Gwadar deep water port is located in the south of Gwadar city in the southwest of Balochistan province, Pakistan. It is 460km away from Karachi in the East and 120km away from Pakistan Iran border in the West. It is adjacent to the Arabian Sea in the Indian Ocean in the South and the Strait of Hormuz and Red Sea in the West. It is a port with strategic position far away from Muscat, capital of Oman. This data is the land cover data of Gwadar and its surrounding areas. The data is from globeland30 with a spatial resolution of 30 meters and a data format of TIFF. The classification images used in the development of globeland30 data set mainly include Landsat's TM5, ETM +, oli multispectral images and HJ-1 multispectral images. Using the Pok based classification method, the total volume accuracy is 83.50%, and the kappa coefficient is 0.78.
WU Hua
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
This dataset records The experiment of soil water content in the lower reaches of the Tarim River (Karl) was carried out by the members of the Xinjiang salt water Regiment (Karl) from September to September, 2020 In order to study the phenotypic characteristics of different plants under high salinity saline water irrigation, and to explore the feasibility of high salinity saline water for vegetation construction.
LI Xinrong HE Mingzhu ZHAO Zhenyong
The data set includes the start time (year, month), location (longitude and latitude), duration (month), drought intensity and vulnerability data of vegetation response to drought in Central Asia from 1982 to 2015, with a spatial resolution of 1 / 12 °. The drought events were identified by the standardized precipitation evapotranspiration index at the time scale of 12 months (spei12) < - 1.0. The specific algorithm of drought characteristics and vegetation vulnerability is detailed in the citation. The dataset has been applied in the study of vegetation vulnerability to drought in Central Asia, and has application prospects in the research fields of spatial-temporal characteristics of drought events, drought-vegetation interaction mechanism, drought risk assessment and so on.
DENG Haoyu
This dataset is derived from the paper: Su, T. et al. (2019). No high tibetan plateau until the Neogene. Science Advances, 5(3), eaav2189. doi:10.1126/sciadv.aav2189 This data contains supplementary material of this article. Researchers discovered well-preserved palm fossil leaves from the Lunpola Basin (32.033°N, 89.767°E), central Tibetan Plateau at a present elevation of 4655 m in 2016. Researchers compared the newly discovered fossil with those present fossil that are most similar, find that there is no similar leaves among present fossil, therefore, researchers proposed the new species <em>S. tibetensis</em> T. Su et Z.K. Zhou sp. nov. Using the climate model, combined with the research of the fossil, researchers rebuilt the paleoelevation of the central Tibetan Plateau, it shows that a high plateau cannot have existed in the core of Tibet in the Paleogene. The data contains the following tables: 1) Table S1. Fossil records of palms around the world. 2) Table S2. Morphological comparisons between fossils from Lunpola Basin and modern palm genera. 3) Table S3. Climate ranges of 12 living genera that show the closest morphological similarity to <em>S. tibetensis</em> T. Su et Z.K. Zhou sp. nov. This dataset also contains the figures in the supplementary material in the article.
SU Tao
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).
CHEN Jiahui YANG Yongping Richard John Abbott SUN Hang
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from phenology camera observation data of the Alpine meadow and grassland ecosystem Superstation from May 1 in 2019 to December 31 in 2019. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The phenology camera adopts a vertical downward method to collect data, with the resolution of 2592*1944. Phenology photos in this data set were taken at 12:10 a day, which has a time error of ±10 min. The image is named as BSDCJZ BEIJING_IR_Year_Month_Day_Time.
LI Xiaoyan
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.
QI Yuan, ZHANG Jinlong, CAO Yongpan, ZHOU Shengming, WANG Hongwei
Land surface temperature is a critical parameter in land surface energy balance. This dataset provides the monthly land surface temperature of UAV remote sensing for typical ground stations in the middle reaches of Heihe River basin from July to September in 2019. The land surface temperature retrieval algorithm is an improved single-channel algorithm, which was applied to the land surface brightness temperature data obtained by the UAV thermal infrared remote sensing sensor, and finally the land surface temperature data with a spatial resolution of 0.4m was obtained.
