Pub Date : 2023-03-31DOI: 10.11922/11-6035.csd.2023.0049.zh
Yang Wang, Xiaohuan Zheng, Y. Ban, Lihua Kong
It has been widely recognized that data have become a basic and strategic resource at home and abroad, ranking as a production factor with land, labor, capital and technology. Scientific data are an important part of data elements. High-quality scientific data are a necessary strategic resource to promote the development of science and technology. Focusing on the characteristics of scientific data (e.g. openness, multi-level integration and evolution, and scientific data life cycle management), this paper analyzes the development and problems of scientific data in China, and puts forward comprehensive suggestions on establishing a sound scientific data ecosystem. By establishing and improving the data basic policy system, building a multi-agent governance framework, and strengthening the capacity of scientific data infrastructure and talent training, the paper aims to gradually create a virtuous circle of scientific data production, utilization, sharing and reuse.
{"title":"Establishing a sound scientific data ecosystem through the implementation of a data element policy","authors":"Yang Wang, Xiaohuan Zheng, Y. Ban, Lihua Kong","doi":"10.11922/11-6035.csd.2023.0049.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0049.zh","url":null,"abstract":"It has been widely recognized that data have become a basic and strategic resource at home and abroad, ranking as a production factor with land, labor, capital and technology. Scientific data are an important part of data elements. High-quality scientific data are a necessary strategic resource to promote the development of science and technology. Focusing on the characteristics of scientific data (e.g. openness, multi-level integration and evolution, and scientific data life cycle management), this paper analyzes the development and problems of scientific data in China, and puts forward comprehensive suggestions on establishing a sound scientific data ecosystem. By establishing and improving the data basic policy system, building a multi-agent governance framework, and strengthening the capacity of scientific data infrastructure and talent training, the paper aims to gradually create a virtuous circle of scientific data production, utilization, sharing and reuse.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41682676","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}
As an essential infrastructure, scientific data repositories play an important role in promoting the practice of open research data. As a bridge between polices and researchers, it makes the data sharing possible. However, it is a challenge to build a repository to be trustworthy and in compliant with FAIR principles. More and more guidelines on how to select a repository and what a trustworthy repository should be like have been brought up. The research elaborates several popular principles and extracts their common requirements. Upon those, the paper analyzes the repositories registered on the website of re3data and summarizes their current development. Moreover, the research focuses on the featured practices in some typical repositories, including domain-specific and generalist data repositories. At last, the research analyzes the development trend of international scientific data repositories in terms of trustworthiness, openness, and ecologization, which can be used as an instructive reference for the construction and development of similar platforms in China.
{"title":"The construction practice and prospects of global scientific data repository platform","authors":"Lulu Jiang, Zeyu Zhang, Zongwen Li, Zongwen Gai, Pengyao Wang, Chengzan Li, Yuanchun Zhou","doi":"10.11922/11-6035.csd.2023.0027.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0027.zh","url":null,"abstract":"As an essential infrastructure, scientific data repositories play an important role in promoting the practice of open research data. As a bridge between polices and researchers, it makes the data sharing possible. However, it is a challenge to build a repository to be trustworthy and in compliant with FAIR principles. More and more guidelines on how to select a repository and what a trustworthy repository should be like have been brought up. The research elaborates several popular principles and extracts their common requirements. Upon those, the paper analyzes the repositories registered on the website of re3data and summarizes their current development. Moreover, the research focuses on the featured practices in some typical repositories, including domain-specific and generalist data repositories. At last, the research analyzes the development trend of international scientific data repositories in terms of trustworthiness, openness, and ecologization, which can be used as an instructive reference for the construction and development of similar platforms in China.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48562234","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}
Pub Date : 2023-03-31DOI: 10.11922/11-6035.csd.2022.0102.zh
Qian Wang, Ce Shang, Chunjing Wang, J. Wan
As a key tributary of the upper Yellow River, originating from Haiyan County, Tibetan Autonomous Prefecture of Haibei, Qinghai Province, Huangshui River flows through Xining City and Haidong City in Qinghai Province, China. Obtaining the basic data of plant communities in Huangshui River Basin is critical to assessing the ecosystem functions and services of the Qinghai-Tibet Plateau and studying its feedback effect on global environmental changes. In this paper, we collated the data from two field surveys of herb communities conducted in the summers of 2021 and 2022 in Huangshui River Basin in Xining City, Haidong City and Haiyan County of Haibei Tibetan Autonomous Prefecture, with a total of 455 quadrats comprising 4,113 records and 277 plant species (55 families and 172 genera). We then calculated the diversity indexes (i.e. species richness, Shannon-Wiener index, Pielou index, and Simpson index) of plant communities. The results can serve as a guide for the future research on plant communities and ecosystems, and biodiversity management in Huangshui River Basin of the Qinghai-Tibet Plateau.
