This data is generated based on meteorological observation data, hydrological station data, combined with various assimilation data and remote sensing data, through the preparation of the Qinghai Tibet Plateau multi-level hydrological model system WEB-DHM (distributed hydrological model based on water and energy balance) coupling snow, glacier and frozen soil physical processes. The time resolution is monthly, the spatial resolution is 5km, and the original data format is ASCII text format, Data types include 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 in the month). If the asc cannot be opened normally in arcmap, please top the first 5 lines of the asc file.
WANG Lei, CHAI Chenhao
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), simulated and output through the WEB-DHM distributed hydrological model of the Indus River basin, with temperature, precipitation, barometric pressure, etc. as input data.
WANG Lei, LIU Hu
This dataset derives from the articles: (1) He, C., Liu, Z., Tian, J., & Ma, Q., (2014). Urban expansion dynamics and natural habitat loss in China: a multiscale landscape perspective. Global change biology, 20(9), 2886-2902.（2）Xu, M., He, C., Liu, Z., Dou, Y. (2016). How Did Urban Land Expand in China between 1992 and 2015? A Multi-Scale Landscape Analysis. PLoS ONE 11 (5): e0154839. To produce this dataset, the nighttime light data, vegetation index data, and land surface temperature data were preprocessed to obtain the multi-source remote sensing data in China from 1992 to 2020, and the economic regionalization, selection of samples, support vector machine classification, and inter-annual correction were used to extract the dynamic information of urban built-up area. According to the accuracy assessment based on Landsat TM/ETM+ data, Kappa coefficient is 0.60, overall accuracy is 92.62% This dataset has been used to assess the impacts of urban expansion on natural habitats and cropland, and can provide data support for understanding China’s urban expansion and its effects.
HE Chunyang, LIU Zhifeng, XU Min , LU Wenlu
This dataset derives from the article: He, C., Liu, Z., Wu, J., Pan, X., Fang, Z., Li, J., Bryan, B.A. (2021). Future global urban water scarcity and potential solutions. Nature Communications, 12, 4667. https://doi.org/10.1038/s41467-021-25026-3. This dataset includes global urban land information under different shared socioeconomic pathways from 2020 to 2070. The production process of this dataset mainly includes: (1) establishing a linear regression model based on the urban land data (downloaded from https://doi.pangaea.de/10.1594/PANGAEA.892684) and urban population data from 1992 to 2016 to calculate the future urban land demand; (2) using the LUSD-urban model to simulate the spatial distribution of urban land. The dataset can support the assessment of effects of future global urban expansion.
HE Chunyang, LIU Zhifeng, YANG Yanjie
This dataset is derived from the article: Huang, M., Wang, Z.C., Pan, X.H., Gong, B.H., Tu, M.Z., & Liu, Z.F. (2022). Delimiting China's urban growth boundaries under localized shared socioeconomic pathways and various urban expansion modes. Earth's Future, 10, e2021EF002572. The dataset shows the urban expansion and urban growth boundaries of China in 2021-2100 under different socioeconomic scenarios and diverse urban expansion modes. To produce this dataset, the patch-based LUSD-urban model was used to simulate the urban expansion with 11 modes under the localized shared socioeconomic pathways, and the morphology approach was used to delimit urban growth boundaries according to the maximum extent of urban expansion. Using this dataset, the authors quantified the impacts of future urban expansion on ecosystem services under different scenarios and diverse modes, as well as the pressure of urban shrinkage, which is helpful to the Chinese government to demarcate urban development boundaries.
HUANG Miao , WANG Zichen , PAN Xinhao , GONG Binghua , TU Mengzhao , LIU Zhifeng
This data set includes 30 m cultivated land and construction land distribution products in Qilian Mountain Area in 2021. The product comes from the land cover classification product of 30 m in Qilian Mountain Area in 2021. The overall accuracy of the product is better than 85%.
YANG Aixia, ZHONG Bo
The dataset of landuse types in Qilian Mountains National Park in 1985 is a vector dataset based on the remote sensing monitoring dataset of the current landuse situation in China by CAS, which is obtained through cropping and splicing operations. The data production production is vector data generated by manual visual interpretation using Landsat TM/ETM remote sensing images as the main data source. 3 datasets for 2000-2020 are raster datasets with 30m resolution based on GlobeLand30 global 30m ground cover data, obtained through mask extraction and other operations. The land use types of all datasets include 10 primary types of cropland, forest, shrubland, grassland, wetland, water, tundra, impervious surface, bareland, glacier, and permanent snow. The data products can detect most of the land cover changes caused by human activities, which is very important in practical applications. This data can be used to analyze the historical land use types in the Qilian Mountains region and to analyze the changes of land use types in the Qilian Mountains region in combination with the current landuse type data.
NIAN Yanyun
Land cover refers to the mulch formed by the current natural and human influences on the earth's surface. It is the natural state of the earth's surface, such as forests, grasslands, farmland, soil, glaciers, lakes, swamps and wetlands, and roads. The Land Cover (LC) dataset is original from MODIS products and preprocessed by format conversion, projection and resampling. The existing format is TIFF and projection is Krasovsky_1940_Albers. The data set has a spatial resolution of 1000 meters and provides one image per year during the period from 2002 to 2020. Land cover products were classified into 17 categories defined by the International Geosphere Biosphere Programme (IGBP), including 11 categories of natural vegetation, 3 categories of land use and Mosaic, and 3 categories of non-planting land.
