Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods

Abstract. In the paper we analyse a multi-date landslide inventory prepared for a mountainous area affected by several landslide types with different degrees of activity, we attempt to quantify the uncertainties associated to the mapping, we measure the evolution of morphological indicators and estimate landslide activity and temporal occurrence. The inventory, covering the period 1956-2010, is prepared for the middle section of the Ubaye valley (southern French Alps) based on the analysis of multi-source documents (geomorphological maps, historical reports of landslide events, field surveys, orthophotographs and SAR (synthetic aperture radar) satellite images). The uncertainties derived from the expert interpretation of different sources of information, the landslide morphological features and the affected land covers are taken into account in relation to the source documents.Morphological indicators are calculated to describe quantitatively the evolution of the landslides (length, area, relative elevation, runout distance). Frequency-area density functions are computed to estimate the changes in the landslide distributions and a Poisson model is used to estimate the probability of reactivation of the observed landslides and the occurrence of new failures. The proposed multi-date inventory and the associated statistics provide additional information to the event catalogue managed by the local policy makers.

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

Analysis of a landslide multi-date inventory in a complex mountain landscape: the Ubaye valley case study

Schlögel

Malet

Reichenbach

et al. 2015

“…Recent developments have taken place in the use of remote sensing techniques and image analysis to evaluate the extension and impact damages of landslides (Fernández et al, 711 2005;Nichol and Wong, 2005;Danneels et al, 2007;Martha et al, 2010). Landslide inventory mapping can also greatly improve from the use of these techniques (Whitworth et al, 2003;Malamud et al, 2004;Kirschbaum et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

The 20 February 2010 Madeira Island flash-floods: VHR satellite imagery processing in support of landslide inventory and sediment budget assessment

Lira¹,

Lousada²,

Falcão³

et al. 2013

On 20 February 2010, an extreme rainfall episode occurred on Madeira Island, which caused an exceptionally strong flash flood and several soil slip-debris flows, producing 45 confirmed deaths and 6 persons declared missing, as well as extensive material damages. In order to understand and quantify the importance of landsliding in routing sediment through mountainous drainage, such as Madeira Island's landscape, it was essential to perform extensive landslide analysis. This study describes the methodology used to semi-automatically detect the landslides, produce the landslide inventory maps and estimate the sediment volume produced during this particular event which ranged from 217 000 m³ to 344 000 m³ and 605 000 m³ to 984 000 m³ for the Funchal and Ribeira Brava basins, respectively. These results contributed to the design and implementation of measures to prevent damages caused by landslides in Madeira Island

“…A reliance upon spectral responses can also result in the misclassification of channel bank erosion and fluvial sedimentation, the misidentification of reactivations, and the division of large landslides into multiple fractions. While the increasing availability of VHR imagery directly enhances the accuracy of manual landslide mapping, the results of automated and semiautomated pixel-based methods that have used VHR imagery are susceptible to large spectral variance between pixels, creating intra-class variability, and are more sensitive to coregistration errors (Moine et al, 2009;Martha et al, 2010;Mondini et al, 2011). Object-based image analysis overcomes many of these issues by accounting for additional metrics such as color, texture, shape, and topography , though the selection of useful object metrics is time intensive and varies from case to case.…”

Section: The Best Way To Map Coseismic Landslidesmentioning

confidence: 99%

Satellite-based emergency mapping using optical imagery: experience and reflections from the 2015 Nepal earthquakes

Williams

Rosser

Kincey

et al. 2018

Abstract. Landslides triggered by large earthquakes in mountainous regions contribute significantly to overall earthquake losses and pose a major secondary hazard that can persist for months or years. While scientific investigations of coseismic landsliding are increasingly common, there is no protocol for rapid (hours-to-days) humanitarian-facing landslide assessment and no published recognition of what is possible and what is useful to compile immediately after the event. Drawing on the 2015 M w 7.8 Gorkha earthquake in Nepal, we consider how quickly a landslide assessment based upon manual satellite-based emergency mapping (SEM) can be realistically achieved and review the decisions taken by analysts to ascertain the timeliness and type of useful information that can be generated. We find that, at present, many forms of landslide assessment are too slow to generate relative to the speed of a humanitarian response, despite increasingly rapid access to high-quality imagery. Importantly, the value of information on landslides evolves rapidly as a disaster response develops, so identifying the purpose, timescales, and end users of a post-earthquake landslide assessment is essential to inform the approach taken. It is clear that discussions are needed on the form and timing of landslide assessments, and how best to present and share this information, before rather than after an earthquake strikes. In this paper, we share the lessons learned from the Gorkha earthquake, with the aim of informing the approach taken by scientists to understand the evolving landslide hazard in future events and the expectations of the humanitarian community involved in disaster response.