Located in China's Inner Mongolia Plateau, Inner Mongolia Autonomous Region is the main area of the Mongolian Plateau in China, and it is also a key research area for desertification and sandification monitoring. The land in this area is severely desertified and sandificated. Carrying out annual monitoring according to the characteristics of desertification land in this region is a necessary support for the research on grasping the dynamic characteristics of desertification and comprehensively analyzing the causes of desertification. We based the monitoring research on the MODIS data from 2001 to 2021 in the Inner Mongolia Autonomous Region and selected FVC, MSAVI, LST, TVDI indicator to generate indicator discriminant values through information overlay analysis, classification, and discrimination. And according to the zoning regulations, we obtained the annual monitoring data of desertification degree from 2001 to 2021 in 4 climate zones in Inner Mongolia. This dataset can provide data support for studies on desertification changes and driving mechanisms and so on.
{"title":"A dataset of desertification degree monitoring in Inner Mongolia from 2001 to 2021","authors":"Ruixia Hou, Xiaoming Cao, Yiming Feng, Lei Xi, Zhi-peng Li, Yundan Xiao, Naijing Zhang, Shengrong Wei","doi":"10.11922/11-6035.csd.2022.0087.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0087.zh","url":null,"abstract":"Located in China's Inner Mongolia Plateau, Inner Mongolia Autonomous Region is the main area of the Mongolian Plateau in China, and it is also a key research area for desertification and sandification monitoring. The land in this area is severely desertified and sandificated. Carrying out annual monitoring according to the characteristics of desertification land in this region is a necessary support for the research on grasping the dynamic characteristics of desertification and comprehensively analyzing the causes of desertification. We based the monitoring research on the MODIS data from 2001 to 2021 in the Inner Mongolia Autonomous Region and selected FVC, MSAVI, LST, TVDI indicator to generate indicator discriminant values through information overlay analysis, classification, and discrimination. And according to the zoning regulations, we obtained the annual monitoring data of desertification degree from 2001 to 2021 in 4 climate zones in Inner Mongolia. This dataset can provide data support for studies on desertification changes and driving mechanisms and so on.","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":"47915590","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.2023.0014.zh
Yifan Wu, Tingting Liu, Na Zhang, Chen Liu
Rice is one of the main crops planted in China, and also the largest component of food consumption. With the rapid development of China, people’s life quality has improved, and higher requirements have been put forward for the quality of rice, which lays stress on not only the yield of rice but also the improvement of its quality. In order to make a survey of the quality of mature rice in the middle and upper reaches of the Yangtze River, according to the inspection method of the national standard for high-quality rice (GB/T 17891-2017), we collected samples of 20 varieties from 15 test sites in the middle and upper reaches of the Yangtze River from 2016 to 2020 to obtain the data of 4 quality indexes of high-quality rice, namely chalkiness, grain length, amylose content, and head rice rate respectively, with a total of 6,000 test results. This dataset will provide a basis for the study of rice seed selection in the middle and upper reaches of the Yangtze River.
{"title":"A dataset of rice quality indexes of 20 varieties in the middle and upper reaches of the Yangtze River from 2016 to 2020","authors":"Yifan Wu, Tingting Liu, Na Zhang, Chen Liu","doi":"10.11922/11-6035.nasdc.2023.0014.zh","DOIUrl":"https://doi.org/10.11922/11-6035.nasdc.2023.0014.zh","url":null,"abstract":"Rice is one of the main crops planted in China, and also the largest component of food consumption. With the rapid development of China, people’s life quality has improved, and higher requirements have been put forward for the quality of rice, which lays stress on not only the yield of rice but also the improvement of its quality. In order to make a survey of the quality of mature rice in the middle and upper reaches of the Yangtze River, according to the inspection method of the national standard for high-quality rice (GB/T 17891-2017), we collected samples of 20 varieties from 15 test sites in the middle and upper reaches of the Yangtze River from 2016 to 2020 to obtain the data of 4 quality indexes of high-quality rice, namely chalkiness, grain length, amylose content, and head rice rate respectively, with a total of 6,000 test results. This dataset will provide a basis for the study of rice seed selection in the middle and upper reaches of the Yangtze River.","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":"45721697","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.2023.0047.zh
Yanhua Zhu, Yuwei Gao, Lianglin Hu, Po Hu
Scientific data standards and specifications are crucial to promote the sharing of data resources. Research on their changes and development is of great significance to give full play to the value of data and enhance the competitiveness of national scientific and technological innovation. In this paper, we surveyed the current situation of scientific data standards and specifications in China and sorted out the new characteristics of standards and specifications at present. We further put forward some thoughts about and suggestions on the construction of scientific data standards and specifications in China, including giving full play to the technical support of data standards and specifications, strengthening the research and development of international standards and association standards, and continuously tracking and evaluating the effect of standard application.
