre and Lisa McKeon (USGS) in February - August, 2016. A Wacom Pro digital tablet was used by USGS staff to trace outlines and make revisions to the PSU margins. Glaciers were digitized at 1:2000 scale, with lowest off-nadir image chosen when multiple WorldView images were available for the same day. File attributes list specific photos used in analysis, including documentation of the off-nadir angle to determine imagery used. Since multiple images in time series contribute to this analysis, if previous image showed perennial snow that was absent from the glacier (bedrock visible), then that portion was deemed "seasonal/perennial snow" in subsequent photos and not included in the digitization of 2015 glacier margins.

This data set contains shapefiles of termini traces from 294 Greenland glaciers, derived using a deep learning algorithm (AutoTerm) applied to satellite imagery. The model functions as a pipeline, imputing publicly availably satellite imagery from Google Earth Engine (GEE) and outputting shapefiles of glacial termini positions for each image. Also available are supplementary data, including temporal coverage of termini traces, time series data of termini variations, and updated land, ocean, and ice masks derived from the Greenland Ice Sheet Mapping Project (GrIMP) ice masks.

The Digital Surficial Geologic-GIS Map of Glacier National Park, Montana is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (glac_surficial_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (glac_surficial_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (glac_geology.gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (glac_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (glac_surficial_geology_metadata_faq.pdf). Please read the glac_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: http://www.google.com/earth/index.html. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (glac_surficial_geology_metadata.txt or glac_surficial_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:100,000 and United States National Map Accuracy Standards features are within (horizontally) 50.8 meters or 166.7 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The files in this dataset are LANDSAT 8 images and maps of parameters derived from these images. Four sets of maps are included, each covering time period between 2013 and 2022: - LANDSAT 8 multispectral image sourced from the Google Earth Engine API - Albedo map computed from this image with a formula of () - GRAI map computed from this image with a formula included in the project's final publication (Podgorski et al 2023) - Debris abundance map derived from the GRAI map The maps show the state of the glacier's surface at the end of the ablation season in each year (end of March/beginning of April).

The dataset presented here contains Transient Snowline Altitude (TSLA) measurements for glaciers in High Mountain Asia (HMA). It is supplementary to a dataset published a PANGAEA (please look there for further information). The only difference between the two datasets is, that the version published here was not filtered for (=includes) "winter maxima", i.e. unrealistically high TSLA values during the winter months.

Upload associated with Scientific Data submission: Glacier thickness and ice volume of the inner tropical Andes.

The dataset includes:

-Ice velocity maps for all 11 glacier regions (5 in Ecuador and 6 in Colombia)

-Ice thickness maps for all 11 glacier regions generated using the 6 different thickness calculation methods

-Multi-model ensemble mean glacier thickness maps for all 11 glacier regions

-Basin-divided ice volumes for each glacier region, with a 1 km, 5 km, and 20 km buffer

-Results of a full parameter sensitivity test for the thickness calculation

All data are saved in 32-bit floating-point geotiff format. The data are freely available under the Creative Commons Attribution Licence, CC BY 4.0.

The feature-tracking code used to derive ice velocities, GIV, is available on github and Zenodo (https://doi.org/10.5281/zenodo.4904544). All other code, including Google Earth Engine download scripts and the ice-thickness inversion code is available on zenodo (https://doi.org/10.5281/zenodo.6323069).

