This dataset is the first 1: 100,000 desert spatial database in China based on the graphic data of desert thematic maps. It mainly reflects the geographical distribution, area size, and mobility of sand dunes in China. According to the system design requirements and relevant standards, the input data is standardized and uniformly converted into a standard format for various types of data input. Build a library to run the delivery system. This project uses the TM image in 2000 as the information source, and interprets, extracts, and edits the coverage of the national land use map and TM digital image information in 2000. It uses remote sensing and geographic information system technology to 1: 100,000 Thematic mapping requirements for scale bar maps were made on the desert, sandy land and gravel Gobi in China. The 1: 100,000 desert map across the country can save users a lot of data entry and editing work when they are engaged in research on resources and the environment. Digital maps can be easily converted into layout maps The dataset properties are as follows: Divided into two folders e00 and shp: Desert map name and province comparison table in each folder 01 Ahsm Anhui 02 Bjsm Beijing 03 Fjsm Fujian 04 Gdsm Guangdong 05 Gssm Gansu 06 Gxsm Guangxi Zhuang Autonomous Region 07 Gzsm Guizhou 08 Hebsm Hebei 09 Hensm Henan 10 Hljsm Heilongjiang 11 Hndsm Hainan 12 Hubsm Hubei 13 Jlsm Jilin Province 14 Jssm Jiangsu 15 Jxsm Jiangxi 16 Lnsm Liaoning 17 Nmsm Inner Mongolia Gu Autonomous Region 18 Nxsm Ningxia Hui Autonomous Region 19 Qhsm Qinghai 20 Scsm Sichuan 21 Sdsm Shandong 22 Sxsm Shaanxi Province 23 Tjsm Tianjin 24 Twsm Taiwan Province 25 Xjsm Xinjiang Uygur Autonomous Region 26 Xzsm Tibet Autonomous Region 27 Zjsm Zhejiang 28 Shxsm Shanxi 1. Data projection: Projection: Albers False_Easting: 0.000000 False_Northing: 0.000000 Central_Meridian: 105.000000 Standard_Parallel_1: 25.000000 Standard_Parallel_2: 47.000000 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) 2. Data attribute table: area (area) perimeter ashm_ (sequence code) class (desert encoding) ashm_id (desert encoding) 3. Desert coding: mobile sandy land 2341010 Semi-mobile sandy land Semi-fixed sandy land 2341030 Gobi 2342000 Saline land 2343000 4: File format: National, sub-provincial and county-level desert map data types are vector shapefiles and E00 5: File naming: Data organization based on the National Basic Resources and Environmental Remote Sensing Dynamic Information Service System is performed on the file management layer of Windows NT. The file and directory names are compound names of English characters and numbers. Pinyin + SM composition, such as the desert map of Gansu Province is GSSM. The flag and county desert map is the pinyin + xxxx of the province name, and xxxx is the last four digits of the flag and county code. The division of provinces, districts, flags and counties is based on the administrative division data files in the national basic resources and environmental remote sensing dynamic information service operation system.
WANG Jianhua, WANG Yimou, YAN Changzhen, QI Yuan
Shule River Basin is one of the three inland river basins in Hexi corridor. In recent years, with the obvious change of climate and the aggravation of human activities, the shortage of water resources and the problem of ecological environment in Shule River Basin have become increasingly prominent. It is of great significance to study the runoff change of Shule River Basin in the future climate situation for making rational water resources planning and ecological environment protection. The Shule River basin boundary is cut from "China's 1:100000 desert sand data set". Taking the 2000 TM image as the data source, it interprets, extracts, revises, and uses remote sensing and geographic information system technology to combine with the 1:100000 scale mapping requirements to carry out thematic mapping of desert, sand and gravel gobi. Data attribute table: Area (area), perimeter (perimeter), ash_ (sequence code), class (desert code), ash_id (desert code). The desert code is as follows: mobile sand 2341010, semi mobile sand 2341020, semi fixed sand 2341030, Gobi 2342000, salt alkali land 2343000. Collect and sort out the basic, meteorological, topographical and geomorphic data of Shule River Basin, and provide data support for the management of Shule River Basin.
This dataset is based on the long sequence (1981-2013)normalized difference vegetation index product(Version 3) of the latest NOAA Global Inventory Monitoring and Modeling System (GIMMS). First, the NDVI data products were re-sampled from the spatial resolution of 1/12 degree to 0.5 degree, then the time series of every year was smoothed by the double-logistic method, and the smoothed curvature was calculated. The maximum curvature of spring was selected as the returning green stage of the vegetation in Spring. This data can be used to analyze the temporal and spatial characteristics of the Holarctic vegetation phenology in Spring.
XU Xiyan
"Hydrological ecological economic process coupling and evolution of Heihe River Basin Management under the framework of water rights" (91125018) project data collection 2 - Dunhuang comprehensive plan for rational utilization of water resources and ecological protection (2011-2020) Planning documents mainly include: 1. Current situation and existing problems of regional water resources utilization; 2. Guiding ideology, basic principles and planning objectives; 3. Analysis of economic, social and ecological water demand; 4. Plan for water resources allocation; 5. Construction of water right system; 6. Main engineering measures; 7. Environmental impact arrangement.
