Current Browsing: glaciers/ice sheets


Glacier height change data of QTP V1.0 (1970-2012)

This product is based on multi-source remote sensing DEM data generation. The steps are as follows: select control points in relatively stable and flat terrain area with Landsat ETM +, SRTM and ICESat remote sensing data as reference. The horizontal coordinates of the control points are obtained with Landsat ETM + l1t panchromatic image as the horizontal reference. The height coordinates of the control points are mainly obtained by ICESat gla14 elevation data, and are supplemented by SRTM elevation data in areas without ICESat distribution. Using the selected control points and automatically generated connection points, the lens distortion and residual deformation are compensated by Brown's physical model, so that the total RMSE of all stereo image pairs in the aerial triangulation results is less than 1 pixel. In order to edit the extracted DEM data to eliminate the obvious elevation abnormal value, DEM Interpolation, DEM filtering and DEM smoothing are used to edit the DEM on the glacier, and kh-9 DEM data in the West Kunlun West and West Kunlun east regions are spliced to form products.

2020-10-14

Greenland ice sheet elevation change data V1.0 (2004-2008)

First of all, the data of ice cover elevation change is obtained by using the data of glas12 in 2004 and 2008. In ideal case, each track is strictly repeated. However, due to the track deviation, it can not be guaranteed that the track is strictly repeated according to the design. The deviation varies from several meters to several hundred meters. The grid of 500m * 500m is taken, and the point falling in the same grid is considered as the weight of the repeated track. The elevation change in 2004-2008 is obtained by subtraction of complex points, and the annual elevation change is obtained. Ice sheet elevation change data

2020-10-14

Ice crack dataset of Antarctican and Greenland V1.0 (2015-2019)

Based on the sentinel-1 hyperspectral wide-band SAR data, using the proposed u-net ice fissure detection method, the ice fissure elevation data of the north and south polar ice sheet are formed. Firstly, the data preprocessing of sentinel-1 hyperspectral wide-band SAR includes radiometric calibration, ice cover range determination and speckle noise removal. In order to suppress the speckle noise of SAR data, and to ensure the ice fracture characteristics, we use ppb method to remove multiplicative noise. This method can not only effectively remove spots, but also retain the characteristics of ice cracks. Secondly, we use the u-net based ice crack detection algorithm to extract ice cracks. In order to obtain the correct ice fracture SAR data samples, we select the SAR samples by comparing the high-resolution optical data of ice fracture to form the ice fracture SAR data samples. Based on the SAR data of ice fracture area and non ice fracture area, we use u-net method to extract ice fracture. Finally, we geocode the detected ice fracture data to form the ice fracture products of the north and south polar.

2020-10-14

Surface cover map in the Antarctic (1999-2003)

A high-resolution remote sensing image mosaic of the entire Antarctic was generated by synthesizing the 1073 images taken by American Landsat 7 during 1999 to 2003 and the medium-resolution MODIS image (taken in 2005) covering south of 82.5°southern latitude. Based on the mosaic, combined with the needs of Antarctic scientific research, Antarctica land cover was divided into six types using the combination method of computer automatic interpretation and artificial assistance. They were blue ice, fissures, bare rocks, water bodies, moraines and firns, and the areas and proportions of the above types were 225,207.29 square kilometers (1.651%), 7153.36 square kilometers (0.052%), 72,958.04 square kilometers (0.535%), 189.43 square kilometers (0.001%), 310.76 square kilometers (0.003%), and 13337392.66 square kilometers (97.758%), respectively. The map is a satellite image map of approximate true color synthesis, and the regions of various cover types are represented by different color blocks. The map mainly provides a reference for popular scientific research, geography education and science popularization.

2020-10-14

Law Dome area Methane concentration (1010-1980)

From 1000 AD to the present, the concentration of methane in the atmosphere has increased significantly in the ice cores of the Antarctic and Arctic. These data came from the Tasmanian laboratory of Australia, where the high resolution data were obtained by using wet extraction of ice core samples, and the same measurement and calibration procedures were applied to all samples. The results are consistent with the results of internationally renowned ice core greenhouse gas laboratories such as the University of Bern, the University of Copenhagen and the University of Ohio. The physical meaning of each variable: First column: time; second column: methane concentration value

2020-10-14

Inventory of glaciers in Pakistan (2003-2004)

This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.

2020-10-09

The dataset of spatio-temporal water resources distribution in the source regions of Yangtze River and Yellow River (1998-2017)

This data is a simulated output data set of 5km monthly hydrological data obtained by establishing the WEB-DHM distributed hydrological model of the source regions of Yangtze River and Yellow River, using temperature, precipitation and pressure as input data, and GAME-TIBET data as verification data. The dataset includes grid runoff and evaporation (if the evaporation is less than 0, it means deposition; if the runoff is less than 0, it means that the precipitation in the month is less than evaporation). This data is a model based on the WEB-DHM distributed hydrological model, and established by using temperature, and precipitation (from itp-forcing and CMA) as input data, GLASS, MODIA, AVHRR as vegetation data, and SOILGRID and FAO as soil parameters. And by the calibration and verification of runoff,soil temperature and soil humidity, the 5 km monthly grid runoff and evaporation in the source regions of Yangtze River and Yellow River from 1998 to 2017 was obtained. If asc can't open normally in arcmap, please delete the blacks space of the top 5 lines of the asc file.

2020-10-01

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