This dataset derives from the articles: (1) He, C., Liu, Z., Tian, J., & Ma, Q., (2014). Urban expansion dynamics and natural habitat loss in China: a multiscale landscape perspective. Global change biology, 20(9), 2886-2902.（2）Xu, M., He, C., Liu, Z., Dou, Y. (2016). How Did Urban Land Expand in China between 1992 and 2015? A Multi-Scale Landscape Analysis. PLoS ONE 11 (5): e0154839. To produce this dataset, the nighttime light data, vegetation index data, and land surface temperature data were preprocessed to obtain the multi-source remote sensing data in China from 1992 to 2020, and the economic regionalization, selection of samples, support vector machine classification, and inter-annual correction were used to extract the dynamic information of urban built-up area. According to the accuracy assessment based on Landsat TM/ETM+ data, Kappa coefficient is 0.60, overall accuracy is 92.62% This dataset has been used to assess the impacts of urban expansion on natural habitats and cropland, and can provide data support for understanding China’s urban expansion and its effects.
HE Chunyang, LIU Zhifeng, XU Min , LU Wenlu
The dataset of landuse types in Qilian Mountains National Park in 1985 is a vector dataset based on the remote sensing monitoring dataset of the current landuse situation in China by CAS, which is obtained through cropping and splicing operations. The data production production is vector data generated by manual visual interpretation using Landsat TM/ETM remote sensing images as the main data source. 3 datasets for 2000-2020 are raster datasets with 30m resolution based on GlobeLand30 global 30m ground cover data, obtained through mask extraction and other operations. The land use types of all datasets include 10 primary types of cropland, forest, shrubland, grassland, wetland, water, tundra, impervious surface, bareland, glacier, and permanent snow. The data products can detect most of the land cover changes caused by human activities, which is very important in practical applications. This data can be used to analyze the historical land use types in the Qilian Mountains region and to analyze the changes of land use types in the Qilian Mountains region in combination with the current landuse type data.
NIAN Yanyun
In this study，a vegetation classification system for the vegetation types in the Qinghai-Tibet Plateau was designed. The integrated classification method，taken into account of multi-source vegetation classification / land cover classification products， was used to produce the actual vegetation map. This integrated classification method followed the principle of data consistency，and the resultant vegetation map was superior over other vegetation maps in terms of reflection of current situation， classification system， and classification accuracy. This vegetation map is timely and could better reflect current vegetation distribution than earlier ones. This vegetation map could be conducive to fully extract vegetation information from multi-source data products with high reliability and consistency. Compared with previous data products，the overall accuracy （78.09%，kappa coefficient is 0.75） of this new vegetation map was found to increase by 18.84%-37.17%，especially for grassland and shrub.
ZHANG Hui, ZHAO Cenliang, ZHU Wenquan
This data is the land cover data at 30m resolution of Southeast Asia in 2015. The data format of the data is NetCDF, and the variable name is "land cover type". The data was obtained by mosaicing and extracting the From-GLC data. Several land cover types, such as snow and ice that do not exist in Southeast Asia were eliminated.The legend were reintegrated to match the new data. The data provide information of 8 land cover types: cropland, forest, grassland, shrub, wetland, water, city and bare land. The overall accuracy of the data is 71% (Gong et al., 2019). The data can provide the land cover information of Southeast Asia for hydrological models and regional climate models.
LIU Junguo
The gridded desertification risk data of The Arabian Peninsula in 2021 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 the Arabian Peninsula in 2021.
XU Wenqiang
This dataset was captured during the field investigation of the Qinghai-Tibet Plateau in June 2021 using uav aerial photography. The data volume is 3.4 GB and includes more than 330 aerial photographs. The shooting locations mainly include roads, residential areas and their surrounding areas in Lhasa Nyingchi of Tibet, Dali and Nujiang of Yunnan province, Ganzi, Aba and Liangshan of Sichuan Province. These aerial photographs mainly reflect local land use/cover type, the distribution of facility agriculture land, vegetation coverage. Aerial photographs have spatial location information such as longitude, latitude and altitude, which can not only provide basic verification information for land use classification, but also provide reference for remote sensing image inversion of large-scale regional vegetation coverage by calculating vegetation coverage.
LV Changhe, ZHANG Zemin
This data set is the land use data of the key areas of Qilian mountain in 2018, spatial resolution 2m. This data set is based on the data of climate, altitude, topography, and land cover type of the Qilian mountain. Through the high-resolution remote sensing images to interprets the surface cover types. For the land types that cannot be reflected by the images, collect relevant data in the field, check and correct the land use types. At the same time, the maps and attribute information are uniformly entered and edited to form land use data in the Qilian Mountain area in 2018.
QI Yuan, ZHANG Jinlong, YAN Changzhen, DUAN Hanchen, JIA Yongjuan
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