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.
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.
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.
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.
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.
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.
(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.
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.
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.
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