Most of the world’s mountain glaciers have been retreating for more than a century in response to climate change. Accurate, spatially explicit information on glacier retreat is pivotal to study the consequences of ice loss on both abiotic and biotic components of the environment. Here, we present a spatially explicit dataset showing positions of glacier fronts since the Little Ice Age (LIA) maxima. The dataset is based on multiple historical archival records including topographical maps; repeated photographs, paintings and aerial or satellite images with supplement of geochronology and our own field data. We provide ESRI shapefiles showing 728 past positions of 93 glacier fronts from all continents, except Antarctica, covering the period between the Little Ice Age maxima and the present. On average, the time series span the past 190 years. From 2 to 46 past positions per glacier are depicted (on average: 7.8). Past positions of the glaciers have been obtained mostly on the basis of the literature, provided in a separate file, complemented with information obtained from topographical maps, historical, aerial or satellite pictures, and with our own field data, dating the position of geomorphological elements in the landscape on the basis of measurements taken in the field, signals and marks reporting the ancient position of the glacier front, and additional approaches for dating older moraines (lichenometry, dendrochronology, radiocarbon chronology).NOTES TO THE DATABASE:Database structure:glacier: glacier nameGLIMS id: glacier id, according to the Global Land Ice Measurements from Space (GLIMS)dating: calculated (or estimated) dating for a given line. source: source followed to draw the lines. Notes to fields of the database:GLIMS id: the database version is glims_db_20200630 (downloaded on February 3rd 2021). Exceptions i) Maladeta: the glacier is not mapped in the GLIMS db, but it appears in the online viewer (https://www.glims.org/maps/glims); ii) Qamanaarsuup Sermia and Popocatepetl: the glacier is not mapped neither in the GLIMS db nor in the online viewer.dating: in the cases a reference is cited in this field, it refers to the source we followed to estimate the age of the moraine ridge / position, sometimes by analogy with surrounding glaciers (cf. main text).source: specifically, we used:1) Articles / theses / maps: one or more figures from a given source were georeferenced, and the lines were redrawn following the original maps;2) Satellite / orthophotogrammetric data: the glacier profile in the specific year was drawn interpreting the satellite / aerial images provided by the sources (i.e., Esri ArcGIS World Imagery, GN orthophotogrammetry, Google Earth, IGN orthophotogrammetry, Regional orthophotogrammetry - Lombardia, Regional orthophotogrammetry - Vallee d Aoste / Valle d Aosta);3) Databases: lines were used as provided by the sources (i.e., GlaRiskAlp, GLIMS, OpenData Trentino);4) unpublished data / field marks: the identification of the moraine / position occurred in the field or using sources not yet published.The complete description of methodologies has been published on this paper:Marta, S., R. S. Azzoni, D. Fugazza, Levan Tielidze, P. Chand, K. Sieron, P. Almond, R. Ambrosini, F. Anthelme, P. A. Gazitúa, R. Bhambri, A. Bonin, M. Caccianiga, S. Cauvy-Fraunié, J. L. C. Lievano, J. Clague, J. A. C. Rapre, O. Dangles, P. Deline, A. Eger, R. C. Encarnación, S. Erokhin, A. Franzetti, L. Gielly, Fabrizio Gili, M. Gobbi, A. Guerrieri, S. Hågvar, N. Khedim, R. Kinyanjui, E. Messager, M. A. Morales-Martínez, G. Peyre, F. Pittino, J. Poulenard, R. Seppi, M. C. Sharma, N. Urseitova, B. P. Weissling, Y. Yang, V. Zaginaev, A. Zimmer, G. A. Diolaiuti, A. Rabatel, and G. F. Ficetola. 2021. The Retreat of Mountain Glaciers since the Little Ice Age: a Spatially Explicit Database. Data 6:10.3390/data6100107. https://www.mdpi.com/2306-5729/6/10/107#When referring to this dataset, please cite the Marta et al. 2021 paper.

Digital polygon data of Status of Glaciers in Tista 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 Surkhab 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.

These polygon features represent digitization of the glacier margins for the 37 named glaciers of Glacier National Park (GNP) and two glaciers on U.S. Forest Service’s Flathead National Forest land, derived from 2015 satellite imagery. The polygons represent only the main body portion of each glacier as it appeared in 2015 satellite imagery. Disconnected patches are not included as this dataset represents only the main body features of the named glaciers in GNP and environs. Polygons were digitized from WorldView imagery acquired on the following source dates: 20150822, 20150912, 20150915, 20150925 (World View 01 satellite). Initial digitization was completed by Melissa Brett, PSU graduate student. This set of polygons represents revisions based on supplemental imagery (20140825, 20141019, 20160915 - WorldView-01, oblique images in USGS collection, GoogleEarth collection), and local knowledge and interpretation by Dan Fagre and Lisa McKeon (USGS) in February - August, 2016. A Wacom Pro digital tablet was used by USGS staff to trace outlines and make revisions to the PSU margins. Glaciers were digitized at 1:2000 scale, with lowest off-nadir image chosen when multiple WorldView images were available for the same day. File attributes list specific photos used in analysis, including documentation of the off-nadir angle to determine imagery used. Since multiple images in time series contribute to this analysis, if previous image showed perennial snow that was absent from the glacier (bedrock visible), then that portion was deemed "seasonal/perennial snow" in subsequent photos and not included in the digitization of 2015 glacier margins.