ZHU Juntao
In this study，a vegetation classification system for the vegetation types in the Qinghai-Tibet Plateau was designed. The integrated classification method，taken into account of multi-source vegetation classification / land cover classification products， was used to produce the actual vegetation map. This integrated classification method followed the principle of data consistency，and the resultant vegetation map was superior over other vegetation maps in terms of reflection of current situation， classification system， and classification accuracy. This vegetation map is timely and could better reflect current vegetation distribution than earlier ones. This vegetation map could be conducive to fully extract vegetation information from multi-source data products with high reliability and consistency. Compared with previous data products，the overall accuracy （78.09%，kappa coefficient is 0.75） of this new vegetation map was found to increase by 18.84%-37.17%，especially for grassland and shrub.
ZHANG Hui, ZHAO Cenliang, ZHU Wenquan
This dataset includes the schematic diagrams and lithologic histograms of the measured sections of typical unconsolidated sediments in Shigatse, Yarlung Tsangpo River Basin, as well as the statistical table of measured sections. The source data comes from a two-month field measurement in Shigatse, Tibet. 16 sections of unconsolidated sediments were measured, and 128 samples were collected, including 89 cosmic nuclide samples and 39 optically stimulated luminescence samples. 16 schematic diagrams and 38 lithologic histograms were shown. The dataset primarily shows the genetic types of typical unconsolidated sediments in the Shigatse area, such as alluvium, eluvium, diluvium, colluvium, and moraine deposits. The exposed range of measured sediment thickness is about 1.6–70 m, the average thickness is about 29 m, and the horizontal distribution is 41–9059 m. The dataset demonstrates the discrete, porous, sandy and weakly cemented structural characteristics of the unconsolidated sediments with high gravel content (80%–95%), and the main gravel diameter distribution is 0.05–0.1m; sorting and roundness of alluvium are good, while the colluvial materials are poor. Fining-upward trends are commonly seen in most sections, and parallel and tabular cross-bedding are occasionally developed. Untangling the sedimentary characteristics of unconsolidated sediments in the Yarlung Tsangpo River Basin is vital to reveal the storage of fluvial solid matter across the basin, and provide important instructions for disaster warning and prevention and control of related features caused by sliding, unloading, and collapse of the ground surface. It is also of great scientific value to reveal the source-sink process and evolution of fluvial and alluvial systems in the Tibet Plateau and its surrounding basins.
LIN Zhipeng, WANG Chengshan , HAN Zhongpeng, BAI Yalige, WANG Xinhang, ZHANG Jian, MA Xinduo
Focusing on the objective of estimating the total amount of unconsolidated sediments in the Yarlung Tsangpo River Basin (YTRB), we marked a series of Quaternary sections of unconsolidated sediments in the whole basin to measure their thickness. The dataset presents a collection of field photos of unconsolidated sediments obtained in the scientific expedition in YTRB in 2020. Specifically, this dataset comprises of 16 composite first–class sub basins, from upstream to downstream, including Dangque–Laiwu Tsangpo, Resu–Lierong Tsangpo, Chaiqu–Menqu, Xiongqu–Wengbuqu, Jiada Tsangpo, Pengji Tsangpo–Sakya Chongqu, Duoxiong Tsangpo, Shabu–Danapu, Nianchu River, Xiangqu–Wuyuma, Manqu, Nimuma–Lhasa River, Gonggapu–Luoburongqu, Niyang River, Yigong Tsangpo–Palong Tsangpo, and Xiangjiang River Basin. A total of 584 sites of unconsolidated sediments were marked. The atlas displays different types of unconsolidated sediments, such as alluvium, eluvium, diluvium, colluvium, eolian, lacustrine and moraine deposits, showing their spatial distribution in hillsides, foothills, floodplains, terraces, alluvial–diluvial fans and glacier fronts. With a scale of 1m benchmarking, it shows the significant difference in distribution of thickness. Generally, the thickness of the eluvium on the upper part of the hillside is about 0.3–2.5m, and the thickness of the alluvium is difficult to bottom out. The thickness of diluvium in the gentle area of the piedmont with steep slope is usually between 5 and 10 m, while the thickness of the deposit at the piedmont gully mouth is related to the scale of the pluvial fan, which can reach tens of meters thick and only 3 to 4 meters thin. From the upstream to the downstream, the thickness of alluvium varies greatly. The bedrock in the canyon area is exposed, and the thickness is almost 0. However, the thickness of alluvium in the upstream river valley is large and difficult to see the bottom interface; The maximum thickness of measured moraine deposits can reach more than 20 m. Aeolian deposits are common in the middle and upper reaches, with a wide range of thickness, ranging from a few meters to more than 20 meters. The dataset provides a wide variety of in–suit photos and measurements of unconsolidated sediments covering the whole basin, showing their characteristics of spatial distribution and genetic types, which lays a material foundation and prior knowledge for further detailed characterization and investigation of unconsolidated sediments. This work presents data for estimating the total accumulation of solid debris deposited in the YTRB, and provides a basis for assessing the risk of natural disasters related to unconsolidated sediments and formulating scientific preventive measures.
