The vegetation biomass data of the North Tibet transect(2017)

Vegetation survey data is essential to study the structure and function of the ecosystems. The North Tibet is abundant in grassland ecosystems, including alpine meadow, alpine grassland, and alpine degraded grassland. Due to the unique geographical location, high altitude and anoxic environment, the community survey data in the North Tibetan Plateau is relatively rare. Based on the accumulation of preliminary work, the research team carried out a more comprehensive vegetation survey in 15 counties of the North Tibetan Plateau in the growing season of 2017. This data set includes biomass data inside and outside the fences of the 23 sampling plots from Nagqu to Ritu of the North Tibet Transect. This data set can be used for productivity spatial analysis and mode calibration.

0 2020-10-14

Water vapor absorption and utilization data set of desert plants in Heihe River Basin (2012-2014)

All data in this data set are original data, including meteorological and soil moisture content, stem sap flow, water potential of plant tissue, isotope characteristics of atmospheric and humidified water vapor, fluorescence tracer image, plant photosynthetic fluorescence, and basic data of five desert plants, Tamarix chinensis, Haloxylon ammodendron, Bawang, Nitraria tangutorum and red sand, which are related to field and indoor control experiments Because of the data of expression regulation. 1. Isotopic data of Tamarix chinensis. After humidifying for 1 hour, 2 hours and 3 hours, the tissue samples of indoor and outdoor plants of plexiglass were collected at the same time. The samples were put forward and processed by low-temperature vacuum distillation glass water extraction system, and then used euro The isotopic data were measured by ea3000 element analyzer and isoprime gas stability mass spectrometer. Tamarix Tamarix samples were collected from Sitan village, Jingtai County, including humidification and control samples. The variation data of isotopic composition can be used to determine the way and amount of water vapor absorbed by plant leaves. 2. Fluorescence section photo data: all the data in this data set are original data, including the structural photos under high-power microscope of Tamarix, Haloxylon ammodendron, Nitraria, Bawang, Hongsha and other desert plant leaves in Sitan village of Jingtai County and Ejin Banner. The specific method is as follows: apply fluorescent dye to the surface of desert plant leaves before humidification, collect plant leaves and stems after humidification for 1 hour, 2 hours and 3 hours, put them in liquid nitrogen, take them back to the laboratory, observe and take photos with fluorescence microscope. It can be used to analyze the tissue and organs of water absorption by desert plant leaves and the direction and path of water migration in plants. 3: Gene transcription and expression data: transcription and expression data of Tamarix chinensis, data collection time: May 25, 2014, location: Sitan village, Jingtai County, Gansu Province, data analysis platform: lllumina hisep TM 2000 platform, obtained by transcriptome analysis of baimaike company. 4. Photosynthetic and fluorescence data: photosynthetic and fluorescence parameters measured by photosynthetic apparatus in the field (Sitan village and Ejin Banner, Jingtai County). 5. Sap flow and environmental data: all data are original data. Sap flow data of desert plants measured by stem flow meter, including Tamarix chinensis, Haloxylon ammodendron, Nitraria tangutorum, red sand and other desert plants (Sitan village, Jingtai County and Ejin Banner), and environmental data monitored by automatic weather station, including temperature and humidity.

0 2020-10-13

Soil observation and leaf area index and aboveground biomass of maize sampling points in Yingke Daman area of Heihe River Basin (2012)

The experimental data of Yingke Daman in Heihe River Basin is supported by the key fund project of Heihe River plan, "eco hydrological effect of agricultural water saving in Heihe River Basin and multi-scale water use efficiency evaluation". Including: soil bulk density, soil water content, soil texture, corn sample biomass, cross-section flow, etc Data Description: 1. Sampling location of Lai and aboveground biomass: Yingke irrigation district; sampling time: May 2012 to September 2012; Lai and aboveground biomass of maize were measured by canopy analyzer (lp-80), and aboveground biomass was measured by sampling drying method; sample number: 16. 2. Soil texture: Sampling location: Yingke irrigation district and Shiqiao Wudou Er Nongqu farmland in Yingke irrigation district; soil sampling depth is 140 cm, sampling levels are 0-20 cm every 10 cm, 20-80 cm every 20 cm, 80-140 cm every 30 cm; sampling time: 2012; measurement method: laboratory laser particle size analyzer; sample number: 38. 3. Soil bulk density: Sampling location: Yingke irrigation district and Daman irrigation district; sampling depth of soil bulk density is 100 cm, sampling levels are 0-50 cm and 50-100 cm respectively; sampling time: 2012; measurement method: ring knife method; number of sample points: 34. 4. Soil moisture content: this data is part of the monitoring content of hydrological elements in Yingke irrigation district. The specific sampling location is: Shiqiao Wudou Er Nongqu farmland in Yingke Irrigation District, planting corn for seed production; soil moisture sampling depth is 140 cm, sampling levels are 0-20 cm every 10 cm, 20-80 cm every 20 cm, 80-140 cm every 30 cm Methods: soil drying method and TDR measurement; sample number: 17. 5. Cross section flow: Sampling location: the farmland of Wudou Er Nong canal in Shiqiao, Yingke irrigation district; measure the flow velocity, water level and water temperature of different canal system sections during each irrigation, record the time and calculated flow, monitor once every 3 hours until the end of irrigation; sampling time: 2012.5-2012.9; measurement method: Doppler ultrasonic flow velocity meter (hoh-l-01, Measurement times: Yingke irrigation data of four times.

