Spatial-temporal Dataset of Salt Marsh Vegetation in Yellow River Delta (1999-2020)
HU Jianfang1GONG Zhaoning*1ZHANG Cheng1QIU Huachang1
1 College of Resources，Environment and Tourism，Capital Normal University，Beijing 100048，China
DOI:10.3974/geodb.2022.01.06.V1
Published:Jan. 2022
Visitors：9665 Data Files Downloaded：276
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Key Words：
Yellow River Delta,salt marsh vegetation,spatial-temporal series,algorithm,1999-2020
Abstract：
Suaeda salsa, phragmites australis and Spartina alterniflora are the typical salt marsh vegetation in Yellow River Delta. The Spatial-temporal Dataset of Salt Marsh Vegetation in Yellow River Delta (1999-2020) was developed based on the Google Earth Engine(GEE) together with Landsat TM/ETM/OLI, Sentinel-2 MSI optical data and Sentinel-1 SAR radar data from 1999 to 2020. Feature optimization algorithm and Random Forest algorithm were used to classify the vegetation. The dataset includes: (1) boundary data of study area; (2) spatial data of salt marsh vegetation in 13 periods from 1999 to 2020; (3) frequency data of Spartina alterniflora, Suaeda salsa and Phragmites australis from 1999 to 2020; (4) 92 sites in situ data; (5) measured spectral data. Among them, the spatial resolution of grid data is 10 m. The dataset is archived in .shp, .tif, and .xlsx data formats, and consists of 64 data files with data size of 172 MB (Compressed into one single file with 31.7 MB).Browse
Foundation Item：
Ministry of Science and Technology of P. R. China (2017YFC0505900)
Data Citation：
HU Jianfang, GONG Zhaoning*, ZHANG Cheng, QIU Huachang. Spatial-temporal Dataset of Salt Marsh Vegetation in Yellow River Delta (1999-2020)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2022. https://doi.org/10.3974/geodb.2022.01.06.V1.
HU Jianfang, GONG Zhaoning, ZHANG Cheng, et al. Development of a dataset of the spatial-temporal distribution of typical salt marsh vegetation in the Yellow River Delta (1999–2020) [J]. Journal of Global Change Data & Discovery, 2022, 6(2): 217–224.
References：
[1]He, Y. L. The mechanism of vegetation differentiation in the lower salt marsh of Yangtze River estuary [D]. East China Normal University, 2014.
[2]Wang, X. H., Li, Y. Z., Meng, H., et al. Distribution pattern of plant community in new-born coastal wetland in the Yellow River Delta [J]. Scientia Geographica Sinica, 2015, 35(8): 1021-1026.
[3]Zhang, C., Gong, Z. N., Qiu, H. C., et al. Mapping typical Salt-marsh species in the Yellow River Delta wetland supported by temporal-spatial-spectral multidimensional features. Science of the Total Environment. 2021, 783: 147061. DOI: 10.1016/j.scitotenv.2021.147061.
[4]Zhang, G. H., Wang, R. Y., Zhao, G. X., et al. Extraction of vegetation information in coastal ecological vulnerable areas from remote sensing data based on phenology parameters and object-oriented method [J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(4): 209-216.
[5]Wu, Y., Zhou, Z. F., Zhao, X., et al. Spatiotemporal variation of vegetation coverage in plateau mountainous areas based on remote sensing cloud computing platform: A case study of Guizhou Province [J]. Carsologica Sinica, 2020, 39(2): 196-205.
[6]Breiman, L. Random Forests [J]. Machine Learning, 2001, 45(1): 5-32.
[7]Ke, Y. C., Shi, Z. K., Li, P. J., et al. Lithological classification and analysis using Hyperion hyperspectral data and Random Forest method [J]. Acta Petrologica Sinica, 2018, 34(7): 2181-2188.
[8]Zhang, X. L., The enviromnental change and degradation of modern Ye1low River Delta coastal wetland [D]. Ocean University of China, 2015.
[9]Miao, S., Wang, R., Li, J. C., et al. Retrieval algorithm of phycocyanin concentration in inland lakes from Sentinel 3A-OLCI images [J]. Journal of Infrared and Millimeter Waves, 2018, 37(5): 621-630.
[10]Lei, G. C. Integrated wetland management: Proceedings of the international conference on integrated wetland management [C]. Beijing: China Ocean Press, 2012.
[11]Serafy, E. S. The proper calculation of income from depletable natural resources [C]. Environmental Accounting for Sustainable Development: A UNDP–World Bank Symposium. Washington D C: World Bank, 1989: 10-18.
[12]Alberts, J. J., Takacs, M., Pattanayek, M. Natural organic matter from a Norwegian lake: possible structural changes resulting from lake acidification [C]. Ghabbour, E.A, Davies, G., (eds). Humic Substances: Versatile Components of Plants, Soil and Water. Cambridge, UK: The Royal Society of Chemistry, 2000.
[13]Zhang, H. S. Geomechanical system theory [D]. Taiyuan: Taiyuan University of Technology, 1998.
Data Product:
| ID |
Data Name |
Data Size |
Operation |
| 0 | Datapaper_SaltMarshVegYRD_1999-2020.pdf | 6790.00kb | DownLoad |
| 1 |
SaltMarshVegYRD1999-2020.rar |
32515.27KB |
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