This data is digitized according to the 1:1,000,000 Vegetation Atlas of China. The 60 maps in the atlas are digitized one by one (polygon attribute), then projected, matched and spliced. Finally, vegetation attributes are assigned to each polygon. The vegetation attributes include: vege_id (vegetation group number), new number, vegetation group and sub group, vegetation Type number, vegetation type, vegetation type group number, vegetation type group, vegetation category, and corresponding attribute information in English. The 1:1,000,000 Vegetation Atlas of China was edited by academician Hou Xueyu, a famous vegetation ecologist, and jointly compiled by more than 250 experts from relevant research institutes of the Chinese Academy of Sciences, relevant ministries and commissions, relevant departments of various provinces and regions, colleges and universities and other 53 units. It was officially published by Science Press in 2001 and publicly distributed at home and abroad. This atlas is another summative achievement of vegetation ecology workers in China for more than 40 years after the publication of "Chinese vegetation" and other monographs. It is the basic map of national natural resources and natural conditions. It reflects in detail the distribution of vegetation units of 11 vegetation type groups, 796 formations and sub formations of 54 vegetation types, horizontal and vertical zonal distribution laws, and also reflects the actual distribution of more than 2000 dominant species of plants, major crops and cash crops in China, as well as the close relationship between dominant species and soil and ground geology. Because this atlas is a kind of realistic vegetation map, it reflects the quality of vegetation in China. This atlas is in quarto format, 280 pages, including 60 vegetation type maps of 1:1,000,000 in China, 1 topography of China at a scale of 1:10,000,000, 1 vegetation map of China and 1 vegetation zoning map of China, with Chinese and English legend. This atlas is the basic map of national natural resources and natural geographical characteristics, and it is the essential scientific data and important basis for the study of global environmental change, biodiversity, environmental protection and monitoring. Vegetation map is the specific expression of existing vegetation spatial distribution on the map. One millionth of China's vegetation map is the most detailed and accurate vegetation map in China so far. The data collection time is 2011-2012, it can serve students and researchers engaged in vegetation ecology research. This data is limited to the internal exchange of the Institute. Alberts projection is adopted for the map, and its parameters are as follows: · coordinate system: geodetic coordinate system · projection: Alberts positive axis equal area double standard weft conic projection · South standard weft: 25 ° n · North standard weft: 47 ° n · central longitude: 105 ° e · coordinate origin: intersection of 105 ° E and the equator · latitudinal migration: 0 · meridional migration: 0
The data include NDVI data of Tibetan Plateau region, with spatial resolution 1000m, time resolution 16d, and time coverage in 2000, 2005, 2010 and 2015.The data source is MOD13A2(C6).NDVI is a kind of vegetation index formed by combining visible light and near-infrared bands of satellites according to the spectral characteristics of vegetation.NDVI is a simple, effective and empirical measure of surface vegetation.The data is of great significance for analyzing the ecological environment of Tibetan Plateau.
PML_V2 terrestrial evapotranspiration and total primary productivity dataset, including gross primary product (GPP), vegetation transpiration (Ec), soil evaporation (Es), vaporization of intercepted rainfall , Ei) and water body, ice and snow evaporation (ET_water), a total of 5 elements. The data format is tiff, the space-time resolution is 8 days, 0.05°, and the time span is 2002.07-2019.08. Based on the Penman-Monteith-Leuning (PML) model, PML_V2 is coupled to the GPP process based on stomatal conductance theory. GPP and ET mutually restrict and restrict each other, which makes PML_V2 in ET simulation accuracy, which is greatly improved compared with the previous model. The parameters of PML_V2 are divided into different vegetation types and are determined on 95 vorticity-related flux stations around the world. The parameters were then migrated globally according to the MODIS MCD12Q2.006 IGBP classification. PML_V2 uses GLDAS 2.1 meteorological drive and MODIS leaf area index (LAI), reflectivity (Albedo), emissivity (Emissivity) as inputs, and finally obtains PML_V2 terrestrial evapotranspiration and total primary productivity data sets.
1:100000 vegetation map of Heihe River Basin, the regional scope is subject to the Heihe river boundary of Huangwei Committee, the area is about 14.29 × 104km2, the data format is GIS vector format, this version is version 3.0. The data is mainly based on ground observation data, integrated with all kinds of remote sensing data, 1:1 million vegetation map, climate, terrain, landform, soil data mapping, and compiled by cross validation. The classification standard, legend unit and system of vegetation map of the people's Republic of China (1:1000000), 2007 are adopted, including vegetation type group, vegetation type, formation and sub formation. The new version mainly unifies the codes of the new formation (74 codes in total, distinguishing the formation and the sub formation). 9 vegetation type groups, 22 vegetation types and 74 formations (sub formations) in version 2.0 were changed into 9 vegetation type groups, 22 vegetation types and 67 formations (7 sub formations).
The vegetation coverage data of theChina-Mongolia-Russia Economic Corridor is based on the Landsat TM data. The dataset includes three-year vegetation coverage data（ 1990, 2000, and 2005）. The Normalized Difference Vegetation Index (NDVI) is extracted first and then converted into vegetation coverage. Since the image is greatly affected by the cloud, this data is replaced by the same period image of the nearby year. The land cover of the corridor is mainly grassland (steppe). Seasonal and one-time precipitation have a great impact on grassland growth. Therefore, there are still different time splicing problems in this data. After that we will propose a solution to the problem and share the new version of the data. For example, based on a large amount of remote sensing data (multi-temporal phase), the maximum synthesis method is used to extract vegetation coverage.
