This phenological data is based on the MOD13A2 data of the Qinghai Tibet Plateau from 2000 to 2015 (with a temporal resolution of 16 days and a spatial resolution of 1km). The NDVI curve is fitted using the segmented Gaussian function in the TIMESAT software. The spring phenology, autumn phenology and the length of the growth season are extracted using the dynamic threshold method. The thresholds of spring phenology and autumn phenology are set to 0.2 and 0.7 respectively. The phenological data were masked. Among them, the mask rules are: 1) The maximum value of NDVI must be met between June and September; 2) The average value of NDVI from June to September shall not be less than 0.2; 3) The average NDVI in winter shall not exceed 0.3.
ZU Jiaxing , ZHANG Yangjian
This data set is the global vegetation productivity data, including total primary productivity (GPP), net primary productivity (NPP) and net ecosystem productivity (NEP). It is simulated by BCC-ESM1 model in Phase 6 of the Coupling Model Comparison Plan (CMIP6) under the historical scenario. The data time range is 1850-2014, the time resolution is month, and the spatial resolution is about 2.8125 °. Analog Data Details Visible Link https://www.wdc-climate.de/ui/cmip6?input=CMIP6.CMIP.BCC.BCC -ESM1。
ZHENG Zhoutao
The feedback of the biosphere to the atmosphere is one of the core contents of global change research. When the atmospheric CO2 concentration rises, the behavior of the terrestrial ecosystem is the main uncertainty factor to predict this feedback effect. Elevated CO2 concentration (eCO2) can directly stimulate plant growth and ecosystem C absorption by increasing carboxylation and inhibiting photorespiration rate. Through the impact of CO2 fertilization effect (CFE) on photosynthesis and carbon sequestration, the terrestrial ecosystem can buffer the surge of atmospheric CO2 concentration, thereby slowing down climate change. In order to study the impact of CO2 enrichment on vegetation productivity, CO2 enrichment experiments were conducted at Naqu Grassland Station (31 ° 38 ′ 31 ″ N, 92 ° 00 ′ 54 ″ E, 4600m above sea level) in the north of the Qinghai Tibet Plateau. The test is designed in zones, with CO2 as the main treatment factor and N as the secondary treatment factor; A total of four experimental treatments span two CO2 concentration levels [ambient CO2 (aCO2), increased CO2 (eCO2):+100ppm]. Considering the low vegetation height and windy weather in the study area, octagonal open top chambers (OTCs) are used to control the carbon dioxide concentration, rather than the free FACE system. The design height of OTC is 2.5 meters, the length of each side is 1.5 meters, and each OTC occupies 7.7 square meters.
ZHANG Yangjian
Vegetation survey data is essential for the study of ecosystem structure and function. The Qinghai Tibet Plateau contains a vast grassland ecosystem, mainly including alpine meadow, alpine grassland, and alpine desertification grassland. Due to the unique geographical location and high altitude anoxic environmental conditions, the community survey data in the northern Tibetan Plateau is relatively scarce. This data set includes the aboveground biomass and coverage data of 47 sampling points on the northern Tibet transect in 2019, and the sampling time is from July to August. The sample size is 50cm × 50cm, dry weight of the plant is weighed after drying. This data set can be used for spatial analysis of productivity and calibration of models.
ZHANG Yangjian, ZHU Juntao
This dataset is global respiration data, including autotrophic respiration (ra) and heterotrophic respiration (rh). It is simulated by TaiESM1 model in Phase 6 of the Coupling Model Comparison Plan (CMIP6) under historical scenarios. The data time range is 1850-2014, the time resolution is month, and the spatial resolution is about 0.9 ° x1.25 °. Analog Data Details Visible Link https://www.wdc-climate.de/ui/cmip6?input=CMIP6.CMIP.AS -RCEC.TaiESM1.historical。
Program for Climate Model Diagnosis and Intercomparison (PCMDI)
The normalized difference vegetation index (NDVI) can accurately reflect the surface vegetation coverage. At present, NDVI time series data based on spot / vegetation and MODIS satellite remote sensing images have been widely used in the research of vegetation dynamic change monitoring, land use / cover change detection, macro vegetation cover classification and net primary productivity estimation at various scales. Evi is similar to the normalized difference vegetation index (NDVI) and can be used to quantify vegetation greenness. However, evi corrects for some atmospheric conditions and canopy background noise and is more sensitive in areas with dense vegetation. It contains an "L" value to adjust the canopy background, a "C" value as the atmospheric drag coefficient, and a value from the blue band (b). These enhancements allow the ratio between R and NIR values to be calculated exponentially while reducing background noise, atmospheric noise and saturation in most cases. This research work mainly focuses on post-processing NDVI and evi data, and gives a more reliable vegetation situation of the Qinghai Tibet Plateau in 2013 and 2018 through transformation of projection coordinate system, data fusion, maximum value synthesis method, elimination of outliers and clipping. The spatial resolution of the data is 0.05 °, and the temporal resolution is month.