Desertification is a kind of land degradation with aeolian sands as the main symbol caused by the uncoordinated human-land relationship in arid, semi-arid and some semi-humid regions of northern China. Data source: edited by the China Institute of Glacial and Frozen Desert and coordinated by the Institute of Geography of the Chinese Academy of Sciences. Based on aerial photographs from the 1970s and field research, a 1: 2 million desert map was drawn. Mapping of the 14 million "Map of the People's Republic of China" published in 1971. First, the data set content 1.Desert_Ch_2009 (desert distribution) 2.Dune_hight_Ch_200 (dune height) 3.Gobi_Ch_200 (Gobi) 4.Wind_eroded_land_Ch_200 (wind erosion data) The fields of the desertification attribute table are as follows: (1) Semifixed (semi-fixed dunes): undulating sandy land (2-1), thicket dunes (2-2), parabolic dunes (2-3), beam nest dunes (2-4), sand ridges And dendritic sand ridge (2-5), honeycomb sand dune (2-6), honeycomb sand ridge (2-7), composite sand ridge (2-8) (2) Fixation (fixed dune): flat sandy land (3-1), grassland bush (3-2), sand ridge (3-3), honeycomb sand dune (3-4) (3) Migratory: Crescent sand dunes and dune chains (1-1), Crescent sand ridges and dunes (1-2), Lattice dunes and Lattice dune chains (1-3), Fish scales Sand dunes (1-4), feathery dunes (1-5), pyramid dunes (1-6), composite dunes and dune chains (1-7), composite dunes (1-8), composite Dome-shaped dunes (1-9), chain-shaped sand hills (sand dunes) (1-10), stacked chain-shaped sand hills (1-11), compound ridge-shaped sand hills (1-12), composite chain-shaped Sand Mountain (1-13), Pyramid Sand Mountain (1-14) (4) class_id: encoding of desertification attributes Projection information PROJCS ["Albers", GEOGCS ["GCS_Beijing_1954", DATUM ["Beijing_1954", SPHEROID ["Krasovsky_1940", 6378245.0,298.3]], PRIMEM ["Greenwich", 0.0], UNIT ["Degree", 0.0174532925199433]], PROJECTION ["Albers_Conic_Equal_Area"], PARAMETER ["False_Easting", 0.0], PARAMETER ["False_Northing", 0.0], PARAMETER ["longitude_of_center", 105.0], PARAMETER ["Standard_Parallel_1", 25.0], PARAMETER ["Standard_Parallel_2", 47.0], PARAMETER ["latitude_of_center", 0.0], UNIT ["Meter", 1.0]]
WANG Jianhua
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Sidalong Station from October 24 to December 31, 2018. The site (38.430°E, 99.931°N) was located on a forest in the Kangle Sunan, which is near Zhangye city, Gansu Province. The elevation is 3059 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (0.5, 3, 13, 24, and 48 m), wind speed and direction profile (windsonic; 0.5, 3, 13, 24, and 48 m), air pressure (1.5 m), rain gauge (24 m), infrared temperature sensors (4 m and 24m, vertically downward), photosynthetically active radiation (4 m and 24m), soil heat flux (-0.05 m and -0.1m), soil temperature/ moisture/ electrical conductivity profile -0.05, -0.1m, -0.2m, -0.4m and -0.6mr), four-component radiometer (24 m, towards south), sunshine duration sensor(24 m, towards south). The observations included the following: air temperature and humidity (Ta_0.5 m, Ta_3 m, Ta_13 m, Ta_24 m, and Ta_48 m; RH_0.5 m, RH_3 m, RH_13 m, RH_24 m, and RH_48 m) (℃ and %, respectively), wind speed (Ws_0.5 m, Ws_3 m, Ws_13 m, Ws_24 m, and Ws_48 m) (m/s), wind direction (WD_0.5 m, WD_3 m, WD_13 m, WD_24 m, and WD_48 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_A, IRT_B) (℃), photosynthetically active radiation (PAR_A, PAR_B) (μmol/ (s m^2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, and Ts_60 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, and Ms_60 cm) (%, volumetric water content),soil water potential (SWP_5cm, SWP_10cm, SWP_20cm, SWP_40cm, and SWP_60cm)(kpa), soil conductivity (Ec_5cm, Ec_10cm, Ec_20cm, Ec_40cm, and Ec_60cm)(μs/cm), sun time (h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The soil water potential in the area is so low that it has exceeded the sensor measurements. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Xiyinghe Station from January 1 to December 31, 2018. The site (101.853E, 37.561N) was located on a alpine meadow in the Menyuan,Qinghai Province. The elevation is 3639 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (2, 4, and 8 m, towards north), wind speed and direction profile (windsonic; 2, 4, and 8 m, towards north), air pressure (1.5 m), rain gauge (4 m), four-component radiometer (4 m, towards south), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (-0.05 m and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (-0.2 and -0.4 m in south of tower), sunshine duration sensor (4 m, towards south). The observations included the following: air temperature and humidity (Ta_2 m, Ta_4 m, and Ta_8 m; RH_2 m, RH_4 m, and RH_8 m) (℃ and %, respectively), wind speed (Ws_2 m, Ws_4 m, and Ws_8 m) (m/s), wind direction (WD_2 m, WD_4 m, and WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s/m^2)), soil heat flux (Gs_5 cm, Gs_10cm) (W/m^2), soil temperature (Ts_20 cm, Ts_40 cm) (℃), soil moisture (Ms_20 cm, Ms_40 cm) (%, volumetric water content), soil water potential (SWP_20cm , SWP_40cm)(kpa) , soil conductivity (Ec_20cm, Ec_40cm)(μs/cm), sun time (h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The meteorological data were missing during Aug. 29 to Oct.18 because of unstable power supply due to battery box flooding; The wind speed and direction profile data were rejected because of sensor failure; The precipitation data were rejected because of program error; The air humidity data before Mar. 2 were rejected due to program error; (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Liancheng Station from January 1 to December 31, 2018. The site (102.833E, 36.681N) was located on a forest in the Tulugou national forest park, which is near Liancheng city, Gansu Province. The elevation is 2912 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4 and 8 m, towards north), wind speed and direction profile (windsonic; 4 and 8 m, towards north), air pressure (1.5 m), rain gauge (2 m), four-component radiometer (4 m, towards south),infrared temperature sensors (2 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (2 duplicates below the vegetation;-0.05 and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation;-0.05 and -0.1m in south of tower), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m and Ta_8 m; RH_4 m and RH_8 m) (℃ and %, respectively), wind speed (Ws_2 m, Ws_4 m, and Ws_8 m) (m/s), wind direction (WD_2 m, WD_4 m, and WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_5 cm, Gs_10 cm) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm) (%, volumetric water content), soil water potential (SWP_5cm,SWP_10cm)(kpa), soil conductivity (EC_5cm,EC_10cm)(μs/cm), sun time (h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The soil heat flux data were wrong during Jan.1 to May 30 because of rodent damage; The data during May. 30 to July 6 were missing because the power supply failure; The air humidity data were rejected due to program error. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
The data is the distribution map of 100,000 deserts in the Tarim River Basin. This data uses 2000 TM images as the data source to interpret, extract and revise, and uses remote sensing and geographic information system technology in combination with the mapping requirements of 1: 100,000 scale to carry out thematic mapping of deserts, sands and gravelly Gobi. Data attribute table: area (area), perimeter (perimeter), ashm_ (sequence code), class (desert code), ashm_id (desert code), of which desert code is as follows: flowing sand 2341010, semi-flowing sand 2341020, semi-fixed sand 2341030, Gobi desert 2342000, saline-alkali land 2343000
WANG Jianhua
This data set is a three-level classification map of Eurasian grassland remote sensing in 2009. The data is in TIF grid format, with a spatial resolution of 1km. The three-level grassland is classified as: temperate meadow grassland, temperate typical grassland, temperate desertification grassland, temperate grassland desertification, and temperate desert. The data is processed according to the ESA global cover 2009 Product global cover map, combined with the historical meteorological data (precipitation, annual accumulated temperature, humidity coefficient, evaporation) and DEM data of ECMWF website. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.
