SeaWiFS NDVI dataset for Sanjiangyuan (1997-2007)

The data set is NDVI data of long time series acquired by SeaWiFS. The time range of the data set is from September 1997 to 2007. In order to remove the noise in NDVI data, the maximum synthesis is carried out. A NDVI image is synthesized every 15 days. The data set is cut out from the global data set, so as to carry out the research and analysis of the source areas of the three rivers separately. The data format of this data set is geotiff, spatial resolution is 4 km, temporal resolution is 15 days, time range: 256 days in 1997 to 365 days in 2007.

0 2021-04-20

NPP dataset of remote sensing for ecological assets assessment in Qinghai-Tibet Plateau

The basic data set of remote sensing for ecological assets assessment of the Qinghai-Tibet Plateau includes the annual Fraction Vegetation Coverage (FVC), Net Primary Productivity (NPP) and Leaf Area Index (LAI) of the Qinghai-Tibet Plateau since 2000, and other ecological parameters based on remote sensing inversion. The FVC data are mainly developed from MODIS NDVI data. NPP estimation method based on algorithm of CASA model.

0 2021-04-09

The 30-m land cover data of Tibetan Plateau (2010)

These data contain two data files: GLOBELAND30 TILES (raw data) and TIBET_ GLOBELAND30_MOSAIC (mosaic data). The raw data were downloaded from the Global Land Cover Data website (GlobalLand3) (http://www.globallandcover.com) and cover the Tibetan Plateau and surrounding areas. The raw data were stored in frames, and for the convenience of using the data, we use Erdas software to splice and mosaic the raw data. The Global Land Cover Data (GlobalLand30) is the result of the “Global Land Cover Remote Sensing Mapping and Key Technology Research”, which is a key project of the National 863 Program. Using the American Landsat images (TM5, ETM+) and Chinese Environmental Disaster Reduction Satellite images (HJ-1), the data were extracted by a comprehensive method based on pixel classification-object extraction-knowledge checks. The data include 10 primary land cover types—cultivated land, forest, grassland, shrub, wetland, water body, tundra, man-made cover, bare land, glacier and permanent snow—without extracting secondary types. In terms of accuracy assessment, nine types and more than 150,000 test samples were evaluated. The overall accuracy of the GlobeLand30-2010 data is 80.33%. The Kappa indicator is 0.75. The GlobeLand30 data use the WGS84 coordinate system, UTM projection, and 6-degree banding, and the reference ellipsoid is the WGS 84 ellipsoid. According to different latitudes, the data are organized into two types of framing. In the regions of 60° north and south latitudes, the framing is carried out according to a size of 5° (latitude) × 6° (longitude); in the regions of 60° to 80° north and south latitudes, the framing is carried out according to a size of 5° (latitude) × 12° (longitude). The framing is projected according to the central meridian of the odd 6° band. GLOBELAND30 TILES: The original, unprocessed raw data are retained. TIBET_ GLOBELAND30_MOSAIC: The Erdas software is used to mosaic the raw data. The parameter settings use the default value of the raw data to retain the original, and the accuracy is consistent with that of the downloading site.

0 2021-04-09

FVC dataset of remote sensing for ecological assets assessment in Qinghai-Tibet Plateau

The basic data set of remote sensing for ecological assets assessment of the Qinghai-Tibet Plateau includes the annual Fraction Vegetation Coverage (FVC), Net Primary Productivity (NPP) and Leaf Area Index (LAI) of the Qinghai-Tibet Plateau since 2000, and other ecological parameters based on remote sensing inversion. The FVC data are mainly developed from MODIS NDVI data. Based on pixel dichotomy model, the vegetation coverage model is developed by using multi-scale remote sensing images, combining with high precision remote sensing parameters such as vegetation community type and distribution characteristics, and the mixed pixel decomposition method is used to construct the vegetation coverage model. All data could be used only after the permission of the data distributor.

0 2021-04-09

Spot vegetation NDVI dataset for Sanjiangyuan (1998-2013)

The data set is extracted from the NDVI data of long time series acquired by VEGETATION sensor on SPOT satellite. The time range of the data set is from May 1998 to 2013. In order to remove the noise in NDVI data, the maximum synthesis is carried out. A NDVI image is synthesized every 10 days. The data set is cut out from the global data set, so as to carry out the research and analysis of the source areas of the three rivers separately. The data format of this data set is geotiff, spatial resolution is 1 km, temporal resolution is 10 days, time range: May 1998 to December 2013.

