In 1970, land use was visually interpreted from MSS images, with an overall interpretation accuracy of more than 90%. Land classification was carried out in accordance with the land use classification system of the Chinese Academy of Sciences. For detailed classification rules, please read the data description document. The 2005 and 2015 data sets were collected from the European Space Agency (ESA) Data acquisition of global land cover types includes five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) and Xinjiang, China. There are 22 land use types in the data set. The IPCC land use classification system is adopted. Please refer to the documentation for specific classification details.
ZHANG Chi Geping Luo
The gridded desertification risk data of Iranian plateau in 2019 was calculated based on the environmentally sensitive area index (ESAI) methodology. The ESAI approach incorporates soil, vegetation, climate and management quality and is one of the most widely used approaches for monitoring desertification risk. Based on the ESAI framework, fourteen indicators were chosen to consider four quality domains. Each quality index was calculated from several indicator parameters. The value of each parameter was categorized into several classes, the thresholds of which were determined according to previous studies. Then, sensitivity scores between 1 (lowest sensitivity) and 2 (highest sensitivity) were assigned to each class based on the importance of the class’ role in land sensitivity to desertification and the relationships of each class to the onset of the desertification process or irreversible degradation. A more comprehensive description of how the indicators are related to desertification risk and scores is provided in the studies of Kosmas (Kosmas et al., 2013; Kosmas et al., 1999). The main indicator datasets were acquired from the Harmonized World Soil Database of the Food and Agriculture Organization, Climate Change Initiative (CCI) land cover of the European Space Agency and NOAA’s Advanced Very High Resolution Radiometer (AVHRR) data. The raster datasets of all parameters were resampled to 500m and temporally assembled to the yearly values. Despite the difficulty of validating a composite index, two indirect validations of desertification risk were conducted according to the spatial and temporal comparison of ESAI values, including a quantitative analysis of the relationship between the ESAI and land use change between sparse vegetation and grasslands and a quantitative analysis of the relationship between the ESAI and net primary production (NPP). The verification results indicated that the desertification risk data is reliable in Iranian plateau in 2019.
The data set is the land cover data set of 2010 and 2020. The spatial range is Dhaka City, Bangladesh. The spatial resolution is 30m and the temporal resolution is year. The data comes from globeland30 (Global geographic information public goods, http://www.globallandcover.com/ ）, acquired after mosaic and reorganization. The data accuracy evaluation of the source data is led by Tongji University and Institute of aerospace information innovation, Chinese Academy of Sciences. The overall accuracy of the data is more than 83.50%. The data set can provide high-precision basic geographic information for related research, and has important applications in resource and environment bearing state identification, natural disaster risk assessment, disaster prevention and mitigation, etc.
YANG Fei YIN Cong
The data set was obtained from UAV aerial photography during the field investigation of the Qinghai Tibet Plateau in August 2020. The data size is 10.1 GB, including more than 11600 aerial photos. The shooting sites mainly include Lhasa, Shannan, Shigatse and other areas along the road, residential areas and surrounding areas. The aerial photos mainly reflect the local land use / cover type, facility agriculture distribution, grassland coverage and other information. The aerial photos have longitude, latitude and altitude information, which can provide better verification information for land use / cover remote sensing interpretation, and can also be used for vegetation coverage estimation, and provide better reference information for land use research in the study area.
LV Changhe LIU Yaqun
The sand drift potential (DP, in vector units (VU)) is calculated by DPi=∑U^2 [U-Ut]*fu where i represents 16 directions: N, NNE, NE, NEE, E, EES, ES, ESS, S, SSW, WS, WWS, W, WWN, NW and NNW; U is the effective sand-moving wind speed at the standard height of 10 m; Ut is the threshold wind velocity, i.e., the minimum wind velocity at the standard height to cause sand particle rolling; and fu is the fraction of time when the wind speed is higher than Ut. The 2 m s-1 bin is adopted in the effective sand-moving wind (sand-moving wind >6 m s-1 at the height of 10 m) directions, corresponding to the mean wind speeds of 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33 and 34 m s-1, to sum all the above results to obtain the final DP in the wind direction. The divisor used in calculating the frequency of effective sand-moving winds from different directions is the total hour number of Julian years (8760 hours for common years or 8784 hours for leap years). The wind speed and wind direction data from 2000 to 2008 were hourly estimates of 10 m u-component of wind and 10 m v-component wind with a horizontal resolution of 0.25°×0.25° generated with the fifth generation of ECMWF atmospheric ReAnalysis of the global climate (ERA5).