TANG Jiakui
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Linze Station from January 1 to December 31, 2018. The site (100.060° E, 39.237° N) was located on a cropland (maize surface) in the Guzhai Xinghua, which is near Zhangye city, Gansu Province. The elevation is 1400 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4 and 8 m, towards north), wind speed and direction profile (windsonic; 4 and 8 m, towards north), air pressure (1 m), rain gauge (4 m), four-component radiometer (4 m, towards south), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (2 duplicates below the vegetation; -0.05 and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (-0.2 and -0.4m), sunshine duration sensor (4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m, Ta_8 m; RH_3 m, RH_4 m, RH_8 m) (℃ and %, respectively), wind speed (Ws_4 m, Ws_8 m) (m/s), wind direction (WD_4 m, WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing long wave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_5cm, Gs_10cm) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm) (%, volumetric water content), soil water potential(SWP_5cm, SWP_10cm), soil conductivity (Ec_5cm,Ec_10cm) (μs/cm), sun time(h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day.The precipitation and the air humidity data were rejected due to program error. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
This dataset is based on the sixth edition of the MODIS normalized difference vegetation index product (2001-2014) jointly released by NASA EOSDIS LP DAAC and the US Geological Survey USGS EROS. The NDVI has a time resolution of 16 days and a spatial resolution of 0.05 degree. First,the NDVI data products were re-sampled from the spatial resolution of 0.05 degree to 0.5 degree, then the time series of every year was smoothed by the double-logistic method, and the smoothed curvature was calculated. The maximum curvature of spring was selected as the returning green stage of the vegetation in Spring. This data can be used to analyze the temporal and spatial characteristics of the Holarctic vegetation phenology in Spring.
NASA EOSDIS LP DAAC, XU Xiyan
By applying Supply-demand Balance Analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, based on which the vulnerability of the water resources system of the basin was evaluated. The IPAT equation was used to set a future water resource demand scenario, setting variables such as future population growth rate, economic growth rate, and unit GDP water consumption to establish the scenario. By taking 2005 as the base year and using assorted forecasting data of population size and economic scale, the future water demand scenarios of various counties and cities from 2010 to 2050 were forecast. By applying the basic structure of the HBV conceptual hydrological model of the Swedish Hydrometeorological Institute, a model of the variation tendency of the basin under climate change was designed. The glacial melting scenario was used as the model input to construct the runoff scenario under climate change. According to the national regulations of the water resources allocation of the basin, a water distribution plan was set up to calculate the water supply comprehensively. Considering of the supply and demand situation, the water resource system vulnerability was evaluated by the water shortage rate. By calculating the (grain production) land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources under the climate change, glacial melt and population growth scenarios was analyzed, and the vulnerability of the agricultural system was evaluated. The Miami formula and HANPP model were used to calculate the human appropriation of net primary biomass and primary biomass in the major counties and cities for the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.
YANG Linsheng, ZHONG Fanglei
This data set is a spatiotemporal variation map of temperate grassland types in Eurasia - three level classification of Inner Mongolia region of China (2009). The data is in TIF grid format with a spatial resolution of 1km. The data is processed on the basis of the existing grass type map of Inner Mongolia grassland. The grassland type map of Inner Mongolia grassland is based on the field survey data, neimengqi County as the unit, the grassland type classification system, on the basis of prediction, the field sample data, remote sensing image and other information data are superposed, and the local historical grassland survey data and relevant data are referred to, and the field plot is modified. We select 2000-2009 historical meteorological data, further analyze and modify the satellite data, and carry out spatial interpolation calculation. The classification of temperate grassland in Inner Mongolia was obtained. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.
TANG Jiakui
By applying Supply-demand Balance Analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, on which basis the vulnerability of the water resources system of the basin was evaluated. The IPAT equation was used to set a future water resource demand scenario, setting variables such as future population growth rate, economic growth rate, and unit GDP water consumption to establish the scenario. By taking 2005 as the base year and using assorted forecasting data of population size and economic scale, the future water demand scenarios of various counties and cities from 2010 to 2050 were predicted. By applying the basic structure of the HBV conceptual hydrological model of the Swedish Hydrometeorological Institute, a model of the variation tendency of the basin under climate change was designed. The glacial melting scenario was used as the model input to construct the runoff scenario under climate change. According to the national regulations for the water resources allocation of the basin, a water distribution plan was set up to calculate the water supply comprehensively. Considering the supply and demand situation, the water resource system vulnerability was evaluated by the water shortage rate. By calculating the (grain production) land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources under the climate change, glacial melt and population growth scenarios was analyzed, and the vulnerability of the agricultural system was evaluated. The Miami formula and HANPP model were used to calculate the human appropriation of net primary biomass and primary biomass in the major counties and cities for the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.
YANG Linsheng, ZHONG Fanglei
By applying supply-demand balance analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, and the results were used to assess the vulnerability of the water resources system in the basin. The IPAT equation was used to establish a future water resource demand scenario, which involved setting various variables, such as the future population growth rate, economic growth rate, and water consumption per unit GDP. By taking 2005 as the base year and using assorted forecasting data of population size and economic scale, the future water demand scenarios of various counties and cities from 2010 to 2050 were predicted. By applying the basic structure of the HBV conceptual hydrological model of the Swedish Hydro-meteorological Institute, a model of the variation trends of the basin under a changing climate was designed. The glacial melting scenario was used as the model input to construct the runoff scenario in response to climate change. According to the national regulations of the water resource allocation in the basin, a water distribution plan was set up to calculate the water supply comprehensively. Considering the supply and demand situation, the water resource system vulnerability was evaluated by the water shortage rate. By calculating the grain production-related land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources in scenarios of climate change, glacial melting and population growth was analysed, and the vulnerability of the agricultural system was evaluated. The Miami formula and HANPP model were used to calculate the human appropriation of net primary biomass and primary biomass in the major counties and cities in the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.
YANG Linsheng, ZHONG Fanglei
一. An overview This data set is a 1:100,000 distribution map of China's deserts as the data source, and it is tailored according to the river basin boundary. It mainly reflects the geographical distribution, area size, mobility and fixation degree of deserts, sandy land and gobi in the upper reaches of the Yellow River.The information source of this data set is Landsat TM image in 2000. Using remote sensing and geographic information system technology, according to the requirements of 1:100,000 scale thematic mapping, the thematic mapping of China's deserts, sandlands and gobi was carried out. 二. Data processing instructions This data set takes the 1:100,000 distribution map of China's deserts as the data source and is tailored according to the basin boundary.The information source of this data set is Landsat TM image in 2000. Using remote sensing and geographic information system technology, according to the requirements of 1:100,000 scale thematic mapping, the thematic mapping of China's deserts, sandlands and gobi was carried out.According to the system design requirements and related standards, the input data is standardized and uniformly converted into various data input standard formats. 三. data content description This data set is divided into desert and non-desert category, the non-desert code is 999. The desert is divided into three categories, namely desert (land), gobi and saline-alkali land, and the classification code is 23410, 2342000 and 2343000 respectively.Among them, deserts (land) are divided into four categories, namely mobile desert (land), semi-mobile desert (land), semi-fixed desert (land) and fixed desert (land). The classification codes are 2341010, 2341020, 2341030 and 2341040. 四. Data usage instructions It can make the resources, environment and other related workers understand the desert type, area and distribution in the upper reaches of the Yellow River, and make the classification and evaluation of the wind and sand hazards in ningmeng river section.