LIN Zhipeng, WANG Chengshan , HAN Zhongpeng, BAI Yalige, WANG Xinhang, HU Taiyu
This data set is a 30m land cover classification product in the Qilian Mountains in 2021. This product is based on the land cover classification product in 2021, based on the Landsat series data and strong geodetic data processing capability of Google Earth engine platform, and is produced by using the ideas and methods of change detection. The overall accuracy is better than 85%. This product is the continuation of land cover classification products from 1985 to 2020. Land cover classification products from 1985 to 2020 can also be downloaded from this website. Among them, the land use products from 1985 to 2015 are five years and one period, and the land use products from 2015 to 2021 are one year and one period.
YANG Aixia, ZHONG Bo, JUE Kunsheng, WU Junjun
The data is the land cover data of the Qinghai Tibet Plateau, with a spatial resolution of 300 meters and a temporal resolution of years. The data includes three periods of 1995, 2005 and 2015. The data is in grid format (TIFF), using the 2000 national geodetic coordinate system, and can be opened using software tools such as ArcGIS and envi. The original data comes from the European Copernicus climate change service data center. With reference to the "land cover classification system" developed by the food and Agriculture Organization of the United Nations, the global land cover types are divided into 22 categories. Because of its high accuracy, consistency and annual update, this data has been widely used in the fields of land use and human activity change monitoring worldwide. Based on the original data, this data is obtained in ArcGIS through clipping, projection, accuracy verification, and quality audit by a second person. The data quality is reliable.
YANG Yaping
Through the investigation of tourist spots, tourist routes and tourist areas at different levels, form photos and video data of tourism resources, tourism services and tourism facilities of scenic spots, scenic spots, corridors and important tourism transportation nodes, tourism villages and tourism towns, record the tourism development status, find problems in tourism development, and form corresponding ideas for the construction of world tourism destinations; The data sources are UAV, tachograph and camera, mobile phone and GPS, and are divided into different folders according to scenic spots and data categories; The data has been checked for many times to ensure its authenticity; This data can provide a traceable basis for the construction of world tourism destinations on the Qinghai Tibet Plateau.
SHI Shanshan
The Surface water body extent and area dataset in pan-Sahel region includes the changes of surface water body (≥1km2) in pan-Sahelian 23 countries during 2000-2020. The dataset was produced based on the global surface water extent dataset (GSWED). Firstly, the misclassification caused by the dynamic threshold in the original GSWED data was eliminated by establishing the mask of area size and observation frequency to obtain an improved surface water data set. Then, the improved surface water surface data set was objected, and manually revised combination with global River widths from Landsat and lake data (HydroLAKES). Finally, based on the revised surface water body data set, the water body extent and area change in the Pan Sahel region in 21 years was counted. The dataset is in the vector file format (.shp) and has the geographic coordinate system of WGS 1984. It not only reduces the redundancy of data but increases the surface water from pixel scale to object scale, which is of more practical significance in geo-analysis. The dataset covers the Sahel and West Africa and provides data support for the assessment and research of surface water resources in the region.
LV Yunzhe , JIANG Min , JIA Li
This data set is a 30m land use / cover classification product in the Sahel region of Africa every five years from 1990 to 2020. The product is based on a collaborative framework of land cover classification integrating machine learning and multiple data fusion, and integrates supervised land cover classification with existing thematic land cover maps by using Google Earth engine (GEE) cloud computing platform. The classification system adopts FROM_ GLC classification system includes 8 categories: cultivated land, forest, grassland, shrub, wetland, water body, impervious surface and bare land. The data set has been verified by a large number of seasonal samples in the Sahel region. The overall accuracy of the data set is about 75%, and the accuracy of change area detection is more than 70%. It is also very similar to FAO and the existing land cover map. The data set can provide data support for the sustainable use of land resources and environmental protection in the Sahel region of Africa.
YU Le
This data is the land cover data at 30m resolution of Southeast Asia in 2015. The data format of the data is NetCDF, and the variable name is "land cover type". The data was obtained by mosaicing and extracting the From-GLC data. Several land cover types, such as snow and ice that do not exist in Southeast Asia were eliminated.The legend were reintegrated to match the new data. The data provide information of 8 land cover types: cropland, forest, grassland, shrub, wetland, water, city and bare land. The overall accuracy of the data is 71% (Gong et al., 2019). The data can provide the land cover information of Southeast Asia for hydrological models and regional climate models.
LIU Junguo
(1) Data content: the data set includes the land use of the Aral Sea basin from 2000 to 2020; (2) Data source and processing method: the data set is from the land cover map of the European Space Agency's climate change initiative（ http://maps.elie.ucl.ac.be/CCI ）On this basis, the boundary data of the Aral Sea basin are masked to extract the land use of the Aral Sea basin. At the same time, the original secondary data are combined into primary data including 7 land use types according to certain rules. The coordinate system is wgs-1984; (3) Data quality description: according to the existing research, the overall accuracy of the data set reaches 80%; (4) The data set can provide basic data support for ecological protection and environmental assessment, and can also be used as the original data of land use simulation.
