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Mapping Tidal Flats with Landsat 8 Images and Google Earth Engine: A Case Study of the China’s Eastern Coastal Zone circa 2015

1
Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
2
College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
3
School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
4
College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(8), 924; https://doi.org/10.3390/rs11080924
Received: 26 February 2019 / Revised: 5 April 2019 / Accepted: 12 April 2019 / Published: 16 April 2019
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Abstract

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. View Full-Text
Keywords: tidal flats mapping; Landsat 8 OLI images; Google Earth Engine (GEE); random forest algorithm (RF) tidal flats mapping; Landsat 8 OLI images; Google Earth Engine (GEE); random forest algorithm (RF)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhang, K.; Dong, X.; Liu, Z.; Gao, W.; Hu, Z.; Wu, G. Mapping Tidal Flats with Landsat 8 Images and Google Earth Engine: A Case Study of the China’s Eastern Coastal Zone circa 2015. Remote Sens. 2019, 11, 924.

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