This vegetation water content data set is derived from the ground synchronous observation in the Luanhe River Basin soil moisture remote sensing experiment, including 55 sampled plots.The vegetation types involved in these sampled plots include grass, corn, potatoes, naked oats and carrots. The data measurement time is from September 13, 2018 to September 26, 2018.
ZHENG Xingming, JIANG Tao
This dataset contains the monthly/yearly surface shortwave band albedo, fraction of absorbed photosynthetically active radiation (fPAR), leaf area index (LAI), vegetation continuous fields (tree cover and non-tree vegetation cover, VCF), land surface temperature (LST), net radiation (RN), evapotranspiration (ET), aboveground autotrophic respiration (RA-ag), belowground autotrophic respiration (RA-bg), gross primary production (GPP) and net primary production (NPP) in China from 2001 to 2018. The spatial resolution are 0.1 degree. Moreover, the dataset also includes these 11 ecosystem variables under climate-driven scenario (i.e., under no human disturbance). So, it can show the relative influences of climate change and human activities on land ecosystem in China during the 21st century.
CHEN Yongzhe, FENG Xiaoming, TIAN Hanqin, WU Xutong, GAO Zhen, FENG Yu, PIAO Shilong, LV Nan, PAN Naiqing, FU Bojie
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
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
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
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
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from phenology camera observation data of the Alpine meadow and grassland ecosystem Superstation from January 1 in 2020 to December 31 in 2020. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The phenology camera adopts a vertical downward method to collect data, with the resolution of 2592*1944. Phenology photos in this data set were taken at 12:10 a day, which has a time error of ±10 min. The image is named as BSDCJZ BEIJING_IR_Year_Month_Day_Time.
LI Xiaoyan
This dataset contains the LAI measurements from the Sidaoqiao in the downstream of the Heihe integrated observatory network from July 25 to October 20 in 2020. The site was located in Ejina Banner in Inner Mongolia Autonomous Region. The elevation is 870 m. There are 1 observation samples, around Sidaoqiao superstation (101.1374E, 42.0012N), which is about 30m×30m in size. Five sub-canopy nodes and one above-canopy node are arranged in each sample. The data is obtained from LAINet measurements; the four-steps are performed to obtain LAI: the raw data is light quantum (level 0); the daily LAI can be obtained using the software LAInet (level 1); further the invalid and null values are screened and using the 7 days moving averaged method to obtain the processed LAI (level 2); for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.
LIU Shaomin, QU Yonghua, CHE Tao, XU Ziwei, REN Zhiguo
This dataset contains the LAI measurements from the Sidaoqiao in the downstream of the Heihe integrated observatory network from July 26 to October 20 in 2020. The site was located in Ejina Banner in Inner Mongolia Autonomous Region. The elevation is 870 m. There are 1 observation samples, around Mixed forest station (101.1335E, 41.9903N), which is about 30 m×30 m in size. Five sub-canopy nodes and one above-canopy node are arranged in each sample. The data is obtained from LAINet measurements; the four-steps are performed to obtain LAI: the raw data is light quantum (level 0); the daily LAI can be obtained using the software LAInet (level 1); further the invalid and null values are screened and using the 7 days moving averaged method to obtain the processed LAI (level 2); for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.
LIU Shaomin, QU Yonghua, CHE Tao, XU Ziwei, REN Zhiguo
The dataset contains the phenological camera observation data of the Sidaoqiao Superstation in the downstream reaches of Heihe integrated observatory network before May 31 and after September 2, 2020. Due to the power failure of phenological camera, the time is lost from May 31 to September 2, 2020. In addition, after the camera was moved and reinstalled, the object in the field of view before May 31 and after September 2 would change, which may cause the inconsistency of the data before and after. The site (101.137° E, 42.001° N) was located on a tamarix (Tamarix chinensis Lour.) surface in the Sidaoqiao, Dalaihubu Town, Ejin Banner, Inner Mongolia Autonomous Region. The elevation is 873 m. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the phenology, firstly, The phenological index needs to be calculated according to the region of interest. Such as, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)). Then, controlling the quality of data, filling the invalid value and filtering smoothing are performed. Finally, the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, peak, growth season end, etc. This data set includes the relative greenness index (GCC) in 2020. Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin, QU Yonghua, CHE Tao, XU Ziwei, REN Zhiguo
This dataset contains phenological camera observation data of the A’rou Superstation in the upperstream reaches of Heihe integrated observatory network from January 1 to December 31 in 2020. The site (100.372° E, 38.856° N) was located in the Daban Village, near Qilian County in Qinghai Province. The elevation is 3033 m.The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the phenology, firstly, The phenological index needs to be calculated according to the region of interest. Such as, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)). Then, controlling the quality of data, filling the invalid value and filtering smoothing are performed. Finally, the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, peak, growth season end, etc. This data set includes the relative greenness index (GCC) in 2020. Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin, QU Yonghua, CHE Tao, XU Ziwei, ZHANG Yang
The dataset contains the phenological camera observation data of the Daman Superstation in the midstream of Heihe integrated observatory network from January 1, 2020 to December 31, 2020. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the phenology, firstly, the phenological index needs to be calculated according to the region of interest. Such as, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)). Then, controlling the quality of data, filling the invalid value and filtering smoothing are performed. Finally, the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, peak, growth season end, etc. This data set includes the relative greenness index (GCC) in 2020. Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin, QU Yonghua, CHE Tao, XU Ziwei, TAN Junlei, REN Zhiguo
This dataset contains the LAI measurements from the Daman superstation in the middle reaches of the Heihe integrated observatory network from June 1 to September 20 in 2020. The site (100.376° E, 38.853°N) was located in the maize surface, near Zhangye city in Gansu Province. The elevation is 1556 m. There are 6 observation samples, each of which is about 30m×30m in size, and the latitude and longitude are (100.376°E, 38.853°N), (100.377° E, 38.858°N), (100.374°E, 38.855°N), (100.374°E, 38.858°N), (100.371°E, 38.854°N), (100.369°E, 38.854°N). Five sub-canopy nodes and one above-canopy node are arranged in each sample. The data is obtained from LAINet measurements; the four-steps are performed to obtain LAI: the raw data is light quantum (level 0); the daily LAI can be obtained using the software LAInet (level 1); further the invalid and null values are screened and using the 7 days moving averaged method to obtain the processed LAI (level 2); for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.
LIU Shaomin, QU Yonghua, CHE Tao, XU Ziwei, TAN Junlei, REN Zhiguo
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
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
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
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
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
This data set includes the monthly synthesis of 30m*30m surface LAI products in Qilian mountain area in 2019. Max value composition (MVC) method was used to synthesize monthly NPP products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun, ZHONG Bo
This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2019. Max value composition (MVC) method was used to synthesize monthly NDVI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun, ZHONG Bo
