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2021年第12期
2019年第02期
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18°N以北中国滨海滩涂湿地分布数据集(1989-2020)


胡忠文1徐月1尹玉蒙1张康永1邬国锋1王晨*2崔丽娟*3
1 深圳大学自然资源部大湾区地理环境监测重点实验室,深圳5180602 生态环境部卫星环境应用中心,北京1000943 中国林业科学研究院湿地研究所,湿地生态功能与恢复北京市重点实验室,北京100091

DOI:10.3974/geodb.2021.10.06.V1

出版时间:2021年10月

网页浏览次数:4473       数据下载次数:194      
数据下载量:39019.46 MB      数据DOI引用次数:

关键词:

海岸带,滩涂湿地,中国,1989-2020

摘要:

滨海滩涂湿地是我国重要的自然资源,同时也是易受人类活动影响的生态脆弱区。作者运用1989-2020年长时间序列遥感影像(Landsat系列影像集:Landsat 8 Surface Reflectance Tier 1/Landsat 7 Surface Reflectance Tier 1/Landsat 5 Surface Reflectance Tier 1),结合部分实地调查数据,基于谷歌地球引擎(Google Earth Engine,GEE)云计算平台,开发了基于监督分类的滨海滩涂湿地空间信息提取方法,经矢量化得到18︒N以北中国滨海滩涂湿地分布数据集(1989-2020)。该数据集时间分辨率为年,空间分辨率为30 m,数据集由256个文件组成,数据量为318 MB(压缩为1个文件,201 MB)。

基金项目:

中华人民共和国科学技术部(2017YFC0506200);国家自然科学基金(51761135022,ALWSD.2016.026,EP/R024537/1)

数据引用方式:

胡忠文, 徐月, 尹玉蒙, 张康永, 邬国锋, 王晨*, 崔丽娟*. 18°N以北中国滨海滩涂湿地分布数据集(1989-2020)[J/DB/OL]. 全球变化数据仓储电子杂志(中英文), 2021. https://doi.org/10.3974/geodb.2021.10.06.V1.

参考文献:

[1] Zhang, X. L., Li, P. Y, Li, P., et al. Present conditions and prospects of study on soastal wetlands in China [J]. Advances in Marine Science, 2005, 23(1): 87-95.
     [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] 王刚. 沿海滩涂的概念界定[J]. 中国渔业经济, 2013(1): 99-104.
     [5] 方如康. 环境学词典[M]. 北京: 科学出版社, 2003.
     [6] 彭建, 王仰麟. 我国沿海滩涂的研究[J]. 北京大学学报(自然科学版), 2000, 36(6): 832-839.
     [7] 苏胜金. 七年全国海岸带和海涂资源综合调查综述[J]. 海洋与海岸带开发, 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. S., 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.
     [18] Breiman, L. Random forests [J]. Machine Learning, 2001, 45(1): 35-32.
     

数据下载:

序号 数据名 数据大小 操作
1 DCTF_China_1989-2020.rar 205958.39KB
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