Abstract
Abstract. Continuous heavy rain for a long duration over mountainous terrain, where the elevation is relatively low and the topography is complex leads to multiple small-scale landslides over a wide area. Detailed investigations of small-scale landslides have been effectively carried out using optical high-resolution satellite images with spatial resolution of about 2 m or less. In this study, the sediment-related disaster caused by heavy rain in northern Kyushu, Japan that occurred in July 2017 was selected as a typical example of small-scale landslide. For this landslide event, the applicability of the conventional superpixel segmentation for landslide separation was examined. The applicability of the representative SLIC and SLICO methods in the superpixel segmentation method by image interpretation in the case of a large number of small-scale landslides in a wide area was assessed. These results suggest that in the case of such a disaster, segmentation by the SLICO method will be better. In addition, the set value of the area size for the area division was systematically examined from the distribution tendency of the average NDVI value in the divided area. It was shown that the landslide region can be extracted with relatively high accuracy by the land cover classification process by the NN method by using the appropriate region size examined by the SLICO method.
Highlights
In recent years sediment-related disasters caused by heavy rainfall over a wide area have occurred frequently in Japan
The tendency that multiple land covers such as landslide surface and forest surface coexist in the rectangle became stronger
With the SLICO method it was possible to divide the landslide more accurately by changing the area according to the outer edge shape of the landslide that varies in different directions
Summary
Some effects of objectbased classification on landslide extraction using high resolution satellite images have been investigated (Helno et al, 2016; Hölbling et al, 2015; Lu et al, 2011; Martha et al, 2011; Martha and Kerl, 2010). While many studies have investigated large-scale landslides using object-based classification, this method has seldom been applied for extracting the distribution of small-scale landslides that frequently occur in mountainous regions with complex terrain. The sediment-related disaster caused by heavy rain in northern Kyushu, Japan that occurred in July 2017 was selected as a typical example of small-scale landslide. For this landslide event, the applicability of the SLICO and SLIC methods that are conventional superpixel segmentation methods for landslide separation was examined. Using the most appropriate segmentation area identified from this study, the extractability of the landslide area by objectoriented land cover classification was evaluated
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