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.

0 2021-07-15

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.

0 2021-07-14

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of Sidaoqiao Superstation-2020)

 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.

0 2021-07-08

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (leaf area index of Sidaoqiao superstation, 2020)

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.

0 2021-07-08

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (leaf area index of Mixed forest station, 2020)

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.

0 2021-07-08

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (leaf area index of Daman Superstation, 2020)

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.

0 2021-07-08

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of Daman Superstation-2020)

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.

0 2021-07-08

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of A’rou Superstation-2020)

 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.

0 2021-07-08

30 m land cover classification product data set of Qilian Mountain Area in 2020 (V2.0)

This dataset contains land cover products in Qilian Mountain Area in 2020. The dataset was produced based on the product in 2019 using change monitoring method on the Google Earth Engine platform using Landsat series data. The overall accuracy of this product is above 85%. This is a continuation of the products from 1985-2019.

0 2021-06-25

Product data set of 30 m human activity parameters in Qilian Mountain Area in 2020 (V2.0)

This data set includes 30 m cultivated land and construction land distribution products in Qilian Mountain Area in 2020. The product comes from the land cover classification product of 30 m in Qilian Mountain Area in 2020. The land cover classification products of 30m in 2020 areproduced using change detection method based on the land cover classification product of 2019 in Google Earth engine platform with the Landsat series data . The overall accuracy of the product is better than 85%. This product is a continuation of the human activity parameter product from 1985 to 2019,which also can be downloaded from this website.

0 2021-06-25