Current Browsing: glacier inventory


Glacier data product in Tibetan Plateau (1976)

The Tibetan Plateau Glacial Data Product-TPG1976 is a glacial attribute product of the Tibetan Plateau around 1976. It was generated by remote sensing visual interpretation method adopting Landsat MSS multispectral data. The temporal coverage of the data were from 1972 to 1979. 61% of the remote sensing data were from 1976 to 1977. The data covered the Tibetan Plateau with a spatial resolution of approximately 60 m. Considering the large error of automatic remote sensing extraction method caused by the impact of cloud, shadow and seasonal snow on glacier area, the remote sensing inversion method adopted manual visual interpretation. By comparing the results of automatic methods and visual interpretation of glacier boundaries based on experts’ experiences, we know that the manual interpretation based on remote sensing images is still the most accurate method to obtain the glacier vector boundary at present. When selecting remote sensing images, the minimum effects of cloud and seasonal snow were mainly considered. Images of summer and cold season were both selected (different from the principle applied in selecting remote sensing image data source for China's second glacier inventory). At the same time, considering the differences in discriminant standards between different interpreters, the comparison of multiple typical regions showed that the relative deviation of manual visual interpretation was less than 4%. Based on the Arc map software platform, the abovementioned remote sensing images were geometrically corrected, and the final glacier vector boundary data were obtained by visual interpretation. According to the format and requirements of the second glacier inventory in China, the glacier code and area statistics were collected, and the elevation attribute data of each glacier was obtained based on the SRTM DEM data, and finally the 1976 glacial data product of the Tibetan Plateau was obtained.

2020-09-15

The Tibetan Plateau glacial data product (2000)

The Tibetan Plateau Glacial Data Product - TPG2000 is a glacial attribute product of the Tibetan Plateau around 2000. It was generated by remote sensing visual interpretation method adopting Landsat5 TM/Landsat7 ETM+ multispectral data. The temporal coverage of the data was from 1999 to 2002. 41% of the remote sensing data were obtained in 2001. They covered the Tibetan Plateau with a spatial resolution of 30 m. Considering the large error of the automatic remote sensing extraction method caused by the impact of clouds, shadows and seasonal snow on glacier areas, the remote sensing inversion method adopted manual visual interpretation. By comparing the results of automatic methods and visual interpretation of glacier boundaries based on experts’ experiences, we know that the manual interpretation based on remote sensing images remains the most accurate method to obtain the glacier vector boundary at present. When selecting remote sensing images, the minimum effects of cloud and seasonal snow were mainly considered. Images of summer and cold season were both selected (different from the principle applied in selecting remote sensing image data source for China's second glacier inventory). At the same time, considering the differences in discriminant standards between different interpreters, the comparison of multiple typical regions showed that the relative deviation of manual visual interpretation was less than 4%. Based on the Arc map software platform, the abovementioned remote sensing images were geometrically corrected, and the final glacier vector boundary data were obtained by visual interpretation. According to the format and requirements of the second glacier inventory in China, the glacier code and area statistics were collected, and the elevation attribute data of each glacier was obtained based on the SRTM DEM data, and finally the Tibetan Plateau glacial data product - TPG2000 was obtained.

2020-09-15

The Tibetan Plateau glacial data product (2013)

The Tibetan Plateau Glacial Data Product-TPG2013 is a glacial attribute product of the Tibetan Plateau around 2013. It was generated by remote sensing visual interpretation method adopting Landsat8 OLI and HJ 1A/1B multispectral data. The temporal coverage of the data were from 2012 to 2014. 86% of the remote sensing data were obtained in 2013. They covered the Tibetan Plateau with a spatial resolution of 30 m. Considering the large error of the automatic remote sensing extraction method caused by the impact of clouds, shadows and seasonal snow on glacier areas, the remote sensing inversion method adopted manual visual interpretation. By comparing the results of automatic methods and visual interpretation of glacier boundaries based on experts’ experiences, we know that the manual interpretation based on remote sensing images remains the most accurate method to obtain the glacier vector boundary at present. When selecting remote sensing images, the minimum effects of cloud and seasonal snow were mainly considered. Images of summer and cold season were both selected (different from the principle applied in selecting remote sensing image data source for China's second glacier inventory). At the same time, considering the differences in discriminant standards between different interpreters, the comparison of multiple typical regions showed that the relative deviation of manual visual interpretation was less than 4%. Based on the Arc map software platform, the abovementioned remote sensing images were geometrically corrected, and the final glacier vector boundary data were obtained by visual interpretation. According to the format and requirements of the second glacier inventory in China, the glacier code and area statistics were collected, the elevation attribute data of each glacier were obtained based on the SRTM DEM data, and, finally, the Tibetan Plateau glacial data product-TPG2013 was obtained.