The major deserts in China include the Taklamakan Desert, Gurban Tunggut Desert, Qaidam Desert, Kumtag Desert, Badain Jaran Desert, Tengger Desert, Ulan Buh Desert, Hobq Desert, MU US Desert, Hunshandake Desert, Hulunbuir Sands, and Horqin Sands. All the desert boundaries were derived from Google Earth Pro® via manual interpretation. We delineated the desert boundaries using the Digital Global Feature Imagery and SpotImage (2011, 10 m resolution) collections of Google Earth Pro®, whose spatial resolution is finer than 30 m. The acquisition time of most images was in 2011.
LI Guoshuai LI Xin
This data set mainly includes daily surface evapotranspiration products in Heihe River Basin (HRB) from 2010 to 2016, with a resolution of 100 meters. Based on multi-source remote sensing data (MODIS Landsat TM/ETM+ data) and regional meteorological data (China meteorological forcing dataset, CMFD), sensitivity parameters of the theoretically robust surface energy balance system (SEBS) model were determined through global sensitivity analysis, and then the parameterization scheme of the model was optimized to improve the estimation accuracy. At the same time, combined with spatial and temporal data fusion algorithm of remote sensing image. Finally, the High-Temporal and Landsat-Like surface evapotranspiration (ET) (HiTLL ET) was obtained over the Heihe Basin. It was validation by the EC measurements from the flux observation stations and ETMap, and the estimation results are consistent with the observation and the spatial and temporal distribution pattern of ETMap. This data set can provide data support for the study of water consumption law and scientific effective management of watershed water resources within HRB, especially for woodland and grassland in the upper stream regions, oasis farmland and desert vegetation in the midstream and downstream regions.
MA Yanfei LIU Shaomin
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.
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 includes the median values of 343 landsat8 data in each 30 meter grid of Gwadar Port Area and its surrounding area from 2014 to 2015. The data includes 12 bands with a spatial resolution of 30 meters, of which the thermal infrared band is 100 meters and the resampling resolution is 30 meters.
The data of greenhouse land is based on Google Earth image interpretation in Lhasa city, 2018, with a spatial resolution of 0.52 meters. Most of the greenhouses in Lhasa are regular rectangles with high reflectivity, which is easy to identify. In the process of interpretation, the open fields with an area of more than 0.10 hectares and roads with a width of more than 7 meters in the greenhouse area of protected agriculture, as well as the greenhouse covered with black textile were removed, while the small empty fields and ridges between the farmland of protected agriculture were not removed. The accuracy of interpretation is 98%. The data well reflects the spatial pattern characteristics of greenhouse land in Lhasa city.
WANG Zhaofeng GONG Dianqing
Remote sensing image refers to the film or photo recording the electromagnetic wave size of various ground objects, mainly divided into aerial photo and satellite photo. The 1-5m remote sensing data set of Yangon deep water port area is from gaofen-2 satellite, with the highest resolution of 1m and the lowest resolution of 5m, including a total of 7 regional images. There are four images in each region, which are band composite images of 5m level and 1m level. The accuracy of 5m level image can meet the needs of most research purposes, and the amount of data is smaller; the accuracy of 1m level image is higher, which can be used for synthesis, verification and other purposes, but the amount of data is larger than 5m level data. In practical use, we can choose 5m or 1m images according to the needs of researchers.
GE Yong LI Qiangzi LI Yi
The meter resolution remote sensing image data of hanbantota area is composed of data fusion and splicing of different satellites. Multispectral remote sensing images with resolution between 0.5 m and 1 m from 2018 to 2019 are selected, and cloud free data with similar time are selected, and the result data set is formed by cutting and splicing according to the research area. The spatial resolution of the data is about 0.6 meters. The data is mainly used to study the high-precision extraction of disaster bearing body elements, such as port facilities, roads and so on. The extracted thematic elements will be used as the basic data of storm surge exposure and vulnerability analysis.
The elevation data set of Hambantota port area with 5-meter resolution is obtained from the stereo image pair data of ZY-3 satellite. ZY-3 carried four optical cameras, including an emmetropic panchromatic TDI CCD camera with a ground resolution of 2.1m, a forward and backward panchromatic TDI CCD camera with a ground resolution of 3.5m, and an emmetropic multispectral camera with a ground resolution of 5.8m. Among them, the three line array stereo image pairs formed by push broom imaging of forward looking and back looking panchromatic cameras can be used for DEM extraction. Through the retrieval of the transit information and data of ZY-3 from 2018 to 2019, the cloudless stereo images of hambantota area are selected for DEM extraction. The steps including defining ground control points, connection points, setting DEM extraction parameters and editing results.