LIU Tie
The gridded desertification risk data of The Arabian Peninsula in 2021 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 the Arabian Peninsula in 2021.
XU Wenqiang
This dataset was captured during the field investigation of the Qinghai-Tibet Plateau in June 2021 using uav aerial photography. The data volume is 3.4 GB and includes more than 330 aerial photographs. The shooting locations mainly include roads, residential areas and their surrounding areas in Lhasa Nyingchi of Tibet, Dali and Nujiang of Yunnan province, Ganzi, Aba and Liangshan of Sichuan Province. These aerial photographs mainly reflect local land use/cover type, the distribution of facility agriculture land, vegetation coverage. Aerial photographs have spatial location information such as longitude, latitude and altitude, which can not only provide basic verification information for land use classification, but also provide reference for remote sensing image inversion of large-scale regional vegetation coverage by calculating vegetation coverage.
LV Changhe, ZHANG Zemin
The data set of land desertification distribution in Sanjiangyuan area is derived from the desertification pattern and change data of Qinghai Tibet Plateau. This data is obtained based on the integration of remote sensing images, auxiliary data and other multi-source data. The main data used and referred to include: 1) remote sensing image data: Landsat was selected to extract the images from June to September as the main data source for land desertification monitoring on the Qinghai Tibet Plateau, and five images were selected to monitor land desertification in 1980, 1990, 2000, 2010 and 2015. 2) auxiliary data: terrain data, soil type data, vegetation type data Land use data, Google Earth image and other auxiliary data are important data in the interpretation of desertification land; 3) The indicators of desertification are wind erosion rate, percentage of quicksand area and vegetation coverage; 4) The area of the source area of the three rivers is 382312 km2. The data set is cut out from the land desertification distribution data of the Qinghai Tibet Plateau, so as to carry out the research and analysis of the source area of the three rivers separately; 5) This data format is ShapeFile format. It is recommended to use ArcMap to open data.
NAN Weige
The Quaternary sediments in the Yarlung Tsangpo River Basin (YTRB) are widely distributed and rich in types. A detailed field geological survey was carried out on the Quaternary sediments in the whole YTRB, including 16 sub-basins. The survey covers Langkazi, Jiangzi, Kangma, Sakya, Razi, Zhongba, Saga, Angren, Xietongmen, Nanmulin, Jiacha, Bomi, Motuo County, Mozhugongka and its surrounding areas. The dataset records the work log, fieldwork photos, and geological profile photos of field geological investigation on different Quaternary sediments in the YTRB. 16 profiles and 40 remote sensing interpretation markers of loose sediments were investigated. It is of great significance to find out the temporal and spatial distribution and change mechanism of Quaternary sediments in YTRB for revealing the evolution of water system, monitoring and protection of plateau ecological environment, soil and water conservation, early warning and prevention of natural disasters, and construction of major infrastructure projects.
LIN Zhipeng, HAN Zhongpeng, WANG Chengshan, BAI Yalige, WANG Xinhang, ZHANG Jian, MA Xinduo, HU Taiyu
The data of cultivated land in 1800 comes from tiehu Inventory, in which the data of Lazi county and Xietongmen County in the modern administrative unit are not recorded. Therefore, the missing data of these two counties should be interpolated.The farmland data in 1900 came from the annals of Lhasa and other county Chronicles.The land area recorded in the data is converted into modern mu units, and the missing counties are calculated using the area's per capita cultivated land and population.Tibetan Plateau with high altitude,cold climate,poor natural conditions and fragile ecological environment become the sensitive and promoter region of global climate change.Studying for Land reclamation of historical period in Qinghai-Tibet Plateau is not only the specific way to participate in the global environmental change, but also can provide the comprehensive research of land use change with abundant regional information,there is important significance for studying history in our country even the whole world of land use/cover change research.The region of Brahmaputra River and its two tributaries in Tibetan Plateau pastoral transitional zone is one of the important typical agricultural area, and is the area with the most intense land reclamation activities and the fastest population growing.Proceeding deep historical data mining in the study area to reconstruct the cropland spatial patterns over the past 300 years has important significance to study the human land use activities under the background of global climate change.
TAO Juanping, WANG Yukun
Hehuang Valley in 1800 and 1900 mainly come from New Records of Xining Mansion, Records of Xunhua Hall and New Records of Gansu, which were written in Qianlong for twenty years. The determination of county administrative boundaries refers to Atlas of Chinese History edited by Tan Qixiang and Comprehensive Table of Administrative Region Evolution in Qing Dynasty edited by Niu Hanping. After collecting cultivated land data, the original farmland data is corrected, the historical cultivated land data is converted into a unified modern unit (km²), and then the grid model is used to spatialize the two periods of cultivated land data. Hehuang Valley is one of the most important agricultural development areas in Qinghai-Tibet Plateau. Especially in Qing Dynasty, after a large number of immigrants settled land, the land cover in this area changed greatly. By sorting out and correcting the data of farmland in 1800 and 1900 recorded in the historical documents of this area, the spatial pattern of cultivated land in Hehuang Valley in 1800 and 1900 was restored, in order to reveal the changes of cultivated land in typical valley agricultural areas of Qinghai-Tibet Plateau.