{"title":"Practice and thinking of scientific data standard in China","authors":"Yanhua Zhu, Yuwei Gao, Lianglin Hu, Po Hu","doi":"10.11922/11-6035.csd.2023.0047.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0047.zh","url":null,"abstract":"Scientific data standards and specifications are crucial to promote the sharing of data resources. Research on their changes and development is of great significance to give full play to the value of data and enhance the competitiveness of national scientific and technological innovation. In this paper, we surveyed the current situation of scientific data standards and specifications in China and sorted out the new characteristics of standards and specifications at present. We further put forward some thoughts about and suggestions on the construction of scientific data standards and specifications in China, including giving full play to the technical support of data standards and specifications, strengthening the research and development of international standards and association standards, and continuously tracking and evaluating the effect of standard application.","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":"47620749","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.2022.0061.zh
Yan Wang, Shuai Liu, Lisha Qiu, Wei Shan
The Mongolian Plateau is characteristic of fragile ecosystem and serious land desertification. As one of the main climate-varied sensitive regions in Asia, it is of great value for the study on temporal and spatial changes of water resources. For the production of this dataset, we used Google Earth engine (GEE) platform to process the high–quality Landsat series satellite images available in the past 32 years (1990–2021). Using the minimum cloud amount synthesis algorithm, we obtained the minimum cloud amount images in the wet season (from June to September) of each year. After calculating NDWI, we further used the OTSU algorithm for threshold segmentation, and extracted the yearly water body data with 30m resolution on the Mongolian Plateau in the wet season for 32 years. The final data results are saved in GeoTIFF format. Through comparison, the average consistency between this dataset and the JRC annual water body data in both permanent water body and maximum water body is 93.0% and 90.9% respectively, which indicates the high reliability of this dataset. The dataset can provide data support for water resource changes, ecological construction planning, environmental protection, etc. on the Mongolian Plateau.
{"title":"A dataset of the annual water bodies in wet seasons on the Mongolian Plateau during 1990–2021","authors":"Yan Wang, Shuai Liu, Lisha Qiu, Wei Shan","doi":"10.11922/11-6035.csd.2022.0061.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0061.zh","url":null,"abstract":"The Mongolian Plateau is characteristic of fragile ecosystem and serious land desertification. As one of the main climate-varied sensitive regions in Asia, it is of great value for the study on temporal and spatial changes of water resources. For the production of this dataset, we used Google Earth engine (GEE) platform to process the high–quality Landsat series satellite images available in the past 32 years (1990–2021). Using the minimum cloud amount synthesis algorithm, we obtained the minimum cloud amount images in the wet season (from June to September) of each year. After calculating NDWI, we further used the OTSU algorithm for threshold segmentation, and extracted the yearly water body data with 30m resolution on the Mongolian Plateau in the wet season for 32 years. The final data results are saved in GeoTIFF format. Through comparison, the average consistency between this dataset and the JRC annual water body data in both permanent water body and maximum water body is 93.0% and 90.9% respectively, which indicates the high reliability of this dataset. The dataset can provide data support for water resource changes, ecological construction planning, environmental protection, etc. on the Mongolian 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":"49587092","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.2021.0046.zh
The complex and varied mountainous conditions and climatic factors in Xinjiang contribute to the production of abundant species of wild medicinal plants. Based on the consecutive 4-year field survey in some areas of Xinjiang, this paper records the wild medicinal plants on the spot. Referring to the wild medicinal plants distributed in Xinjiang recorded in national and regional flora and monographs, domestic and foreign medicinal plant academic journals, major public specimen banks and databases, we reorganized a dataset of wild medicinal plant resources for heat-cleaning and detoxifying effects in Xinjiang. This dataset involves 127 species of angiosperms, 3 species of ferns, 2 species of gymnosperms, one species of lichens, and 4 species of other plants, including 13 items of information about medicinal plants: Chinese name, English name, Latin name, alias, phylum, order, family, genus, nature and flavor, efficacy, medicinal parts, habitat distribution, and picture. It can provide data support for the diversity research and protection of wild medicinal plants with heat clearing effect, detoxifying clearing, and the combination of the both effects as well as the medicine research on medicinal plants in Xinjiang.