XUE Xian, DU Heqiang
The data is 100,000 desert distribution map over the north_slope_of_Tianshan River Basin. This data uses 2000 TM image as data source to interpret, extract and revise. Remote sensing and geographic information system technology are combined with the mapping requirements of 1: 100,000 scale to carry out thematic mapping of deserts, sands and gravelly Gobi. Data attribute table: area (area), perimeter (perimeter), ashm_ (sequence code), class (desert code) and ashm_id (desert code), of which the desert code is as follows: mobile sand 2341010, semi-mobile sand 2341020, semi-fixed sand 2341030, Gobi desert 2342000 and saline-alkali land 2343000.
WANG Jianhua, YAN Changzhen
Data for 100000 desert map qaidam river basin, cutting since China 1:100000 desert sand data set, the data of TM images in 2000 data sources, to interpret, extraction, revision, using remote sensing and geographic information system technology combining 1:100000 scale mapping, the desert, sand and gravel gobi for thematic mapping.The desert codes are as follows: mobile sandy land 2341010, semi-mobile sandy land 2341020, semi-fixed sandy land 2341030, gobi desert 2342000, saline alkaline land 2343000.
WANG Jianhua
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Dunhuang Station from January 1 to December 31, 2020. The site (93.708° E, 40.348° N) was located on a wetland in the Dunhuang west lake, Gansu Province. The elevation is 990 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4m and 8 m, towards north), wind speed and direction profile (windsonic; 4m and 8 m, towards north), air pressure (1 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), soil heat flux (-0.05 and -0.1m ), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation in the south of tower, -0.05 and -0.2 m), photosynthetically active radiation (4 m, towards south), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m, Ta_8 m; RH_2 m, RH_4 m, RH_8 m) (℃ and %, respectively), wind speed (Ws_4 m, Ws_8 m) (m/s), wind direction (WD_4 m, WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_0.05m, Ts_0.2m) (℃), soil moisture (Ms_0.05m, Ms_0.2m) (%, volumetric water content), soil conductivity (Ec_0.05m, Ec_0.2m)(μs/cm), sun time(h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day and missing records were denoted by -6999.. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column.
ZHAO Changming, ZHANG Renyi
1. The data is digitized in the map of the development degree of desertification in daqintara (1958) from the drawing. The specific information of the map is as follows: * chief editor: zhu zhenda, qiu xingmin * editor: wang yimou * drawing: feng yu-sun, yao fa-fen, wu wei, wang jianhua, wang zhou-long * cartographic unit: desert laboratory, Chinese academy of sciences * publishing house: xi 'an map publishing house, unified isbn: 12461.26 二. The data is stored in ESRI Shapefile format, including the following layers: 1, * desertification development degree map (1958) : desertification1958.shp 2, * double river: river_double-shp 3, * single river: river_single-shp 4, Road: SHP 5, Lake: lake.shp 6, street: Stree. SHP 7, Railway: Railway. SHP 8, forest belt: Tree_networks 9. Residential land: residential. SHP 10. Map: map_margin.shp 三, desertification development degree figure property fields and encoding attribute: (1) desertification degree (Type) : a flow of sand (Semi - shifting Sandy Land), sand form class (Shapes), grass (Grassland), forest Land, Woodland and forest density (W_density), the cultivated Land (Farmland) (2) sand Shapes: Barchan Dunes, Flat Sandy Land, undulated Sandy Land, Vegetated Dunes (3) the grass (Grassland) (4) Woodland: Woodland. (5) woodland density (W_density): Sparse Woodlot (6) Farmland: Dryfarming and Abandoned Farmland, Irrigated Fields
WANG Jianhua, ZHU Zhenda, QIU Xingmin, YAO Fafen, FENG Yusun
The vegetation regulation mechanism project of soil water cycle in arid desert areas belongs to the national natural science foundation "environment and ecological science in western China" major research plan, led by li xinrong, a researcher of the institute of environment and engineering in dry and cold areas, Chinese academy of sciences, with the running time of 2003.1-2005.12. Remittance data of the project: 1. Dataset of observation field of shapotou railway vegetation sand fixation protection system (excel) Plant and soil information in the vegetation-sand fixation zone established in 1956, 1964, 1981 and 1987.Since the establishment of the observation field, long-term soil moisture and vegetation surveys have been conducted. This database records the soil moisture data after the neutron tube installation in August 2002, the vegetation data from 2003 to 2005 (vegetation structure, herb structure, shrub structure, etc.), and the soil physical and chemical properties data (particle size, total N,P2O5,K2O, hydrolyzed N) of the irregular surveys. 2. Physiological data set of desert plant stress (excel) From 2003 to 2005, the physiological and biochemical characteristics of typical plant communities and their dominant species in steppe desert under natural and simulated environmental conditions were analyzed.(including photosynthetic transpiration, fluorescence, biochemistry and other indicators) 3. Soil infiltration and evapotranspiration data set (excel) Precipitation infiltration process, soil water dynamics and evapotranspiration of fixed sand dunes monitored by desert artificial vegetation using TDR and Lysimeters from 2002 to 2005. 4. Data set of comprehensive survey on soil and vegetation in the southeastern margin of tengger desert (excel) In 2003-2004, silver (sichuan), yan (latour) highway, silver (sichuan) (state) highway through the tengger desert area, set up along the road of eight samples, 449 samples of soil conductivity, Ph, organic matter, total nitrogen (content) and vegetation (plants, coverage, average height, biomass, strains, coverage, high average, biomass).
LI Xinrong
Background: this data interchange is the first data interchange of the key project of "integrated study of eco-hydrological processes in heihe basin", "genomics research on drought tolerance mechanism of typical desert plants in heihe basin".The main research targets of the key projects is a typical sand desert plants are Holly, using the current international advanced a new generation of gene sequencing technology to the whole genome sequence and gene transcription of Holly group sequence decoding, so as to explore related to drought resistance gene and gene groups, and transgenic technology in model to verify their drought resistance in plants. Process and content: as genome sequencing requires special sequencing equipment, the project is huge and the process is complex (mainly including genome library construction, sequencing, data analysis and genome assembly), so it needs to be completed by a professional sequencing company.After contacting with sequencing companies, we learned that before sequencing an unknown genome, the size and complexity of the genome should be predicted, which is a necessary prerequisite for designing sequencing schemes and strategies.Therefore, in 2013, we mainly predicted the chromosome composition, genome size and complexity of sand Holly, and successfully established the extraction and purification method of its genomic DNA.The results showed that the plant was diploid, the genome was composed of 9 staining lines (18 lines of diploid), and the genome size was 1.07G.The quality test results of the genomic DNA indicated that the requirements of the obtained DNA complex sequencing have been sent to the sequencing company for library construction and sequencing, which is now in progress.In addition, in order to obtain a large number of uniform plant materials, we have discussed the induction of callus, which has been successful.Due to these reasons, we were unable to complete the genome sequencing and submit the relevant data of sand Holly in accordance with the original plan of the project this year, mainly because we did not count the predicted contents of the genome before. Data usage: the data obtained in this year on ploidy, karyotype composition and genome size of lycopodium SPP.The success of the callus induction provides a high-quality material guarantee for the subsequent transcriptome sequencing and drought-resistance mechanism research experiments, and it is also a new contribution to the cytological and physiological research of the plant.