LUO Jing, WU Zhilei, WU Zhilei, CHEN Qiong
The data set records the current situation of land use in Qinghai Province. The data is divided by cultivated land, garden land, woodland, grassland, residential land, industrial and mining land, transportation land, water conservancy facilities land and unused land. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 8 data tables Land use status 2002.xls Land use status in 2003.xls Land use status 2004.xls Land use status 2006.xls Land use status 2007.xls Land use status in 2008.xls Land use status in 2009.xls The structure of 2012. XLS data table is the same. For example, there are four fields in the data table of land use status in 2002 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
The data set records the statistical data of land use status in Huangnan Prefecture of Qinghai Province from 2003 to 2012, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Land use status in Huangnan Prefecture, 2003.xls Land use status of Huangnan Prefecture, 2006.xls Land use status in Huangnan Prefecture, 2008 1.xls Land use status in Huangnan Prefecture, 2008 2.xls Land use status of Huangnan Prefecture, 2012.xls Land use status in Huangnan Prefecture, 2004.xls Land use status of Huangnan Prefecture, 2006.xls Land use status of Huangnan Prefecture 2007.xls Current situation of land use in Huangnan Prefecture The data table structure is the same. For example, there are four fields in the data table of land use status in Huangnan Prefecture in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
The data set records the statistical data of land use status in Haixi Prefecture of Qinghai Province from 2003 to 2007, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 8 data tables Land use status of Haixi Prefecture, 2003.xls Land use status of Haixi Prefecture 2007.xls Land use status of Haixi Prefecture, 2008.xls Land use status of Haixi Prefecture in 2008 Land use status of Haixi Prefecture, 2012.xls Land use status of Haixi Prefecture, 2006.xls Land use status of Haixi Prefecture 2007.xls Land use status of Haixi Prefecture, 2004.xls The data table structure is the same. For example, there are four fields in the data table of land use status in Haixi Prefecture in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
This data set records the statistical data of land use status in Hainan prefecture of Qinghai Province from 2003 to 2007, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Land use status in Hainan Province, 2003.xls Land use status in Hainan Province, 2006-2007.xls Land use status in Hainan Province, 2008 1.xls Land use status in Hainan Province, 2008 2.xls Land use status in Hainan, 2012.xls Land use status in Hainan Province, 2004.xls Land use status in Hainan, 2006.xls Land use status in Hainan, 2007.xls Land use status in Hainan Province The data table structure is the same. For example, there are four fields in the data table of land use status in Hainan in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
This data set records the statistical data of land use status in Haidong area of Qinghai Province from 2003 to 2012, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Land use status in Haidong region, 2003.xls Land use status in Haidong area 2007.xls Land use status in Haidong region, 2008 1.xls Land use status in Haidong region, 2008 2.xls Land use status in Haidong region, 2004.xls Land use status in Haidong region, 2006.xls Land use status in Haidong area 2007.xls Land use status in Haidong region, 2008 3.xls Land use status of Haidong City, 2012.xls The data table structure is the same. For example, there are four fields in the 2003 data table of land use status in Haidong region Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
This data set records the statistical data of land use status in Haibei Prefecture of Qinghai Province from 2003 to 2007, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Land use status of Haibei Prefecture, 2003.xls Land use status of Haibei Prefecture 2006 2007.xls Land use status of Haibei Prefecture, 2008.xls Land use status of Haibei Prefecture 2008.xls Land use status of Haibei Prefecture, 2012.xls Land use status of Haibei Prefecture, 2004.xls Land use status of Haibei Prefecture, 2006.xls Land use status of Haibei Prefecture 2007.xls Land use status of Haibei Prefecture, 2008.xls The data table structure is the same. For example, there are four fields in the data table of land use status in Haibei Prefecture in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
This data set records the statistical data of land use status in Guoluo Prefecture of Qinghai Province from 2003 to 2007, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Present situation of land use in Guoluo Prefecture, 2003.xls Current situation of land use in Guoluo Prefecture, 2006-2007.xls Current situation of land use in Guoluo Prefecture, 2008.xls Present situation of land use in Guoluo Prefecture, 2008.xls Current situation of land use in Guoluo Prefecture, 2012.xls Present situation of land use in Guoluo Prefecture, 2004.xls Current situation of land use in Guoluo Prefecture, 2006.xls Present situation of land use in Guoluo Prefecture, 2007.xls Current situation of land use in Guoluo Prefecture, 2008.xls The data table structure is the same. For example, there are four fields in the data table of land use status in Luozhou in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