{"title":"A dataset of wild medicinal plant resources for heat-clearing and detoxifying effects in Xinjiang from 2017 to 2020","authors":"","doi":"10.11922/11-6035.nasdc.2021.0046.zh","DOIUrl":"https://doi.org/10.11922/11-6035.nasdc.2021.0046.zh","url":null,"abstract":"The complex and varied mountainous conditions and climatic factors in Xinjiang contribute to the production of abundant species of wild medicinal plants. Based on the consecutive 4-year field survey in some areas of Xinjiang, this paper records the wild medicinal plants on the spot. Referring to the wild medicinal plants distributed in Xinjiang recorded in national and regional flora and monographs, domestic and foreign medicinal plant academic journals, major public specimen banks and databases, we reorganized a dataset of wild medicinal plant resources for heat-cleaning and detoxifying effects in Xinjiang. This dataset involves 127 species of angiosperms, 3 species of ferns, 2 species of gymnosperms, one species of lichens, and 4 species of other plants, including 13 items of information about medicinal plants: Chinese name, English name, Latin name, alias, phylum, order, family, genus, nature and flavor, efficacy, medicinal parts, habitat distribution, and picture. It can provide data support for the diversity research and protection of wild medicinal plants with heat clearing effect, detoxifying clearing, and the combination of the both effects as well as the medicine research on medicinal plants in Xinjiang.","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":"43304343","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.2021.0048.zh
Xinjiang is rich in wild medicinal resources, which are mainly distributed in Tianshan Mountains and Altai Mountains in Northern Xinjiang. Trollius chinensis is one of the plants with heat clearing and detoxifying effects. It has been listed as one of the recommended Chinese medicinal materials for poverty alleviation in China’s traditional Chinese medicine industry, with a good market prospect. This dataset is formed based on the field study on Trollius chinensis in Altay, Xinjiang. It consists of three types of multi-source data: 81 Trollius chinensis sample point data, 42 environmental variable data and one Trollius chinensis distribution data. Among them, the data of sample points were collected through field study in July, 2018 as well as June and July, 2019; The number of environmental variables includes 39 climatic data and 3 topographic data. The climatic data are obtained by interpolating the data of 6 meteorological stations in Altay region from 1995 to 2018, and the topographic data are obtained based on elevation data processing; the distribution data of Trollius chinensis are obtained through inputting the sample point data and environmental variable data into MaxEnt model for analysis. This dataset can provide a data basis for predicting the function evaluation of the potential suitable growth area of Altay Golden Lotus. Referring to the distribution area of Altay Golden Lotus, suitable growth areas can be selected for introduction and artificial cultivation. The dataset can support the resource development and species diversity protection of Altay wild Golden chinensis, and prop up the research of scientific researchers on such medicinal plants. Moreover, it also has certain value for the scientific research of other wild medicinal plants in this field.
{"title":"A dataset of Trollius chinensis distribution in Altay, Xinjiang during 1995–2018","authors":"","doi":"10.11922/11-6035.nasdc.2021.0048.zh","DOIUrl":"https://doi.org/10.11922/11-6035.nasdc.2021.0048.zh","url":null,"abstract":"Xinjiang is rich in wild medicinal resources, which are mainly distributed in Tianshan Mountains and Altai Mountains in Northern Xinjiang. Trollius chinensis is one of the plants with heat clearing and detoxifying effects. It has been listed as one of the recommended Chinese medicinal materials for poverty alleviation in China’s traditional Chinese medicine industry, with a good market prospect. This dataset is formed based on the field study on Trollius chinensis in Altay, Xinjiang. It consists of three types of multi-source data: 81 Trollius chinensis sample point data, 42 environmental variable data and one Trollius chinensis distribution data. Among them, the data of sample points were collected through field study in July, 2018 as well as June and July, 2019; The number of environmental variables includes 39 climatic data and 3 topographic data. The climatic data are obtained by interpolating the data of 6 meteorological stations in Altay region from 1995 to 2018, and the topographic data are obtained based on elevation data processing; the distribution data of Trollius chinensis are obtained through inputting the sample point data and environmental variable data into MaxEnt model for analysis. This dataset can provide a data basis for predicting the function evaluation of the potential suitable growth area of Altay Golden Lotus. Referring to the distribution area of Altay Golden Lotus, suitable growth areas can be selected for introduction and artificial cultivation. The dataset can support the resource development and species diversity protection of Altay wild Golden chinensis, and prop up the research of scientific researchers on such medicinal plants. Moreover, it also has certain value for the scientific research of other wild medicinal plants in this field.","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":"49515348","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}
Automatic recognition of desert plant types by machine vision can support the research on wind prevention and sand fixation, ecosystem value assessment, vegetation restoration and reconstruction, and reduce the dependence on plant expert identification. At present, the research on the machine discrimination model of desert plants mainly relies on the standardized high-quality plant specimen images, lacking the desert plant images obtained under complex natural conditions. This dataset provides typical desert plant images of Xinjiang that can be used for the model training of deep learning image classification, including 15,550 digital camera images of desert plants in Xinjiang obtained under different seasons, natural backgrounds and lighting conditions, and covering 19 typical desert plant types. Suaeda salsa has the smallest number of images and Artemisia desertorum has the biggest, 465 and 1,240 respectively, with a median of 800, which has met the training needs of mainstream deep learning model. This dataset can provide basic data for desert plant image segmentation, target detection and automatic recognition.
