Nighttime light remote sensing has been an increasingly important proxy for human activities including socioeconomics and energy consumption. Defense Meteorological Satellite Program-Operational Linescan System from 1992 to 2013 and Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite since 2012 are the most widely used datasets. Despite urgent needs for long-term products and pilot explorations in synthesizing them, the publicly available long-term products are limited. We propose a Night-Time Light convolutional Long Short-Term Memory (NTLSTM) network, and apply the network to produce annual Prolonged Artificial Nighttime-light DAtaset (PANDA) in China from 1984 to 2020. Model assessments between modelled and original images show that on average the Root Mean Squared-Error (RMSE) reaches 0.73, the coefficient of determination (R2) reaches 0.95, and the linear slope is 0.99 at pixel level, indicating a high confidential level of the data quality of the generated product. In urban areas, the modelled results can well capture temporal trends in newly built-up areas but slightly underestimate the intensity within old urban cores. Socioeconomic indicators (built-up areas, Gross Domestic Product, population) correlates better with the PANDA than with previous products in the literature, indicating its better potential in finding different controls of nighttime-light variances in different phases. Besides, the PANDA delineates different urban expansion types, outperforms other products in representing road networks, and provides potential nighttime-light sceneries in early years. PANDA provides the opportunity to better bridge the cooperation between human activity observations and socioeconomic or environmental fields
ZHANG Lixian, REN Zhehao, CHEN Bin, GONG Peng, FU Haohuan, XU Bing
Since the first Industrial Revolution, human activity has profoundly affected all spheres of the earth, and this influence will continue to expand and intensify. As an ecosystem unit with global significance, the Qinghai-Tibet Plateau (QTP) is also an important ecological security barrier in China, playing a crucial role in soil and water conservation, biodiversity conservation, water conservation and carbon balance. However, in the past 30 years, with the expansion of the scope and rapid growth of the intensity of human activities on the QTP, a series of ecological and environmental issues caused by human activities have become increasingly prominent and seriously affected the ecological functions of the QTP. The comprehensive spatial dataset that records human activity intensity will contribute to a deeper understanding of the intensity and scope of human activities in the region, reveal the law of change of human activities in the context of climate warming, and have important significance for further quantitative identification of the impact of human activities and climate change on the ecosystem, as well as promoting the sustainable development of the region. In this study, the human footprint index method was adopted to evaluate the intensity of human activity on the QTP, which used six types of spatial data as indicators of human activities, including population density, land use, grazing density, night lighting, railway and road. The dataset records indicators of human activity intensity in the seven phases, namely, 1990, 1995, 2000, 2005, 2010, 2015 and 2017. The optimization and adjustment of the human footprint method in this dataset mainly include: (1) Six kinds of data including population density, land use, night lighting, grazing density, road and railway were selected to calculate the intensity of human activities; (2) Adjust the assignment of different land use types; (3) The maximum intensity threshold of population density was set at 50 people/km2, and the logarithmic method was used to assign the value. (4) The cattle and sheep density data were used to characterize the grazing density, and the maximum intensity threshold was set as 1000 sheep units/km2, and the logarithmic method was used to assign the value. (5) The corrected DMSP/OLS night lighting data were used for assigning values; (6) Divide the road into five grades, namely expressway, national road, provincial road, county road and other roads, and assign values respectively; (7) The maximum influence range of railway is set as 3.5km; (8) Using glacier and lake spatial data for quality control . The dataset contains the data from "Duan, Q., & Luo, L. (2020). A dataset of human footprint over the Qinghai-Tibet Plateau during 1990–2015. China Scientific Data, 5(3). https://doi.org/10.11922/csdata.2019.0082.zh", and the newly produced data of 2017. This dataset can provide spatial data for exploring the characteristics and rules of spatial changes of human activities in the Qinghai-Tibet Plateau, and can also provide support for exploring the interaction between human activities and ecological environment in the region. it can play a guiding role in promoting the ecological environment protection and sustainable development of the entire Qinghai-Tibet Plateau.