HE Junxian, GU Lifei
From October to November 2018, we used both live traps and camera traps to collect mammal diversity and distributions along the elevational gradients at the Yarlung Zangbo Grand Canyon National Nature Reserve. Small mammal diversity and abundance were collected at 5 elevational belts range between 2600m and 3500m above sea level, with a total of 2776 live trap nights. We collected 439 individuals and 878 tissue samples of small mammals during the first field sampling. We also located 60 camera traps along elevational gradient range between 1050m and 3960m asl, and plan to collect the camera trapping data in May 2019. Small mammal data consist of richness, abundance, traits, environmental gradients etc, and could be used to model relationship between environmental gradients and traits concatenated by richness matrix. Camera trap data could inventory endangered species in the region, and provide information to identify biodiversity hotspots and conservation priorities.
LI Xueyou
From April to June 2019, we used both live traps and camera traps to collect mammal diversity and distributions along the elevational gradients at the Yarlung Zangbo Grand Canyon National Nature Reserve. We set 64 trap lines for small mammals inventory, with a total of 11456 live trap nights. We collected 1061 individuals and 2394 tissue samples of small mammals during the field sampling. We also retrived images of 60 camera traps placed between October 2018 and April 2019. We obtained 4638 pictures of wild animals and 654 captures of anthopogenic activities. The camera traps were reset in the same locations after renew batteries and memory cards. Small mammal data consist of richness, abundance, traits, environmental gradients etc, and could be used to model relationship between environmental gradients and traits concatenated by richness matrix. Camera trap data could inventory endangered species in the region, and provide information to identify biodiversity hotspots and conservation priorities.
LI Xueyou
The interaction mechanism project between major road projects and the environment in western mountainous areas belongs to the major research plan of "Environment and Ecological Science in Western China" of the National Natural Science Foundation. The person in charge is Cui Peng researcher of Chengdu Mountain Disaster and Environment Research Institute, Ministry of Water Resources, Chinese Academy of Sciences. The project runs from January 2003 to December 2005. Data collected for this project: Engineering and Environmental Centrifugal Model Test Data (word Document): Consists of six groups of centrifugal model test data, namely: Test 1. Centrifugal Model Test of Soil Cutting High Slope (6 Groups) Test 2. Centrifugal Model Experiment of Backpressure for Slope Cutting and Filling (4 Groups) Test 3. Centrifugal Model Experimental Study on Anti-slide Piles and Pile-slab Walls (10 Groups) Test 4. Centrifugal Model Tests for Different Construction Timing of Slope (5 Groups) Test 5. Migration Effect Centrifugal Model Test (11 Groups) Test 6. Centrifugal Model Test of Water Effect on Temporary Slope (8 Groups) The purpose, theoretical basis, test design, test results and other information of each test are introduced in detail.
CUI Peng
The research project on the breeding strategies of desert plants in hexi region of gansu province belongs to the national natural science foundation "environment and ecological science in western China" major research plan, led by professor an lizhe of lanzhou university. The project runs from January 2004 to December 2007. Remittance data of the project: 1. Effect of super - dry preservation on seeds The data is in Word format and contains a lot of analysis charts. A comparative study was conducted on the changes of vitality of overlord seeds and rhizoma coptidis seeds stored at 45℃, room temperature and 15℃ respectively, and the effects of dampening treatment, artificial aging and ultra-dry treatment on electrical conductivity and physiological activity indexes of seeds were conducted.The details are as follows: Energy change of seeds was preserved at 45℃ FIG. 1 germination rate (%) of overlord seeds stored at 45℃、FIG. 2 germination index of overlord seeds stored at 45℃、FIG. 3 vigor index of the seeds stored at 45℃. Change of seed vigor at room temperature FIG. 4 germination rate (%) of overlord seeds stored at room temperature、FIG. 5 germination index of overlord seeds stored at room temperature、FIG. 6 vigor index of overlord seeds preserved at room temperature. 15℃ preservation of seed vitality changes FIG. 7 germination rate of overlord seeds stored at 15℃ (%)、FIG. 8 germination index of alba seeds stored at 15℃、FIG. 9 vigor index of the seeds stored at 15℃. Changes of seed vigor of rhizoma coryzae at 45℃ FIG. 10 germination rate (%) of rhizoma coptidis seeds stored at 45℃、FIG. 11 germination index of the seeds of rhizoma coryzae at 45℃、FIG. 12 vigor index of seeds of corydalis corydalis preserved at 45℃. Changes of seed vigor of rhizoma coryzae at room temperature FIG. 13 germination rate (%) of rhizoma corydalis seeds preserved at room temperature、FIG. 14 germination index of seeds preserved at room temperature、FIG. 15 vigor index of seeds of corydalis corydalis preserved at room temperature Changes of seed vigor of rhizoma corydalis in 15℃ storage FIG. 16 germination rate (%) of rhizoma coptidis seeds stored at 15℃、FIG. 17 germination index of the seeds of rhizoma coptidis preserved at 15℃、FIG. 18 vigor index of seeds of corydalis sativus preserved at 15℃ Effect of slow wetting treatment on relative conductivity of seeds FIG. 28 changes in the relative conductivity of arrobatus seeds without dampening treatment、FIG. 29 changes of relative conductivity of overlord seeds after slow wetting treatment、FIG. 31 changes of relative electrical conductivity of seeds of rhizoma coryzae after dampening treatment Effects of artificial aging treatment on seed of archaea chinensis l FIG. 34 effects of artificial aging treatment on germination rate of overlord seeds、FIG. 35 effect of artificial aging treatment on seed vigor index、FIG. 36 effects of artificial aging treatment on the relative conductivity of overlord seeds Effects of artificial aging treatment on seeds of coryza sativa l FIG. 37 effect of artificial aging treatment on germination rate of seeds of coryza sativa l、FIG. 38 effect of artificial aging treatment on seed vigor index of rhizoma coryzae、FIG. 39 effects of artificial aging treatment on the relative electrical conductivity of the seeds of coryza sativa l Effects of artificial aging on the content of aldehydes in seeds after 15 days FIG. 52 effects of artificial aging treatment on the content of aldehydes in the seeds after 15 day、FIG. 53 effects of artificial aging treatment on the content of aldehydes in seeds of prunus chinense after 15 days, Effect of super - dry treatment on physiological activity index of seed Table 31 effect of super - dry treatment on physiological activity index of monkshood seed Table 32 influence of hyperdrying treatment on physiological activity index of seeds of coryza sativa l 2. Micromorphological and structural characteristics of the skin of desert plants (including experimental conditions, microscopic images of the skin microstructure and analysis of distribution of 47 plants, genus, species code, list of length and weight of long and short axes of seeds, and list of seed elements)
AN lizhe
In this paper, we review evidence for a major biotic turnover across the Oligocene/Miocene in the Tibetan Plateau region. Based on the recent study of six well-preserved fossil sites from the Cenozoic Lunpola and Nima basins in the central Tibetan Plateau, we report a regional changeover from tropical/subtropical ecosystems in the Late Oligocene ecosystem (26–24 Ma) to a cooler, alpine biota of the Early Miocene (23–18 Ma). The Late Oligocene fossil biota, comprising of fish (climbing perch), insects and plants (palms), shows that the hinterland of the Tibetan Plateau was a warm lowland influenced by tropical humidity from the Indian Ocean. In the Early Miocene, the regional biota became transformed, with the evolution and diversification of the endemic primitive snow carp. Early Miocene vegetation was dominated by temperate broad-leaved forest with abundant conifers and herbs under a cool climate, and mammals included the hornless rhinoceros, Plesiaceratherium, a warm temperate taxon. This dramatic ecosystem change is due to a cooling linked to the uplift of Tibetan region, from a Late Oligocene paleo-elevation of no greater than 2300 m a.s.l. in the sedimentary basin to a paleo-elevation of about 3000 m a.s.l. Another factor was the Cenozoic global climatic deterioration toward to an ice-house world.
