Abstract
Landslide identification is a fundamental step enabling the assessment of landslide susceptibility and determining the associated risks. Landslide identification by conventional methods is often time-consuming, therefore alternative techniques, including automatic approaches based on remote sensing data, have captured the interest among researchers in recent decades. By providing a highly detailed digital elevation model (DEM), airborne laser scanning (LiDAR) allows effective landslide identification, especially in forested areas. In the present study, object-based image analysis (OBIA) was applied to landslide detection by utilizing LiDAR-derived data. In contrast to previous investigations, our analysis was performed on forested and agricultural areas, where cultivation pressure has degraded specific landslide geomorphology. A diverse variety of aspects that influence OBIA accuracy in landslide detection have been considered: DEM resolution, segmentation scale, and feature selection. Finally, using DEM delivered layers and OBIA, landslide was identified with an overall accuracy (OA) of 85% and a kappa index (KIA) equal to 0.60, which illustrates the effectiveness of the proposed approach. In the end, a field investigation was performed in order to evaluate the results achieved by applying an automatic OBIA approach. The advantages and challenges of automatic approaches for landslide identification for various land use were also discussed. Final remarks underline that effective landslide detection in forested areas could be achieved while this is still challenging in agricultural areas.
Highlights
A landslide is a movement of a mass of rocks, debris, or earth down a slope [1]
Using digital elevation model (DEM) delivered layers and object-based image analysis (OBIA), landslide was identified with an overall accuracy (OA) of 85% and a kappa index (KIA) equal to 0.60, which illustrates the effectiveness of the proposed approach
Remote sensing technologies have increased in importance and they are mainly focused on data delivered by synthetic aperture radar (SAR), high resolution multi-spectral images [10] and digital elevation models (DEMs) obtained from space or airborne sensors [3,11,12,13]
Summary
A landslide is a movement of a mass of rocks, debris, or earth down a slope [1]. This natural hazard can lead to severe consequences such as economic and infrastructure damage and human casualties [2]. DEM-derived layers such as the slope angle layer, an elevation percentile layer and two topographic openness layers (with kernel sizes of 25 m and 250 m) together with multi-temporal, ortho-rectified, panchromatic, color, and infrared aerial photos, have been applied to stratified OBIA classification This allows for the mapping of six geomorphological features with an overall accuracy of 84%. For rural areas, typical landslide features can be degraded, or even vanished, due to direct or surrounding agricultural activities Having considered these issues, an open question remains to be explored: Based on DEM derivatives only, what OBIA approach is scalable to various land cover conditions? IInn tthhiiss ssttuuddyy,, tthhrreeee ddaattaasseettss wweerree uusseedd:: AA llaannddsslliiddee iinnvveennttoorryy mmaapp ffrroomm tthhee PPoolliisshh GGeeoollooggiiccaall IInnssttiittuuttee ddaattaabbaassee ((SSeeccttiioonn 33..11)),, LLiiDDAARR ddaattaa ((SSeeccttiioonn 33..22;; wwhhiicchh aalllloowweedd uuss ttoo ddeerriivvee aa hhiigghh rreessoolluuttiioonn DDEEMM)) aanndd tthhee OOppeenn SSttrreeeett MMaapp ddaattaabbaassee ((rrooaaddss,, SSeeccttiioonn 33..33..11aannddrriivveerraannddssttrreeaammss,,SSeeccttiioonn33..33..22)). Slides are mostly located on the slopes of valleys and water reservoirs and places where backward erosion increases slope failure, such as areas around roads and rivers [23,25]
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