1.Daily 0.05°×0.05° land surface soil moisture dataset of Qilian Mountain area (2019,SMHiRes,V2)

    This dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2019. The dataset was produced by utilizing the optimized wavelet-coupled-RF downscaling model (RF-OWCM) to downscale the “AMSR-E and AMSR2 TB-based SMAP Time-Expanded Daily 0.25°×0.25° Land Surface Soil Moisture Dataset in Qilian Mountain Area (SMsmapTE, V1)”. The auxiliary datasets participating in the downscaling model include GLASS Albedo/LAI/FVC, Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST – Tibetan Plateau (TRIMS LST-TP) by Ji Zhou and Lat/Lon information.

    CHAI Linna, ZHU Zhongli, LIU Shaomin

    276 6 Open Access 2021-06-02

    2.Land Surface Soil Moisture Dataset of SMAP Time-Expanded Daily 0.25°×0.25° over Qinghai-Tibet Plateau Area (SMsmapTE, V1)

    This dataset contains land surface soil moisture products with SMAP time-expanded daily 0.25°×0.25°in Qinghai-Tibet Plateau Area. The dataset was produced based on the Random Forest method by utilizing passive microwave brightness temperature along with some auxiliary datasets. The temporal resolution of the product in 1980,1985,1990,1995 and 2000 is monthly, by using SMMR, SSM/I, and SSMIS brightness temperature from 19 GHz V/H and 37 GHz V channels. The temporal resolution of the product between June 20, 2002 and Dec 30, 2018 is daily, by utilizing AMSR-E and AMSR2 brightness temperature from 6.925 GHz V/H, 10.65 GHz V/H, and 36.5 GHz V channels. The auxiliary datasets participating in the Random Forest training include the IGBP land cover type, GTOPO30 DEM, and Lat/Lon information.

    CHAI Linna, ZHU Zhongli, LIU Shaomin

    doi: 10.11888/Soil.tpdc.270948 3059 104 Open Access 2020-09-26

    3.Grading map of agricultural suitability on the Tibet Plateau (2018)

    This study takes the land resources in the Qinghai-Tibet Plateau as the evaluation object, and clarifies the current situation in the region suitable for agriculture, forestry, animal husbandry production and the quantity, quality and distribution of the reserve land resources. Through field investigations, collect relevant data from the study area, and combine relevant literature and expert experience to determine the evaluation factors (altitude, slope, annual precipitation, accumulated temperature, sunshine hours, soil effective depth, texture, erosion, vegetation type, NDVI). The grading and standardization are carried out, and the weights of each evaluation factor are determined by principal component analysis. The weighted index and model are used to determine the total score of the evaluation unit. Finally, the ArcGis natural discontinuity classification method is used to obtain the Qingshang Plateau. And the grades of farmland, forestry and grassland suitability drawings of the Qinghai-Tibet Plateau with a resolution of 90m were given. Finally, the results are verified and analyzed.

    YAO Minglei

    doi: 10.11888/Socioeco.tpdc.270483 2762 74 Open Access 2019-01-25

    4.Night light data on the Tibetan Plateau (2000, 2005, 2010)

    The data include the night light data of Tibetan Plateau with a spatial resolution of 1km*1km, a temporal resolution of 5 years and a time coverage of 2000, 2005 and 2010.The data came from Version 4 dmsp-ols products. DMSP/OLS sensors took a unique approach to collect radiation signals generated by night lights and firelight.DMSP/OLS sensors, working at night, can detect low-intensity lights emitted by urban lights, even small-scale residential areas and traffic flows, and distinguish them from dark rural backgrounds.Therefore, DMSP/OLS nighttime light images can be used as a representation of human activities and become a good data source for human activity monitoring and research.

    FANG Huajun

    1729 36 Open Access 2019-01-03

    5.Refined spatial distribution data set of GDP in hanbantota port area (2015)

    The refined spatial distribution data set of GDP in Hambantota port area is obtained by downscaling the GDP data of Sri Lanka in 2015 with 100m spatial resolution based on the land use data and POI data obtained from high-resolution remote sensing images. The land use data are obtained by interactive correction after classification of high-resolution (0.5m) satellite images of Digital Globe and the POI data is obtained through the Internet map. The functional areas are determined based on the POI data, and the weight is determined by the statistics of 100-meter scale GDP of different functional areas. Finally, under the control of regional GDP, the GDP of different functional areas is allocated according to the weight proportion, and the fine scale GDP distribution data with 30 meters spatial resolution is obtained.