This data set includes six data files, which are: (1) soil temperature and moisture data of alpine meadow elevation gradient_ Dangxiong, Tibet (2019-2020). This data is the hourly observation data of temperature and water content at different soil depths (5cm and 20cm) of the alpine meadow at 4400m, 4500m, 4650m, 4800m, 4950m and 5100m above sea level in Dangxiong, Tibet during 2019-2020. (2) Meteorological environment data of Sejila Mountain Forest line_ Linzhi, Tibet (2019), the data is the hourly meteorological environment (including wind speed, air temperature 1 m away from the surface, relative humidity 1 m away from the surface, air temperature 3 m away from the surface, relative humidity 3 m away from the surface, atmospheric pressure, total radiation, net radiation, photosynthetically active radiation, 660 nm) of the forest line of Sejila Mountain in Linzhi, Tibet in 2019 Hourly observation data of red light radiation, 730nm infrared radiation, surface temperature, atmospheric long wave radiation, surface long wave radiation, underground 5cm-20cm-60cm heat flux, underground 5cm-20cm-60cm soil temperature and humidity, rainfall and snow thickness, among which some observation data are missing due to equipment power failure in plateau area, which has been explained in the data. (3) NDVI of vegetation at major meteorological stations_ In the Qinghai Tibet Plateau (2020), NDVI survey data and average values of vegetation near 25 meteorological stations are included. (4) Land use survey data set_ Along the Sichuan Tibet Railway (2019), including 35 survey points along the Sichuan Tibet railway land use survey data, including survey time, location, latitude and longitude, altitude, slope aspect, main vegetation types and dominant species. (5) Leaf area index survey data_ The leaf area index (LAI) of main vegetation types along Sichuan Tibet Railway (2019) was measured by SunScan canopy analyzer and lai-2200. (6) Survey data of soil temperature and humidity_ Along the Sichuan Tibet Railway (2019), including 34 survey points along the Sichuan Tibet Railway: location, longitude and latitude, altitude, soil surface temperature, soil moisture at 30cm, the data were recorded as 3 repeated measurements at each survey point. The data set can be used to analyze and study the change law of vegetation environment in Qinghai Tibet Plateau.
ZHOU Guangsheng, LV Xiaomin, LUO Tianxiang, DU Jun, WANG Yuhui, ZHOU Huailin
Agricultural irrigation consumes a large amount of available freshwater resources and is the most immediate human disturbance to the natural water cycle process, with accelerated regional water cycles accompanied by cooling effects. Therefore, estimating irrigation water use (IWU) is important for exploring the impact of human activities on the natural water cycle, quantifying water resources budget, and optimizing agricultural water management. However, the current irrigation data are mainly based on the survey statistics, which is scattered and lacks uniformity, and cannot meet the demand for estimating the spatial and temporal changes of IWU. The Global Irrigation Water Use Estimation Dataset (2011-2018) is calculated by the satellite soil moisture, precipitation, vegetation index, and meteorological data (such as incoming radiation and temperature) based on the principle of soil water balance. The framework of IWU estimation in this study coupled the remotely sensed evapotranspiration process module and the data-model fusion algorithm based on differential evolution. The IWU estimates provided from this dataset have small bias at different spatial scales (e.g., regional, state/province and national) compared to traditional discrete survey statistics, such as at Chinese provinces for 2015 (bias = −3.10 km^3), at U.S. states for 2013 (bias = −0.42 km^3), and at various FAO countries (bias = −10.84 km^3). Also, the ensemble IWU estimates show lower uncertainty compared to the results derived from individual precipitation and soil moisture satellite products. The dataset is unified using a global geographic latitude and longitude grid, with associated metadata stored in corresponding NetCDF file. The spatial resolution is about 25 km, the time resolution is monthly, and the time span is 2011-2018. This dataset will help to quantitatively assess the spatial and temporal patterns of agricultural irrigation water use during the historical period and support scientific agricultural water management.
ZHANG Kun, LI Xin, ZHENG Donghai, ZHANG Ling, ZHU Gaofeng
The data set records the land use status of Yushu prefecture in Qinghai Province from 2003 to 2012, and the data is divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains eight data tables, each of which has the same structure. For example, there are four fields in the data table from 1978 to 2004 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
The data set records the land use status of Xining City in Qinghai Province from 2003 to 2012, and the data is divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains 9 data tables with the same structure. For example, the data table in 2003 has four fields: Field 1: area at the beginning of the year Field 2: area reduced during the year Add field area within 3 years Field 4: year end area
Qinghai Provincial Bureau of Statistics
According to the distribution of cultivated land in 18 districts and counties in the "One River and Two Tributaries" region of Tibet Autonomous Region, a 5km × 5km grid was adopted, covering all cultivated land and greenhouse land. A total of 1092 5km × 5km grids were set up, and each grid contains a number. Data processing method: the fishnet tool in ArcGIS 10.3 is used to generate the grid covering the administrative boundaries of 18 districts and counties in the "one river, two rivers" region of Tibet Autonomous Region, and then the intersect tool is used to generate the grid covering cultivated land. The data can be used to collect soil samples of cultivated land in "One River and Two Tributaries" area of Tibet Autonomous Region.
GONG Dianqing
The data of greenhouse land is based on Google Earth image interpretation in Lhasa city, 2018, with a spatial resolution of 0.52 meters. Most of the greenhouses in Lhasa are regular rectangles with high reflectivity, which is easy to identify. In the process of interpretation, the open fields with an area of more than 0.10 hectares and roads with a width of more than 7 meters in the greenhouse area of protected agriculture, as well as the greenhouse covered with black textile were removed, while the small empty fields and ridges between the farmland of protected agriculture were not removed. The accuracy of interpretation is 98%. The data well reflects the spatial pattern characteristics of greenhouse land in Lhasa city.