DUAN Quntao, LUO Lihui
China's land cover data set includes 5 products: 1) glc2000_lucc_1km_China.asc, a Chinese subset of global land cover data based on SPOT4 remote sensing data developed by the GLC2000 project. The data name is GLC2000.GLC2000 China's regional land cover data is directly cropped from global cover data. For data description, please refer to http : //www-gvm.jrc.it/glc2000/defaultGLC2000.htm 2) igbp_lucc_1km_China.asc, a Chinese subset of global land cover data based on AVHRR remote sensing data supported by IGBP-DIS, the data name is IGBPDIS; IGBPDIS data was prepared using the USGS method, using April 1992 to March 1992 The AVHRR data developed global land cover data with a resolution of 1km. The classification system adopts a classification system developed by IGBP, which divides the world into 17 categories. Its development is based on continents. Applying AVHRR for 12 months to maximize synthetic NDVI data, 3) modis_lucc_1km_China_2001.asc, a subset of MODIS land cover data products in China, the data name is MODIS; MODIS China's regional land cover data is directly cropped from global cover data, and its data description please refer to http://edcdaac.usgs.gov/ modis / mod12q1v4.asp. 4. umd_lucc_1km_China.asc, a Chinese subset of global land cover data based on AVHRR data produced by the University of Maryland, the data name is UMd; the five bands of UMd based on AVHRR data and NDVI data are recombined to suggest a data matrix, using Methodology carried out global land cover classification. The goal is to create data that is more accurate than past data. The classification system largely adopts the classification scheme of IGBP. 5) westdc_lucc_1km_China.asc, China ’s 2000: 100,000 land cover data organized and implemented by the Chinese Academy of Sciences, combined with Yazashi conversion (the largest area method), and finally obtained a land use data product of 1km across the country, data name WESTDC. WESTDC China's regional land cover data is based on the results of a 1: 100,000 county-level land resource survey conducted by the Chinese Academy of Sciences. The land use data were merged and converted into a vector (the largest area method). The Chinese Academy of Sciences resource and environment classification system is adopted. 2: Data format: ArcView GIS ASCII 3: Mesh parameters: ncols 4857 nrows 4045 xllcorner -2650000 yllcorner 1876946 cellsize 1000 NODATA_value -9999 4: Projection parameters: Projection ALBERS Units METERS Spheroid Krasovsky Parameters: 25 00 0.000 / * 1st standard parallel 47 00 0.000 / * 2nd standard parallel 105 00 0.000 / * central meridian 0 0 0.000 / * latitude of projection's origin 0.00000 / * false easting (meters) 0.00000 / * false northing (meters)
RAN Youhua
This data set includes year-by-year nighttime light annual images (totally 28 images) in Northern Equatorial Africa and Sahel from 1992 to 2020. By establishing the calibration relationship by fitting the median NPP-VIIRS nighttime light radiance and the DMSP-OLS nighttime light DN values, the DMSP-OLS nighttime light stable data from 1992 to 2013 were calibrated, and the synthesized DMSP-OLS data after 2013 are generated based on NPP-VIIRS nighttime light data. The spatial resolution is 0.00833 ° (about 1km); Raster data type is GeoTIFF. The grid pixel value is radiance with unit 10 − 9 w ∙ cm − 2 ∙ sr − 1. This data set can be used for the study on human activities in Northern Equatorial Africa and Sahel, such as the analysis of temporal and spatial changes of human activities.
YUAN Xiaotian , JIA Li , JIANG Min
The data set contains annual NPP-VIIRS night time light data images of equatorial northern Africa and the Sahel region from 2013 to 2020. Based on the monthly average night time light image data of visible infrared imaging radiometer Suite (VIIRS) of national polar orbiting partnership (NPP) satellite, this dataset is generated by separating the unstable night light caused by biomass combustion from the stable night light information caused by human activities. The spatial resolution of the data is 500 m, and the grid data type is GeoTIFF. The grid pixel value is radiance, and the unit is 10 − 9 w ∙ cm − 2 ∙ SR − 1. The data set improves the ability of noctilucent images to identify small-scale, scattered and unstable urban information in northern equatorial Africa and Sahel to a certain extent, and can be further applied to the research on human activities in northern equatorial Africa and Sahel.
