1:100000 desert distribution dataset of Shule river basin (2000)

Shule River Basin is one of the three inland river basins in Hexi corridor. In recent years, with the obvious change of climate and the aggravation of human activities, the shortage of water resources and the problem of ecological environment in Shule River Basin have become increasingly prominent. It is of great significance to study the runoff change of Shule River Basin in the future climate situation for making rational water resources planning and ecological environment protection. The Shule River basin boundary is cut from "China's 1:100000 desert sand data set". Taking the 2000 TM image as the data source, it interprets, extracts, revises, and uses remote sensing and geographic information system technology to combine with the 1:100000 scale mapping requirements to carry out thematic mapping of desert, sand and gravel gobi. Data attribute table: Area (area), perimeter (perimeter), ash_ (sequence code), class (desert code), ash_id (desert code). The desert code is as follows: mobile sand 2341010, semi mobile sand 2341020, semi fixed sand 2341030, Gobi 2342000, salt alkali land 2343000. Collect and sort out the basic, meteorological, topographical and geomorphic data of Shule River Basin, and provide data support for the management of Shule River Basin.

0 2020-09-15

1:1 million wetland data of Heilongjiang Province (2000)

The data is tailored from "China's 1:1 million wetland data". "China's 1:1 million wetland data" mainly reflects the national wetland information in the 2000's, which is expressed by the decimal system of geographical coordinates. The main contents include: types of wetland, water supply types of wetland, soil types, main vegetation types, geographical areas, etc. The information classification and coding standard of China sustainable development information sharing system has been implemented. Data source of the database: 1:20 swamp map (internal version), 1:500000 swamp map of Qinghai Tibet Plateau (internal version), 1:1 million swamp survey data and 1:4 million swamp map of China; processing steps: data source selection, preprocessing, digitization and coding of swamp wetland elements, data editing and processing, establishment of topological relationship, edge connection processing, projection conversion, place name and other attribute databases Link and get property data.

0 2020-09-15

Field LAI dataset in the Heihe River Basin (2012)

The ground sample data was collected by LAI-2000 canopy analyzer, and the collection area was located in Dayekou, Wuxing Village (2012) and other areas. The main measure of vegetation is corn. The LAI value of the corn was obtained using the LAI2000, and the observation was repeated twice in a pattern of “one up and four down”. The leaf area of each leaf of the corn plant was obtained using CD202, and a total of three corns were collected.

0 2020-09-15

Species list and distribution of Phrynocephalus and Eremias lizards in the Junggar Depression

1) Data content: species list and distribution data of Phrynocephalus and Eremais in Junggar basin, including class, order, family, genus, species, and detailed distribution information including country, province, city and county; 2) Data source and processing method: Based on the field survey of amphibians and reptiles in Junggar Basin and adjacent arid desert area from 2007 to 2019, and recording the species composition and distribution range of Phrynocephalus and Eremias in this area; 3) Data quality description: the investigation, collection and identification of samples are all conducted by professionals, and the collection of samples, longitude, latitude and altitude information are checked to ensure the quality of distribution data; 4) Data application results and prospects: Through comprehensive analysis of the dataset, the list of species diversity and distribution can provide important data for biodiversity cataloguing in arid central Asia, and provide scientific basis for assessing biodiversity pattern and formulating conservation strategies.

0 2020-09-11

Genome Assembly of Alpine Species Salix Brachista on the Tibetan Plateau (2019)

