As a basic properties of forest vegetation, forest succession law is the basis understanding . Typical evergreen broad-leaved forest a zonal vegetation in the subtropical area of China. The existing vegetation is mostly in different secondary succession stages due to human and natural disturbance. lant species is an important indicator of the forest ecosystem and the basis for the study o structure, function and dynamics of evergreen broad-leaved forest. According to CNERN monitoring standards, Zhejiang Tiantong Forest Ecosystem Observation and Research Station three three succession plots and established a dataset species composition 2008 to 2017. The succession plots are evergreen shrub plot, Schima Superba forest plot and Castanopsis fargesii forest plot, respectively species name, abundance, mean diameter and biomass of woody plants the plots. The database provides critical data for the study and application of succession, community assembly and forest restoration in subtropical typical evergreen broad-leaved forests.
{"title":"A dataset of species composition and biomass in successional stages of Tiantong typical evergreen broad-leaved forest (2008–2017)","authors":"Qingsong Yang, Haibo Yang, Zemei Zheng, Heming Liu, Fang-fang Yao, Shan-Qun Jiang, Xihua Wang","doi":"10.11922/11-6035.csd.2022.0077.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0077.zh","url":null,"abstract":"As a basic properties of forest vegetation, forest succession law is the basis understanding . Typical evergreen broad-leaved forest a zonal vegetation in the subtropical area of China. The existing vegetation is mostly in different secondary succession stages due to human and natural disturbance. lant species is an important indicator of the forest ecosystem and the basis for the study o structure, function and dynamics of evergreen broad-leaved forest. According to CNERN monitoring standards, Zhejiang Tiantong Forest Ecosystem Observation and Research Station three three succession plots and established a dataset species composition 2008 to 2017. The succession plots are evergreen shrub plot, Schima Superba forest plot and Castanopsis fargesii forest plot, respectively species name, abundance, mean diameter and biomass of woody plants the plots. The database provides critical data for the study and application of succession, community assembly and forest restoration in subtropical typical evergreen broad-leaved forests.","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":"41938331","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 one of the most important water quality parameters on the radar screen of environmental protection sectors, water transparency reveals the turbidity degree of water and plays an important role in the primary productivity of water body and water ecosystem. As an independent island water system, Hainan Province has abundant surface inland water resources and plentiful river runoff. However, due to the influence of dry and wet monsoons and topography, the aquatic systems are characterized by uneven spatial and temporal distribution, and there are few studies on the water quality of inland water bodies on Hainan Island. In this study, we took Sanya River in Sanya, Hainan Province as the study area, and used the QAAv6-based semi-analytic model to retrieve the water transparency of Sanya River in time series from 2019 to 2021 based on the GEE cloud computing platform and the massive Sentinel-2 surface reflectance data stored in Google Cloud. With regard to the extraction of dynamic water area from Sanya River, we adopted the algorithm combining the normalized water body index NDWI with OTUS automatic threshold segmentation to extract the small river water. The data are stored in GeoTiff raster format, and the pixel transparency value and coordinate information are stored at the same time for easy reading and analysis by relevant GIS software. The inversion of long time series transparency based on the GEE cloud database is highly efficient. The dataset can serve as valuable scientific evidence for the water quality monitoring, water pollution control, and aquatic ecological protection of Sanya River.