2020-09-15

Data set of spatial and temporal distribution of water resources in Yarlung Zangbo River from 1998 to 2016

This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation). This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation).

2020-08-27

The second glacier inventory dataset of China (version 1.0) (2006-2011)

China's second glacier inventory uses the high-resolution Landsat TM/ETM+ remote sensing satellite data as the main glacier boundary data source and extracts the data source with the latest global digital elevation model, SRTM V4, as the glacier attribute, using the current international ratio threshold segmentation method to extract the glacier boundary in bare ice areas. The ice ridge extraction algorithm is developed to extract the glacier ice ridge, and it is used for the segmentation of a single glacier. At the same time, the international general algorithm is used to calculate the glacier attributes, so that the vector data and attribute data that contain the glacier information of the main glacier regions in west China are obtained. Compared with some field GPS field measurement data and higher resolution remote sensing images (such as from QuickBird and WorldView), the glacial vector data in the second glacier inventory data set of China have higher positioning accuracy and can meet the requirements for glacial data in national land, water conservancy, transportation, environment and other fields. Glacier inventory attributes: Glc_Name, Drng_Code, FCGI_ID, GLIMS_ID, Mtn_Name, Pref_Name, Glc_Long, Glc_Lati, Glc_Area, Abs_Accu, Rel_Accu, Deb_Area, Deb_A_Accu, Deb_R_Accu, Glc_Vol_A, Glc_Vol_B, Max_Elev, Min_Elev, Mean_Elev, MA_Elev, Mean_Slp, Mean_Asp, Prm_Image, Aux_Image, Rep_Date, Elev_Src, Elev_Date, Compiler, Verifier. For a detailed data description, please refer to the second glacier inventory data description.

2020-07-29

Glacier distribution map in the Sanjiangyuan based on the second glacier inventory (2008)

This data set is extracted from the second Glacier Inventory Data Set of China for Three River Source area. The file is SHP format. The attribute data are as follows: Glc_Name (glacier name), Drng_Code (basin code), FCGI_ID (first glacier catalogue code), GLIMS_ID (GLIMS glacier code), Mtn_Name (mountain system name), Pref_Name (administrative division), Glc_Long (glacier longitude), Glc_Lati (glacier latitude), Glc_Area (glacier area), Abs_Accu (absolute area accuracy), Rel_Accu (relative area accuracy), Deb_Area (surface Moraine Area), Deb_A_Accu (absolute accuracy of surface moraine Area), Deb_R_Accu (relative accuracy of surface moraine area)、Glc_Vol_A (estimation of glacier volume 1)、Glc_Vol_B (estimation of glacier volume 2)、Max_Elev (maximum glacier elevation)、Min_Elev (minimum glacier elevation)、Mean_Elev (average glacier elevation)、MA_Elev (median area height of glacier)、Mean_Slp (average glacier slope)、Mean_Asp (average glacier slope direction)、Prm_Image (major remote sensing data)、Aux_Image (auxiliary remote sensing data)、Rep_Date (glacier catalogue represents date)、Elev_Src (elevation data source)、Elev_Date (elevation represents date)、Compiler (glacier cataloguing editor)、Verifier (glacier cataloguing verifier).

2020-07-28

An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018

Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).