YUAN Xiaotian , JIA Li , JIANG Min
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 include the night light data of Tibetan Plateau with a spatial resolution of 1km*1km, a temporal resolution of 5 years and a time coverage of 2000, 2005 and 2010.The data came from Version 4 dmsp-ols products. DMSP/OLS sensors took a unique approach to collect radiation signals generated by night lights and firelight.DMSP/OLS sensors, working at night, can detect low-intensity lights emitted by urban lights, even small-scale residential areas and traffic flows, and distinguish them from dark rural backgrounds.Therefore, DMSP/OLS nighttime light images can be used as a representation of human activities and become a good data source for human activity monitoring and research.
FANG Huajun
Anthropogenic heat is one of the products of urbanization, which refers to the heat produced by human activities and released into the atmosphere, mainly from various types of energy consumption and biological metabolism. This data set is the surface anthropogenic heat emission flux data of 500m × 500m spatial resolution in China's land surface area from 2000 to 2016 (2000 / 2004 / 2008 / 2012 / 2016). Data sources and processing methods: (1) through the collection of energy consumption data and socio-economic data of provinces and cities in 2000-2016, the annual average AHF of prefecture level cities (prefectures, districts and leagues) is estimated by the inventory method; (2) The AHF estimation model is established based on multi-source remote sensing data, and the grid AHF is obtained; (3) the AHF estimation results of time series are analyzed and tested, and the deviation values are corrected to improve the accuracy of the AHF estimation results. It is of great significance to understand and master the anthropogenic heat emission and its change for understanding the impact of urbanization on climate, environment and society.
HU Deyong
This data set is the human activity data of Qilian Mountain in 2019. Based on the data of mining, illegal house renovation, new roads, land leveling and ecological restoration in Qilian Mountains, the high-resolution remote sensing images to compare the changes before and after statistical analysis. In the Qilian Mountains area, the changes of land types are investigated and verified block by block; in the areas with suspicious maps, re-interpretation and verification; in the areas with unreflecting images, field verification is carried out to collect relevant data, check and correct the location. At the same time, it further checks the attribute information of mining, illegal house renovation, new roads, land leveling and ecological restoration in the Qilian Mountains in 2019, and unifies the input and editing of the patches and their attributes, forming the data set of human activities in the Qilian Mountains in 2018, realizing the current situation and timeliness of ecological management in the Qilian Mountains, and providing data support for the monitoring of human activities in Qilian Mountains in 2019.
QI Yuan, ZHANG Jinlong, ZHOU Shengming, LI Na, WANG Hongwei
This dataset is the data of human activities in the key areas of Qilian Mountain in 2018, spatial resolution 2m. This dataset focuses on mine mining, urban expansion, cultivated land development, hydropower construction, and tourism development in the key areas of Qilian Mountain.Through high-resolution remote sensing images, compare the changes before and after the statistics. For the maps of the landforms in the Qilian Mountains, check and verify them one by one; re-interpret the plots that are suspicious of the map; collect the relevant data in the field that cannot be reflected by the images, check and correct the location. At the same time, unified input and editing of map attribute information. Generating a data set of human activities in the key areas of the Qilian Mountains in 2018.