{"title":"A dataset of water transparency of Sanya River based on Sentinel-2 data during 2019–2021","authors":"Ruiting Qiu, Shenglei Wang, Jiankang Shi, Junsheng Li, Fangfang Zhang, Wenzhi Zhang, Yue Mei","doi":"10.11922/11-6035.noda.2022.0015.zh","DOIUrl":"https://doi.org/10.11922/11-6035.noda.2022.0015.zh","url":null,"abstract":"As one of the most important water quality parameters on the radar screen of environmental protection sectors, water transparency reveals the turbidity degree of water and plays an important role in the primary productivity of water body and water ecosystem. As an independent island water system, Hainan Province has abundant surface inland water resources and plentiful river runoff. However, due to the influence of dry and wet monsoons and topography, the aquatic systems are characterized by uneven spatial and temporal distribution, and there are few studies on the water quality of inland water bodies on Hainan Island. In this study, we took Sanya River in Sanya, Hainan Province as the study area, and used the QAAv6-based semi-analytic model to retrieve the water transparency of Sanya River in time series from 2019 to 2021 based on the GEE cloud computing platform and the massive Sentinel-2 surface reflectance data stored in Google Cloud. With regard to the extraction of dynamic water area from Sanya River, we adopted the algorithm combining the normalized water body index NDWI with OTUS automatic threshold segmentation to extract the small river water. The data are stored in GeoTiff raster format, and the pixel transparency value and coordinate information are stored at the same time for easy reading and analysis by relevant GIS software. The inversion of long time series transparency based on the GEE cloud database is highly efficient. The dataset can serve as valuable scientific evidence for the water quality monitoring, water pollution control, and aquatic ecological protection of Sanya River.","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":"43520789","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}
The grassland-livestock balance system plays an important role in reconciling the protection of grassland resources with the sustainable development of animal husbandry. The accurate estimation of the grassland-livestock balance is the basis for the implementation of the grassland-livestock balance system. In order to explore the accurate calculation method of the important parameter, i.e. livestock grass demand in the calculation system, in this study, we set up an experimental area in the natural grassland of Hulunbuir, Inner Mongolia, to simulate a real grazing scene, and conducted 4 grazing experiments controlling the sheep number and grazing time. We obtained multispectral images of the experimental area before and after grazing were with the help of the UAV remote sensing, and carried out a quadrat survey. After data sorting and preprocessing, we produced this dataset of high spatial resolution image data and accurate quadrat investigation data. It can be applied to the research on the daily grass demand of grazing sheep on Hulunbuir meadow steppe in autumn. This dataset provides data support for the accurate estimation of local grassland-livestock balance, and is of great significance for the adaptive dynamic management of grass and livestock.
{"title":"A dataset of UAV multispectral images for the grassland-livestock balance","authors":"Zhongming Jin, Tianci Hu, Chengxiang Jiang, Jingwei Qi, R. Yan, Leifeng Guo","doi":"10.11922/11-6035.csd.2022.0070.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0070.zh","url":null,"abstract":"The grassland-livestock balance system plays an important role in reconciling the protection of grassland resources with the sustainable development of animal husbandry. The accurate estimation of the grassland-livestock balance is the basis for the implementation of the grassland-livestock balance system. In order to explore the accurate calculation method of the important parameter, i.e. livestock grass demand in the calculation system, in this study, we set up an experimental area in the natural grassland of Hulunbuir, Inner Mongolia, to simulate a real grazing scene, and conducted 4 grazing experiments controlling the sheep number and grazing time. We obtained multispectral images of the experimental area before and after grazing were with the help of the UAV remote sensing, and carried out a quadrat survey. After data sorting and preprocessing, we produced this dataset of high spatial resolution image data and accurate quadrat investigation data. It can be applied to the research on the daily grass demand of grazing sheep on Hulunbuir meadow steppe in autumn. This dataset provides data support for the accurate estimation of local grassland-livestock balance, and is of great significance for the adaptive dynamic management of grass and livestock.","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":"44077649","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}
Fractional snow cover (FSC) is a quantitative description of the ratio of snow cover area (SCA) per image element to the spatial extent of the image element. Using the MODIS global surface reflectance product MOD09GA as the source data, this dataset takes advantage of the Google Earth Engine (GEE) platform to establish the Based NDVI Bivariate Linear Regression Model (BV-BLRM) showing the relation between the FSC and the Normalized Difference Snow Index (NDVI), and the Normalized Difference Snow Index (NDSI). Compared with the Root Mean Square Error (RMST) of MOD10A1 V6 data, the RMST of the FSC data prepared by the BV-BLRM has increased by 45%. Based on the model, we obtained a dataset of MODIS-based daily FSC time-series data with one kilo-meter spatial resolution in the Holarctic region (45°N to 90°N). The time series of this dataset is from February 24, 2000 to November 18, 2019, with a temporal resolution of one day and a spatial resolution of one km. The dataset is expected to provide quantitative information of snow distribution for regional climate simulation, hydrological models, etc.