The checklist and distribution database of alpine subnival plants mainly includes the collection information and identification information of alpine subnival plants. Between them, the collection information document includes species name, genus name, family name, habitat, altitude, longitude and latitude, collector and collection time; while the identification information document includes species name, genus name, family name, determinavit and identification time. The collected information in the database comes from the first-hand data in the field, while the identification information comes from the identification results of famous botany experts in the world. The quality of data in database is high. It can not only be used in the study of flora and regionalization, but also lay a solid foundation for the study of plant diversity, ecosystem and global climate change response.
This data set is provided by the author of the paper: Huang, R., Zhu, H.F., Liang, E.Y., Liu, B., Shi, J.F., Zhang, R.B., Yuan, Y.J., & Grießinger, J. (2019). A tree ring-based winter temperature reconstruction for the southeastern Tibetan Plateau since 1340 CE. Climate Dynamics, 53(5-6), 3221-3233. In this paper, in order to understand the past few hundred years of winter temperature change history and its driving factors, the researcher of Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences and CAS Center for Excellence in Tibetan Plateau Earth Sciences. Prof. Eryuan Liang and his research team, reconstructed the minimum winter (November – February) temperature since 1340 A.D. on southeastern Tibetan Plateau based on the tree-ring samples taken from 2007-2016. The data set contains minimum winter temperature reconstruction data of Changdu on the southeastern TP during 1340-2007. See attachments for data details: A tree ring-based winter temperature reconstruction for the southeasternTibetan Plateau since 1340 CE.pdf
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.
Vegetation functional type (PFT) is a combination of large plant species according to the ecosystem function and resource utilization mode of plant species. Each planting functional type shares similar plant attributes, which simplifies the diversity of plant species into the diversity of plant function and structure.The concept of vegetation-functional has been advocated by ecologists especially ecosystem modelers.The basic assumption is that globally important ecosystem dynamics can be expressed and simulated through limited vegetative functional types.At present, vegetation-functional model has been widely used in biogeographic model, biogeochemical model, land surface process model and global dynamic vegetation model. For example, the land surface process model of the national center for atmospheric research (NCAR) in the United States has changed the original land cover information into the applied vegetation-functional map (Bonan et al., 2002).Functional vegetation has been used in the dynamic global vegetation model (DGVM) to predict the changes of ecosystem structure and function under the global change scenario. 1. Functional classification system of vegetation 1 Needleleaf evergreen tree, temperate 2 Needleleaf evergreen tree, boreal 3 Needleleaf deciduous tree 4 Broadleaf evergreen tree, tropical 5 Broadleaf evergreen tree, temperate 6 Broadleaf deciduous tree, tropical 7 Broadleaf deciduous tree, temperate 8 Broadleaf deciduous tree, boreal 9 Broadleaf evergreen shrub, temperate 10 Broadleaf deciduous shrub, temperate 11 Broadleaf deciduous shrub, boreal 12 C3 grass, arctic 13 C3 grass 14 C4 grass 15 Crop 16 Permanent wetlands 17 Urban and built-up lands 18 Snow and ice 19 Barren or sparsely vegetated lands 20 Bodies of water 2. Drawing method China's 1km vegetation function map is based on the climate rules of land cover and vegetation function conversion proposed by Bonan et al. (Bonan et al., 2002).Ran et al., 2012).MICLCover land cover map is a blend of 1:100000 data of land use in China in 2000, the Chinese atlas (1:10 00000) the type of vegetation, China 1:100000 glacier map, China 1:10 00000 marshes and MODIS land cover 2001 products (MOD12Q1) released the latest land cover data, using IGBP land cover classification system.The evaluation shows that it may be the most accurate land cover map on the scale of 1km in China.Climate data is China's atmospheric driven data with spatial resolution of 0.1 and temporal resolution of 3 hours from 1981 to 2008 developed by he jie et al. (2010).The data incorporates Princeton land-surface model driven data (Sheffield et al., 2006), gewex-srb radiation data (Pinker et al., 2003), TRMM 3B42 and APHRODITE precipitation data, and observations from 740 meteorological stations and stations under the China meteorological administration.According to the evaluation results of RanYouhua et al. (2010), GLC2000 has a relatively high accuracy in the current global land cover data set, and there is no mixed forest in its classification system. Therefore, the mixed forest in the MICLCover land cover diagram USES GLC2000 (Bartholome and Belward, 2005).The information in xu wenting et al., 2005) was replaced.The data can be used in land surface process model and other related researches.
The dataset contains phenological camera observation data collected at the Arou Superstation in the midstream of the Heihe integrated observatory network from June 13 to November 16, 2018. The instrument was developed with data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures high-quality data with a resolution of 1280×720 by looking-downward. The calculation of the greenness index and phenology are following 3 steps: (1) calculate the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) according to the region of interest, (2) perform gap-filling for the invalid values, filtering and smoothing, and (3) determine the key phenological parameters according to the growth curve fitting (such as the growth season start date, Peak, growth season end, etc.) There are also 3 steps for coverage data processing: (1) select images with less intense illumination, (2) divide the image into vegetation and soil, and (3) calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (GCC), phenological phase and fractional cover (FC). Please refer to Liu et al. (2018) for sites information in the Citation section.