Literature DB >> 32863440

Tracking annual changes of coastal tidal flats in China during 1986-2016 through analyses of Landsat images with Google Earth Engine.

Xinxin Wang1, Xiangming Xiao2, Zhenhua Zou2, Bangqian Chen3, Jun Ma1, Jinwei Dong4, Russell B Doughty2, Qiaoyan Zhong1, Yuanwei Qin2, Shengqi Dai1, Xiangping Li1, Bin Zhao1, Bo Li1.   

Abstract

Tidal flats (non-vegetated area), along with coastal vegetation area, constitute the coastal wetlands (intertidal zone) between high and low water lines, and play an important role in wildlife, biodiversity and biogeochemical cycles. However, accurate annual maps of coastal tidal flats over the last few decades are unavailable and their spatio-temporal changes in China are unknown. In this study, we analyzed all the available Landsat TM/ETM+/OLI imagery (~ 44,528 images) using the Google Earth Engine (GEE) cloud computing platform and a robust decision tree algorithm to generate annual frequency maps of open surface water body and vegetation to produce annual maps of coastal tidal flats in eastern China from 1986 to 2016 at 30-m spatial resolution. The resulting map of coastal tidal flats in 2016 was evaluated using very high-resolution images available in Google Earth. The total area of coastal tidal flats in China in 2016 was about 731,170 ha, mostly distributed in the provinces around Yellow River Delta and Pearl River Delta. The interannual dynamics of coastal tidal flats area in China over the last three decades can be divided into three periods: a stable period during 1986-1992, an increasing period during 1993-2001 and a decreasing period during 2002-2016. The resulting annual coastal tidal flats maps could be used to support sustainable coastal zone management policies that preserve coastal ecosystem services and biodiversity in China.

Entities:  

Keywords:  China coastal zone; Google Earth Engine; Pixel and frequency-based algorithm; Tidal flats; Time series Landsat images

Year:  2018        PMID: 32863440      PMCID: PMC7449126          DOI: 10.1016/j.rse.2018.11.030

Source DB:  PubMed          Journal:  Remote Sens Environ        ISSN: 0034-4257            Impact factor:   10.164


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