{"title":"A dataset of quadrat sampling of 455 herb quadrats in Huangshui River Basin, Qinghai Province","authors":"Qian Wang, Ce Shang, Chunjing Wang, J. Wan","doi":"10.11922/11-6035.csd.2022.0102.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0102.zh","url":null,"abstract":"As a key tributary of the upper Yellow River, originating from Haiyan County, Tibetan Autonomous Prefecture of Haibei, Qinghai Province, Huangshui River flows through Xining City and Haidong City in Qinghai Province, China. Obtaining the basic data of plant communities in Huangshui River Basin is critical to assessing the ecosystem functions and services of the Qinghai-Tibet Plateau and studying its feedback effect on global environmental changes. In this paper, we collated the data from two field surveys of herb communities conducted in the summers of 2021 and 2022 in Huangshui River Basin in Xining City, Haidong City and Haiyan County of Haibei Tibetan Autonomous Prefecture, with a total of 455 quadrats comprising 4,113 records and 277 plant species (55 families and 172 genera). We then calculated the diversity indexes (i.e. species richness, Shannon-Wiener index, Pielou index, and Simpson index) of plant communities. The results can serve as a guide for the future research on plant communities and ecosystems, and biodiversity management in Huangshui River Basin of the Qinghai-Tibet Plateau.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44916852","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}
Pub Date : 2023-03-31DOI: 10.11922/11-6035.csd.2022.0092.zh
Weidong Ma, Wei Jia, Xingyun Feng, Yuantao Zhou, Pengyan Su, Dan Wei, Chunying Mao, Yimeng Ji, Fenggui Liu, Jing’ai Wang
Highland barley is the dominant crop that can best adapt to the natural environment of the Qinghai-Tibet Plateau characterized by Alpine low temperature, hypoxia and strong radiation. In order to obtain the spatial distribution of the highland barley planting areas on the Qinghai-Tibet Plateau, we adopted a highland barley extraction method based on multi-element fusion of partition classification. First, we impose restrictions on the range of highland barley map spots of different agricultural partitions in terms of altitude, slope, precipitation and hydrological factors. Second, we optimized the optimal band for highland barley extraction through the OIF index partition. Finally, we used the object-oriented classification method to extract the planting areas of highland barley on the Qinghai-Tibet Plateau. The accuracy test of confusion matrix shows that the overall accuracy is 91.74% and Kappa coefficient is 0.83. According to the extraction results of highland barley on the Qinghai-Tibet Plateau, the total planting area of highland barley is about 2.74×105 hm2. The dataset improves the understanding of the existing highland barley spatial distribution pattern from the administrative unit scale to the patch scale. And it can provide data reference for optimizing the spatial distribution pattern of highland barley planting in the future.