0 2020-10-13

Source region of Yellow River - land cover and vegetation type ground verification point dataset

The dataset is the ground verification point dataset of land cover and vegetation type in the Source Region of Yellow River (in the north of Zaling Lake, Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.

0 2020-10-13

Hoh Xil - land cover and vegetation type ground verification point dataset

The dataset is the ground verification point dataset of land cover and vegetation type in the Hoh Xil (in the northwest of Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.

0 2020-10-13

Source region of the Yangtze River - land cover and vegetation type ground verification point dataset

The dataset is the ground verification point dataset of land cover and vegetation type in the Source Region of the Yangtze River (in the south of Qinghai Province) which collected during August 2018. In the dataset, the homogeneous patches are considered as the main targets of this collection. They are easy to be recognized out and distinguished from other vegetation types. And these samples have high representativeness comparing with other land surface features. In each sample, the geographical references, longitude and latitude (degree, minute, second), time (24h) and elevation (0.1m) are recorded firstly according to GPS positioning. Vegetation types, constructive species, characteristics, land types and features, landmarks, etc. are recorded into the property table manually for checking in laboratory. At last, each sample place has been taken at least 1 photography. In this dataset, 90% or more samples have been taken 2 or more in field landscape photographs for land use type and vegetation classification examination. We have carefully examined the position accuracy of each sample in Google Earth. After 2 rounds of checking and examination, the accuracy and reliability of the property of each sample have been guaranteed.

0 2020-10-13

MODIS NDVI based phenology for the Three-River-Source National Park from 2001 to 2018

This dataset is land surface phenology estimated from 16 days composite MODIS NDVI product (MOD13Q1 collection6) in the Three-River-Source National Park from 2001 to 2018. The spatial resolution is 250m. The variables include Start of Season (SOS) and End of Season (EOS). Two phenology estimating methods were used to MOD13Q1, polynomial fitting based threshold method and double logistic function based inflection method. There are 4 folders in the dataset. CJYYQ_phen is data folder for source region of the Yangtze River in the national park. HHYYQ_phen is data folder for source region of Yellow River in the national park. LCJYYQ_phen is data folder for source region of Lancang River in the national park. SJY_phen is data folder for the whole Three-River-Source region. Data format is geotif. Arcmap or Python+GDAL are recommended to open and process the data.

0 2020-10-13

GF-1 NDVI dataset in Maduo County (2016)

This is the vegetation index (NDVI) for Maduo County in July, August and September of 2016. It is obtained through calculation based on the multispectral data of GF-1. The spatial resolution is 16 m. The GF-1 data are processed by mosaicking, projection coordinating, data subsetting and other methods. The maximum synthesis is then conducted every month in July, August, and September.

0 2020-10-13

Long term vegetation index dataset of the Yellow River upstream – Spot vegetation (1998-2011)

