The data set mainly includes observation data of each tree in the super site, and the observation time is from June 2, 2008 to June 10, 2008. The super site is set around the Dayekou Guantan Forest Station. Since the size of the super site is 100m×100m, in order to facilitate the forest structure parameter survey, the super site is divided into 16 sub-sample sites, and tally forest measurement is performed in units of sub-samples. The tally forest measurement factors include: diameter, tree height, height under branch, crown width in transversal slope direction, crown width in up and down slope direction, and tindividual tree growth status. The measuring instruments are mainly: tape, diameter scale, laser altimeter, ultrasonic altimeter, range pole and compass. The data set also records the center point latitude and longitude coordinates of 16 sub-samples (measured by Z-MAX DGPS). The data set can be used for verification of remote sensing forest structure parameter extraction algorithm. The data set, together with other observation data of the super site, can be used for reconstruction of forest 3D scenes, establishment of active and passive remote sensing mechanism models, and simulation of remote sensing images,etc.
CHEN Erxue, BAI Lina, WANG Bengyu, TIAN Xin, LIU Qingwang, CAO Bin, Yang Yongtian, Zhihai Gao, Bingxiang Tan, GUO Zhifeng, WANG Xinyun, FU Anmin, ZHANG Zhiyu, NI Wenjian, WANG Qiang, BAO Yunfei, WANG Dianzhong, ZHANG Yang, ZHAO Liqiong, LIANG Dashuang, WANG Shunli, ZHAO Ming, LEI Jun, NIU Yun, LUO Longfa
The main contents of this data set are forest, shrub and grassland sample plot survey data.The fixed samples are located in the drainage ditch valley of qilian mountain and the dayaokou valley where the hydrology observation and test site of the water source conservation forest research institute of gansu province is located. The information of the sample is as follows: Number elevation quadrat size longitude latitude surface type G1 2715 20 × 20 100 ° 17 '12 "38 ° 33' 29" qinghai spruce forest G2 2800 20×36 100°17 '07 "38°33' 27" moss spruce forest G3 2840 20×20 100°17 '37 "38°33' 05" moss spruce forest G4 2952 20 × 20 100 ° 17 '59 "38 ° 32' 47" qinghai spruce forest G5 3015 20 × 20 100 ° 18 '06 "38 ° 32' 42" qinghai spruce forest G6 3100 20 × 20 100 ° 18 '13 "38 ° 32' 31" thicket qinghai spruce forest G7 3300 23.5 × 20 thickets qinghai spruce forest G8 2800 20×20 100°13 '30 "38°33' 29" moss spruce forest B1 2700 12.8×25 moss spruce forest B2 2800 20×20 100°17 '38 "38°32' 59" moss spruce forest B3 2900 20×20 100°17 '59 "38°32' 51" grass spruce forest B4 3028 20×20 100°17 '59 "38°32' 39" moss spruce forest B5 3097 20×20 100°18 '02 "38°32' 32" moss spruce forest B6 3195 20 × 20 100 ° 18 '06 "38 ° 32' 25" qinghai spruce forest B7 2762 20 × 20 100 ° 17 '08 "38 ° 33' 21" qinghai spruce forest B8 2730 20×20 100°17 '06 "38°33' 27" moss spruce forest GM1 3690 5×5 100°18 '02 "38°32' 02" caragana scrub (middle) GM2 3690 5×5 100°18 '02 "38°32' 02" caragana scrub (rare) GM3 3700 5×5 100°18 '03 "38°32' 03" caragana + jilaliu shrub (dense) GM4 3600 5×5 100°18 '10 "38°32' 06" caragana + jila willow thicket (middle) GM5 3600 5×5 100°18 '10 "38°32' 06" caragana + jila willow shrub (sparse) GM6 3600 5×5 100°18 '10 "38°32' 06" caragana + jila willow thicket (dense) GM7 3500 5×5 100°18 '14 "38°32' 08" caragana + jila willow thicket (middle) GM8 3500 5×5 100°18 '14 "38°32' 08" caragana + jila willow thicket (dense) GM9 3500 5×5 100°18 '14 "38°32' 08" caragana + jila willow thicket (rare) GM10 3400 5×5 100°18 '18 "38°32' 12" golden pheasant scrub (rare) GM11 3400 5×5 100°18 '18 "38°32' 12" golden pheasant + golden raspberry shrub (dense) GM12 3400 5×5 100°18 '18 "38°32' 12" golden pheasant scrub (rare) GM13 3300 5 × 5 100 ° 18 '21 "38 ° 32' 21" giraliu thicket GM14 3300 5 × 5 100 ° 18 '21 "38 ° 32' 21" caragana + jila shrub GM15 3300 5 × 5 100 ° 18 '21 "38 ° 32' 21" caragana + jila shrub YC3 2700 1×1 100°17 '14 "38°33' 33" needle thatch field YC4 2750 1×1 100°17 '18 "38°33' 32" needle thatch field YC5 2800 1×1 100°17 '21 "38°33' 33" needle thatch field YC6 2850 1×1 100°17 '25 "38°33' 33" needle thatch field YC7 2900 1×1 100°17 '31 "38°33' 32" aster + needle thatch field YC8 2950 1×1 100°17 '44 "38°33' 23" needle thatch