{"title":"A dataset of spatial distribution of highland barley planting area on the Qinghai-Tibet Plateau (2019)","authors":"Weidong Ma, Wei Jia, Xingyun Feng, Yuantao Zhou, Pengyan Su, Dan Wei, Chunying Mao, Yimeng Ji, Fenggui Liu, Jing’ai Wang","doi":"10.11922/11-6035.csd.2022.0092.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0092.zh","url":null,"abstract":"Highland barley is the dominant crop that can best adapt to the natural environment of the Qinghai-Tibet Plateau characterized by Alpine low temperature, hypoxia and strong radiation. In order to obtain the spatial distribution of the highland barley planting areas on the Qinghai-Tibet Plateau, we adopted a highland barley extraction method based on multi-element fusion of partition classification. First, we impose restrictions on the range of highland barley map spots of different agricultural partitions in terms of altitude, slope, precipitation and hydrological factors. Second, we optimized the optimal band for highland barley extraction through the OIF index partition. Finally, we used the object-oriented classification method to extract the planting areas of highland barley on the Qinghai-Tibet Plateau. The accuracy test of confusion matrix shows that the overall accuracy is 91.74% and Kappa coefficient is 0.83. According to the extraction results of highland barley on the Qinghai-Tibet Plateau, the total planting area of highland barley is about 2.74×105 hm2. The dataset improves the understanding of the existing highland barley spatial distribution pattern from the administrative unit scale to the patch scale. And it can provide data reference for optimizing the spatial distribution pattern of highland barley planting in the future.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46644189","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}
Pub Date : 2023-03-31DOI: 10.11922/11-6035.csd.2022.0080.zh
Kai Li, Juanle Wang, Wenjing Cheng, Mengmeng Hong
Mongolia Plateau is located in arid and semi-arid areas, and hydrology and water resources are important constraints for the development of its resources and environment. Grasping the temporal and spatial distribution of water bodies on the Mongolian Plateau is of great significance for indicating the temporal and spatial characteristics of water resources and the water environment and their impacts on and responses to regional climate change as well as disaster prevention and reduction. However, as the vast Plateau spans both China and Mongolia, it is a great challenge to accurately and automatically obtain large-scale and long time series water bodies at the basin scale. In this research, we adopted the method of combining local deep learning training and Google Earth Engine (GEE) distributed computing to endow GEE with deep learning computing capabilities so that GEE could rapidly and automatically deploy deep learning models. Based on this, we obtained the distribution of surface water in the growing season of the Mongolia Plateau from 2013 to 2022 with a spatial resolution of 30 meters. 5,000 verification points were manually selected, and the overall verification rate was 88.0%. The dataset is in the form of TIFF grid, containing 28 tile images of with 5°×5°×10 years, with a data volume of 339 MB (88.1 MB compressed, 189 GB in RAW). The data volume in the raw format is 189 GB. With the method used in this dataset, users can automatically and efficiently map water bodies in the cloud platform, which makes it possible to automatically and efficiently process large-scale and long-time series water bodies in arid and semi-arid regions. This is a valuable dataset for application and promotion.
{"title":"A dataset of annual surface water distribution in the growing season on the Mongolia Plateau from 2013 to 2022","authors":"Kai Li, Juanle Wang, Wenjing Cheng, Mengmeng Hong","doi":"10.11922/11-6035.csd.2022.0080.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0080.zh","url":null,"abstract":"Mongolia Plateau is located in arid and semi-arid areas, and hydrology and water resources are important constraints for the development of its resources and environment. Grasping the temporal and spatial distribution of water bodies on the Mongolian Plateau is of great significance for indicating the temporal and spatial characteristics of water resources and the water environment and their impacts on and responses to regional climate change as well as disaster prevention and reduction. However, as the vast Plateau spans both China and Mongolia, it is a great challenge to accurately and automatically obtain large-scale and long time series water bodies at the basin scale. In this research, we adopted the method of combining local deep learning training and Google Earth Engine (GEE) distributed computing to endow GEE with deep learning computing capabilities so that GEE could rapidly and automatically deploy deep learning models. Based on this, we obtained the distribution of surface water in the growing season of the Mongolia Plateau from 2013 to 2022 with a spatial resolution of 30 meters. 5,000 verification points were manually selected, and the overall verification rate was 88.0%. The dataset is in the form of TIFF grid, containing 28 tile images of with 5°×5°×10 years, with a data volume of 339 MB (88.1 MB compressed, 189 GB in RAW). The data volume in the raw format is 189 GB. With the method used in this dataset, users can automatically and efficiently map water bodies in the cloud platform, which makes it possible to automatically and efficiently process large-scale and long-time series water bodies in arid and semi-arid regions. This is a valuable dataset for application and promotion.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49142484","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}
Pub Date : 2023-03-31DOI: 10.11922/11-6035.csd.2022.0082.zh
Shuxing Xu, Juanle Wang
Desertification is one of the most serious eco-environmental and socio-economic problems in the world. Mongolia is a hot area of global desertification because of its fragile ecological environment and serious land degradation. In this study, based on Google Earth Engine platform, we selected Landsat7 remote sensing images, normalized vegetation index (NDVI), surface albedo (Albedo), improved soil adjusted vegetation index (MSAVI) and topsoil grain size index (TGSI) as desertification discrimination indexes, and combined the geographical division with desertification inversion characteristic space models to complete the fine inversion of Mongolian desertification information. In this way, we obtained a dataset of desertification distribution in Mongolia in 2015. The quality and accuracy of this dataset are verified by referring to field survey data and high-resolution Google Earth images. The overall evaluation accuracy is 87.00% and the Kappa coefficient is 83.19%. This dataset directly reflects the spatial distribution of different degrees of desertification in Mongolia, and can provide detailed and reliable data support for the delineation of key areas for desertification control and the formulation of restoration strategies in Mongolia. It is of great significance for the ecological environment and green and sustainable development of the China-Mongolia-Russia Economic Corridor.
