China Scientific Data最新文献

{"title":"A dataset of district/county-level population distribution of China’s six national censuses","authors":"Haoran Wu, Liang Gao, Dongdong Song, Yitao Yang, Changxing Xu, Xiaobao Yang","doi":"10.11922/csdata.2021.0034.zh","DOIUrl":"https://doi.org/10.11922/csdata.2021.0034.zh","url":null,"abstract":"Spatialized population data are of great importance for supporting the studies on transportation, geography, socio-economy, sustainable development etc. In this paper, we collected the district/county-level population data of China’s six population censuses starting from 1949. With the administrative division in 2015 (the year of the Sixth Population Census) as the benchmark of the geographical region of the districts and counties, we calibrated and spatialized the population data to obtain a dataset of district/county-level population distribution of China’s six national censuses.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44529083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dataset of poetry literature landscape of famous Chinese mountains from pre-Qin period to Tang and Song Dynasties","authors":"Xiaohan Du, Di Hu, Daiwei Li, Sifan Zhou, Tianyi Bai","doi":"10.11922/csdata.2021.0016.zh","DOIUrl":"https://doi.org/10.11922/csdata.2021.0016.zh","url":null,"abstract":"Literary landscape refers to the landscape closely related to the life, study, work, writing and literary activities of literati, which has certain literary connotations. Most ancient men of letters enjoyed visiting famous mountains and rivers. Most of the famous peaks in China have witnessed the footprints of poets and many famous poems. In this study, we collected and collated the famous Chinese mountains as well as the information about relevant poetry and writers from pre-Qin period to Tang and Song Dynasties, and obtained a dataset of poetry literature landscape of famous Chinese mountains from pre-Qin period to Tang and Song Dynasties. The dataset includes the famous mountain table, the poetry table, and the poet table, recording the information of the famous mountains, the poems and the poets as well as the the relationship between them. This dataset can be used to study the relationship between the formation, development and evolution of literary landscape of famous mountains and the creation of poetry, so as to explore the influence of literary works and literary activities on literary landscape. Moreover, it can also support the study on revealing the formation, development and evolution path of literary landscape.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45019772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dataset of topographic factors with 12.5 m spatial resolution in the Yellow River Basin","authors":"Yanmin Jiang, Haijing Shi, Shaowei Zhang, J. Shui, X. Cao, Cheng Zheng","doi":"10.11922/11-6035.ncdc.2021.0017.zh","DOIUrl":"https://doi.org/10.11922/11-6035.ncdc.2021.0017.zh","url":null,"abstract":"The Yellow River Basin is a key area of soil and water conservation and ecological protection in China, due to its complex topography and great variation in climate. The study on changes of topographical factors is of great significance for the prevention and control of soil and water loss as well as vegetation restoration in this area. This dataset consists of 11 topographic factors, namely slope gradient, slope aspect, comprehensive curvature, plane curvature, sectional curvature, aspect variability, slope variability, topographic relief, surface roughness, surface cutting depth and the coefficient of variation of elevation. The data were sourced from ALOS DEMs with a resolution of 12.5m, which were then processed through four steps: preprocessing, framing, projection and topographic index calculation. Verified by GLA14 altimetry data, the ALOS 12.5 m DEM data, with a high absolute vertical accuracy, are much better in expressing terrain characteristics than data of GDEM with 30 m resolution and STRM DEM data with 90 m resolution. This dataset can provide data support for scientific research in the Yellow River Basin, such as vegetation restoration, quantitative evaluation of soil erosion, and land hydrological analysis.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45383980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dataset of UAV remote sensing images of Lanzhou Botanical Garden in 2021","authors":"Zhiyuan Gong, Xuemei Li, Guigang Wang, Qiuping Li, Kaixin Zhao, Zhengrong Zhang, Xinyu Liu","doi":"10.11922/11-6035.csd.2022.0044.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0044.zh","url":null,"abstract":"In the study, we used a small drone carrying a visual monitoring camera to obtain the spectral reflectance information of the red, green and blue bands in Lanzhou Botanical Garden. And on the basis of the drone data of Lanzhou Botanical Garden from March First to December 30, 2021, we prepared a dataset of remote sensing images. During the data collection process, the UAV conducted 72 aerial surveys, and took a total of 15,696 images. After aerial triangulation and orthophoto correction, we spliced the images to obtain the digital orthophoto map (DOM), and further calculated the vegetation index results. This dataset includes two kinds of data: DOM and vegetation index. Lanzhou Botanical Garden is located in Anning District, Lanzhou City, Gansu Province, with rich vegetation varieties and high spatial heterogeneity in the study area. This dataset has certain potential for researchers to explore in urban vegetation phenology, urban ecological monitoring, and urban thermal environment.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49434684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dataset of time series of climate variables in the karst areas of Southwest China from 1951 to 2014","authors":"Xingqi Wu, Q. Cheng, Lingwei Wei, Xiaofei Hu, J. Ni","doi":"10.11922/11-6035.csd.2022.0004.