field YC9 2980 1×1 100°17 '48 "38°33' 25" needle thatch field The sample geodesic tree data were surveyed from July to August 2007.The survey included: 1. Basic survey of sample plots in drainage ditch basin: A) sample land setting: sample land number, elevation, slope direction, slope position, slope, soil layer thickness, sample land size, longitude and latitude, community type, soil type, operation status, age B) survey of each wood in the sample plots: sample plot number, tree number, tree species, tree classification, chest diameter, tree height, undershoot height, crown radius 2. Soil profile survey record sheet Including forest/vegetation status, major tree species, forest age, soil name, surface soil erosion, parent rock and material, drainage conditions, land use history, soil profile (soil layer, moisture, color, texture, structure, root system, gravel content) 3. Standard ground cover factor Standard land area, dominant tree species, stand/vegetation origin, elevation, slope direction, slope position, slope, cutting and utilization method, afforestation land preparation type, survey method, canopy coverage, living ground cover, dead cover cover, litter thickness (undivided strata, semi-decomposed layer, decomposed layer) 4. Canopy survey: 5. Draft quadrat (1m×1m) survey record sheet Including species name, number, coverage, average height 6. Results of determination of soil physical properties in source forest of qilian mountain (land sample survey) Contains the soil physical properties measurement process (+ wet mud weight aluminum box, aluminum box, soil moisture content, suddenly bulk density, etc.), bringing biomass measurement (total fresh weight of shrub and herb, fresh weight of sample, sample dry weight, etc.), litter dry weight (including mosses) layer and the largest capacity calculation process (of moss and litter thickness, total fresh weight, fresh weight of samples, the dry weight of the sample, soaking for 24 h after heavy, maximum water holding capacity, the largest water depth, the biggest hold water rate, maximum moisture capacity) 7. Bush sample survey: Including species name, number, coverage, average height 8. Standard sample land setting and questionnaire for each wooden inspection ruler Including tree species, tree classification, age, chest diameter, number of height, undershoot height, crown radius 9. Litter layer survey record sheet Including litter (decomposed layer, semi-decomposed layer, decomposed layer) thickness 10. Update survey records: Including tree species, natural regeneration (height <30cm, height 31-50cm, height >51cm), artificial regeneration (height <30cm, height 31-50cm, height >51cm) This data set can provide ground measured data for remote sensing inversion of forest structure parameters.
WANG Shunli, LUO Longfa, WANG Rongxin, CHE Zongxi, JING Wenmao
The forest hydrology experimental area of Heihe River integrated remote sensing experiment includes the dense observation area of Dayekou basin and the dense observation area of Pailugou basin. Due to the concentrated distribution of the fixed sample plots in the drainage ditch basin, these sample plots lack of representativeness to the forest of the whole dayokou basin, so in June 2008, 43 temporary forest sample plots were set up in the whole dayokou basin. The data set is the ground observation data of the 43 temporary plots. In addition to the measurement and recording of stand status and site factors, Lai was also observed. The instruments used to measure each wood in the sample plot are mainly tape, DBH, flower pole, tree measuring instrument and compass. The DBH, tree height, height under branch, crown width in cross slope direction, crown width along slope direction and single tree growth were measured for each tree. WGS84 latitude and longitude coordinates of the center point of the sample plot were measured with different hand-held GPS, and the positioning error was about 5-30m. Other observation factors include: Forest Farm, slope direction, slope position, slope, soil thickness, canopy density, etc. The implementation time of these temporary sample plots is from 2 to 30 June 2008. The data set can provide ground data for the development of remote sensing inversion algorithm of forest structure parameters.