I. Overview The long-term sequence China Vegetation Index dataset is mainly for the normalized vegetation index (NDVI), based on four bands synthesized every 10 days from 1 April 1998 to 31 December 2011 with a spatial resolution of 1 km. Spectral reflectance and 10-day maximized NDVI dataset. Ⅱ. Data processing description The VEGETATION sensor was launched by SPOT-4 in March 1998, and has received SP0T VGT data for global vegetation coverage observation since April 1998. It has a very complete and efficient image ground processing mechanism system. The VEGETATION data is mainly received by the Kiruna ground station in Sweden. The image quality monitoring center in Toulouse, France is responsible for image quality and provides related parameters (such as calibration coefficients). Finally, the image processing and archiving center of VITO Institute in Belgium Global VEGETATION data archiving and user orders. Among them, VGT-P (prototype) data products mainly provide scientific researchers with high-quality physical quantity prototype data in order to facilitate their research and development of algorithms and application models. The data undergoes strict systematic error correction and resampling into a longitude and latitude network projection, the pixel resolution is lkm, and the pixel brightness value is the reflectivity of the ground features on the top layer of the atmosphere. In addition to providing four bands of raw data, relevant auxiliary parameters such as atmospheric conditions, system information (solar zenith angle, azimuth, field of view, and reception time) and terrain data are also provided according to user needs. VGT-S (synthesis) products provide atmospheric-corrected surface reflectance data, and use multi-band synthesis techniques to obtain a normalized vegetation index (w) data set with lkm resolution. VGI-S products include the spectral reflectance and NDVI data set (s1) of four bands synthesized daily, the spectral reflectance of four bands synthesized every 10 days, and the maximum NDVI data set (S10) every 10 days to reduce cloud and The impact of BRDF, while S10 was also resampled into 4km resolution (S10.4) and 8km resolution (S10.8) datasets. VGT-S products are widely used for their high time resolution. This data set contains the spectral reflectance of four bands synthesized every 10 days and the 10-day maximized NDVI data set (S10). The pre-processing of SPOT source data includes atmospheric correction, radiation correction, and geometric correction. NDVI data with a maximum of 10 days of synthesis is generated, and the values ​​of -1 to -0.1 are set to -0.1, and then formula YDN = (JNDVI +0.1) /0.004 Convert to a YDN value from 0 to 250. Ⅲ. Data content description The long-term sequence China Vegetation Index dataset is mainly for the normalized vegetation index (NDVI), based on four bands synthesized every 10 days from 1 April 1998 to 31 December 2011 with a spatial resolution of 1 km. Spectral reflectance and 10-day maximized NDVI dataset. The SPOT-VEGETATION-NDVI data set contains .zip compressed files with time resolution from April 1, 1998 to December 31, 2011. After decompression, it is an ESRI-GRID file with a scene every 10 days. The SPO-VEGETATION-NDVI data set naming rules are: v-yymmdd, where v is the abbreviation of vegetation, yymmdd represents the date of the file, and is the main identifier that distinguishes other files. Ⅳ. Data usage description An important feature of the Vegetation Index product is that it can be converted into leaf crown biophysical parameters. Vegetation index (VI) also plays an "intermediate variable" in the acquisition of vegetation biophysical parameters (such as foliar index LAI, green shade, fAPAR, etc.). The relationship between vegetation indices and vegetation biophysical parameters is currently being studied using globally representative ground, aircraft and satellite observation datasets. These data can be used to evaluate the performance of the VI algorithm before satellite launch, and also provide the conversion coefficient between the vegetation index product and the biophysical characteristics of the leaf crown. The use of biophysical data is part of the Vegetation Index Verification Program. Vegetation index products will play a major role in several Earth Observation System (EOS) studies and are also part of global and regional biosphere model products in recent years.

0 2020-10-12

Long term vegetation SPOT vegetation index dataset of the QinghaiLake River Basin (1998-2008)

The VEGETATION sensor sponsored by the European Commission was launched by SPOT-4 in March 1998. Since April 1998, SPOTVGT data for global vegetation coverage observation has been received by Kiruna ground station in Sweden. The image quality monitoring center in Toulouse, France is responsible for image quality and provides relevant parameters (such as calibration coefficient number). Finally, the Belgian flemish institute for technological research (Vito)VEGETATION processing Centre (CTIV) is responsible for preprocessing into global data of 1km per day. Pretreatment includes atmospheric correction, radiation correction, geometric correction, production of 10 days to maximize the synthesized NDVI data, setting the value of -1 to -0.1 to -0.1, and then converting to the DN value of 0-250 through the formula DN= (NDVI+0.1)/0.004. The dataset is a long-time series vegetation index dataset of Qinghai Lake Basin, which is mainly aimed at normalized difference vegetation index (NDVI). It includes spectral reflectance of four bands synthesized every 10 days from 1998 to 2008 and maximum NDVI for 10 days, with a spatial resolution of 1km and a temporal resolution of 10 days.

0 2020-10-10