Evapotranspiration over the Qinghai Tibet Plateau is calculated by etwatch, a land surface evapotranspiration remote sensing model based on multi-scale and multi-source data. Etwatch adopts the method of combining the residual term method with P-M formula to calculate evapotranspiration. Firstly, according to the characteristics of the data image, the suitable model is selected to retrieve the evapotranspiration on a sunny day; the remote sensing model is often lack of data because the weather conditions can not obtain a clear image. In order to obtain the daily continuous evapotranspiration, the penman Monteith formula is introduced, and the evapotranspiration results on a sunny day are regarded as the "key frame", and the surface impedance information of the key frame is used as the basis to construct the surface impedance Based on the daily meteorological data, the time series data of evapotranspiration are reconstructed. Through the data fusion model, the high spatial and temporal resolution evapotranspiration data set is constructed by combining the low and medium resolution evapotranspiration temporal variation information with the high resolution evapotranspiration spatial difference information, so as to generate the 8 km resolution evapotranspiration of the Qinghai Tibet Plateau Data sets (1990-2015).
Net primary productivity (NPP), as the basis of ecosystem material and energy cycle, can reflect the carbon sequestration capacity of vegetation at regional and global scales, and is an important indicator to evaluate the quality of terrestrial ecosystem. Based on the principle of light use efficiency model, the productivity model of ecosystem in national barrier area was established by coupling remote sensing, meteorology, vegetation and soil type data. In the selection of parameters, the photosynthetic effective radiation (APAR) was calculated from GIMMS NDVI 3gv1.0 data, vegetation map of China, total solar radiation and temperature and humidity data. Compared with the soil water molecular model, the regional evapotranspiration model can simplify the parameters and enhance the operability of the model. The net primary productivity (NPP) of terrestrial vegetation in 1990-2015 over the Qinghai Tibet Plateau was estimated based on the parameterized model with par and actual light use efficiency as input variables of CASA model.
High spatial and temporal resolution remote sensing image plays a very important role in land use change detection, disaster monitoring and bio-geochemical parameter estimation.Currently, Landsat multi-spectral series satellite data (including Landsat TM, ETM+ and OLI multi-spectral bands) is one of the most widely used multi-spectral data.Taking the One Belt And One Road key node area as the research area, and based on the data of Landsat TM/ETM+/OLI series with good quality from 2000 to 2016, python was used to clip the data in the research area with the masks .To solve the partial data missing problem, MODIS imagery on the missing date and Landsat-MODIS data pair of adjacent phases are combined for spatio-temporal fusion to obtain Landsat-like data.Finally, the high spatial and temporal resolution remote sensing images of 34 key node area during 2001 to 2016 lasted for 8 to 16 days was obtained.
YIN Zhixiang LING Feng
The built-up area can reflect the scale, form and actual use of urban construction land in a certain period of time, which provides a basis for analyzing and studying the current situation of land use, rationally utilizing the land in the built-up area and planning the land for urban construction and development. Based on the satellite images covering 34 key nodes from 1999 to 2003 and 2013 to 2014, the supervised and unsupervised data classification process is adopted, and the data driven and knowledge driven are reasonably combined to produce the regional built-up area distribution data of key nodes in 2000 and 2014. The preliminary test shows that the information quality of the built-up area is better than other global information data extracted by automatic processing of earth observation data. In addition, the Balanced Accuracy is 0.83 and the Omission Error is 0.22. The data is raster data in TIFF format, including five unique values of 0, 1, 2, 3 and 4, in which 0 is NoData, 1 is water surface, 2 is land without built-up area, 3 is built-up area in 2014, and 4 is built-up area in 2000.
ZHOU Pu LING Feng
The dataset of urban land and urbanization index on the Tibetan Plateau mainly includes the spatial distribution data of all urban land on the Tibetan Plateau (2019) and urbanization index of different scales (2018). The dataset of urban land was obtained by the visual interpretation of Google Earth images (2019), and the residential place and residential area data of "1:250000 national basic geographic database - 2015 edition". The dataset of urbanization index was based on the composite night light index (CNLI) at the regional, provincial, watershed, prefecture, and county scales calculated from the night light data of Luojia-1. Our dataset will support the study of optimizing the ecological security barrier system in the key urbanization areas of the Tibetan Plateau
He Chunyang Liu Zhifeng Wang Yihang
The data set is based on a series of microwave remote sensing data, including Special Sensor Microwave Imager (SSM/I), Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E), etc., which can be used as a reference for primary productivity. The data is from Liu et al. (2015), and the specific calculation method is shown in the article. The source data range is global, and Tibetan Plateau region is selected in this data set. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.
The data set is based on the Lai 3g calculated by GIMMS AVHRR sensor, which represents the greenness of vegetation. The data is from Chen et al. (2019), and the specific calculation method is shown in the article. The source data range is global, and Tibetan plateau region is selected in this data set. This data integrates the original semi monthly scale data into the monthly data, and the processing method is to take the maximum value of two periods of Lai in a month, so as to achieve the effect of removing noise as much as possible. This data set is one of the most widely used Lai data, and is often used to evaluate the temporal and spatial patterns of vegetation greenness, which has practical significance and theoretical value.