GONG Dianqing
This dataset contains land cover products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset was produced by two steps. Firstly, land cover product in 2015 is produced using time series Landsat-8/OLI data. In view of the different NDVI time series curves of various land features with time variation, the knowledge of different land features is summarized, the extraction rules of different land features are set, and the land cover classification map in 2015 is obtained. The classification system refers to IGBP and FROM_LC classification system. It is divided into 10 categories: cultivated land, woodland, grassland, shrub, wetland, water body, impermeable surface, bare land, glacier and snow cover. According to the accuracy evaluation of Google Earth high-definition image and field survey data, the overall accuracy of land cover classification products in 2015 is as high as 92.19%. Secondly, taking the land cover classification products in 2015 as the base map, a large number of samples are selected according to the proportion of different types. Based on the Landsat series data and powerful data processing ability of Google Earth Engine platform, the random forest classifier is selected to train the band information and NDVI, MNDWI, NDBI and other indices by using the idea of in-depth learning. The land of each five-year period from 1985 to 2017 is produced. By comparing two classified products in 2015, it is concluded that the land cover classified products based on Google Earth Engine platform have good consistency with those based on time series method. In conclusion, the land cover data set in the core area of Qilian Mountains has high overall accuracy , and the method based on sample training of Google Earth Engine platform can expand the existing classification products in time and space, and the frequency of every five years can reflect more land cover type change information in long time series.
ZHONG Bo, JUE Kunsheng
This data set is the land use data of the key areas of Qilian mountain in 2018, spatial resolution 2m. This data set is based on the data of climate, altitude, topography, and land cover type of the Qilian mountain. Through the high-resolution remote sensing images to interprets the surface cover types. For the land types that cannot be reflected by the images, collect relevant data in the field, check and correct the land use types. At the same time, the maps and attribute information are uniformly entered and edited to form land use data in the Qilian Mountain area in 2018.
QI Yuan, ZHANG Jinlong, YAN Changzhen, DUAN Hanchen, JIA Yongjuan
The dataset is the land cover of Qing-Tibet Plateau in 2014. The data format is a TIFF file, spatial resolution is 300 meters, including crop land, grassland, forest land, urban land, and so on. The dataset offers a geographic fundation for studying the interaction between urbanization and ecological reservation of Qing-Tibet Plateau. This land cover data is a product of CCI-LC project conducted by European Space Agency. The coordinate reference system of the dataset is a geographic coordinate system based on the World Geodetic System 84 reference ellipsoid. There are 22 major classes of land covers. The data were generated using multiple satellite data sources, including MERIS FR/RR, AVHRR, SPOT-VGT, PROBA-V. Validation analysis shows the overall accuracy of the dataset is more than 70%, but it varies with locations and land cover types.
DU Yunyan
This dataset is the spatial distribution map of the marshes in the source region 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 30 m, 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.
WANG Guangjun
Based on 2015 ESA global land cover data (ESA GlobCover, 300 m grid), combined with the Tsinghua university global land cover data (FROM GLC, 30 m grid), NASA MODIS global land cover data (MCD12Q1, 300 m grid), the United States Geological Survey (USGS global land data (GFSAD30, 30 m), Japanese global forest data (PALSAR/PALSAR - 2, 25 m), we build the LUCC classification system in the Belt and Road's region and the rest of the data transformation rules of the classification system. We also build the land cover classification confidence function and the rules of fusing land classification to finish the integration and modification of land cover products and finally completed the land use data in the Belt and Road's region V1.0 (64 + 1 countries, 2015, 1 km x 1 km grid, the first level classification).
XU Erqi
Land cover dataset of MODIS is a product that describes the types of land cover based on the data obtained from Terra and Aqua observations for one year. The land cover dataset contains 17 major land cover types, including 11 natural vegetation types, 3 land development and mosaic types and 3 non-vegetation land types according to the International Geosphere Biosphere Project (IGBP). MCD12Q1 adopts five different land cover classification schemes. The main technology of information extraction is supervised decision tree classification. Based on MCD12Q1 data from 2001 to 2016, MatLab was used to tailor the masks of 18 key nodes in Southeast Asia and middle East. Finally, the annual land cover dataset of 18 key nodes in Southeast Asia from 2001 to 2016 was obtained.
GE Yong, LING Feng, ZHANG Yihang
The MODIS Terra MOD09A1 Version 6 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 m reflectance bands is a quality layer and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.Based on MCD12Q1 data from 2001 to 2016, MatLab was used to tailor the masks of 18 key nodes in Southeast Asia and middle East. Finally. This dataset is based on the data of MOD09A1 V6 synthesized in 8 days from 2001 to 2016 downloaded by the National Aeronautics and Space Administration (NASA). The spatial resolution is 500 meters, and MatLab is used to mask cut the data in the research area, and Finally, the land cover data of 18 key nodes from 2001 to 2016 were obtained.. The 18 key regions covered by the data mainly include: Bangkok, Port of Myanmar, Chittagong, Colombo, Dhaka, Gwadar, Hambantot, Huangjing and Malacca, Kwantan, Maldives, Mandalay, Sihanouk, Vientiane, Yangon, etc.).
