Abstract
Tidal flats are associated with complicated depositional and ecological environments, and have changed considerably as a result of the erosion and sedimentation caused by tidal energy; consequently, the surface sediment distribution in tidal flats must be constantly monitored and mapped. Although several studies have been conducted with the aim of classifying intertidal surface sediments using various remote sensing methods combined with field survey, most of these studies were unable to consider various sediment types, due to the low spatial resolution of remotely sensed data. Therefore, previous studies were unable to efficiently describe precise surface sediment distribution maps. In the present study, unmanned aerial vehicle (UAV) red, green, blue (RGB) orthoimagery was used in combination with a field survey (232 samples) to produce a large-scale classification map for surface sediment distribution, in accordance with sedimentology standards, using an object-based method. The object-based method is an effective technique that can classify surface sediment distribution by analyzing its correlations with spectral reflectance, grain size, and tidal channels. Therefore, we distinguished six sediment types based on their spectral reflectance and sediment properties, such as grain composition and statistical parameters. The accuracy assessment of the surface sediment classification based on these six types indicated an overall accuracy of 72.8%, with a kappa coefficient of 0.62 and 5-m error range related to the Global Positioning System (GPS) device. We found that 11 samples were misclassified due to the effects of sun glint and cloud caused by the UAV system and shellfish beds, while 14 misclassified samples were influenced by surface water related to the elevation, tidal channels, and sediment properties. These results indicate that large-scale classification of surface sediment with high accuracy is possible using UAV RGB orthoimagery.
Highlights
Tidal flats are affected by tidal and wave flows in rivers and seawater in estuaries, leading to their complicated depositional and ecological environments [1]
They recommended that a precise digital elevation model (DEM) and appropriate classification method be used for surface sediment classification
Choi et al [11] attempted to quantitatively estimate the spatial relationship of intertidal sediment environments and surface sediment classification using a frequency ratio model with spectral reflectance grades of IKONOS data. They concluded that object-based classification was an effective approach to surface sediment classification based on high-resolution remote sensing data, and that topographic features, surface water, and grain size were significantly correlated with spectral reflectance
Summary
Tidal flats are affected by tidal and wave flows in rivers and seawater in estuaries, leading to their complicated depositional and ecological environments [1]. Ryu et al [2] applied Landsat ETM+ to analyze the correlation between spectral reflectance and critical grain size (0.0625 mm, 0.125 mm, and 0.25 mm) under different tidal environment conditions They found that a critical grain size of 0.25 mm was appropriate for classification of the surface sediment distribution using Landsat ETM+ band 4, with the exception of close tidal channels in the upper areas of tidal flats. They found that surface water and regional topographic features affected spectral reflectance. This grain size criteria was applied to classification procedure based on object-based method
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