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Data Details

Tidal Flats Dataset Covers Coastal Region in North of 18°N Latitude of China (1989-2020)


HU Zhongwen1XU Yue1YIN Yumeng1ZHANG Kangyong1WU Guofeng1WANG Chen*2CUI Lijuan*3
1 MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area，Shenzhen University，Shenzhen 518060，China2 Center for Satellite Application on Ecology and Environment，Ministry of Ecology and Environment，Beijing 100094，China3 Institute of Wetland Research，Chinese Academy of Forestry，Beijing Key Laboratory of Wetland Ecological Function and Restoration，Beijing 100091，China

DOI:10.3974/geodb.2021.10.06.V1

Published:Oct. 2021

Visitors：4070       Data Files Downloaded：487      
Data Downloaded：97950.91 MB      Citations：

Key Words：

coastal zone,tidal flats,China,1989-2020

Abstract：

Costal tidal flats are critical regions for climate change, sea level rises and human activities. Based on time-series remote sensing images and field survey data, the authors developed a supervised classification method for mapping tidal flats on the Google Earth Engine (GEE) to obtain the Tidal Flats Dataset Covers Coastal Region in North of 18°N Latitude of China (1989-2020). The dataset is yearly based during the more than 30 years and has a spatial resolution of 30 m. The dataset is consisted of 256 data files in .shp data format with the data size of 318 MB (Compressed to one single file with 201 MB).Browse

Foundation Item：

Ministry of Science and Technology of P. R. China (2017YFC0506200)；National Natural Science Foundation of China (51761135022, ALWSD.2016.026, EP/R024537/1)

Data Citation：

HU Zhongwen, XU Yue, YIN Yumeng, ZHANG Kangyong, WU Guofeng, WANG Chen*, CUI Lijuan*.Tidal Flats Dataset Covers Coastal Region in North of 18°N Latitude of China (1989-2020)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2021. https://doi.org/10.3974/geodb.2021.10.06.V1.

HU Zhongwen ,XU Yue, YIN Yumeng. et al. Tidal flats dataset covers coastal region in north of 18°N latitude of China (1989–2020) [J]. Journal of Global Change Data & Discovery, 2022, 6(1): 125–132.

References：

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     [2] Yao, H. Characterizing landuse changes in 1990-2010 in the coastal zone of Nantong, Jiangsu province, China [J]. Ocean & Coastal Management, 2013, 71: 108-15.
     [3] Wen, Q., Zhang, Z., Xu, J., et al. Spatial and temporal change of wetlands in Bohai rim during 2000-2008: An analysis based on satellite images [J]. Journal of Remote Sensing, 2011, 15(1): 183-200.
     [4] Wang, G. Definition of coastal tidal flats [J]. Chinese Fishery Economy, 2013, (01): 99-104.
     [5] Fang, R, K. Environmental dictionary [M]. Beijing: Science Press, 2003.
     [6] Peng, J., Wang, Y, L. Study of coastal Tidalflats in China [J]. Journal of Peking University (Natural Science), 2000, 36(6): 832-839.
     [7] Su, S. J. Comprehensive survey of coastal zone and tideland resources in China in the past seven years [J]. Marine and coastal zone development, 1988, (2): 30-32.
     [8] Wessel, P., Smith, W. H. F. A global, self-consistent, hierarchical, high-resolution shoreline database [J]. Journal of Geophysical Research Solid Earth, 1996, 101(B4): 8741-8743.
     [9] Foga, S., Scaramuzza, P. L., Guo, S., et al. Cloud detection algorithm comparison and validation for operational Landsat data products [J]. Remote Sensing of Environment, 2017, 194: 379-390.
     [10] Zhang, K. Y., Dong, X. Y., Liu, Z. G., et al. Mapping tidal flats with Landsat 8 images and Google Earth Engine: A case study of the China's eastern coastal zone circa 2015 [J]. Remote Sensing, 2019, 11(8): 924.
     [11] Cheng, B., Liu, Y., Liu, X., et al. Research on extraction method of coastal aquaculture areas on high resolution remote sensing image based on multi-features fusion [J]. Remote Sensing Technology and Application, 2018, 33(2): 296-304.
     [12] Gong, C., Gang, D., Wang, D. Remote sensing monitoring water area of Dongting lake based on MNDWI [J]. Journal of Water Resources Research, 2015, 04(3): 234-239.
     [13] Dong, Z. Y., Wang, Z. M., Liu, D. W., et al. Mapping wetland areas using Landsat-derived NDVI and LSWI: A case Study of West Songnen Plain, Northeast China [J]. Journal of the Indian Society of Remote Sensing, 2014, 42(3): 569-576.
     [14] Nguyen, C. T., Chidthaisong, A., Diem, P. K., et al. A Modified bare soil index to identify bare land Features during agricultural Fallow-Period in Southeast Asia using Landsat 8 [J]. Land, 2021, 10(3): 1-18.
     [15] Guo, B., Zang, W. Q., Zhang, R. Soil salizanation information in the Yellow River Delta based on feature surface models using Landsat 8 OLI Data [J]. Ieee Access, 2020, 11(1): 288-300.
     [16] Zhang, J. H., Feng, L. L., Yao, F. M. Improved maize cultivated area estimation over a large scale combining MODIS-EVI time series data and crop phenological information [J]. Isprs Journal of Photogrammetry and Remote Sensing, 2014, 94: 102-113.
     [17] Malik, M., Shukla, J. P., MishraI, S. Relationship of LST, NDBI and NDVI using Landsat-8 data in Kandaihimmat Watershed, Hoshangabad, India [J]. Indian Journal of Geo-Marine Sciences, 2019, 48(1): 25-31.
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Data Product:

ID Data Name Data Size Operation
0Datapaper_DCTF_China_1989-2020.pdf7078.00kbDownLoad
1 DCTF_China_1989-2020.rar 205958.39KB
Co-Sponsors

Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences

The Geographical Society of China

Parteners

Committee on Data for Science and Technology (CODATA) Task Group on Preservation of and Access to Scientific and Technical Data in/for/with Developing Countries (PASTD)

Jomo Kenyatta University of Agriculture and Technology

Digital Linchao GeoMuseum