    DONG Wen

    doi: 10.11888/Socioeco.tpdc.271049 70 4 Open Access 2020-12-24

    6.Dynamic downscaling simulation data set nested between global climate model and WRF model (1995-2060)

    This data set is the result of dynamic downscaling simulation of CORDEX region 8 (Central Asia) using WRF model driven by MPI-ESM-HR1.2 model data in CMIP6 plan. The data include 2m temperature (variable T2) and precipitation and precipitation was divided into convective (variable RAINC) and non-convective (variable RAINNC) precipitation. The time period includes historical test (1995-2014), near future (2021-2040) and medium future (2041-2060). The future time period includes SSP1-2.6 and SSP5-8.5. The time resolution of the simulation is once every 6 hours, the spatial resolution is 25km, the number of vertical layers is 51, a whole year in 1994 is used as spin up, the SST update is used, and the parameterized scheme combination with good performance in this area is selected. The data set can better reflect the future climate change characteristics of Central Asia and the Qinghai Tibet Plateau, and provide guidance for relevant countries to adapt to climate change.

    LUO Yong, ZHOU Jiewei, SHI Wen

    doi: 10.11888/Meteoro.tpdc.271700 110 0 Protection period 2021-09-07

    7.Paleoclimate data of a 300-m thick Oligocene strata borehole in Qujing area, Yunnan

    Qujing basin, located in the east of Yunnan Province, is a long and narrow faulted basin with a north-south trend. Thick and continuous Cenozoic sediments are preserved in the basin, which can be divided into Xiaotun Formation, Caijiachong Formation, and Ciying Formation from bottom to top. These sediments are ideal materials to explore the southeast escape and deformation affected by the India-Eurasian plate collision in the early Cenozoic and the formation and evolution history of the Indian monsoon. A total of a 320.1-meter core covering the entire Ciying Formation and the Caijiachong Formation was obtained through the continuous drilling mission carried out in the center of the basin in the previous study. The mass-specific magnetic susceptibility, anhysteretic remanent magnetization (ARM), and saturation isothermal remanent magnetization (SIRM) from parts of samples of Caijiachong core (320.1m) have been measured, and several important magnetic parameters were determined, including the high and low-frequency magnetic susceptibility (χlf), SIRM and ARM, thus providing important basic information for further mult-index climate reconstruction.

    YAN Maodu

    doi: 10.11888/Paleoenv.tpdc.271703 74 0 Protection period 2021-09-15

    8.Paleoclimate data of a 400 m-thick Paleocene strata borehole in the Xiaojinggu area, Yunnan

    Simao basin is located in the south of Yunnan Province and the southeast of Qinghai Tibet Plateau. It belongs to the Sanjiang tectonic domain in the east of Tethys tectonic domain. Thick and continuous early Cenozoic strata are preserved in the basin, so it is an ideal material to restore the tectonic evolution history of the region and the southeast side of the plateau. a continuous and complete high-resolution sequence (361.86 m in thickness) of the Mengyejing Formation was obtained through the continuous drilling in the previous study. The mass-specific magnetic susceptibility, anhysteretic remanent magnetization, and saturation isothermal remanent magnetization from parts of samples of Xiaojinggu core (250 m) have been measured, and several important magnetic parameters were determined, including the high and low-frequency magnetic susceptibility (χlf), SIRM, and ARM. These records will provide an important insight into the paleoclimate change covering the Mengyejing Formation.

    YAN Maodu

    doi: 10.11888/Paleoenv.tpdc.271704 79 0 Protection period 2021-09-15

    9.Datasets of key technologies and demonstration for vegetation restoration and reconstruction in desertification land of Amu darya valley(2019)

    The Northwest Institute of Ec-Environment and Resources of the Chinese Academy of Sciences organized a team of 9 and 5 people to carry out the research on "key technologies and demonstration for vegetation restoration and reconstruction in desertification land " from the middle and lower reaches of the Amu Darya River basin to the surrounding area of the Aral Sea from April 3, 2019 to April 30, 2019 and from September 16 to 28, 2019, respectively, and investigated the middle and lower reaches of the Amu Darya River basin to the surrounding area of the Aral Sea The site includes Tashkent, Samarkand, Navoi, Bukhara, Nukus, muinak, etc., with a total length of more than 4000 kilometers. It mainly conducts UAV low altitude remote sensing, plant community investigation, soil type, climate and soil moisture status comprehensive investigation in different degree of degradation desertification areas, and samples of plant, soil are taken. A total of 30 sample plots were investigated, and data sets of desertification degree and distribution characteristics, vegetation type and distribution, soil type and physical and chemical properties were obtained.