{"title":"A dataset of desert plant images for deep learning recognition in Xinjiang in 2020–2021","authors":"Yapeng Wang, Quansheng Li, Gulimila Kezierbieke, Shen Yan, Tingting Liu, Wei Sun, Shanshan Cao","doi":"10.11922/11-6035.nasdc.2021.0050.zh","DOIUrl":"https://doi.org/10.11922/11-6035.nasdc.2021.0050.zh","url":null,"abstract":"Automatic recognition of desert plant types by machine vision can support the research on wind prevention and sand fixation, ecosystem value assessment, vegetation restoration and reconstruction, and reduce the dependence on plant expert identification. At present, the research on the machine discrimination model of desert plants mainly relies on the standardized high-quality plant specimen images, lacking the desert plant images obtained under complex natural conditions. This dataset provides typical desert plant images of Xinjiang that can be used for the model training of deep learning image classification, including 15,550 digital camera images of desert plants in Xinjiang obtained under different seasons, natural backgrounds and lighting conditions, and covering 19 typical desert plant types. Suaeda salsa has the smallest number of images and Artemisia desertorum has the biggest, 465 and 1,240 respectively, with a median of 800, which has met the training needs of mainstream deep learning model. This dataset can provide basic data for desert plant image segmentation, target detection and automatic recognition.","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":"44390770","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.2022.0062.zh
Located in the plateau region of the hinterland of Eurasia, the Mongolian Plateau has vegetation covers, including forest, forest steppe, typical steppe, desert steppe, and gobi desert, etc. It is effective to use FAPAR data to monitor the changes of biodiversity on the Mongolian Plateau. Based on FAPAR data, we used ArcGIS and Python program to synthesize monthly maximum values, and in combination with the dynamic habitat index (DHI), we obtained a dataset of dynamic habitat indexes with a resolution of 500m from 2001 to 2018, including the spatial distribution of long-term series DHI on the Mongolian Plateau. The geographic scope of the dataset covers Inner Mongolia Autonomous Region of China, all over Mongolia and Southern Russia, with a time series of 2001–2018. The data is stored in “.tif” format. Through data sharing, the dataset is expected to provide data support for the research on the spatial distribution of the biodiversity and species richness as well as the prediction of species distribution in the future on the Mongolian Plateau.
{"title":"A dataset of dynamic habitat indexes with a resolution of 500m on the Mongolian Plateau (2001-2018)","authors":"","doi":"10.11922/11-6035.csd.2022.0062.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0062.zh","url":null,"abstract":"Located in the plateau region of the hinterland of Eurasia, the Mongolian Plateau has vegetation covers, including forest, forest steppe, typical steppe, desert steppe, and gobi desert, etc. It is effective to use FAPAR data to monitor the changes of biodiversity on the Mongolian Plateau. Based on FAPAR data, we used ArcGIS and Python program to synthesize monthly maximum values, and in combination with the dynamic habitat index (DHI), we obtained a dataset of dynamic habitat indexes with a resolution of 500m from 2001 to 2018, including the spatial distribution of long-term series DHI on the Mongolian Plateau. The geographic scope of the dataset covers Inner Mongolia Autonomous Region of China, all over Mongolia and Southern Russia, with a time series of 2001–2018. The data is stored in “.tif” format. Through data sharing, the dataset is expected to provide data support for the research on the spatial distribution of the biodiversity and species richness as well as the prediction of species distribution in the future on the Mongolian 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":"43639357","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.2022.0065.zh
Located in the highland of Eurasia, the Mongolian Plateau, a typical arid and semi-arid region, is highly sensitive to climate change. Vegetation phenology is the most intuitive and sensitive biological indicator of seasonal and interannual changes of climatic conditions. Vegetation phenology data can be used to explore the ecological situation and climate change of the Mongolian Plateau. Based on THE MODIS land cover Dynamic Product (MCD12Q2 C6), in this paper, we used non-parametric Theil-Sen Median trend analysis and Mann-Kendall significance test to extract sub-datasets, Mosaic, projection transformation and cropping. In this way, we obtained the data of four phenological periods (namely the beginning time, the end time, the length of the growing season and the peak time of the growing season) and the change trend data of vegetation of 500m resolution on the Mongolian Plateau from 2001 to 2019. This dataset can reflect the spatio-temporal changes of vegetation phenology in 19 years on the Mongolian Plateau. Combined with climate factors such as temperature and precipitation, it can be used to explore the response and feedback mechanism of vegetation phenology change to environmental factors, and provide data support for vegetation change analysis, climate change, carbon cycle and other studies.