0 2021-03-28

GIMMS NDVI3g dataset for Sanjiangyuan (1982-2015)

The data set is NDVI data of long time series acquired by NOAA's Advanced Very High Resolution Radiometer (AVHRR) sensor. The time range of the data set is from 1982 to 2015. In order to remove the noise in NDVI data, maximum synthesis and multi-sensor contrast correction are carried out. A NDVI image is synthesized every half month. The data set is widely used in the analysis of long-term vegetation change trend. The data set is cut out from the global data set, so as to carry out the research and analysis of the source areas of the three rivers separately. The data format of this data set is GeoTIFF with spatial resolution of 8 km and temporal resolution of 2 weeks, ranging from 1982 to 2015. Data transfer coefficient is 10000, NDVI = ND/10000.

0 2021-03-28

MODIS vegetation index dataset in Sanjiangyuan (2000-2018)

The data set is MODIS vegetation index data (MOD13Q1). The source areas of the three rivers are extracted to carry out the research and analysis of the source areas of the three rivers separately. MOD13Q1 is a 16-day composite vegetation index, including normalized vegetation index (NDVI) and enhanced vegetation index (EVI). The spatial scope of Sanjiang Source covers two MODIS files (h25v05 and h26v05). Data storage format is hdf. Each file contains 12 bands: Normalized Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Data Quality (VI Quality), Red Reflectance, Near Infrared Reflectance (NIR Reflectance), Blue Reflectance, Mid Infrared Reflectance, Observation. Viewzenith angle, sun zenith angle, relative azimuth angle, composite day of the year and pixel reliability. The data format of this data set is hdf, spatial resolution is 250m, temporal resolution is 16 days, time range: February 2000 to October 2018.

0 2021-03-28

GF-1 NDVI dataset in Maduo County (2016)

This is the vegetation index (NDVI) for Maduo County in July, August and September of 2016. It is obtained through calculation based on the multispectral data of GF-1. The spatial resolution is 16 m. The GF-1 data are processed by mosaicking, projection coordinating, data subsetting and other methods. The maximum synthesis is then conducted every month in July, August, and September.

0 2021-03-28

The desertification risk map of Iranian plateau in 2019

The gridded desertification risk data of Iranian plateau in 2019 was calculated based on the environmentally sensitive area index (ESAI) methodology. The ESAI approach incorporates soil, vegetation, climate and management quality and is one of the most widely used approaches for monitoring desertification risk. Based on the ESAI framework, fourteen indicators were chosen to consider four quality domains. Each quality index was calculated from several indicator parameters. The value of each parameter was categorized into several classes, the thresholds of which were determined according to previous studies. Then, sensitivity scores between 1 (lowest sensitivity) and 2 (highest sensitivity) were assigned to each class based on the importance of the class’ role in land sensitivity to desertification and the relationships of each class to the onset of the desertification process or irreversible degradation. A more comprehensive description of how the indicators are related to desertification risk and scores is provided in the studies of Kosmas (Kosmas et al., 2013; Kosmas et al., 1999). The main indicator datasets were acquired from the Harmonized World Soil Database of the Food and Agriculture Organization, Climate Change Initiative (CCI) land cover of the European Space Agency and NOAA’s Advanced Very High Resolution Radiometer (AVHRR) data. The raster datasets of all parameters were resampled to 500m and temporally assembled to the yearly values. Despite the difficulty of validating a composite index, two indirect validations of desertification risk were conducted according to the spatial and temporal comparison of ESAI values, including a quantitative analysis of the relationship between the ESAI and land use change between sparse vegetation and grasslands and a quantitative analysis of the relationship between the ESAI and net primary production (NPP). The verification results indicated that the desertification risk data is reliable in Iranian plateau in 2019.

0 2021-01-14

NDVI Dataset of Typical Stations in Midstream of Heihe River Basin Based on UAV Remote Sensing (2019, V1)

NDVI is a very important vegetation index for the research of vegetation growth and land cover classification. This dataset provides a monthly land surface albedo of UAV remote sensing with a spatial resolution of 0.2 m. It measured in the midstream of Heihe River Basin during the vegetation growth season over typical stations in 2019. The pix4D mapper software was used for image mosaic and NDVI calculation.

0 2020-07-31