DENG Tao
The birds along the Zhamo Highway in Medog and Bome counties are investigated by mist net method and point count method. According to the 400-meter elevation span, elevation transects were set up in the survey area. Four elevation transects are set up in the north slope from Gangcun to Galong Temple in Bome County, from low to high, and nine elevation transects are set up in the south slope from Jiefang Bridge to Galongla in Medog County. So that we can make a breakthrough understanding the formation and maintenance mechanism of bird diversity in this region. The data of bird diversity and distribution will be used to further explore the key scientific issues such as the impact of climate change on bird diversity and adaptation strategies, and the response and protection strategies of bird species diversity under the global climate change.
DONG Feng
The Grassland Degradation Assessment Dataset in agricultural and pastoral areas of the Qinghai-Tibet Plateau (QTP) is a data set based on the 500m Global Land Degradation Assessment Data (2015), which is evaluated according to the degree of grassland degradation or improvement. In this dataset, the grassland degradation of the QTP was divided into two evaluation systems. At the first level, the grassland degradation assessment was divided into 3 types, including no change type, improvement type and degradation type. At the second level, the grassland degradation assessment on the QTP was divided into 9 types, among which the type with no change was class 1, represented by 0. There were 4 types of improvement: slight improvement (3), relatively significant improvement (6), significant improvement (9) and extremely significant improvement (12). The degradation types can be divided into 4 categories: slight degradation (-3), relatively obvious degradation (-6), obvious degradation (-9) and extremely obvious degradation (-12). This dataset covers all grassland areas on the QTP with a spatial resolution of 500m and a time of 2015. The projection coordinate system is D_Krasovsky_1940_Albers. The data are stored in TIFF format, named “grassdegrad”, and the data volume is 94.76 MB. The data were saved in compressed file format, named “500 m grid data of grassland degradation assessment in agricultural and pastoral areas of the Qinghai-Tibet Plateau in 2015”. The file volume is 2.54 MB. The data can be opened by ArcGIS, QGIS, ENVI, and ERDAS software, which can provide reference for grassland ecosystem management and restoration on the QTP.
LIU Shiliang, SUN Yongxiu, LIU Yixuan
The compilation basis of frozen soil map includes: (1) frozen soil field survey, exploration and measurement data; (2) aerial photo and satellite image interpretation; (3) topo300 1km resolution ground elevation data; (4) temperature and ground temperature data. Among them, the distribution of permafrost in the Qinghai Tibet Plateau adopts the research results of nanzhuo Tong et al. (2002). Using the measured annual average ground temperature data of 76 boreholes along the Qinghai Tibet highway, regression statistical analysis is carried out to obtain the relationship between the annual average ground temperature and latitude, elevation, and based on this relationship, combined with the gtopo30 elevation data (developed under the leadership of the center for earth resources observation and science and technology, USGS) Global 1 km DEM data) to simulate the annual mean ground temperature distribution over the whole Tibetan Plateau. Taking the annual average ground temperature of 0.5 ℃ as the boundary between permafrost and seasonal permafrost, the boundary between discontinuous Permafrost on the plateau and island Permafrost on the plateau is delimited by referring to the map of ice and snow permafrost in China (1:4 million) (Shi Yafeng et al., 1988); in addition, the division map of Permafrost on the big and small Xing'an Mountains in the Northeast (Guo Dongxin et al., 1981), the distribution map of permafrost and underground ice around the Arctic (b According to rown et al. 1997) and the latest field survey data, the Permafrost Boundary in Northeast China has been revised; the Permafrost Boundary in Northwest mountains mostly uses the boundary defined in the map of ice and snow permafrost in China (1:4 million) (Shi Yafeng et al., 1988). According to the data, the area of permafrost in China is about 1.75 × 106km2, accounting for about 18.25% of China's territory. Among them, alpine permafrost is 0.29 × 106km2, accounting for about 3.03% of China's territory. For more information, please refer to the specification of "1:4 million map of glacial and frozen deserts in China" (Institute of environment and Engineering in cold and dry areas, Chinese Academy of Sciences, 2006)
WANG Tao
The dataset is the 30 meter resolution leaf area index (LAI) product from 2010 to 2019 over the Tibetan Plateau. The LAI product was retrieved using Landsat time series data and physically based radiative transfer model, and it is the annual maximum synthetic leaf area index product. When validated with the simulation data set, the root-mean-square error (RMSE) was 1.16. Leaf area index highly integrates the horizontal coverage and vertical structure of vegetation, and is an important structural parameter of the vegetation canopy, which can provide data product support for the research and applications in land surface process simulation, resources survey, ecological environment monitoring, global change research and other fields.
ZHANG Zhaoming
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
The dataset includes FPAR, GPP, NPP, evapotranspiration product and LAI product. FPAR product is and LAI product are obtained from the MODIS Terra MOD15A2H dataset, GPP and NPP product are obtained from the MOD15A2H dataset and evapotranspiration product is obtained from MODIS Terra MOD16A2 dataset from 2000 to 2019 over the Tibetan Plateau,which is downloaded from USGS, and their formats are converted from .hdf to .tif by GDAL.The quality assessment files are also included for aboved products,and they are stored in an efficient bit-encoded manner.The MODIS products play an important role in forest, agriculture, ecology.