Supplementary data to Kraaijenbrink, Bierkens, Lutz and Immerzeel, 2017. Impact of a global temperature rise of 1.5 degrees Celsius on Asia’s glaciers, Nature. Model code can be found here.

Please note that all data is provided in 7z-archives. To extract the data use the open source software 7zip.

Model input: Raster data

The raster data that is required to run the model is available for the entire High Mountain Asia (complete-hma.7z) and for each RGI v5.0 sub-region (<region-name>.7z). The 7z-archives hold separate folders for each glacier, which are named by RGI glacier ID. The rasters for each glacier are in GeoTIFF format, have a 30 m resolution, are in local UTM projection (WGS84 datum), and are clipped to the RGI glacier extent.

Rasters present for each glacier are:

classification.tif     The debris classification made in google earth engine.
debris-thickness-50cm.tif Debris thickness estimation based on Landsat 8 surface temperature.
ice-thickness.tif     Ice thickness determined using the Glabtop2 model
ls8-composite-b456.tif   Landsat 8 warmest-pixel optical composite (bands RED, NIR, SWIR1)
ls8-composite-tsurf.tif  Landsat 8 warmest-pixel surface temperature composite
srtm-elevation.tif     SRTM 1 arc second elevation data
srtm-slope.tif       Slope of the SRTM 1 arc second data

Model input: RDS data

The general model input data (mbg-model-rds-data.7z) is stored in R’s binary RDS format and R is required to open and read the data.

Files present in the 7z-archive are:

dP_factors_2006-2100.rds  Precipitation changes (delta factors) up to 2100
dT_degrees_2006-2100.rds  Temperature changes (Kelvin) up to 2100
glacier-data.rds      Glacier centroids with current climate and mass balance input
ostrem_meancurve.rds    The Östrem curve used by the model
rgi-subregions.rds     RGI sub-region polygons for Asia

Output data

Region-aggregated output is available in ESRI Shapefile format for the RGI sub-regions, major river basins, and for a 1×1 degree grid (output-shapefiles.7z). The attribute tables of all shapefiles hold data on the occurrence of debris as well as current glacier area and volume, and volume projections for the end of century.

The available shapefile attributes are:

count   number of glaciers
a_total   total glacier area (m2)
a_debris  glacier area covered by debris (m2)
a_ela    glacier area below modelled ELA (m2)
a_ela_deb  glacier area below modelled ELA covered by debris (m2)
v_total   total glacier volume (m3)
v_debris  glacier volume covered by debris (m3)
v_ela    glacier volume below modelled ELA (m3)
v_ela_deb  glacier volume below modelled ELA covered by debris (m3)
m_total_gt total glacier mass (gigaton)
volST_EOC  volume remaining in end of century under a stable current temperature
vol15_EOC  volume remaining in end of century under 1.5 degree scenario
vol26_EOC  volume remaining in end of century for the RCP2.6 model ensemble
vol45_EOC  volume remaining in end of century for the RCP4.5 model ensemble
vol60_EOC  volume remaining in end of century for the RCP6.0 model ensemble
vol85_EOC  volume remaining in end of century for the RCP8.5 model ensemble

Global Land Ice Measurements from Space (GLIMS) is an international initiative with the goal of repeatedly surveying the world's estimated 200,000 glaciers. GLIMS uses data collected by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument aboard the Terra satellite and the LANDSAT series of satellites, along with historical observations. The GLIMS initiative has created a unique glacier inventory, storing information about the extent and rates of change of all the world's mountain glaciers and ice caps. The GLIMS Glacier Database was built up from data contributions from many glaciological institutions, which are managed by Regional Coordinators, who coordinate the production of glacier mapping results for their particular region. The GLIMS Glacier Database provides students, educators, scientists, and the public with reliable glacier data from these analyses. New glacier data are continually being added to the database.The GLIMS Glacier Viewer was developed to provide the public with easy access to the GLIMS Glacier Database. This Web application allows users to view and query several thematic layers, including glacier outlines, Regional Coordinator institution locations, the World Glacier Inventory, and more. GLIMS data can be downloaded into a number of GIS-compatible formats, including ESRI Shapefiles, MapInfo tables, Geographic Mark-up Language (GML), and Keyhole Mark-up Language (KML) suitable for viewing in Google Earth.

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.