This dataset is collected from the paper: Chen, J.*#, Huang, Y.*#, Brachi, B.*#, Yun, Q.*#, Zhang, W., Lu, W., Li, H., Li, W., Sun, X., Wang, G., He, J., Zhou, Z., Chen, K., Ji, Y., Shi, M., Sun, W., Yang, Y.*, Zhang, R.#, Abbott, R. J.*, & Sun, H.* (2019). Genome-wide analysis of Cushion willow provides insights into alpine plant divergence in a biodiversity hotspot. Nature Communications, 10(1), 5230. doi:10.1038/s41467-019-13128-y. This data contains the genome assembly of alpine species Salix brachista on the Tibetan Plateau, it contains DNA, RNA, Protein files in Fasta format and the annotation file in gff format. Assembly Level: Draft genome in chromosome level Genome Representation: Full Genome Reference Genome: yes Assembly method: SMARTdenovo 1.0; CANU 1.3 Sequencing & coverage: PacBio 125.0; Illumina Hiseq X Ten 43.0; Oxford Nanopore Technologies 74.0 Statistics of Genome Assembly: Genome size (bp): 339,587,529 GC content: 34.15% Chromosomes sequence No.: 19 Organellas sequence No.: 2 Genome sequence No.: 30 Maximum genome sequence length (bp): 39,688,537 Minimum genome sequence length (bp): 57,080 Average genome sequence length (bp): 11,319,584 Genome sequence N50 (bp): 17,922,059 Genome sequence N90 (bp): 13,388,179 Annotation of Whole Genome Assembly: Protein:30,209 tRNA:784 rRNA:118 ncRNA:671 Please see attachments for more details of annotation. The tables in the Supplementary Information of this article can also be found in this dataset. The table list is represented in attachments. The accession no. of genome assembly is GWHAAZH00000000 (https://bigd.big.ac.cn/gwh/Assembly/663/show).

0 2020-09-07

MODIS Lai data of 34 key nodes in Pan third pole region (2002-2016)

Leaf area index (leaf area index), also known as leaf area coefficient, refers to the multiple of total plant leaf area in land area per unit land area, which is a better dynamic index to reflect the size of crop population. Leaf area index (LAI) is an important structural parameter of forest ecosystem. It represents the density of leaves and canopy structure characteristics, and affects the physiological and biochemical processes such as photosynthesis, respiration and transpiration in the canopy. It is a key parameter to describe the material and energy exchange between soil, vegetation and atmosphere, and is also an important variable for estimating various ecological processes and functions. Based on MODIS leaf area index data from 2000 to 2016, the mcd15a3h product data of Pan third pole key node area were trimmed, and the 4-day leaf area index data of key node area from 2002 to 2016 were obtained. Data projection: sinusoidal projection The data area is 34 key nodes of Pan third pole (Abbas, Astana, Colombo, Gwadar, Mengba, Teheran, Vientiane, etc.).

0 2020-08-24

Remote sensing products of vegetation parameters in Heihe River Basin (2019)

This data set includes the normalized vegetation index, vegetation coverage, vegetation net primary productivity, grassland biomass, forest stock vegetation parameters of the Heihe River Basin from May 2019 to October 2019, and the spatial resolution is 10m. In this dataset, remote sensing data sources such as GF-1, GF-6, Sentinel-2, and ZY-3, combined with basic meteorological and ground monitoring data, are used to retrieve vegetation parameters such as band ratio method, mixed pixel decomposition model and CASA model to generate monthly vegetation index remote sensing products of Qilian Mountain in the growing season. This data set provides data support for the diagnosis of regional eco-environmental problems and the dynamic assessment of eco-environment by constructing a high spatial-temporal resolution eco-environmental monitoring data set based on high-resolution satellites.

0 2020-08-23

Remote sensing products of vegetation parameters in key areas of Qilian Mountains (2019)

This data set includes the normalized vegetation index, vegetation coverage, vegetation net primary productivity, grassland biomass, forest stock vegetation parameter remote sensing products in the key area of Qilian mountain from May 2019 to October 2019, and the spatial resolution is 10m. In this data set, remote sensing data sources such as GF-1, GF-6, Sentinel-2, and ZY-3, combined with basic meteorological and ground monitoring data, are used to retrieve vegetation parameters such as band ratio method, mixed pixel decomposition model and CASA model to generate monthly vegetation index remote sensing products of Qilian Mountain in the growing season. This data set provides data support for the diagnosis of regional eco-environmental problems and the dynamic assessment of eco-environment by constructing a high spatial-temporal resolution eco-environmental monitoring data set based on high-resolution satellites.

0 2020-08-23

WATER: Dataset of forest structure parameter survey at the temporary forest sampling plot in the Dayekou watershed foci experimental area (2008)

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.

0 2020-08-20

WATER: Dataset of forest structure parameter survey at the super site around the Dayekou Guantan Forest Station

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.

0 2020-08-20