QI Yuan, ZHANG Jinlong, JIA Yongjuan, ZHOU Shengming, WANG Hongwei
This data set includes 30 m cultivated land and construction land distribution products in Qilian Mountains from 1985 to 2017. The product is based on Landsat series data production. In the production of cultivated land products, the normalized vegetation index NDVI is used, and the production rules are set by using the prior knowledge of Crop Phenology and crop planting types. In the production of construction land products, the normalized vegetation index NDVI is used by using the Landsat series data synthesized in summer. NDBI, MNDWI and other indicators, assisted by DEM, lighting data to set product production rules. Taking the products of 2015 as an example, the accuracy of the products of cultivated land and construction land in 2015 is 89.43% and 91.89% respectively, based on the high-definition image of Google Earth and field survey data.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This data set includes 30 m farmland and buildingland distribution products in Qilian Mountains from 1985 to 2017. The product is based on Landsat series data. In the production of farmland products, the Normalized Difference Vegetation Index (NDVI) is used, and the production rules are set by using the prior knowledge of crop phenology and planting types. In the production of buildingland products, the normalized NDVI is used by using the Landsat series data synthesized in summer. other indexs like NDBI, MNDWI and other data like DEM, lighting data are used together to set product production rules. Taking the products of 2015 as an example, the accuracy of the products of farmland andbuildingland in 2015 is 89.43% and 91.89% respectively, based on the high-definition image of Google Earth and field survey data.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This data set includes 30 m farmland and construction land distribution products in Qilian Mountains from 1985 to 2017. The product is based on Landsat series data production. In the production of farmland products, the Normalized Difference Vegetation Index (NDVI) is used, and the production rules are set by using the prior knowledge of crop phenology and planting types. In the production of buildingland products, the normalized NDVI is used by using the Landsat series data synthesized in summer. other indexs like NDBI, MNDWI and other data like DEM, lighting data are used together to set product production rules. Taking the products of 2015 as an example, the accuracy of the products of farmland andbuildingland in 2015 is 89.43% and 91.89% respectively, based on the high-definition image of Google Earth and field survey data.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This data set includes 30 m farmland and buildingland distribution products in Qilian Mountains from 1985 to 2017. The product is based on Landsat series data production. In the production of farmland products, the Normalized Difference Vegetation Index (NDVI) is used, and the production rules are set by using the prior knowledge of crop phenology and planting types. In the production of buildingland products, the normalized NDVI is used by using the Landsat series data synthesized in summer. other indexs like NDBI, MNDWI and other data like DEM, lighting data are used together to set product production rules. Taking the products of 2015 as an example, the accuracy of the products of farmland andbuildingland in 2015 is 89.43% and 91.89% respectively, based on the high-definition image of Google Earth and field survey data.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This data set includes 30 m farmland and bulidingland distribution products in Qilian Mountains in 2018. The product is based on Landsat-8/OLI data. In the production of farmland products, the Normalized Difference Vegetation Index (NDVI) is used, and the production rules are set by using the prior knowledge of crop phenology and planting types. In the production of buildingland products, the normalized NDVI is used by using the Landsat series data synthesized in summer. other indexs like NDBI, MNDWI and other data like DEM, lighting data are used together to set product production rules. The accuracy of the products of farmland andbuildingland in 2018 is 90.05% and 90.97% respectively, based on the high-definition image of Google Earth and field survey data.
ZHONG Bo, JUE Kunsheng
This data set is the human activity data in the key areas of Qilian Mountain in 2020. Based on the data of mining, illegal house renovation, new roads, land leveling and ecological restoration in the key areas of Qilian Mountain, the Gaofen-1, Gaofen-2 and ZY3 high-resolution remote sensing images to compare the changes before and after statistical analysis. in the key areas of Qilian Mountain, the changes of land types are investigated and verified block by block; in the areas with suspicious maps, re-interpretation and verification; in the areas with unreflecting images, field verification is carried out to collect relevant data, check and correct the location. At the same time, it further checks the attribute information of mining, illegal house renovation, new roads, land leveling and ecological restoration in the key areas of Qilian Mountain in 2020, and unifies the input and editing of the patches and their attributes, forming the data set of 2m spatial resolution human activities in the key areas of Qilian Mountain in 2020, realizing the current situation and timeliness of ecological management in the key areas of Qilian Mountain, and providing data support for the monitoring of human activities in the key areas of Qilian Mountain in 2020.
QI Yuan, ZHANG Jinlong, YUAN Jing, ZHOU Shengming, WANG Hongwei
The vegetation index mainly reflects the differences between the visible light, near-infrared reflection and soil background. The vegetation index can be used to quantitatively describe the growth of vegetation under certain conditions. At present, normalized vegetation index (NDVI) is an important data source for detecting vegetation growth status, vegetation coverage and eliminating some radiation errors. NDVI can reflect the background influence of plant canopy, such as soil, wet ground, snow, dead leaves, roughness, etc., and it is related to vegetation coverage. Landsat satellite data product is an important data source for NDVI estimation. Taking 31 key nodes and 3 major projects in the third pole as the research area, based on the data of Landsat-5 and landsat-8 from 2000 to 2016, the NDVI of different areas was cut and estimated, and finally the 16 day time series ten meter (30M) high-resolution NDVI data of key node areas in the third pole from 2000 to 2016 was obtained.