{"title":"A dataset of MODIS-based daily FSC time-series data with one kilo-meter spatial resolution in the Holarctic region (2000–2019)","authors":"Yuan Ma, Jian Wang, Hongyu Zhao, Donghang Shao, Weiguo Wang, Haojie Li, Hongyi Li","doi":"10.11922/11-6035.ncdc.2021.0031.zh","DOIUrl":"https://doi.org/10.11922/11-6035.ncdc.2021.0031.zh","url":null,"abstract":"Fractional snow cover (FSC) is a quantitative description of the ratio of snow cover area (SCA) per image element to the spatial extent of the image element. Using the MODIS global surface reflectance product MOD09GA as the source data, this dataset takes advantage of the Google Earth Engine (GEE) platform to establish the Based NDVI Bivariate Linear Regression Model (BV-BLRM) showing the relation between the FSC and the Normalized Difference Snow Index (NDVI), and the Normalized Difference Snow Index (NDSI). Compared with the Root Mean Square Error (RMST) of MOD10A1 V6 data, the RMST of the FSC data prepared by the BV-BLRM has increased by 45%. Based on the model, we obtained a dataset of MODIS-based daily FSC time-series data with one kilo-meter spatial resolution in the Holarctic region (45°N to 90°N). The time series of this dataset is from February 24, 2000 to November 18, 2019, with a temporal resolution of one day and a spatial resolution of one km. The dataset is expected to provide quantitative information of snow distribution for regional climate simulation, hydrological models, etc.","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":"48962476","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}
Mao’er Mountain is a typical area of natural secondary forest with strong spatial heterogeneity. As a result, the scientific assessment of ecosystem service function value in Mao’er Mountain can provide data support for researches on the decision-making for forest management and the relation between supply and demand for natural forests. This paper provides a dataset of six categories of ecological indicators in Mao’er Mountain, including water conservation, soil conservation, carbon fixation and oxygen release, nutrient accumulation, atmospheric purification, biodiversity conservation, which are assessed based on the Specifications for Assessment of Forest Ecosystem Services in China (LY/T 1721-2008). The raw data mainly include GF-1 remote sensing data, meteorological data, forest resource inventory data for management, etc. The data accuracy is strictly ensured through a series of processes such as vectorization, correction, and registration. The dataset in this paper includes the physical and value quantities of 18 specific ecological indicators in the ecosystem service assessment system. The data can be divided into two types: monthly data and yearly data according to the time resolution with unified spatial resolution of 16 m. During the process of index calculation, we strictly screened and controlled the input data and model parameters in this study, so as to ensure the reliability of the dataset through comparison and verification. This dataset covers most of the indicators in the forest ecosystem service assessment system, which can provide fine-grained data support for the study on natural secondary forest in cold temperate zones in China.
{"title":"A dataset of the ecosystem service assessment in Mao’er Mountain National Forest Park during 2018–2019","authors":"Guangsheng Chen, Weitao Zou, Zekun Xu, Weipeng Jing","doi":"10.11922/11-6035.noda.2022.0013.zh","DOIUrl":"https://doi.org/10.11922/11-6035.noda.2022.0013.zh","url":null,"abstract":"Mao’er Mountain is a typical area of natural secondary forest with strong spatial heterogeneity. As a result, the scientific assessment of ecosystem service function value in Mao’er Mountain can provide data support for researches on the decision-making for forest management and the relation between supply and demand for natural forests. This paper provides a dataset of six categories of ecological indicators in Mao’er Mountain, including water conservation, soil conservation, carbon fixation and oxygen release, nutrient accumulation, atmospheric purification, biodiversity conservation, which are assessed based on the Specifications for Assessment of Forest Ecosystem Services in China (LY/T 1721-2008). The raw data mainly include GF-1 remote sensing data, meteorological data, forest resource inventory data for management, etc. The data accuracy is strictly ensured through a series of processes such as vectorization, correction, and registration. The dataset in this paper includes the physical and value quantities of 18 specific ecological indicators in the ecosystem service assessment system. The data can be divided into two types: monthly data and yearly data according to the time resolution with unified spatial resolution of 16 m. During the process of index calculation, we strictly screened and controlled the input data and model parameters in this study, so as to ensure the reliability of the dataset through comparison and verification. This dataset covers most of the indicators in the forest ecosystem service assessment system, which can provide fine-grained data support for the study on natural secondary forest in cold temperate zones in China.","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":"47181493","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 : 2022-12-31DOI: 10.11922/11-6035.nasdc.2021.0063.zh