2020-06-23

Glacier inventory dataset of Nepal (2000)

This glacier inventory is supported by the International Centre for Integrated Mountain Development (ICIMOD) and the United Nation Environment Programme/Regional Resources Centre, Asia and The Pacific (UNEP/RRC-AP)。 1、The glacier inventory uses the remote sensing data of Landsat,reflecting the current status of glaciers in Nepal in 2000. 2、The spatial coverage of the glacier inventory: Nepal 3、Contents of the glacier inventory: glacier location, glacier code, glacier name, glacier area, glacier length, glacier thickness, glacier stocks, glacier type, glacier orientation, etc. 4、Data Projection: Grid Zone IIA Projection: Lambert conformal conic Ellipsoid: Everest (India 1956) Datum: India (India, Nepal) False easting: 2743196.40 False northing: 914398.80 Central meridian: 90°00'00"E Central parallel: 26°00'00"N Scale factor: 0.998786 Standard parallel 1: 23°09'28.17"N Standard parallel 2: 28°49'8.18"N Minimum X Value: 1920240 Maximum X Value: 2651760 Minimum Y Value: 914398 Maximum Y Value: 1188720 Grid Zone IIB Projection: Lambert conformal conic Ellipsoid: Everest (India 1956) Datum: India (India, Nepal) False easting: 2743196.40 False northing: 914398.80 Central meridian: 90°00'00"E Central parallel: 26°00'00"N Scale factor: 0.998786 Standard parallel 1: 21°30'00"N Standard parallel 2: 30°00'00"N Minimum X Value: 1823188 Maximum X Value: 2000644 Minimum Y Value: 1306643 Maximum Y Value: 1433476 For a detailed data description, please refer to the data file and report.

2020-06-09

Glacier inventory dataset of Bhutan (2000)

This glacier inventory is supported by the International Centre for Integrated Mountain Development (ICIMOD) and the United Nations Environment Programme/Regional Resource Centre, Asia and The Pacific (UNEP/RRC-AP). 1.The glacier inventory incorporates topographic map data, and reflects the status of glaciers in the region in 2000. 2.The spatial coverage of the glacier inventory includes the following: Pa Chu Sub-basin,Mo Chu Sub-basin,Thim Chu Sub-basin,Pho Chu Sub-basin,Mangde Chu Sub-basin, Chamkhar Chu Sub-basin,Kuri Chu Sub-basin,Dangme Chu Sub-basin,Northern Basin, etc. 3.The glacier inventory includes the following data fields: glacier location, glacier code, glacier name, glacier area, glacier length, glacier thickness, glacier stocks, glacier type, glacier orientation, etc. 4.Data projection: Projection: Polyconic Ellipsoid: Everest (India 1956) Datum: Indian (India, Nepal) False easting: 2,743,196.4 False northing: 914,398.80 Central meridian: 90°0'00'' E Central parallel: 26°0'00' N Scale factor: 0.998786 For a detailed description of the data, please refer to the data file and report.

2020-06-04

The Randolph Glacier Inventory (RGI) (2012-2017)

The Randolph Glacier Inventory (RGI) is a complete inventory of global glacier outlines published by GLIMS (Global Land Ice Measurements from Space). It is currently available in six versions: Version 1.0 was published in February 2012, version 2.0 was published in June 2012, version 3.0 was published in April 2013, version 4.0 was published in December 2014, version 5.0 was published in July 2015, and version 6.0 was published in July 2017. The data sets include four versions, which are 6.0, 5.0, 4.0 and 3.2 (revision, August 2013). The data are organized according to different regions. In each region, each glacier record includes a shape file (.shp file and its corresponding .dbf, .prj, and .shx files) and a .csv file of height measurement data. The data are from GLIMS: Global Land Ice Measurements from Space (http://www.glims.org/RGI/) Data quality checks include geometry, topology, and certain attributes, and the following checks were performed: 1) All polygons were checked by the ArcGIS Repair Geometry tool. 2) Glaciers with areas less than 0.01 square kilometres were removed. 3) The topology was checked with the Does Not Overlap rule. 4) The attribute sheet was checked by Fortran subroutines and Python scripts for data quality.

2020-06-03