{"title":"A dataset of desertification distribution in Mongolia in 2015","authors":"Shuxing Xu, Juanle Wang","doi":"10.11922/11-6035.csd.2022.0082.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0082.zh","url":null,"abstract":"Desertification is one of the most serious eco-environmental and socio-economic problems in the world. Mongolia is a hot area of global desertification because of its fragile ecological environment and serious land degradation. In this study, based on Google Earth Engine platform, we selected Landsat7 remote sensing images, normalized vegetation index (NDVI), surface albedo (Albedo), improved soil adjusted vegetation index (MSAVI) and topsoil grain size index (TGSI) as desertification discrimination indexes, and combined the geographical division with desertification inversion characteristic space models to complete the fine inversion of Mongolian desertification information. In this way, we obtained a dataset of desertification distribution in Mongolia in 2015. The quality and accuracy of this dataset are verified by referring to field survey data and high-resolution Google Earth images. The overall evaluation accuracy is 87.00% and the Kappa coefficient is 83.19%. This dataset directly reflects the spatial distribution of different degrees of desertification in Mongolia, and can provide detailed and reliable data support for the delineation of key areas for desertification control and the formulation of restoration strategies in Mongolia. It is of great significance for the ecological environment and green and sustainable development of the China-Mongolia-Russia Economic Corridor.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48183322","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}
Pub Date : 2023-03-31DOI: 10.11922/11-6035.nasdc.2021.0055.zh
Ruyi Yang, Shanshan Cao, Wei Sun, Min-Uk An, Jifang Liu, Xiaoli Wang, Fantao Kong
Solanaceous vegetables are popular dishes for residents, and they are also one type of the important cultivated vegetables in China. The price of solanaceous vegetables is non-steady with large fluctuations. Therefore, it is important to master the historical price trends and fluctuation laws, which can provide scientific guidance and support for ensuring the stability of vegetable supply prices and guiding the orderly operation of the market. Through data collection, stability test and HP filtering method decomposition, we formed a dataset of the HP filter model-based research on price volatility of solanaceous vegetables at Beijing Xinfadi Agricultural and Sideline Products Wholesale Market from 2012 to 2018. The varieties cover five kinds of solanaceous vegetables (eggplant, tomato, green pepper, bean and cucumber), including market, date, weekly/monthly average price, weekly/monthly average volatility, weekly/monthly maximum price, weekly/monthly minimum price, weekly/monthly average HP filter trend value, weekly/monthly average HP filter fluctuation value. The dataset can reflect the weekly and monthly changes of the trading prices of solanaceous vegetables in Beijing Xinfadi Market, and provide scientific data foundation for the research on as well as monitoring and early warning of the price fluctuation of solanaceous vegetables in Beijing.