YE Aizhong
The vegetation data of the Antarctic Peninsula were obtained from the Antarctic Pioneer vegetation cover classification data of the spatio-temporal three-level environmental big data platform by applying pure image element PPI to extract the end element spectra of mosses, lichens, rocks, sea and snow and applying the linear Mixture Model (LMM) to calculate them. The characteristic vegetation cover of the Fildes Peninsula was obtained based on its correlation with the linear relationship of abundance. The data format is geotiff format. The data content is the vegetation cover of the typical zone of the Antarctic Peninsula in a typical year. In this research work, tif raster format products were generated by post-processing the typical annual vegetation cover of the typical area of the Antarctic Peninsula, and the value of the main body of the raster is the vegetation cover. The vegetation cover of the Antarctic Peninsula typical area obtained in this study is a mosaic of Antarctic pioneer plant abundance data products, including the plant abundance data products in and around the Antarctic Peninsula. The typical area of the Antarctic Peninsula including Adley, north and south were mosaicked by ArcGIS to obtain six vegetation cover maps identified by spectral angle matching method (SAM) and spectral information scatter method (SID) including 2008, 2017 and 2018.
YE Aizhong
Soil moisture is an important boundary condition of earth-atmosphere exchanges, and it has been defined as an essential climate variable by GCOS. Vegetation optical depth is a physical variable to measure the attenuation of vegetation in microwave radiative transfer model, and it has been proved to be a good indicator of vegetation water content and biomass. This dataset uses the multi-channel collaborative algorithm (MCCA) to retrieve both soil moisture and polarized vegetation optical depth with SMAP brightness temperature. The algorithm uses a self-constraint relationship between land parameters and an analytical relationship between brightness temperature at different channels to perform the retrieval process. The MCCA does not depend on other auxiliary data on vegetation properties and can be applied to a variety of satellites. The soil moisture product from this dataset includes the soil moisture content in the unfrozen period and the liquid water content in the frozen period. Both horizontal- and vertical-polarization vegetation optical depth are retrieved. So far as we know, it is the first polarization-dependent vegetation optical depth product at L-band. This dataset was validated by 19 dense soil moisture observation networks (9 core validation sites used by SMAP team and 13 sites not used by them), and the widely used soil climate analysis network (SCAN). It was found that ubRMSE (unbiased root mean square error) of MCCA retrieved soil moisture is generally smaller than that of other SMAP products.
ZHAO Tianjie, PENG Zhiqing , YAO Panpan, SHI Jiancheng
The vegetation type map was created by the random forest (RF) classification approach, based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. According to vegetation characteristics, four types include alpine swamp meadow (ASM), alpine meadow (AM), alpine steppe (AS), and alpine desert (AD) were classified in this map. Based on a spatial resolution of 30 m, the map can provide more detailed vegetation information.
ZHOU Defu, ZOU Defu, ZOU Defu, Zhao Lin, ZHAO Lin, Liu Guangyue, LIU Guangyue, Du Erji, DU Erji, LI Zhibin , LI Zhibin, Wu Tonghua, WU Xiaodong, CHEN Jie CHEN Jie
The data include raw sequencing result of plant DNA in surface sediments of 33 lakes in the Qinghai-Tibetan Plateau and arid northwestern China. We used PowerMax Soil Kit of Qiagen company in Germany to extract DNA, then used universal plant primer g-h (Taberlet et et al., 2007) to amplify P6 loop of chloroplast trnL (UAA) intron in the sample. The PCR products were then sent to Fasteris company in Switzerland for the next-generation paired-end sequencing. The sequencing instrument is Illumina Nextseq 550. The data quality score (Q30) is 81.97.
LIU Xingqi, JIA Weihan
1) Data content It includes the observation year, latitude and longitude, altitude, ecosystem type and soil layer (soc0-100 (kgcm-2); 0-100 represents soil layer), underground biomass content. 2) Data sources This part of the data is obtained from the literature, specific literature sources refer to the documentation. 3) Data quality description The data cover a wide range, including comprehensive indicators, showing the content of soil organic carbon under different soil layers, with high integrity and accuracy, which can meet the estimation of soil carbon storage of grassland in Qinghai Tibet Plateau. 4) Data application achievements and Prospects It provides basic data for predicting the carbon source sink effect of soil and realizing the sustainable development of ecosystem carbon in the future.
HU Zhongmin
1) Data content It includes the observation year, longitude and latitude, ecosystem type, annual rainfall, drought index, annual net primary productivity, aboveground biomass, underground biomass and other data. 2) Data sources One part is from literature (1980-1995), the other part is from field sampling (2005-2006). 3) Data quality description The data has a long observation year, a large time span, a wide coverage, and many indicators, which has high integrity and accuracy, and can meet the estimation of grassland carbon storage in the Qinghai Tibet Plateau. 4) Data application achievements and Prospects It provides basic data for predicting the carbon source sink effect and realizing the sustainable development of ecosystem carbon in the future.
