Abstract
This study aims to introduce effective unmanned aerial system (UAS) survey conditions for coastal sediment classification, including muddy sand, sand, gravel, and shells in a tidal flat area. UAS images with resolutions ranging from 2 to 60 mm are used as an implication of survey altitudes. The UAS images are used for sediment classification using random forest (RF) and support vector machine (SVM) methods. The results showed that RF is more effective in sediment classification while the general accuracy pattern was similar. The accuracy decreased with lower spatial resolutions. Notably, there is a significant drop of accuracy with a resolution coarser than 40 mm. Considering the training data selection, classification accuracy, and survey efficiency, it is suggested that 40 mm UAS images would provide optimal condition with acceptable accuracy for coastal sediment classification using RF model. To gain higher accuracy, a lower flight altitude is required, which will elongate the survey time significantly. Given the fact that this study is the first approach to test various UAS survey conditions for coastal sediment classifications in a field condition; the methodology and findings of this study can serve as a guideline framework for future coastal UAS sediment mapping.
Highlights
G rain size of coastal sediments is a key component for understanding coastal topography, habitat of marine organisms, and geological processes [1]–[3]
The grain size analysis of 40 ground truth samples revealed that the sediments of the study area consist of muddy sandy gravel, sandy gravel, gravelly sand, gravelly muddy sand by folk’s classification (Fig. 4)
This study is designed to identify the optimal resolution of Unmanned Aerial System (UAS) surveys for coastal sediment mapping
Summary
G rain size of coastal sediments is a key component for understanding coastal topography, habitat of marine organisms, and geological processes [1]–[3]. There is a recent trend in coastal sediment mapping of a vigorous participation of Unmanned Aerial Systems (UAS hereafter) by taking the advantages of their high flexibility, accuracy, and efficiency for survey [1], [3]–[8]. VázquezTarrío et al.[5] analyzed grain roughness and size distribution in a braided, gravel bed river at the 2 cm resolution for grain sizes ranging from sand to gravel by using Unmanned Aerial System optical imagery and Structure from Motion (SfM) photogrammetry. Arif et al [7] classified river bed sediments for grain size from silt to boulder based on object based classification at 1 cm resolution UAS image. [1] used UAS images at 26 cm resolution, tidal channel network, 50 cm DEM, and tidal channel density data for classification of tidal flat sediments ranging from silt to gravelly sand with an accuracy of 72.8% Kim el al. [1] used UAS images at 26 cm resolution, tidal channel network, 50 cm DEM, and tidal channel density data for classification of tidal flat sediments ranging from silt to gravelly sand with an accuracy of 72.8%
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