{"title":"A dataset of vegetation phenology and change trends with a resolution of 500m of on the Mongolian Plateau (2001–2019)","authors":"","doi":"10.11922/11-6035.csd.2022.0065.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0065.zh","url":null,"abstract":"Located in the highland of Eurasia, the Mongolian Plateau, a typical arid and semi-arid region, is highly sensitive to climate change. Vegetation phenology is the most intuitive and sensitive biological indicator of seasonal and interannual changes of climatic conditions. Vegetation phenology data can be used to explore the ecological situation and climate change of the Mongolian Plateau. Based on THE MODIS land cover Dynamic Product (MCD12Q2 C6), in this paper, we used non-parametric Theil-Sen Median trend analysis and Mann-Kendall significance test to extract sub-datasets, Mosaic, projection transformation and cropping. In this way, we obtained the data of four phenological periods (namely the beginning time, the end time, the length of the growing season and the peak time of the growing season) and the change trend data of vegetation of 500m resolution on the Mongolian Plateau from 2001 to 2019. This dataset can reflect the spatio-temporal changes of vegetation phenology in 19 years on the Mongolian Plateau. Combined with climate factors such as temperature and precipitation, it can be used to explore the response and feedback mechanism of vegetation phenology change to environmental factors, and provide data support for vegetation change analysis, climate change, carbon cycle and other studies.","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":"41466532","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.2023.0006.zh
Grassland is the dominant vegetation type on the Mongolian Plateau. It is not only an important part of the ecological environment of the Mongolian Plateau, but also an important resource base for the development of animal husbandry in the Mongolian Plateau. As one of the evaluation indicators of grassland productivity, the grass yield has guiding significance for striking the balance between grassland and livestock. However, due to the long-term dependence on artificial investigation, there is a shortage of products for estimating grass yield in a large range, high spatial resolution and continuous time. Taking Mongolia as the research area, in this paper, we used Landsat8 remote sensing image, MODIS remote sensing data and meteorological data in combination with the measured sample data of grass yield in the field survey to obtain the relationship between the measured grass yield and the vegetation index NDVI, surface temperature and precipitation through the depth neural network. In this way, we constructed the estimation model of Mongolia's domestic grass yield suitable for the characteristics of the region. Moreover, we establish a deep neural network estimation model for grass yield, and retrieved the temporal and spatial distribution map of grass yield in Mongolia from 2017 to 2021. The precision verification experiment shows that the model based on deep learning has a high precision, with an RMSE of 12.14 g/m2 and an estimation accuracy of 81%, which can provide a method and data reference for the estimation of domestic grassland in Mongolia.
{"title":"A dataset of grass yield estimation with 30m resolution in Mongolia during 2017-2021","authors":"","doi":"10.11922/11-6035.csd.2023.0006.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2023.0006.zh","url":null,"abstract":"Grassland is the dominant vegetation type on the Mongolian Plateau. It is not only an important part of the ecological environment of the Mongolian Plateau, but also an important resource base for the development of animal husbandry in the Mongolian Plateau. As one of the evaluation indicators of grassland productivity, the grass yield has guiding significance for striking the balance between grassland and livestock. However, due to the long-term dependence on artificial investigation, there is a shortage of products for estimating grass yield in a large range, high spatial resolution and continuous time. Taking Mongolia as the research area, in this paper, we used Landsat8 remote sensing image, MODIS remote sensing data and meteorological data in combination with the measured sample data of grass yield in the field survey to obtain the relationship between the measured grass yield and the vegetation index NDVI, surface temperature and precipitation through the depth neural network. In this way, we constructed the estimation model of Mongolia's domestic grass yield suitable for the characteristics of the region. Moreover, we establish a deep neural network estimation model for grass yield, and retrieved the temporal and spatial distribution map of grass yield in Mongolia from 2017 to 2021. The precision verification experiment shows that the model based on deep learning has a high precision, with an RMSE of 12.14 g/m2 and an estimation accuracy of 81%, which can provide a method and data reference for the estimation of domestic grassland in 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":"48968549","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}
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