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
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