LING Feilong, HE Qisheng, ZHANG Xuelong, WANG Shunli, ZHAO Ming, LEI Jun, NIU Yun, LUO Longfa, CHEN Erxue
The data is a fisheye photo above the interception barrel of the Picea crassifolia plot in the Tianlaochi small watershed of Qilian Mountain. The plot has a latitude and longitude of 38.44N, 99.91E, and an altitude of 2793m. Photo DSC_0008——DSC_0097 corresponds to Fisheye photos above interception barrels 1 to 90 respectively. The camera is directly above the interception barrel and the lens is 1m above the ground. It is used to estimate the cover or LAI of Qinghai spruce forest, and the pictures are processed with Gap Light Analyzer software.
ZHAO Chuanyan, MA Wenying
These are the meteorological, soil, vegetation and other data observed by the Gongga Mountain Forest Ecosystem Test Station on the eastern margin of the Tibetan plateau, primarily from 2005 to 2008. Meteorological data: temperature, air pressure, relative humidity, dew point temperature, water pressure, ground temperature, soil temperature (5 cm, 10 cm, 20 cm, and 40 cm), 10-minute average wind, 10-minute maximum wind speed, precipitation, total radiation, net radiation. Tree layer biological observation data: diameter at breast height, tree height, life form Shrub layer biological observation data: tree number, height, coverage, life form, aboveground biomass, underground biomass Herb layer biological observation data: tree (strain) number, average height, coverage, life type, aboveground biomass, underground biomass Leaf area index: tree layer leaf area index, shrub layer leaf area index, grass layer leaf area index Soil organic matter and nutrients: soil organic matter, total nitrogen, total phosphorus, total potassium, nitrate nitrogen, ammonium nitrogen, available nitrogen (alkali-hydrolysable nitrogen), available phosphorus, available potassium, slowly available potassium, PH value in aqueous solution Soil water content: depth, water content
WANG Xiaodan
This data set is the transcriptome data of Tibetan pigs, which are the control group and the experimental group respectively. There are three individuals in the control group without any treatment. The experimental group is also three individuals. They are attacked with FMDV at the concentration of ID50. All samples are the transcriptome sequence results of the spleen samples of Tibetan pigs. Both the experiment and sample collection were carried out in Lanzhou. The numbers are z1-z6, and each data is divided into R1 and R2, indicating the results of double headed sequencing. Z1-Z3 is the individual results of the control group, and z4-z6 is the individual results of the experimental group. By comparing and analyzing the data of the control group and the experimental group, we can find out the response of the immune system in vivo when FMDV attacks the body of Tibetan pigs, find the immune genes and immune pathways that are activated when FMDV is attacked, find the related genes and pathways for the ability of resistance of Tibetan pigs to FMDV, and increase the immunity to FMDV in the future breeding process of domestic pigs Epidemic ability provides theoretical basis.
DUAN Ziyuan
In order to describe the distribution pattern of the genetic diversity of the main domesticated animals in the Qinghai Tibet Plateau and its surrounding areas, and to clarify the related genetic background. In 2019, we extracted total DNA from 21 local chicken tissue samples collected in Pakistan and Thailand, built a database and re sequenced the genome. Sequencing produced a batch of 140g genome re sequencing raw data. To provide basic data for the study of the adaptation of domestic animals to the extreme environment of the Qinghai Tibet Plateau, to explore the historical events of domestication, migration and expansion of the main domesticated animals in the region, and to further explore the adaptation mechanism of domesticated animals to the poor environment such as hypoxia, high cold and dry.
LI Yan
The Pan Third Pole is sensitive to global climate change, its warming rate is more than twice of the global rate, and it is affected by the synergy of westerlies and monsoons. How to respond to climate change will have a profound impact on regional ecological security. However, the estimation of NPP by current products is still uncertain. For this reason, this product combines multi-source remote sensing data, including AVHRR NDVI, MODIS reflectivity data, a variety of climate variables (temperature, precipitation, radiation, VPD) and a large number of field measured data, and uses machine learning algorithm to retrieve the net primary production capacity of Pan third polar ecosystem.