ZHOU Ji LIU Shaomin WANG Ziwei
Surface albedo is a critical parameter in land surface energy balance. This dataset provides the monthly land surface albedo of UAV remote sensing for typical ground stations in the middle reaches of Heihe river basin during the vegetation growth stage in 2019. The algorithm for calculating albedo is an empirical method, which was developed based on a comprehensive forward simulation dataset based on 6S model and typical spectrums. This method can effectively transform the surface reflectance to the broadband surface albedo. The method was then applied to the surface reflectance acquired by UAV multi-spectral sensor and the broadband surface albedo with a 0.2-m spatial resolution was eventually obtained.
ZHOU Ji LIU Shaomin DONG Weishen
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.
QI Yuan, ZHANG Jinlong, CAO Yongpan, ZHOU Shengming, WANG Hongwei
NDVI is a very important vegetation index for the research of vegetation growth and land cover classification. This dataset provides a monthly land surface albedo of UAV remote sensing with a spatial resolution of 0.2 m. It measured in the midstream of Heihe River Basin during the vegetation growth season over typical stations in 2019. The pix4D mapper software was used for image mosaic and NDVI calculation.
ZHOU Ji LIU Shaomin JIN Zichun
This dataset contains the LAI measurements from the Sidaoqiao in the downstream of the Heihe integrated observatory network from June 1 to September 20 in 2019. The site was located in Ejina Banner in Inner Mongolia Autonomous Region. The elevation is 870 m. There are 1 observation samples, around Mixed forest station (101.1335E, 41.9903N), which is about 30 m×30 m in size. Five sub-canopy nodes and one above-canopy node are arranged in each sample. The data is obtained from LAINet measurements; the four-steps are performed to obtain LAI: the raw data is light quantum (level 0); the daily LAI can be obtained using the software LAInet (level 1); further the invalid and null values are screened and using the 7 days moving averaged method to obtain the processed LAI (level 2); for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.
LIU Shaomin QU Yonghua XU Ziwei
The dataset contains the phenological camera observation data of the Xiyinghe station in the midstream of Shiyanghe integrated observatory network from January 1 to December 31, 2019. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, 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 period and coverage (Fc).
ZHAO Changming ZHANG Renyi
The dataset contains the phenological camera observation data of the Liancheng station in the midstream of Datonghe integrated observatory network from April 26 to October 31, 2019. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 2592×1944. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, 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 period and coverage (Fc).
ZHAO Changming ZHANG Renyi
The dataset contains the phenological camera observation data of the Guazhou station in the midstream of Shulehe integrated observatory network from March 26 to October 31, 2019. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 2592×1944. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, 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 period and coverage (Fc).
ZHAO Changming ZHANG Renyi
This dataset contains the LAI measurements from the Sidaoqiao in the downstream of the Heihe integrated observatory network from June 1 to September 20 in 2019. The site was located in Ejina Banner in Inner Mongolia Autonomous Region. The elevation is 870 m. There are 1 observation samples, around Sidaoqiao superstation (101.1374E, 42.0012N), which is about 30m×30m in size. Five sub-canopy nodes and one above-canopy node are arranged in each sample. The data is obtained from LAINet measurements; the four-steps are performed to obtain LAI: the raw data is light quantum (level 0); the daily LAI can be obtained using the software LAInet (level 1); further the invalid and null values are screened and using the 7 days moving averaged method to obtain the processed LAI (level 2); for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.
LIU Shaomin QU Yonghua XU Ziwei
This data is the spatial distribution map of ecological shelters in Nursultan, the capital of Kazakhstan in 2018. The types of features in the map mainly include shelter forests, roads, buildings, lakes and rivers. The data source is four sentinel images in August 2018, with a resolution of 10 meters. At the same time, overlay the vector map of OSM global features. The data set is more accurate after correction. Through visual interpretation and field investigation, the extraction of shelter forest spot has high precision. The data reflects the spatial distribution of urban ecological shelters in Nursultan, the capital of Kazakhstan. At the same time, it has an important reference value for the long-term monitoring of the spatial and temporal pattern of shelter forests.
WANG Yongdong
The dataset contains the phenological camera observation data of the Daman Superstation in the midstream of Heihe integrated observatory network from August 28,2019 to December 31, 2019. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, 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 period and coverage (Fc). Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin QU Yonghua XU Ziwei
The dataset contains the phenological camera observation data of the Mixed forest station in the downstream of Heihe integrated observatory network from August 28, 2019 to December 31, 2019. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, 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). Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin QU Yonghua XU Ziwei