{"title":"A dataset of the HP filter model-based research on price volatility of solanaceous vegetables in Beijing Xinfadi Market from 2012 to 2018","authors":"Ruyi Yang, Shanshan Cao, Wei Sun, Min-Uk An, Jifang Liu, Xiaoli Wang, Fantao Kong","doi":"10.11922/11-6035.nasdc.2021.0055.zh","DOIUrl":"https://doi.org/10.11922/11-6035.nasdc.2021.0055.zh","url":null,"abstract":"Solanaceous vegetables are popular dishes for residents, and they are also one type of the important cultivated vegetables in China. The price of solanaceous vegetables is non-steady with large fluctuations. Therefore, it is important to master the historical price trends and fluctuation laws, which can provide scientific guidance and support for ensuring the stability of vegetable supply prices and guiding the orderly operation of the market. Through data collection, stability test and HP filtering method decomposition, we formed a dataset of the HP filter model-based research on price volatility of solanaceous vegetables at Beijing Xinfadi Agricultural and Sideline Products Wholesale Market from 2012 to 2018. The varieties cover five kinds of solanaceous vegetables (eggplant, tomato, green pepper, bean and cucumber), including market, date, weekly/monthly average price, weekly/monthly average volatility, weekly/monthly maximum price, weekly/monthly minimum price, weekly/monthly average HP filter trend value, weekly/monthly average HP filter fluctuation value. The dataset can reflect the weekly and monthly changes of the trading prices of solanaceous vegetables in Beijing Xinfadi Market, and provide scientific data foundation for the research on as well as monitoring and early warning of the price fluctuation of solanaceous vegetables in Beijing.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46029283","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}
Pub Date : 2023-03-31DOI: 10.11922/11-6035.csd.2022.0083.zh
Juanle Wang, Shuxing Xu, Fei Yang, Kai Li, Yating Shao
The Mongolian Plateau is in the interior of Northeast Asia, and is extremely vulnerable to climate change and the deleterious effects of human activities. Mongolia is an important component unit of the Mongolian Plateau, and its resources, environment and ecological problems are closely related to the ecological barrier and resource security in northern China and the sustainable development of the China-Mongolia-Russia Corridor. However, there is still a lack of high-precision land cover data products suitable for the regional characteristics of Mongolia. In this study, according to the landscape pattern of Mongolia, we constructed a land cover classification system suitable for Mongolia; and based on the object-oriented remote sensing interpretation method, we adopted the split-scene interpretation to select a variety of indexes. According to certain rules and classification thresholds, we obtained a dataset of land cover classifications with a spatial resolution of 30m in Mongolia in 2005 and 2015. The land cover classifications of Mongolia includes 11 categories: forest, meadow steppe, real steppe, desert steppe, bare land, sand, desert, ice and snow, water, cropland and built areas. Based on multi-source validation point information and high-resolution Google Earth images, we completed an overall quality assessment and a single classification quality assessment of land cover classification results in Mongolia. In 2005, the overall classification accuracy is 78.85% and the Kappa coefficient is 0.77. In 2015, the overall classification accuracy is 80.49% and the Kappa coefficient is 0.78. The average annual classification accuracy is 79.67%, which meets the accuracy requirements. The dataset can directly reflect the changes of land cover pattern and trend in Mongolia and provide basic scientific data to support the sustainable development of Mongolia.
{"title":"A dataset of land cover classifications with a spatial resolution of 30m in Mongolia in 2005 and 2015","authors":"Juanle Wang, Shuxing Xu, Fei Yang, Kai Li, Yating Shao","doi":"10.11922/11-6035.csd.2022.0083.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0083.zh","url":null,"abstract":"The Mongolian Plateau is in the interior of Northeast Asia, and is extremely vulnerable to climate change and the deleterious effects of human activities. Mongolia is an important component unit of the Mongolian Plateau, and its resources, environment and ecological problems are closely related to the ecological barrier and resource security in northern China and the sustainable development of the China-Mongolia-Russia Corridor. However, there is still a lack of high-precision land cover data products suitable for the regional characteristics of Mongolia. In this study, according to the landscape pattern of Mongolia, we constructed a land cover classification system suitable for Mongolia; and based on the object-oriented remote sensing interpretation method, we adopted the split-scene interpretation to select a variety of indexes. According to certain rules and classification thresholds, we obtained a dataset of land cover classifications with a spatial resolution of 30m in Mongolia in 2005 and 2015. The land cover classifications of Mongolia includes 11 categories: forest, meadow steppe, real steppe, desert steppe, bare land, sand, desert, ice and snow, water, cropland and built areas. Based on multi-source validation point information and high-resolution Google Earth images, we completed an overall quality assessment and a single classification quality assessment of land cover classification results in Mongolia. In 2005, the overall classification accuracy is 78.85% and the Kappa coefficient is 0.77. In 2015, the overall classification accuracy is 80.49% and the Kappa coefficient is 0.78. The average annual classification accuracy is 79.67%, which meets the accuracy requirements. The dataset can directly reflect the changes of land cover pattern and trend in Mongolia and provide basic scientific data to support the sustainable development of Mongolia.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47428507","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}
Pub Date : 2023-03-31DOI: 10.11922/11-6035.nasdc.2022.0014.zh
Tianshan spruce (Picea schrenkiana var. tianschanica ) is an important tree for water conservation, windbreak and sand fixation in the mountains of Xinjiang, and plays an important role in the formation and maintenance of forest ecosystem in Xinjiang. Ecological factors such as temperature, precipitation, soil and topography are important data bases for studying forest ecosystem. Based on multi-source data such as remote sensing image, DEM, meteorological data and soil data in Yili region of Xinjiang, we used multi-scale segmentation, nearest neighbor classification and spatial analysis to generate a dataset of ecological factors in the distribution area of Tianshan spruce in Yili region in 2014, covering six ecological factors, namely temperature, precipitation, sunshine duration, slope, slope direction and soil type. In this dataset, strict procedures of data quality control were carried out by TTA Mask accuracy verification and other methods to ensure the accuracy and reliability of the data. The dataset can provide data support for forest ecosystem health assessment.