WANG Tao
The data set is based on the GPP simulated by 16 dynamic global vegetation models (TRENDY v8) under S2 Scenario (CO2+Climate) and represents the gross primary productivity of the ecosystem. Data was derived from Le Qu é r é Et al. (2019). The range of source data is global, and the Qinghai Tibet plateau region is selected in this data set. Original data is interpolated into 0.5*0.5 degree by the nearest neighbor method in space, and the original monthly scale is maintained in time. The data set is the standard model output data, which is often used to evaluate the temporal and spatial patterns of gross primary productivity, and compared with other remote sensing observations, flux observations and other data.
STEPHEN Sitch
In order to study the population evolution history and local adaptive genetic mechanism of main domesticated equine animals in Qinghai Tibet Plateau and its surrounding areas, and to establish the corresponding germplasm genetic resource bank. We have sequenced 236 horse samples collected in Qinghai Province, Tibet Autonomous Region and Xinjiang Autonomous Region by the end of 2018, including Tibetan horse, Tibetan donkey, plain domestic donkey and Jiama plain local breed. Seventy five samples (including 73 donkey samples and two horse samples) were sequenced for mitochondrial genome and D-loop sequencing. A number of genomic data were generated by sequencing, which provided data for tracing the domestication, migration, expansion and other historical events of horse domesticated animals in this area, and further exploring the adaptation mechanism of equine animals to the harsh environment such as hypoxia, high temperature and dryness.
LI Yan
Forest change (including forest loss and gain) is a complex ecological process influenced by natural and human activities, and has important impacts on global material cycles and energy flows. Based on long-term tree-canopy cover (TCC) data, the Bi-temporal class-probabilities model was used to detect forest changes, and a dataset of forest change of the Natural Forest Conversion Program area in northeast China from 1986 to 2018 was obtained (spatial resolution 30 meters with a temporal resolution of 1 year). The method of stratified random sampling was used to select 1000 points in the reserve and visual interpretation was carried out to evaluate the accuracy of forest change. The results show that the accuracy of forest loss (producer's accuracy = 85.21%; user's accuracy = 84.26%) and forest restoration (producer's accuracy = 87.74%; user's accuracy = 88.31%) are both high, which can effectively reflect the forest change status of the protected area.
WANG Jianbang , HE Zhuoyu , WANG Chunling , FENG Min, PANG Yong, YU Tao , LI Xin
In order to describe the distribution pattern of the genetic diversity of the main domesticated animals in the Qinghai Tibet Plateau and its surrounding areas, and to clarify the related genetic background. In 2019, we selected two breeds of pigs in Yunnan Province as low altitude reference, collected RNA tissue samples from the brain of two breeds of pigs, extracted total RNA, built a database and sequenced the transcriptome. Sequencing produced a batch of 322g transcriptome sequencing raw data. To provide basic data for the study of the adaptation of domestic animals to the extreme environment of the Qinghai Tibet Plateau, to explore the historical events of domestication, migration and expansion of the main domesticated animals in the region, and to further explore the adaptation mechanism of domesticated animals to the poor environment such as hypoxia, high cold and dry.
PENG Minsheng
In order to study the population evolution history and local adaptive genetic mechanism of the main domesticated equine animals in the Qinghai Tibet Plateau and its surrounding areas, and to establish the corresponding germplasm genetic resource bank. We sequenced the whole genome of 100 horse species collected in Qinghai Province, Tibet Autonomous Region and Xinjiang Autonomous Region, including Tibetan horses, Tibetan donkeys, Pingyuan donkeys and local breeds of Jiama plain. A lot of genomic data were generated by sequencing, which provided data for tracing the historical events of domestication, migration and expansion of the main domesticated equine animals in this area, and further exploring the adaptation mechanism of equine animals to the poor environment such as hypoxia, high cold and dry.
