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.
Highlights
Tidal flats, often defined as sandy and muddy flats, are the important parts of coastal zones and highly productive, providing numerous minerals as well as biological and oceanic resources for human beings [1,2]
Coastal vegetation is likely to colonize the upper margins of intertidal flats with years of vegetation succession [6,7], which can be defined as supratidal vegetated flats [8], or salt marshes in terms of the presence of halophytic vegetation (Figure 1) [9,10]
A 30 m tidal flat map circa 2015 (Figure 8) covering the coastal zone from Hangzhou Bay to Yalu River was obtained by using random forest (RF) machine learning algorithm with Landsat time-series images via Google Earth Engine (GEE)
Summary
Often defined as sandy and muddy flats, are the important parts of coastal zones and highly productive, providing numerous minerals as well as biological and oceanic resources for human beings [1,2]. Coastal vegetation is likely to colonize the upper margins of intertidal flats (supratidal flats) with years of vegetation succession [6,7], which can be defined as supratidal vegetated flats (hereinafter referred to as vegetated flats) [8], or salt marshes in terms of the presence of halophytic vegetation (Figure 1) [9,10]. The upper limit of supratidal flats is artificial borders (roads, seawalls and offshore buildings) [10], and the tidal flats that were situated inside the artificial borders and reclaimed for coastal development were explicitly excluded
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