    LI Xinrong, HE Mingzhu, ZHAO Zhenyong

    doi: 10.11888/Soil.tpdc.270457 2651 5 Requestable 2020-01-01

    10.SRTM DEM data on the Tibetan Plateau (2012)

    This data set is mainly the SRTM terrain data obtained by International Center for Tropical Agriculture (CIAT)with the new interpolation algorithm, which better fills the data void of SRTM 90. The interpolation algorithm was adpoted from Reuter et al. (2007). SRTM's data organization method is as follows: divide a file into 24 rows (-60 to 60 degrees) and 72 columns (-180 to 180 degrees) in every 5 degrees of latitude and longitude grid, and the data resolution is 90 meters. Data usage: SRTM data are expressed as elevation values with 16-bit values (-/+/32767 m), maximum positive elevation of 9000m, and negative elevation (12000m below sea level). For null data use the -32767 standard.

    doi: 10.11888/Geogra.tpdc.270486 5275 402 Requestable 2019-01-31

    11.County level statistics data of Tibetan Plateau (1980-2015)

    The data set contains agricultural economic data of all counties and regions in the Tibetan Plateau in 1980-2015, and covering the total number of households and total population in rural areas, agricultural population, rural labor force, cultivated land, paddy field area, the dry land area, power of agricultural machinery, agricultural vehicles, mechanical ploughing area, irrigation area, consumption of chemical fertilizers electricity use, gross output value of agriculture, forestry, animal husbandry and fishery, the output of cattle, pig, sheep, meat, poultry, and fish, the sown area of grain, the output of grain, cotton, oil and all kinds of crops, and characteristic agricultural products and livestock production and other relevant data.The data came from the statistical yearbook of the provinces included in the Tibetan Plateau.The data are of good quality and can be used to analyze the socio-economic and agricultural development of qinghai-tibet plateau.

    LV Changhe

    4307 1360 Open Access 2019-02-02

    12.Cold and Arid Research Network of Lanzhou university (phenology camera observation data set of Liancheng Station, 2020)

    The data set contains the phenological camera observation data of Liancheng of the cold and arid area scientific observation network of Lanzhou University in Datong River Basin from March 1, 2020 to December 31, 2020. The longitude and latitude of the observation points are 102.737e, 36.692n and the altitude is 2903m. The data is processed by the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward manner. The shooting data resolution is 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenological period, it is necessary to calculate the relative greenness index according to the region of interest (GCC, green chromatographic coordinate formula is GCC = g / (R + G + b), and R, G and B are the pixel values of red, green and blue channels of the image), then fill in the invalid values and filter and smooth them, and finally determine the key phenological period parameters according to the growth curve fitting, such as the start date, peak End date of growing season, etc; For the coverage, firstly, the data is preprocessed, the image with less strong illumination is selected, and then the image is divided into vegetation and soil. The proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, The filtered result is the final time series coverage. This data set includes relative greenness index (GCC) and coverage. Due to the built-in clock error of phenological camera, the images before March 1 are taken at night and cannot be used, so the data is missing.

    ZHAO Changming, ZHANG Renyi

    doi: 10.11888/Ecolo.tpdc.271610 198 6 Open Access 2021-07-13

    13.Cold and Arid Research Network of Lanzhou university (Phenology camera observation data set of Minqin Station, 2020)

    The data set includes phenological camera observation data of Minqin station of Lanzhou University cold and dry area scientific observation network in Shiyang River Basin from August 11, 2020 to December 31, 2020. The longitude and latitude of observation points are 103.668e, 39.208n, and the altitude is 1020m. The data is processed by the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way with a resolution of 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenology, we need to calculate the relative greenness index (GCC, green chromic coordinate formula is GCC = g / (R + G + b), R, G and B are the pixel values of red, green and blue channels of the image) according to the region of interest, and then fill in the invalid values and filter smoothing, finally determine the key phenology parameters according to the growth curve fitting, such as the start date of the growth season, the peak value of the growth season, the peak value of the growth season End date of growing season, etc; For the coverage, firstly, the data is preprocessed, and the image with low illumination is selected. Then, the image is divided into vegetation and soil, and the proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the extraction of time series data, the original coverage data is smoothed and filtered according to the time window specified by the user, The filtered result is the final time series coverage. This dataset includes relative greenness index (GCC). The phenological camera will be installed on August 11, 2020.