HU Zhongmin
This data set is hyperspectral observation data of typical vegetation along Sichuan Tibet Railway in September 2019, using the airborne spectrometer of Dajiang M600 resonon imaging system. Including the hyperspectral data observed in the grassland area of Lhasa in 2019, with its own latitude and longitude. The hyperspectral survey was mainly sunny. Before flight, whiteboard calibration was carried out; when data were collected, there was a target (that is, the standard reflective cloth suitable for the grass), which was used for spectral calibration; there were ground mark points (that is, letters with foam plates), and the longitude and latitude coordinates of each mark were recorded for geometric precise calibration. The DN value recorded by Hyperspectral camera of UAV can be converted into reflectivity by using Spectron Pro software. Hyperspectral data is used to extract spectral characteristics of different vegetation types, vegetation classification, inversion of vegetation coverage and so on.
ZHOU Guangsheng, JI Yuhe, LV Xiaomin, SONG Xingyang
Grassland actual net primary production (NPPa) was calculated by CASA model. CASA model was calculated with the combination of satellite-observed NDVI and climate (e.g. temperature, precipitation and radiation) as the driving factors, and other factors, such as land-use change and human harvest from plant material, were reflected by the changes of NDVI. CASA NPP was determined by two variables, absorbed photosynthetically active radiation’ (APAR) and the light-use efficiency (LUE). Grassland potential net primary production (NPPp) was calculated by TEM model. TEM is one of process-based ecosystem model, which was driven by spatially referenced information on vegetation type, climate, elevation, soils, and water availability to calculate the monthly carbon and nitrogen fluxes and pool sizes of terrestrial ecosystems. TEM can be only applied in mature and undisturbed ecosystem without take the effects of land use into consideration due to it was used to make equilibrium predications. Grassland potential aboveground biomass (AGBp) was estimated by random forest (RF) algorithm, using 345 AGB observation data in fenced grasslands and their corresponding climate data, soil data, and topographical data.
NIU Ben, ZHANG Xianzhou
Based on a large number of measured aboveground biomass data of grassland, the temperate grassland types were divided according to the vegetation type map of China in 1980s Based on the Landsat remote sensing data of engine platform, the random forest model of grassland aboveground biomass and remote sensing data was constructed for different grassland types. On the basis of reliable verification, the annual aboveground biomass of grassland from 1993 to 2019 was estimated, and the annual spatial data set of aboveground biomass of temperate grassland in Northern China from 1993 to 2019 was formed. Aboveground biomass is defined as the total amount of organic matter of vegetation living above the ground in unit area. The original grid value has been multiplied by a factor of 100, unit: 0.01 g / m2 (g / m2). This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.
ZHANG Na
Based on a large number of measured aboveground biomass data of grassland, the temperate grassland types were divided according to the vegetation type map of China in 1980s Based on the Landsat remote sensing data of engine platform, the random forest model of grassland aboveground biomass and remote sensing data was constructed for different grassland types. On the basis of reliable verification, the annual aboveground biomass of grassland from 1993 to 2019 was estimated, and the annual spatial data set of aboveground biomass of temperate grassland in Northern China from 1993 to 2019 was formed. Aboveground biomass is defined as the total amount of organic matter of vegetation living above the ground in unit area. The original grid value has been multiplied by a factor of 100, unit: 0.01 g / m2 (g / m2). This data set can provide a scientific basis for the dynamic monitoring and evaluation of temperate grassland resources and ecological environment in northern China.
ZHANG Na
1) Data content: the main ecological environment data retrieved from remote sensing in Pan third polar region, including PM2.5 concentration, forest coverage, Evi, land cover, and CO2; 2) data source and processing method: PM2.5 is from the atmospheric composition analysis group web site at Dalhousie University, and the forest coverage data is from MODIS Vegetation continuum Fields (VCF), CO2 data from ODIAC fossil fuel emission dataset, EVI data from MODIS vehicle index products, and land cover data from ESA CCI land cover. 65 pan third pole countries and regions are extracted, and others are not processed; 3) data quality description: the data time series from 2000 to 2015 is good; 4) data application achievements and prospects: it can be used for the analysis of ecological environment change.
LI Guangdong
Data set contains tree age of trees growing at different glacier moraines in the central Himalayas. The data were obtained using tree ring samples. Cores samples were collected (almost near to the ground level to estimate the minimum age of the related moraine) using an increment borer. Samples were processed by using standard dendrochronological techniques.
SIGDEL Shalik Ram, ZHNAG Hui, ZHU Haifeng, SHER Muhammad, LIANG Eryuan
Thematic data on desertification in Western Asia, includes two parts: Distribution Map of Sandy Land in Western Asia, Distribution Map of Grassland Degradation in Western Asia. The spatial resolution of the data is 30m. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences, the spatial resolution of data is 30 m. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101. The map of artificial oasis pattern in Amu river basin is based on Landsat TM and ETM image data in 2015. Firstly, with the help of eCognition software, the object-oriented classification is carried out. Secondly, the classification results are checked and corrected manually.
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 2020. 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.
WANG Xufeng
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