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0004.zh","url":null,"abstract":"The areas with karst topography in Southwest China have a fragile ecological environment and the ecosystem there is vulnerable to climate change and human activities. Due to the influence of the karst topography, the spatial distribution of weather stations in this area is uneven which, together with the slight difference of meteorological observation time series of each observation station and the limited number of stations, makes it difficult for the observed data to be used in the study on the realationship between terrestrial ecosystems and climate change. In this study, we used the local smooth thin plate spline function from the ANUSPLIN software version 4.3, combining with the Shuttle Radar Topography Mission (SRTM) digital elevation model, to spatially interpolate four monthly climatic variables (i.e. temperature, precipitation, sunshine percentage, and wet days with daily precipitation <0.1 mm). In this way, we finally obtained three sets of gridded data in different formats with a resolution of 1km. The error statistics show that the error of the interpolation results is relatively low, especially with a high accuracy of the temperature interpolation. The gridded data of the four climate variables can truly reflect the spatial distribution of climates in the karst areas. Further analyses show that from 1951 to 2014, the distribution of temperature and precipitation showed a decreasing trend from the southeast to the northwest. The overall change of temperature showed an upward trend, and the change trend of precipitation was not significant. The distribution of sunshine percentage gradually decreased from the middle to the two sides, and the sunshine percentage showed an overall decline trend. The distribution patterns of wet days are inversely related to altitudes. This dataset can provide data support for the regional research on climate, the relationship between vegetation, rocky desertification and climate change, the relationship between land use and land cover changes, as well as the climate–driven terrestrial ecological model simulations.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48579978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dataset of the Poyang Lake coastlines in dry and wet seasons during 2000–2020","authors":"Fangdi Sun, Ling Zhang, Zhongyuan Chen","doi":"10.11922/11-6035.csd.2022.0076.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0076.zh","url":null,"abstract":"Located in the south of Jiangxi Province, Poyang Lake is China’s largest freshwater lake directly connected to the Yangtze River. In the wet seasons, the water area of the lake can reach more than 3000 km2 and in the dry seasons, it can drop to lower than 800 km2. As one of the important lakes in the mainstream of Yangtze River, it plays an important role in flood storage, biodiversity protection and the economic development of the watershed. Based on high-frequency MODIS data, we obtained series of Poyang Lake coastlines in wet and dry seasons during 2000–2020. When compared with two visual interpretation results in wet and dry seasons, the overall accuracy of this dataset is higher than 85%. This dataset can delineate the tempo-spatial dynamics of the lake inundations and can serve as scientific data for the research on ecological protection and water resource management in the watershed of Poyang Lake.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48210465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dataset of benthic material distribution of coastal coral reefs in Hainan during 2018–2020","authors":"Hongrong Wu, Lanwei Zhu, Yanggan Fu, Dong Shi","doi":"10.11922/11-6035.noda.2022.0010.zh","DOIUrl":"https://doi.org/10.11922/11-6035.noda.2022.0010.zh","url":null,"abstract":"Based on the Sentinel-2 high resolution remote sensing image data, we adopted the object-oriented multi-scale segmentation technology and the nearest neighbor algorithm to classify benthic materials of offshore coral reefs in Wenchang City, Danzhou City, Sanya City of Hainan Province during 2018-2020. Then, combining with the remote sensing images of Gaofen-2 satellite and field validation data we verified the accuracy of the classification results. The dataset can be used to analyze the temporal and spatial changes of benthic materials in coastal coral reefs and assess the health status of coral reefs. Moreover, it can serve as a data foundation for the management and protection of coral reefs in Hainan Island.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44592546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dataset of multi-modal peach images for object detection","authors":"Fengyi Wang, Y. Rao, Qing Luo, Tong Zhang, Tianyu Wan, Jingyao Zhang, Yulong Shi","doi":"10.11922/11-6035.csd.2022.0055.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0055.zh","url":null,"abstract":"Reliable and accurate detection of fruits during the whole growth period has always been one sticking point and important bottleneck for achieving precise, intelligent and efficient orchard management. In order to deal with the insufficiency of sample scale and diversity in actual production scenes, we built this dataset by focusing on the application of fruit detection in typical orchard operation stages, such as fruit thinning, bagging and picking operations based on in-field shooting and data post-processing. The dataset covers the acquisition, classification, labeling, storage and use of multi-modal peach images during fruit thinning, bagging and picking stages under the different natural circumstances, including complex weather, illumination and occlusion. The dataset involves various modalities, such as visible light, depth and infrared with a total volume of 8.27GB. It can provide fundamental and valuable image resources for the following research areas, e.g., multi-modal image data fusion and object detection. In addition, the dataset can also be used as a standard library for deep learning modeling in big data environment with the important practical application value for promoting the research on fruit object detection.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42957855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dataset of slope length and slope steepness factors based on soil loess equation with 12.5 m spatial resolution in the Yellow River Basin","authors":"Cheng Zheng, Haijing Shi, Shaowei Zhang, J. Shui, X. Cao, Yanmin Jiang","doi":"10.11922/11-6035.ncdc.2021.0021.zh","DOIUrl":"https://doi.org/10.11922/11-6035.ncdc.2021.0021.zh","url":null,"abstract":"The Yellow River Basin is one of the areas worst affected by soil and water loss in China. The slope length and slope steepness factors are the basic data for the study on soil loss equation, and they are also important parameters in soil erosion models of USLE and CSLE. In this study, we obtained the dataset of four key factors of soil erosion equation, namely slope steepness factor, slope length factor, LS factor and slope length through regional LS factor calculation tool based on ALOS 12.5 m DEM data. This dataset can be used for regional soil erosion assessment, land use as well as the measures and planning of soil and water conservation.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41928656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preface to the special issue of Ecological Environment Monitoring Data","authors":"Guoqing Li","doi":"10.11922/11-6035.noda.2022.0021.zh","DOIUrl":"https://doi.org/10.11922/11-6035.noda.2022.0021.zh","url":null,"abstract":"","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46988954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}