    ZHAO Changming, ZHANG Renyi

    doi: 10.11888/Ecolo.tpdc.271592 273 6 Open Access 2021-07-06

    14.Cold and arid research network of Lanzhou university (phenology camera observation data set of Sidalong Station, 2020)

    The data set includes phenological camera observation data of Sidalong station of Lanzhou University cold and dry area scientific observation network in Heihe River Basin from February 3, 2020 to December 31, 2020. The longitude and latitude of observation points are 99.926e, 38.428n, and the altitude is 3146m. The data is processed by the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way with a resolution of 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenology, we need to calculate the relative greenness index (GCC, green chromic coordinate formula is GCC = g / (R + G + b), R, G and B are the pixel values of red, green and blue channels of the image) according to the region of interest, and then fill in the invalid values and filter smoothing, finally determine the key phenology parameters according to the growth curve fitting, such as the start date of the growth season, the peak value of the growth season, the peak value of the growth season End date of growing season, etc; For the coverage, firstly, the data is preprocessed, and the image with low illumination is selected. Then, the image is divided into vegetation and soil, and the proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the extraction of time series data, the original coverage data is smoothed and filtered according to the time window specified by the user, The filtered result is the final time series coverage. This dataset includes relative greenness index (GCC). Photos before February 3 cannot be used due to equipment failure.

    ZHAO Changming, ZHANG Renyi

    doi: 10.11888/Ecolo.tpdc.null 172 6 Open Access 2021-07-06

    15.Cold and Arid Research Network of Lanzhou university (Phenology camera observation data set of Xiyinghe Station, 2020)

    The data set includes the phenological camera observation data of xiyinghe station of Lanzhou University cold and dry area scientific observation network in Shiyang River Basin from January 1, 2020 to December 31, 2020. The longitude and latitude of the observation points are 101.855e, 37.561n, and the altitude is 3616m. The data is processed by the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way with a resolution of 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenology, we need to calculate the relative greenness index (GCC, green chromic coordinate formula is GCC = g / (R + G + b), R, G and B are the pixel values of red, green and blue channels of the image) according to the region of interest, and then fill in the invalid values and filter smoothing, finally determine the key phenology parameters according to the growth curve fitting, such as the start date of the growth season, the peak value of the growth season, the peak value of the growth season End date of growing season, etc; For the coverage, firstly, the data is preprocessed, and the image with low illumination is selected. Then, the image is divided into vegetation and soil, and the proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the extraction of time series data, the original coverage data is smoothed and filtered according to the time window specified by the user, The filtered result is the final time series coverage. This data set includes the relative greenness index (GCC).

    ZHAO Changming, ZHANG Renyi

    doi: 10.11888/Ecolo.tpdc.271591 176 6 Open Access 2021-07-06

    16.Cold and arid research network of Lanzhou university (phenology camera observation data set of Guazhou Station, 2020)

    The data set includes phenological camera observation data of Guazhou station of Lanzhou University cold and dry area scientific observation network in Shule River Basin from March 10, 2020 to December 31, 2020. The longitude and latitude of observation points are 95.673e, 41.405n, and the altitude is 2014m. The data is processed by the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way with a resolution of 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenology, we need to calculate the relative greenness index (GCC, green chromic coordinate formula is GCC = g / (R + G + b), R, G and B are the pixel values of red, green and blue channels of the image) according to the region of interest, and then fill in the invalid values and filter smoothing, finally determine the key phenology parameters according to the growth curve fitting, such as the start date of the growth season, the peak value of the growth season, the peak value of the growth season End date of growing season, etc; For the coverage, firstly, the data is preprocessed, and the image with low illumination is selected. Then, the image is divided into vegetation and soil, and the proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the extraction of time series data, the original coverage data is smoothed and filtered according to the time window specified by the user, The filtered result is the final time series coverage. This dataset includes relative greenness index (GCC). The data before March 10 has been covered due to the memory card reaching the upper limit; Adjust camera orientation on May 31.