LI Yan
Forest is an important terrestrial ecosystem, accounting for about one-third of the total land area. It plays an important role in regulating climate, providing habitat for species, and maintaining global ecosystem balance. The dynamic change of the tree-canopy cover will affect the structure, composition, and function of the forest ecosystem. Landsat data were used to derive the 30-m tree-canopy cover dataset based on the machine learning method. The dataset of the rate of tree-canopy cover change in the Eastern Himalayas from 1990 to 2020 was generated using the annual tree-canopy cover data. The results show that the average tree-canopy cover in this region had increased from 40.67% (1990) to 43.43% (2020), an increase of 2.76%, indicating that the forests in the Eastern Himalayas improved in the past few decades.
WANG Chunling , WANG Jianbang , HE Zhuoyu , FENG Min
The data includes: zooplankton species list; zooplankton density; microscopy; high-throughput sequencing; complete data; constructing an original data set for lakes on the Qinghai-Tibet Plateau. Zooplankton is an indispensable link in lake water ecological investigation, and it is a link between the system The location of the food web is an important carrier for the material circulation and energy flow of the food web. The systematic investigation and study of the composition and biodiversity of the zooplankton in the lakes on the Qinghai-Tibet Plateau is particularly important for understanding the stability and resilience of the lake ecosystem on the Qinghai-Tibet Plateau. In addition, Zooplankton are very sensitive to environmental changes, and changes in their structure and functional groups can indicate the intensity and magnitude of environmental pressure.
LI Yun
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.
Climatic warming alters the onset, duration and cessation of the vegetative season. While prior studies have shown a tight link between thermal conditions and leaf phenology, less is known about the impacts of phenological changes on tree growth. Here, we assessed the relationships between the start of the thermal growing season (TSOS) and tree growth across the extratropical Northern Hemisphere using 3451 tree-ring chronologies and daily climatic data for 1948-2014. An earlier TSOS promoted growth in regions with high ratios of precipitation to temperature but limited growth in cold dry regions. Path analyses indicated that an earlier TSOS enhanced growth primarily by alleviating thermal limitations on wood formation in boreal forests and by lengthening the period of growth in temperate and Mediterranean forests. Semi-arid and dry subalpine forests, however, did not benefit from an earlier onset of growth and a longer growing season, presumably due to associated water loss and/or more frequent early spring frosts. These broadly relevant patterns of how climatic impacts on wood phenology affect tree growth at regional to hemispheric scales, enhance our understanding of how future phenological changes may affect the carbon sequestration capacity of extra-tropical forest ecosystems.
LIANG Eryuan, GAO Shan
This data set includes the PM2.5 mass concentration of atmospheric aerosol particles at Southeast Tibet station, Ali station, mostag station, Everest station and Namuco station (unit: mm) μ g/m3）。 Aerosol PM2.5 fine particles refer to particles with aerodynamic equivalent diameter less than or equal to 2.5 microns in the ambient air. It can be suspended in the air for a long time, which has an important impact on air quality and visibility. The higher its concentration in the air, the more serious the air pollution. The concentration characteristic data of PM2.5 is output at the frequency of obtaining a set of data every 5 minutes, which can realize the analysis of aerosol mass concentration at different time scales such as hour, day and night, season and interannual, which provides the analysis of changes and influencing factors of aerosol mass concentration at different locations in the Qinghai Tibet plateau at different time scales, as well as the evaluation of local air quality, It provides important data support. This data is an update of the published data set of PM2.5 concentration of aerosol particles at different stations on the Qinghai Tibet Plateau (2018 and 2019).
WU Guangjian
We develop a DBH recording meter that can automatically record DBH at high frequencies and high precision in the field by designing a high-precision displacement sensor and temperature compensation algorithm. With the developed software, tree growth dynamics can be evaluated online in real time through remote computers or smartphones。The data set is collected through field test and demonstration at Qilian mountain station in Gansu Province and Beijing forest station by using the DBH recording meter. The data table includes the control values measured manually and the measured values of VI (displacement), RI (tree perimeter) and CI (tree diameter) collected by different tree species at different stations. The development of this automatic DBH recording meter promote the automation, intelligent level and independent innovation of vegetation ecological monitoring in China. The dynamic changes of DBH of trees serve the national ecosystem monitoring network, the construction of national "two screens and three belts" ecological security barrier and the demand for large-scale, all-weather and three-dimensional monitoring of vulnerable ecological areas. It plays an important supporting role in promoting the construction of ecological civilization in China.
ZHANG Lin, GAO Liyao, WU Dongxiu
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.
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