天山云杉(Picea schrenkiana var. tianschanica)是新疆山区重要的保水、防风、固沙乔木，在新疆森林生态系统的形成和维持中起着重要作用。温度、降水、土壤、地形等生态因子是研究森林生态系统的重要数据基础。基于新疆伊犁地区遥感影像、DEM、气象数据和土壤数据等多源数据，采用多尺度分割、最近邻分类和空间分析等方法，构建了2014年伊犁地区天山云杉分布区生态因子数据集，包括温度、降水、日照时数、坡度、坡度方向和土壤类型6个生态因子。在该数据集中，通过TTA Mask精度验证等方法对数据进行了严格的质量控制程序，以确保数据的准确性和可靠性。该数据集可为森林生态系统健康评价提供数据支持。
{"title":"A dataset of ecological factors in the distribution area of Tianshan spruce in Yili region in 2014","authors":"","doi":"10.11922/11-6035.nasdc.2022.0014.zh","DOIUrl":"https://doi.org/10.11922/11-6035.nasdc.2022.0014.zh","url":null,"abstract":"Tianshan spruce (Picea schrenkiana var. tianschanica ) is an important tree for water conservation, windbreak and sand fixation in the mountains of Xinjiang, and plays an important role in the formation and maintenance of forest ecosystem in Xinjiang. Ecological factors such as temperature, precipitation, soil and topography are important data bases for studying forest ecosystem. Based on multi-source data such as remote sensing image, DEM, meteorological data and soil data in Yili region of Xinjiang, we used multi-scale segmentation, nearest neighbor classification and spatial analysis to generate a dataset of ecological factors in the distribution area of Tianshan spruce in Yili region in 2014, covering six ecological factors, namely temperature, precipitation, sunshine duration, slope, slope direction and soil type. In this dataset, strict procedures of data quality control were carried out by TTA Mask accuracy verification and other methods to ensure the accuracy and reliability of the data. The dataset can provide data support for forest ecosystem health assessment.","PeriodicalId":57643,"journal":{"name":"China Scientific Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42271314","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}
Pub Date : 2023-03-31DOI: 10.11922/11-6035.csd.2022.0095.zh
Menghan Li, Haiyu Li, M. Gao, Renfeng Che, Junchen Zhou, Yueyu Sui
Soil temperature has a great influence on soil humification and mineralization, so it is of great significance to collect soil temperature observations at different spatial scales. Located in the core area of black soil, Hailun Agricultural Ecological Experiment Station (hereinafter referred to as Hailun Ecological Station) is a state-level long-term positioning research station of China Ecosystem Research Network. The soil temperature data of Hailun Ecological Station is of great significance for studying the soil water and heat process in black soil area. The data in this dataset are collected at Helen Ecological Station by the MILOS520 and MAWS301 automatic monitoring systems developed by VAISALA Company of Finland. We used the “Eco-meteorological Workstation” software process the observation data, and ensured the accuracy of the monitoring soil temperature data through analysis, inspection and reasonable optimization. This dataset consists of 11 data tables, namely the yearly soil temperature data of 11 years from 2010 to 2020, respectively. Each data table includes hourly soil temperature data of 0 cm, 5 cm, 10 cm, 15 cm, 20 cm, 40 cm, 60 cm and 100 cm. The publication of this dataset aims to provide scientific basis for the protection and long-term use of black soil.
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