    ZHAO Changming, ZHANG Renyi

    doi: 10.11888/Ecolo.tpdc.271609 170 6 Open Access 2021-07-06

    17.Cold and Arid Research Network of Lanzhou university (Phenology camera observation data set of Suganhu Station, 2020)

    The data set includes the phenological camera observation data of suganhu station of Lanzhou University cold and dry area scientific observation network in haertang River Basin of Qaidam Basin from January 1, 2020 to December 31, 2020. The longitude and latitude of the observation points are 94.125 ° e, 38.992n, and the altitude is 2798m. The data is processed by the software package independently developed by Beijing Normal University. The phenological camera collects data in a downward way with a resolution of 2592 * 1944, and the shooting time and frequency can be specified. For the calculation of greenness index phenology, we need to calculate the relative greenness index (GCC, green chromic coordinate formula is GCC = g / (R + G + b), R, G and B are the pixel values of red, green and blue channels of the image) according to the region of interest, and then fill in the invalid values and filter smoothing, finally determine the key phenology parameters according to the growth curve fitting, such as the start date of the growth season, the peak value of the growth season, the peak value of the growth season End date of growing season, etc; For the coverage, firstly, the data is preprocessed, and the image with low illumination is selected. Then, the image is divided into vegetation and soil, and the proportion of vegetation pixels in the calculation area of each image is calculated as the corresponding coverage of the image. After the extraction of time series data, the original coverage data is smoothed and filtered according to the time window specified by the user, The filtered result is the final time series coverage. This data set includes the relative greenness index (GCC).

    ZHAO Changming, ZHANG Renyi

    doi: 10.11888/Ecolo.tpdc.271590 190 6 Open Access 2021-07-06

    18.Camera-trapping survey of the mammal diversity in the Qilian Mountain:Camera-trapping data of Lanzhou university in the Sidalong of Qilian Mountain (2020)

    This dataset contains infrared camera data from January 2020 to October 2020 for the Sidalong sample area in the Qilian Mountains region of Lanzhou University. The typical habitats in the sample area of Teradalong are forests, the main tree species are Qilian round cypress and Qinghai spruce, and the typical mammals are red deer, musk deer, roe deer and blue eared-pheasant.. The main steps of infrared camera data processing include. 1. data storage, setting up directories to store photos and video files on computers, mobile hard disks or other storage media. 2. Processing of mistaken or invalid photos. Delete wind-blown, exposure, no animal presence or arbitrary form of invalid photos. 3. species identification. (1) Animal identification image library construction, each survey unit to establish a library of animal identification images, the library is mainly used for the training of species identification personnel, to facilitate their rapid grasp of species identification characteristics, accurate identification of species. (2) Processing of effective photos: for photos (videos) that can accurately identify species, fill in the name, number and environmental information of the animals in the automatic camera (video) recording form; if there are two or more animals on a photo, fill in one line each; for photos that cannot accurately identify species, fill in the column of the name of the animal that cannot be identified, and fill in the number and environmental information, and fill in the photo processing For poultry and livestock, fill in the name and number of animals and poultry and livestock; for people, fill in the name of the animal as "herder, tourist, forest ranger", etc. (3) other information: environmental information records, according to the photos (video), fill in the following environmental information: temperature: according to the temperature shown on the photos to fill in. Weather: sunny, cloudy, rain, snow. Need to judge carefully. Snow: with or without. Behavior: foraging, drinking, hunting, mating, fighting, fighting for food, repelling, playing, running, resting, walking, alerting, etc. Animal age: young, subspecies, female, male, unknown. Published observation data include: file number, file format, folder number, camera number, deployment point number, shooting date, shooting time, working days (days), element, species name, young, sub, female, male, unknown, total, behavior, temperature (℃), weather, snow.