Qin Qiu, Shanshan Cao, Quansheng Li, Wei Sun, Lei Wang
This dataset is a collection of UAV visible light images of Tianshan spruces (superior mountain forest tree species in Xinjiang) for deep learning training. Tianshan spruces are the most important conifer species in ecological function in Tianshan region, Xinjiang. It is particularly important to effectively identify and divide Tianshan spruce forest through remote sensing technology, which provides important support for collecting the information of Tianshan spruce single factors. In this study, combining with mountain terrain and environmental factors, we developed a UAV field operation plan to collect visible light remote sensing image data of Nanshan Practice Forest Farm of Xinjiang Agricultural University, which were spliced into orthophographic projective images after data filtering, geometric correction and ortho correction and other pre-processing methods. We then adopted Labelme software to plot and classify Tianshan spruces and obtained a dataset of 1,128 UAV visible light images of Tianshan spruces for deep learning training in 2017.
{"title":"A dataset of UAV visible light images of Tianshan spruces for deep learning training in 2017","authors":"Qin Qiu, Shanshan Cao, Quansheng Li, Wei Sun, Lei Wang","doi":"10.11922/11-6035.nasdc.2021.0063.zh","DOIUrl":"https://doi.org/10.11922/11-6035.nasdc.2021.0063.zh","url":null,"abstract":"This dataset is a collection of UAV visible light images of Tianshan spruces (superior mountain forest tree species in Xinjiang) for deep learning training. Tianshan spruces are the most important conifer species in ecological function in Tianshan region, Xinjiang. It is particularly important to effectively identify and divide Tianshan spruce forest through remote sensing technology, which provides important support for collecting the information of Tianshan spruce single factors. In this study, combining with mountain terrain and environmental factors, we developed a UAV field operation plan to collect visible light remote sensing image data of Nanshan Practice Forest Farm of Xinjiang Agricultural University, which were spliced into orthophographic projective images after data filtering, geometric correction and ortho correction and other pre-processing methods. We then adopted Labelme software to plot and classify Tianshan spruces and obtained a dataset of 1,128 UAV visible light images of Tianshan spruces for deep learning training in 2017.","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":"44668277","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 : 2022-12-31DOI: 10.11922/11-6035.csd.2022.0003.zh
Lingwei Wei, Xiaofei Hu, Q. Cheng, Xingqi Wu, J. Ni
Bioclimatic variables are indicators reflecting the integrated relationship between living things and climate. They are often used to interprete the relationships between species, vegetations and climate in global change research, and further simulate the geographical distribution patterns of both species and vegetations, as well as their functional characteristics. Regional bioclimate datasets, however, have been rarely reported. Based on an ANUSPIN interpolated dataset (covering temperature, precipitation and sunshine percentage) of 1km-resolution climate variables in China at 30-year basis averaged from 1951 to 1980 and from 1981 to 2010, respectively, we calculated 9 kinds bioclimatic variables in this study, namely mean temperature of the coldest month, mean temperature of the warmest month, absolute maximum temperature, absolute minimum temperature, annual growing degree days above 0°C and 5°C, growing season precipitation, annual drought index and annual moisture index. We plotted their spatial distribution map and analyzed their spatial pattern and trend statistically. Comparative analysis shows that the variation range of corresponding variables is very narrow, and the statistical variables are nearly the same. Therefore, the error of this dataset mainly comes from the spatial distribution dataset of basic climatic factors, and the secondary error in the process is tiny.This dataset provides reasonable environmentally mechanistic explanations for research on the relationships between species, vegetations and climate, and offers a convenient and diverse way for researchers to use bioclimatic variables to simulate species distribution patterns, vegetation structures and functions.
