Mapping Tidal Flats with Landsat 8 Images and Google Earth Engine: A Case Study of the China's Eastern Coastal Zone circa 2015
Mapping Tidal Flats with Landsat 8 Images and Google Earth Engine: A Case Study of the China's Eastern Coastal Zone circa 2015
复制标题
利用 Landsat 8 图像和 Google Earth Engine 绘制滩涂地图：以 2015 年左右中国东部沿海地区为例
DOI：
10.3390/rs11080924
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发表时间：
2019-04
期刊：
影响因子：
5
通讯作者：
Wu Guofeng
中科院分区：
文献类型：
--
作者：
Zhang Kangyong;Dong Xuanyan;Liu Zhigang;Gao Wenxiu;Hu Zhongwen;Wu Guofeng
Accurate and up-to-date tidal flat mapping is of much importance to learning how coastal ecosystems work in a time of anthropogenic disturbances and rising sea levels, which will provide scientific instruction for sustainable management and ecological assessments. For large-scale and high spatial-resolution mapping of tidal flats, it is difficult to obtain accurate tidal flat maps without multi-temporal observation data. In this study, we aim to investigate the potential and advantages of the freely accessible Landsat 8 Operational Land Imager (OLI) imagery archive and Google Earth Engine (GEE) for accurate tidal flats mapping. A novel approach was proposed, including multi-temporal feature extraction, machine learning classification using GEE and morphological post-processing. The 50 km buffer of the coastline from Hangzhou Bay to Yalu River in China’s eastern coastal zone was taken as the study area. From the perspective of natural attributes and unexploited status of tidal flats, we delineated a broader extent comprising intertidal flats, supratidal barren flats and vegetated flats, since intertidal flats are major component of the tidal flats. The overall accuracy of the resultant map was about 94.4% from a confusion matrix for accuracy assessment. The results showed that the use of time-series images can greatly eliminate the effects of tidal level, and improve the mapping accuracy. This study also proved the potential and advantage of combining the GEE platform with time-series Landsat images, due to its powerful cloud computing platform, especially for large scale and longtime tidal flats mapping.
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DOI：
--
发表时间：
2005
期刊：
Hydrographic Surveying and Charting
影响因子：
--
作者：
Li Si-hai;Liu Zhen-min;He Guang-shun;Xia Deng-wen;Wei Hong-Yu
通讯作者：
Li Si-hai;Liu Zhen-min;He Guang-shun;Xia Deng-wen;Wei Hong-Yu
DOI：
10.1109/jstars.2016.2616514
发表时间：
2016-11
影响因子：
5.5
作者：
Xiangyang Liu;Zhiqiang Gao;Jicai Ning;Xiang Yu;Yuanyuan Zhang
通讯作者：
Xiangyang Liu;Zhiqiang Gao;Jicai Ning;Xiang Yu;Yuanyuan Zhang
通过Google Earth Engine分析Landsat图像追踪1986-2016年中国沿海滩涂的年度变化
影响因子：
13.5
作者：
Wang Xinxin;Xiao Xiangming;Zou Zhenhua;Chen Bangqian;Ma Jun;Dong Jinwei;Doughty Russell B.;Zhong Qiaoyan;Qin Yuanwei;Dai Shengqi;Li Xiangping;Zhao Bin;Li Bo
通讯作者：
Li Bo
DOI：
--
发表时间：
2004
期刊：
Advances in Marine Science
影响因子：
--
作者：
Liu Yong-xue
通讯作者：
Liu Yong-xue
影响因子：
3
作者：
Zhenghong Tang;Yao Li;Yue Gu;Weiguo Jiang;Yuan Xue;Q. Hu;Ted Lagrange;A. Bishop;J. Drahota;Ruopu Li
通讯作者：
Zhenghong Tang;Yao Li;Yue Gu;Weiguo Jiang;Yuan Xue;Q. Hu;Ted Lagrange;A. Bishop;J. Drahota;Ruopu Li