GE Yong, LING Feng, ZHANG Yihang
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
This dataset, based on night light data and macro statistical data, uses remote sensing inversion method（1km*1km）to obtain the poverty rate in different regions within each country. It has three advantages. a) The calculation unit can be adjusted according to the boundaries of administrative regions to reflect the poverty rate of sub-regions within the large country and scale, which is rare in statistically data. b) The survey and summary cycle limits the updating of national and sub-regional poverty rate, while the method based on night light data is more convenient. c) Due to the continuous annual data of night light, the difficulty of obtaining regional poverty rate in a long period was overcome. In view of the three outstanding advantages mentioned above, this data set can support to achieve the research subjects and provide scientific data for understanding the basic situation of poverty along the Silk Roads.
ZHANG Qian, Linxiu ZHANG
As the roof of the world, the water tower of Asia and the third pole of the world, the Qinghai Tibet Plateau is an important ecological security barrier for China and even Asia. With the rapid development of social economy, human activities have increased significantly, and the impact on the ecological environment is growing. In this paper, eight factors including cultivated land, construction land, National Road, provincial road, railway, expressway, GDP and population density were selected as the threat factors, and the attributes of the threat factors were determined based on the expert scoring method to evaluate the habitat quality of the Qinghai Tibet Plateau, so as to obtain six data sets of the habitat quality of the agricultural and pastoral areas of the Qinghai Tibet Plateau in 1990, 1995, 2000, 2005, 2010 and 2015. The production of habitat quality data sets will help to explore the habitat quality of the Qinghai Tibet Plateau and provide effective support for the government to formulate sustainable development policies of the Qinghai Tibet Plateau.
LIU Shiliang, LIU Yixuan, SUN Yongxiu, LI Mingqi
1) In mountainous areas, due to the complex topographic and geological background conditions, landslides are very easy to occur triggered by external factors such as rainfall, snow melting, earthquake and human engineering activities, resulting in the loss of life and property and the destruction of the natural environment. In order to meet the safety of project site construction, the rationality of land use planning and the urgent needs of disaster mitigation, it is necessary to carry out regional landslide sensitivity evaluation. When many different evaluation results are obtained by using a variety of different methods, how to effectively combine these results to obtain the optimal prediction is a technical problem that is still not difficult to solve at present. It is still very lack in determining the optimal strategy and operation execution of the optimal method for landslide sensitivity evaluation in a certain area. 2) Using the traditional classical multivariate classification technology, through the evaluation of model results and error quantification, the optimal evaluation model is combined to quickly realize the high-quality evaluation of regional landslide sensitivity. The source code is written based on the R language software platform. The user needs to prepare a local folder separately to read and store the software operation results. The user needs to remember the folder storage path and make corresponding settings in the software source code. 3) The source code designs two different modes to display the operation results of the model. The analysis results are output in the standard format of text and graphic format and the geospatial mode that needs spatial data and is displayed in the standard geographic format. 4) it is suitable for all people interested in landslide risk assessment. The software can be used efficiently by experienced researchers in Colleges and universities, and can also be used by government personnel and public welfare organizations in the field of land and environmental planning and management to obtain landslide sensitivity classification results conveniently, quickly, correctly and reliably. It can serve regional land use planning, disaster risk assessment and management, disaster emergency response under extreme induced events (earthquake or rainfall, etc.), and has great practical guiding significance for the selection of landslide monitoring equipment and the reasonable and effective layout and operation of early warning network. It can be popularized and applied in areas with serious landslide development
YANG Zhongkang
Monthly average daytime as well as nighttime data of the Universal Thermal Climate Index (UTCI) for 354 cities in China. The time range of the data is from January 2012 to December 2021, with a temporal resolution of month-by-month. The spatial resolution is 1 km. The data is mainly based on the MYD07 atmosphere profile dataset and MYD11 land surface temperature dataset provided by MODIS, and incorporates the wind speed provided by ERA5 reanalysis data. The urban boundary is demarcated according to the 2018 data provided by Global Urban Boundary-GUB dataset. All the data are resampled to 1 km, in order to maintain the uniform spatial resolution. With the rapid urbanization and global warming, the data are useful for studying the spatiotemporal patterns of urban thermal comfortable and related analysis.
王 晨光 , WANG Chenguang, WANG Chenguang, 占 文凤 ZHAN Wenfeng
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