{"title":"A dataset of spatial distribution of bioclimatic variables in China at 1 km resolution","authors":"Lingwei Wei, Xiaofei Hu, Q. Cheng, Xingqi Wu, J. Ni","doi":"10.11922/11-6035.csd.2022.0003.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0003.zh","url":null,"abstract":"Bioclimatic variables are indicators reflecting the integrated relationship between living things and climate. They are often used to interprete the relationships between species, vegetations and climate in global change research, and further simulate the geographical distribution patterns of both species and vegetations, as well as their functional characteristics. Regional bioclimate datasets, however, have been rarely reported. Based on an ANUSPIN interpolated dataset (covering temperature, precipitation and sunshine percentage) of 1km-resolution climate variables in China at 30-year basis averaged from 1951 to 1980 and from 1981 to 2010, respectively, we calculated 9 kinds bioclimatic variables in this study, namely mean temperature of the coldest month, mean temperature of the warmest month, absolute maximum temperature, absolute minimum temperature, annual growing degree days above 0°C and 5°C, growing season precipitation, annual drought index and annual moisture index. We plotted their spatial distribution map and analyzed their spatial pattern and trend statistically. Comparative analysis shows that the variation range of corresponding variables is very narrow, and the statistical variables are nearly the same. Therefore, the error of this dataset mainly comes from the spatial distribution dataset of basic climatic factors, and the secondary error in the process is tiny.This dataset provides reasonable environmentally mechanistic explanations for research on the relationships between species, vegetations and climate, and offers a convenient and diverse way for researchers to use bioclimatic variables to simulate species distribution patterns, vegetation structures and functions.","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":"66164894","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}
Water use efficiency can reveal the coupling relation between water dissipation and carbon sequestration in terrestrial ecosystems. The understanding the temporal and spatial dynamics of water use efficiency and its response to complex climate change is a prerequisite for dealing with future climate change and man-made disturbances. The ecological model method is considered to be an effective way to assess the carbon and water dynamics of the regional large-scale ecosystem. This study is based on the Arid Region Ecosystem Model (AEM), using site flux data to optimize, verify, and parameterize the model, so as to build a dataset of water use efficiency of the arid region ecosystems in Central Asia. The time span of this dataset is 1980-2020, involving six types of ecosystems, namely coniferous forests, broad-leaved forests, grasslands, phreatophyte shrubs, non-phreatophyte shrubs and farmland. The study region covers five Central Asian countries (Kazakhstan, Uzbekistan, Tajikistan, Kyrgyzstan and Turkmenistan) and Xinjiang of China. Comparing the simulated results with the observed data, we found that they were highly consistent with each other. This dataset provides support for understanding the carbon-water dynamics of ecologically fragile areas under the background of global change and maintaining the stability of the ecosystem.
{"title":"A dataset of water use efficiency in the ecosystems of Central Asian arid regions during 1980–2020","authors":"Shihua Zhu, X. Hang, Xiaoping Xie, Liangxiao Sun, X. Fang, Liangzhong Cao, Yachun Li","doi":"10.11922/11-6035.csd.2021.0079.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2021.0079.zh","url":null,"abstract":"Water use efficiency can reveal the coupling relation between water dissipation and carbon sequestration in terrestrial ecosystems. The understanding the temporal and spatial dynamics of water use efficiency and its response to complex climate change is a prerequisite for dealing with future climate change and man-made disturbances. The ecological model method is considered to be an effective way to assess the carbon and water dynamics of the regional large-scale ecosystem. This study is based on the Arid Region Ecosystem Model (AEM), using site flux data to optimize, verify, and parameterize the model, so as to build a dataset of water use efficiency of the arid region ecosystems in Central Asia. The time span of this dataset is 1980-2020, involving six types of ecosystems, namely coniferous forests, broad-leaved forests, grasslands, phreatophyte shrubs, non-phreatophyte shrubs and farmland. The study region covers five Central Asian countries (Kazakhstan, Uzbekistan, Tajikistan, Kyrgyzstan and Turkmenistan) and Xinjiang of China. Comparing the simulated results with the observed data, we found that they were highly consistent with each other. This dataset provides support for understanding the carbon-water dynamics of ecologically fragile areas under the background of global change and maintaining the stability of the ecosystem.","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":"44889148","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}
With the characteristics of high spatial resolution, high geometric positioning accuracy, and a single-scene image observation width of up to 45 kilometers, GF-2 data can be widely used in agriculture, forestry and other fields. We collected Gaofen No. 2 data (GF-2 PMS) image data from 592 sites in Hainan Province during 2017-2021, and carried out atmospheric correction of the data by using the visible light near-infrared iteration algorithm, so as to obtain a dataset of GF-2 surface reflectance products of Hainan Province during 2017–2021.