GONG Chengjuan
1) Data content: the main ecological environment data retrieved from remote sensing in Pan third polar region, including PM2.5 concentration, forest coverage, Evi, land cover, and CO2; 2) data source and processing method: PM2.5 is from the atmospheric composition analysis group web site at Dalhousie University, and the forest coverage data is from MODIS Vegetation continuum Fields (VCF), CO2 data from ODIAC fossil fuel emission dataset, EVI data from MODIS vehicle index products, and land cover data from ESA CCI land cover. 65 pan third pole countries and regions are extracted, and others are not processed; 3) data quality description: the data time series from 2000 to 2015 is good; 4) data application achievements and prospects: it can be used for the analysis of ecological environment change.
LI Guangdong
The dataset is the MODIS Terra surface reflectance products from 2000 to 2019 over the Tibetan Plateau,each period of data contains 13 files: 7 surface reflectence files, 3 observation angle files, 2 quality control files and 1 time description file. The dataset is download from USGS and its format is converted from .hdf to .tif by GDAL.The sur_refl_qc_500m and sur_refl_state_500m layers are the quality identification documents,which are stored in an efficient bit-encoded manner.The MODIS surface reflectance play an important role in forest, water resources, climate change.
GONG Chengjuan
Ecological carrying capacity refers to the maximum population scale with a certain level of social and economic development that can be sustainably carried by the ecosystem without damaging the production capacity and functional integrity of the ecosystem, per person/square kilometer. Spatial distribution data of ecological carrying capacity were calculated based on NPP data simulated by VPM model and FAO production and trade data of agriculture, forestry and animal husbandry. Based on NPP data and combined with the land use data of cci-ci and biomass ratio parameters of various ecosystems, ANPP data was obtained to serve as ecological supply quantity. Based on agricultural, forestry and animal husbandry production and trade data and combined with population data, per capita ecological consumption standards of countries along the One Belt And One Road line were obtained, and then national scale data space was rasterized. The spatial rasterized ecological bearing data are obtained by dividing the ecological supply data with the per capita ecological consumption standard.
YAN Huiming
The data set collected long-term monitoring projects from multiple stations for atmosphere, hydrology and soil in the North Tibetan Plateau. The data set consisted of monitoring data obtained from the automatic weather station (AWS) and the atmospheric boundary layer tower (PBL) in the field. The sensors for temperature, humidity and pressure were provided by Vaisala of Finland; the sensors for wind speed and direction were provided by Met One of America, the radiation sensors were provided by APPLEY of America and EKO of Japan; the gas analyzers were provided by Licor of America; the soil water content instrument, ultrasonic anemometers and data collectors were provided by CAMPBELL of America. The observation system was maintained by professionals regularly (2-3 times a year), the sensors were calibrated and replaced, and the collected data were downloaded and reorganized. The data set was processed by forming a time continuous sequence after the raw data were quality-controlled. It met the accuracy level of the original meteorological observation data of the National Weather Service and the World Meteorological Organization (WMO). The quality control included the elimination of the missing data and the systematic error caused by the failure of the sensor.
HU Zeyong
The data is the distribution map of 100,000 deserts in Qinghai Lake Basin. This data uses 2000 TM image as the data source for interpretation, extraction and revision. Remote sensing and geographic information system technology are combined with the mapping requirements of a scale of 1: 100,000 to carry out thematic mapping of deserts, sands and gravelly Gobi. Data attribute table: area (area), perimeter (perimeter), ashm_ (sequence code), class (desert code) and ashm_id (desert code), of which the desert code is as follows: mobile sand 2341010, semi-mobile sand 2341020, semi-fixed sand 2341030, Gobi desert 2342000 and saline-alkali land 2343000.
WANG Jianhua, YAN Changzhen
In the previous project, three different types of desert investigation and observation sites in the lower reaches of Heihe River were set up. Different kinds of desert plants with the same average growth and size as the observation site were selected for the above ground biomass and underground biomass total root survey. The dry weight was the dry weight at 80 ℃, and the root shoot ratio was the dry weight ratio of the underground biomass to the aboveground biomass. Species: Elaeagnus angustifolia, red sand, black fruit wolfberry, bubble thorn, bitter beans, Peganum, Tamarix and so on.
SU Peixi
This data set includes two infrared cameras and environmental parameter data sets of three terrestrial vertebrates deployed in Qilian Mountain reserve. The equipment is deployed near Sidalong in Qilian Mountain reserve, with a time span of (2020.8-2021.10). Due to equipment maintenance and insufficient illumination, some data are discontinuous, but the data of the two equipment can complement each other and reconstruct all the information of observation points in Qilian Mountain reserve from August 2020 to October 2021. One of the two devices is equipped with an infrared camera, which collects 4994 photos, which can be matched with the above sensor photos, or the ecological factor information before and after taking photos. 1. Wild animals and temperature, humidity, light, pressure and network signal strength information in Qilian Mountain reserve. The acquisition interval is once every half an hour 2. Data source: "development of terrestrial vertebrate monitoring equipment", 2016yfc0500104, completed by: Institute of zoology, Chinese Academy of Sciences, raw data, unprocessed 3. The sensor data acquisition interval is every half an hour. The temperature accuracy is plus or minus 0.1 degrees and the humidity accuracy is plus or minus 0.5%. The photo data is divided into trigger and timing. The trigger data is generally triggered by wild animals in the field of vision of the infrared camera; the timing photo data is dynamically adjusted according to the battery power, and the acquisition interval is between 1-12 hours. 4. This data can be used to record the ambient temperature in the reserve. Combined with the infrared camera data, it can be used to analyze the activity rhythm of wild animals, coexistence analysis and distribution limiting factors.
QIAO Huijie
The data set is based on the field observation and survey along the roads in Sichuan, Qinghai and Tibet. 100 * 100m sample plots are selected along the roads, and 1m * 1m or 2m * 2m sample plots are selected according to the vegetation distribution. The survey content involves the weather, geographical location, geomorphic characteristics, slope direction, slope position, soil type, vegetation type, plant community name, surface characteristics, human activity mode and vegetation status in the sample plot. For the investigation of basic information and vegetation status of the sample plot, the methods of artificial observation and tool measurement are adopted. In the vegetation status, the vegetation name refers to "herb species in Qinghai Province", mainly investigating its height, coverage, life form and other information. The summary of the survey results of the data set can be used as a reference to supplement the herb diversity of the Qinghai Tibet Plateau. The data set is the vegetation survey content of the actual sample plot, one file per day, and the file naming method is: year + day. For example, 20200712 represents the questionnaire content on July 12, 2020, and 202007023 represents the questionnaire content on July 23, 2020.
LI Jingji
The data set is the physiological and ecological parameters of the dominant species of each ecosystem in Heihe River Basin. According to the requirements of tesim model, the data set divides Heihe River basin into seven ecosystems: deciduous broad-leaved forest ecosystem (BRD), evergreen coniferous forest ecosystem (CNF), agricultural field ecosystem (CRP), desert ecosystem (DST), meadow grassland ecosystem (MDS) Shrubbery ecosystem (SHB) and grassland ecosystem (STP). Some of the data in this data set are based on the measured data, some are obtained by reference documents, but after verification, they are applied to the Heihe River Basin. For the data in this data, each parameter of each ecosystem has three values, which are the value in the model, the minimum value and the maximum value of this parameter. The data can provide input parameters for the ecological process model, and the data set is still in further optimization.
