Towards the Optimal Pixel Size of Dem for Automatic Mapping of Landslide Areas

Pawłuszek, Kamila; Borkowski, Andrzej; Tarolli, Paolo

doi:10.5194/isprs-archives-xlii-1-w1-83-2017

Cited by 21 publications

(20 citation statements)

References 21 publications

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“…Although many papers have argued the influence of pixel size on the accuracy of specific modelling, the selection of pixel resolution is exceptionally based on the inherent spatial variability of the input data for any scientific justification, mostly drawing on information theory (Borkowski and Meier 1994;Hengl 2006). In contrast, some papers have demonstrated that the selection of the finest DTM resolution is not always the optimal choice (Pawłuszek et al 2014;Mora et al 2014;Penna et al 2014;Tarolli and Tarboton 2006;Pawłuszek et al 2017). The selection of an inappropriate spatial resolution for DTM may result in misjudgement of landslide identification or misinterpretation of landslide features or morphology (Mora et al 2014).…”

Section: Methodsmentioning

confidence: 99%

Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution

2018

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The automatic detection of landslides after major events is a crucial issue for public agencies to support disaster response. Pixel-based approaches (PBAs) are widely used in the literature for various applications. However, the accuracy of PBAs in the case of automatic landslide mapping (ALM) is affected by several issues. In this study, we investigated the sensitivity of ALM using PBA through digital terrain models (DTMs). The analysis, carried out in a study area of Poland, consisted of the following steps: (1) testing the influence of selected DTM resolutions for ALM, (2) assessing the relevance of diverse landslide morphological indicators for ALM, and (3) assessing the sensitivity to landslide features for a selected size of moving window (kernel) calculations for ALM. Ultimately, we assessed the performance of three classification methods: maximum likelihood (ML), feed-forward neural network (FFNN), and support vector machine (SVM). This broad analysis, as combination of grid cell resolution, surface derivatives calculation, and performance classification methods, is the challenging aspect of the research. The results of almost 500 experimental tests provide valuable guidelines for experts performing ALM. The most important findings indicate that feature sensitivity in the case of kernel size increases with coarser DTM resolution; however, the peak of the optimal feature performance for the selected study area and landslide type was demonstrated for a resolution of 20 m. Another finding indicated that in combining a set of topographic variables, the optimal performance was acquired for a DTM resolution of 30 m and the support vector machine classification. Moreover, the best performance of the identification is represented for SVM classification.

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Section: Methodsmentioning

confidence: 99%

Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution

2018

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“…Li et al [17] also considered slope, aspect, and texture layers as useful for landslide detection. According to previous findings [5,13,36], a hillshade layer is especially informative for landslide detection, therefore, it was used in this research [13,36]. The above mentioned topographical layers are currently widely applied in various landslide-related studies, therefore broad descriptions of these layers is presented in [3,5,9,13,17,36].…”

Section: Generation Of First and Second-order Dem-derivativesmentioning

confidence: 99%

“…To mitigate the negative effects of landslide occurrence, an effective risk assessment is unavoidable [3]. Landslide identification in a specific study area is a fundamental step, which enables an assessment of landslide susceptibility and the associated risk [3][4][5]. Landslide identification can be carried out using a variety of techniques, which are mainly divided into conventional and remote sensing techniques [6,7].…”

Section: Introductionmentioning

confidence: 99%

Multi-Aspect Analysis of Object-Oriented Landslide Detection Based on an Extended Set of LiDAR-Derived Terrain Features

Pawłuszek

Marczak

Borkowski

et al. 2019

IJGI

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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.

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“…On the other hand, semi-automatic and automatic methods of landslide identification do not necessarily offer better performances, and can even lead to larger uncertainties when applied to very high resolution images (van Westen et al, 2006;Guzzetti et al, 2012;Pawłuszek et al, 2017). Moreover, some semi-automatic techniques still need visual interpretation over a significant test area for calibration (e.g., Đurić et al, 2017), and automatic methods may require a combination of images of different portions of the spectrum, or of satellite and aerial images, that should be acquired within a narrow time window to be 10 (Santangelo et al, 2015).…”

Section: Uncertaintiesmentioning

confidence: 99%

Two multi-temporal datasets to track the enhanced landsliding after the 2008 Wenchuan earthquake

Fan¹,

Scaringi²,

Yang³

et al. 2018

Preprint

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Abstract. We release two datasets that track the enhanced landsliding induced by the Mw 7.9 2008 Wenchuan earthquake over a portion of the Longmen mountains, at the eastern margin of the Tibetan plateau (Sichuan, China). The first dataset is a geo-referenced multi-temporal polygon-based inventory of pre- and coseismic landslides, post-seismic remobilisations of coseismic landslide debris, and post-seismic landslides (new failures). The inventory covers 462.5 km2 in the earthquake's epicentral area, from 2005 to 2015. The second dataset records the debris flows that occurred from 2008 to 2017 in a larger area (~ 17,000 km2), together with information on their triggering rainfalls recorded by a network of rain gauges. For some well-monitored event, we provide detailed information on rainfall, discharge, flow depth and density. The datasets can be used to analyse, at various scales, the patterns of enhanced landsliding caused by the earthquake. They can be compared to inventories relative to past or new earthquakes or other triggers to reveal common or distinctive controlling factors. To our knowledge, no other inventories that track the temporal evolution of earthquake-induced mass wasting have been made freely available thus far. Our datasets are available at https://doi.org/10.5281/zenodo.1405490. We also encourage other researchers to share their datasets to facilitate research on post-seismic geological hazards.

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Towards the Optimal Pixel Size of Dem for Automatic Mapping of Landslide Areas

Cited by 21 publications

References 21 publications

Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution

Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution

Multi-Aspect Analysis of Object-Oriented Landslide Detection Based on an Extended Set of LiDAR-Derived Terrain Features

Two multi-temporal datasets to track the enhanced landsliding after the 2008 Wenchuan earthquake

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