{"title":"A dataset of GF-2 surface reflectance products of Hainan Province during 2017–2021","authors":"Yueguan Yan, Hujun Liang, Hao Zhang, Lianchong Zhang, Hong-wei Zhang, Zhenzhen Cui, Shanjing Chen","doi":"10.11922/11-6035.noda.2022.0014.zh","DOIUrl":"https://doi.org/10.11922/11-6035.noda.2022.0014.zh","url":null,"abstract":"With the characteristics of high spatial resolution, high geometric positioning accuracy, and a single-scene image observation width of up to 45 kilometers, GF-2 data can be widely used in agriculture, forestry and other fields. We collected Gaofen No. 2 data (GF-2 PMS) image data from 592 sites in Hainan Province during 2017-2021, and carried out atmospheric correction of the data by using the visible light near-infrared iteration algorithm, so as to obtain a dataset of GF-2 surface reflectance products of Hainan Province during 2017–2021.","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":"43785427","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 : 2022-12-31DOI: 10.11922/11-6035.csd.2022.0037.zh
Juyang Wu
Tree biomass equations are the most commonly used method to estimate tree and forest biomasses at various spatial and temporal scales because of their minor damage, ease of use, and high relative accuracy. The systematic compilation of models for the biomass of mixed evergreen and deciduous broad-leaved forest, a unique and essential class of typical vegetation types in the subtropical zone of China, has not been reported so far. In this paper, we compiled a species list through an inventory of 28.9 hm2 of mixed evergreen deciduous broad-leaved forests in southwestern Hubei Province with fixed detection sample plots, the species list was compiled by this study, and used it to retrieve, collect and establish a model dataset of woody plant biomass in subtropical mixed evergreen and deciduous broad-leaved forest. The species list contains a total of 665 biomass models in 167 groups. Each model corresponds to the plant species name, Latin name, plant life type, plant components calculated by the model, model independent variables, measurement units and ranges of independent variables, model correlation coefficient or coefficient of determination, and model sample size. is also recorded. By establishing this dataset, this paper not only provides essential information for in-depth research on the productivity and carbon sink of this specific vegetation but also provides a scientific basis for the management of this type of forest, the conservation of biodiversity, and the evaluation of forest ecological benefits.
{"title":"亚热带常绿落叶阔叶混交林木本植物生物量模型数据集","authors":"Juyang Wu","doi":"10.11922/11-6035.csd.2022.0037.zh","DOIUrl":"https://doi.org/10.11922/11-6035.csd.2022.0037.zh","url":null,"abstract":"Tree biomass equations are the most commonly used method to estimate tree and forest biomasses at various spatial and temporal scales because of their minor damage, ease of use, and high relative accuracy. The systematic compilation of models for the biomass of mixed evergreen and deciduous broad-leaved forest, a unique and essential class of typical vegetation types in the subtropical zone of China, has not been reported so far. In this paper, we compiled a species list through an inventory of 28.9 hm2 of mixed evergreen deciduous broad-leaved forests in southwestern Hubei Province with fixed detection sample plots, the species list was compiled by this study, and used it to retrieve, collect and establish a model dataset of woody plant biomass in subtropical mixed evergreen and deciduous broad-leaved forest. The species list contains a total of 665 biomass models in 167 groups. Each model corresponds to the plant species name, Latin name, plant life type, plant components calculated by the model, model independent variables, measurement units and ranges of independent variables, model correlation coefficient or coefficient of determination, and model sample size. is also recorded. By establishing this dataset, this paper not only provides essential information for in-depth research on the productivity and carbon sink of this specific vegetation but also provides a scientific basis for the management of this type of forest, the conservation of biodiversity, and the evaluation of forest ecological benefits.","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":"42752292","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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