    ZHANG Lixun

    doi: 10.11888/Ecolo.tpdc.271614 66 0 Protection period 2021-07-14

    19.Camera-trapping survey of the mammal diversity in the Qilian Mountain:Camera-trapping data of Lanzhou university in the Haxi of Qilian Mountain (2019-2020)

    This dataset contains infrared camera data from July 2019 to October 2020 for the Haxi sample area in the Qilian Mountains region of Lanzhou University. The typical habitat in the Haxi sample area is forest, the main tree species are Qilian round cypress and Qinghai spruce, and the typical mammals are red deer, musk deer, roe deer and blue eared-pheasant.. The area is heavily grazed and has frequent human activities. The main steps of infrared camera data processing include. 1. data storage, setting up directories to store photos and video files on computers, mobile hard disks or other storage media. 2. Processing of mistaken or invalid photos. Delete wind-blown, exposure, no animal presence or arbitrary form of invalid photos. 3. species identification. (1) Animal identification image library construction, each survey unit to establish a library of animal identification images, the library is mainly used for the training of species identification personnel, to facilitate their rapid grasp of species identification characteristics, accurate identification of species. (2) Processing of effective photos: for photos (videos) that can accurately identify species, fill in the name, number and environmental information of the animals in the automatic camera (video) recording form; if there are two or more animals on a photo, fill in one line each; for photos that cannot accurately identify species, fill in the column of the name of the animal that cannot be identified, and fill in the number and environmental information, and fill in the photo processing For poultry and livestock, fill in the name and number of animals and poultry and livestock; for people, fill in the name of the animal as "herder, tourist, forest ranger", etc. (3) other information: environmental information records, according to the photos (video), fill in the following environmental information: temperature: according to the temperature shown on the photos to fill in. Weather: sunny, cloudy, rain, snow. Need to judge carefully. Snow: with or without. Behavior: foraging, drinking, hunting, mating, fighting, fighting for food, repelling, playing, running, resting, walking, alerting, etc. Animal age: young, subspecies, female, male, unknown. Published observation data include: file number, file format, folder number, camera number, deployment point number, shooting date, shooting time, working days (days), element, species name, young, sub, female, male, unknown, total, behavior, temperature (℃), weather, snow.

    ZHANG Lixun

    doi: 10.11888/Ecolo.tpdc.271613 58 0 Protection period 2021-07-14

    20.Camera-trapping survey of the mammal diversity in the Qilian Mountain:Camera-trapping data of Lanzhou university in the Qifeng of Qilian Mountain (2019-2020)

    This dataset contains infrared camera data from January 2020 to November 2020 from Qifeng sample area in Qilian Mountains region of Lanzhou University. It belongs to the Sunan Yugu Autonomous County, Zhangye City, Gansu Province, in the northwest of the Sunan Yugu Autonomous County, the western part of the Western Corridor and the northern foot of the Qilian Mountains, east of Daxiang, south of Qilian County and Tianjun County, Qinghai Province, west of Subei County, Jiuquan City, and north of Jiuquan Suzhou District, Jiayuguan City and Yumen City. The typical habitats in the Qifeng sample area are desert and alpine bare rock, and typical mammals include snow leopard, lynx, white-lipped deer and blue sheep. The main steps of infrared camera data processing include. 1. data storage, setting up directories to store photos and video files on computers, mobile hard disks or other storage media. 2. Processing of mistaken or invalid photos. Delete wind-blown, exposure, no animal presence or arbitrary form of invalid photos. 3. species identification. (1) Animal identification image library construction, each survey unit to establish a library of animal identification images, the library is mainly used for the training of species identification personnel, to facilitate their rapid grasp of species identification characteristics, accurate identification of species. (2) Processing of effective photos: for photos (videos) that can accurately identify species, fill in the name, number and environmental information of the animals in the automatic camera (video) recording form; if there are two or more animals on a photo, fill in one line each; for photos that cannot accurately identify species, fill in the column of the name of the animal that cannot be identified, and fill in the number and environmental information, and fill in the photo processing For poultry and livestock, fill in the name and number of animals and poultry and livestock; for people, fill in the name of the animal as "herder, tourist, forest ranger", etc. (3) other information: environmental information records, according to the photos (video), fill in the following environmental information: temperature: according to the temperature shown on the photos to fill in. Weather: sunny, cloudy, rain, snow. Need to judge carefully. Snow: with or without. Behavior: foraging, drinking, hunting, mating, fighting, fighting for food, repelling, playing, running, resting, walking, alerting, etc. Animal age: young, subspecies, female, male, unknown. Published observation data include: file number, file format, folder number, camera number, deployment point number, shooting date, shooting time, working days (days), element, species name, young, sub, female, male, unknown, total, behavior, temperature (℃), weather, snow.

    ZHANG Lixun

    doi: 10.11888/Ecolo.tpdc.271612 53 0 Protection period 2021-07-14