PENG Hongchun
The dataset is the 30m resolution burned area product from 1980s to 2019 over the Tibetan Plateau. The dataset is produced using Landsat time series land surface reflectance and machine learning algorithm, and the overall accuracy is over 90%. It can provide data product support for the research and applications in fire monitoring, carbon emission studies, ecological environment monitoring, global change research and other fields.
ZHANG Zhaoming
Data content: the data set product contains the 30-meter resolution product of suspended solids concentration in the water body of the Qinghai-Tibet Plateau, which can be used as the key parameters for ecosystem-related research in Qinghai-Tibet Plateau. Data sources and processing methods: Product inversion is mainly based on the Landsat series data, by extracting the effective aquatic reflectance, to obtain the water composition information. This product is the preliminary result of extracting the concentration information of suspended solids in water using the empirical / semi-empirical method. Data quality: the overall accuracy is high, and the product will be further optimized in combination with the measured data of scientific research. Results and prospects of data application: the data set will be continuously updated and can be used for the study and analysis of ecosystem change in the Qinghai-Tibet Plateau.
LIU Huichan
On the basis of physiological and biochemical analysis of photosynthetic organs (leaves or assimilating branches) of typical desert plants in heihe river basin collected in mid-july 2011, some photosynthetic organs of desert plants were collected in mid-july 2012 and put into a liquid nitrogen tank and brought back to the laboratory for determination. Physiological analysis indexes mainly include: soluble protein unit: mg/g;Free amino acid unit: g/g;Chlorophyll content unit: mg/g;Superoxide dismutase (SOD) unit: U/g FW;Catalase (CAT) unit: U/(g•min);POD unit: U/(g•min);Proline (Pro) unit: g/g; Soluble sugar unit: g/g;Malondialdehyde (MDA) is given in moles per liter.
SU Peixi
As determined in mid-august 2013, planting species: bubbly spines (different habitats are mid-range intermountain lowland and gobi), red sand (different habitats are mid-range gobi and downstream gobi). Using the brother company of LI - 6400 Portable Photosynthesis System (Portable Photosynthesis System, LI - COR, USA) and LI - 3100 leaf area meter, etc., to the desert plant photosynthetic physiological characteristics were observed. The symbolic meaning of the observed data is as follows: Obs，observation frequency ； Photo ，net photosynthetic rate，μmol CO2•m–2•s–1； Cond stomatal conductance，mol H2O•m–2•s–1 ; Ci, Intercellular CO2 concentration, μmol CO2•mol-1； Trmmol，transpiration rate，mmol H2O•m–2•s–1； Vpdl，Vapor pressure deficit，kPa； Area，leaf area，cm2； Tair，free air temperature ，℃； Tleaf，Leaf temperature，℃； CO2R，Reference chamber CO2 concentration，μmol CO2•mol-1； CO2S，Sample chamber CO2 concentration，μmol CO2•mol-1； H2OR，Reference chamber moisture，mmol H2O•mol-1； H2OS，Sample chamber moisture，mmol H2O•mol-1； PARo，photon flux density，μmol•m–2•s–1； RH-R，Reference room air relative humidity，%； RH-S，Relative humidity of air in sample room，%； PARi，Photosynthetic effective radiation，μmol•m–2•s–1； Press，barometric pressure，kPa； Others are the state parameters of the instrument at the time of measurement.
SU Peixi
Net Primary Productivity (NPP) reflects the efficiency of plant fixation and conversion of light energy as a compound. It refers to the amount of organic matter accumulated per unit time and unit area of green plants. It is the organic matter produced by plant photosynthesis. The remainder of the Gross Primary Productivity (GPP) minus Autotrophic Respiration (RA), also known as net primary productivity. As an important part of the surface carbon cycle, NPP not only directly reflects the production capacity of vegetation communities under natural environmental conditions, but also is an important component to measure regional land use/cover change. The net primary productivity data product uses the light energy utilization (GLOPEM) model algorithm to invert multiple scale raster data products obtained from various satellite remote sensing data (Landsat, MODIS, etc.), which is also the main factor for determining and regulating ecological processes.
LIU Tie
At the end of September and the beginning of October, 2013, desert plants in typical areas of heihe basin stopped their growth period to conduct year-end ecological survey. There are altogether 8 survey and observation fields, which are: piedmont desert, piedmont gobi, middle reaches desert, middle reaches gobi, middle reaches desert, lower reaches desert, lower reaches gobi and lower reaches desert, with a size of 40m×40m. Three 20m×20m large quadrats were fixed in each observation field, named S1, S2 and S3, and regular shrub surveys were conducted.Each large quadrat was fixed with 4 5m x 5m small quadrats, named A, B, C, D, for the herbal survey.
SU Peixi
This data is digitized from the "Naiman Banner Desertification Types and Land Consolidation Zoning Map" of the drawing. The specific information of this map is as follows: * Editors: Zhu Zhenda and Qiu Xingmin * Editor: Feng Yushun * Re-photography and Mapping: Feng Yushun, Liu Yangxuan, Wen Zi Xiang, Yang Taiyun, Zhao Aifen, Wang Yimou, Li Weimin, Zhao Yanhua, Wang Jianhua * Field trips: Qiu Xingmin and Zhang Jixian * Cartographic unit: compiled by Desert Research Office of Chinese Academy of Sciences * Publishing House: Shanghai China Printing House * Scale: 1: 150000 * Published: May 1984 * Legend: Severe Desertification Land, Intensely Developed Desertification Land, Developing Desertification Land, Potential Desertification Land, Non-desertification Land, Fluctuating Sandy Loess Plain, Forest and Shrub, Saline-alkali Land, Mountain Land, Cultivated Land and Midian Land 2. File Format and Naming Data is stored in ESRI Shapefile format, including the following layers: Naiman banner desertification type map, rivers, roads, reservoirs, railways, zoning 3. Data Attributes Desertification Class Vegetation Background Class Desertified land and cultivated sand dunes under development. Midland in Saline-alkali Land Severely desertified land Reservoir Trees and shrubbery Mountain Strongly developing desertified land Potential desertified land Lakes Non-desertification land Undulating sand-loess plain 2. Projection information: Angular Unit: Degree (0.017453292519943295) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_Beijing_1954 Spheroid: Krasovsky_1940 Semimajor Axis: 6378245.000000000000000000 Semiminor Axis: 6356863.018773047300000000 Inverse Flattening: 298.300000000000010000
ZHU Zhenda, QIU Xingmin, FENG Yusun, ZHAO Yanhua, WANG Jianhua, ZHAO Aifen, WANG Yimou, LI Weimin, ZHANG Jixian, LIU Yangxuan, WEN Zixiang
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