LI Xinyan
This data comes from "China's 1:100000 land use data". China's 1:100000 land use data is constructed in three years based on LANDSAT MSS, TM and ETM Remote sensing data by means of satellite remote sensing, organized by 19 research institutes affiliated to the Chinese Academy of Sciences under the national macro survey and dynamic research on remote sensing of resources and environment, a major application project of the eighth five year plan of the Chinese Academy of Sciences. Using a hierarchical land cover classification system, this data divides the whole country into six first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class categories. This is the most accurate land use data product in China, which has played an important role in the national land resource survey, hydrological and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, ZHOU Wancun, WU Shixin
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.
CHEN Jun
The land cover classification product is the second phase product of the ESA Climate Change Initiative (CCI), with a spatial resolution of 300 meters and a temporal coverage of 1992-2015. The spatial coverage is latitude -90-90 degrees, longitude -180-180 degrees, and the coordinate system is the geographic coordinate WGS84. The classification of the surface coverage is based on the Land Cover Classification System (LCCS) of the Food and Agriculture Organization of the United Nations. When the data are used for scientific research purposes, the ESA CCI Land Cover project should be acknowledged. In addition, the published article should be send to contact@esalandcover-cci.org.
XU Xiyan
The aim of the simultaneous observation of river surface temperature is obtaining the river surface temperature of different places, while the sensor of thermal infrared go into the experimental areas of artificial oases eco-hydrology on the middle stream. All the river surface temperature data will be used for validation of the retrieved river surface temperature from thermal infrared sensor and the analysis of the scale effect of the river surface temperature, and finally serve for the validation of the plausibility checks of the surface temperature product from remote sensing. 1. Observation sites and other details Ten river sections were chosen to observe surface temperature simultaneously in the midstream of Heihe River Basin on 3 July and 4 July, 2012, including Sunan Bridge, Binhe new area, Heihe Bridge, Railway Bridge, Wujiang Bridge, Gaoya Hydrologic Station, Banqiao, Pingchuan Bridge, Yi’s Village, Liu’s Bridge. Self-recording point thermometers (observed once every 6 seconds) were used in Railway Bridge and Gaoya Hydrologic Station while handheld infrared thermometers (observed once of the river section temperature for every 15 minutes) were used in other eight places. 2. Instrument parameters and calibration The field of view of the self-recording point thermometer and the handheld infrared thermometer are 10 and 1 degree, respectively. The emissivity of the latter was assumed to be 0.95. All instruments were calibrated on 6 July, 2012 using black body during observation. 3. Data storage All the observation data were stored in excel.
HE Xiaobo, Jia Shuzhen
Er’ba Reservoir surface temperature of water body can offer in situ calibration data for TASI, WiDAS and L band sensor used in aerospace experiment. Observation Site: This site is 14 KM away from East of ZhangYe city. It’s located in Er’ba village, JianTan town, ZhangYe city. The coordinates of this site: 38°54′57.14" N, 100°36′57.39" E. Observation Instrument: The observation system consists of two SI-111 infrared radiometers (Campbell, USA) and two 109SS temperature probes (Campbell, USA). Two SI-111 sensors, one installed vertically downward to water surface, another face to south of zenith angle 35°. Temperature probes float under water surface at 0 cm. SI-111 sensor installed at 3.0 m height, 3.4 m away from water edge. Observation Time: This site operates from 27 May, 2012 to 27 September, 2012. Observation data laagered by every 5 seconds uninterrupted. Output data contained sample data of every 5 seconds and mean data of 1 minute. Accessory data： Water surface infrared temperature (by SI-111), sky infrared temperature (by SI-111), water surface temperature (by 109ss) can be obtained. Dataset is stored in *.dat file, which can be read by Microsoft excel or other text processing software (UltraEdit, et. al). Table heads meaning: TarT_Atm, Sky infrared temperature (℃) @ facing south of zenith angle 35°; SBT_Atm, body temperature of SI-111 sensor (℃) measured sky; TarT_Sur, water surface infrared temperature @ 3.0 m height; SBT_Sur, body temperature of SI-111 sensor (℃) measured water surface; WaterT_1, WaterT_2, water surface temperature (℃) measured by 109SS temperature probes. Dataset is stored day by day, named as: data format + site name + interval time + date + time. The detailed information about data item showed in data header introduction in dataset.
MA Mingguo
Ⅰ. Overview This data set is based on Landsat MSS, TM and ETM Remote sensing data by means of satellite remote sensing. Using a hierarchical land cover classification system, the data divides the whole region into six first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅱ. Data processing description The data set is based on Landsat MSS, TM and ETM Remote sensing data as the base map, the data set projection is set as Alberts equal product projection, the scale is set at 1:24,000 for human-computer interactive visual interpretation, and the data set storage form is ESRI coverage format. Ⅲ. Data content description The data set adopts a hierarchical land cover classification system, which is divided into 6 first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅳ. Data use description The data can be mainly used in national land resources survey, climate change, hydrology and ecological research.
XUE Xian, DU Heqiang
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