Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy

Yordanov, V.; Brovelli, M. A.

doi:10.1007/s12518-020-00344-1

Cited by 13 publications

(4 citation statements)

References 43 publications

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“…The advantages of using RRIMs compared to orthophotos for applying computer vision approaches are better highlighted at lower scale representation. To show this fact, details of the RRIM implementation in comparison to orthophotos and hillshade visualization are depicted in Figure 9, while the full-scale RRIMs for the period 2021-2022 are added as an Appendix A.1-A.4 (available [56]). For a full comparison with the orthophotos, the latter can be accessed as mentioned in Section 2.3.2 from the https://openaerialmap.org/ (OpenAerialMap.org, accessed on 30 October 2022) platform.…”

Section: Rrim Implementationmentioning

confidence: 99%

Estimating Landslide Surface Displacement by Combining Low-Cost UAV Setup, Topographic Visualization and Computer Vision Techniques

Yordanov

Truong

Brovelli

2023

Drones

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Many techniques are available for estimating landslide surface displacements, whether from the ground, air- or spaceborne. In recent years, Unmanned Areal Vehicles have also been applied in the domain of landslide hazards, and have been able to provide high resolution and precise datasets for better understanding and predicting landslide movements and mitigating their impacts. In this study, we propose an approach for monitoring and detecting landslide surface movements using a low-cost lightweight consumer-grade UAV setup and a Red Relief Image Map (a topographic visualization technique) to normalize the input datasets and mitigate unfavourable illumination conditions that may affect the further implementation of Lucas–Kanade optical flow for the final displacement estimation. The effectiveness of the proposed approach in this study was demonstrated by applying it to the Ruinon landslide, Northern Italy, using the products of surveys carried out in the period 2019–2021. Our results show that the combination of different techniques can accurately and effectively estimate landslide movements over time and at different magnitudes, from a few centimetres to more than several tens of meters. The method applied is shown to be very computationally efficient while yielding precise outputs. At the same time, the use of only free and open-source software allows its straightforward adaptation and modification for other case studies. The approach can potentially be used for monitoring and studying landslide behaviour in areas where no permanent monitoring solutions are present.

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Section: Rrim Implementationmentioning

confidence: 99%

Estimating Landslide Surface Displacement by Combining Low-Cost UAV Setup, Topographic Visualization and Computer Vision Techniques

Yordanov

Truong

Brovelli

2023

Drones

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“…Moreover, ODM has a built-in integration to directly upload the resulted orthophotos to OpenAerialMap (www.openaerialmap.org) which is a tool for searching and sharing crowdsourced UAV images under open license, where such orthophotos can be in great asset for landslide inventory compiling and susceptibility analyses (Yordanov and Brovelli, 2020;Yordanov et al, 2021) .…”

Section: Opendronemapmentioning

confidence: 99%

Landslide Surveys Using Low-Cost Uav and Foss Photogrammetric Workflow

Yordanov

Biagi²,

Truong³

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Unmanned Aerial Vehicle have found their usage in various academic and industry domains, mainly due to the versatility of options that one aircraft can offer from onboard sensors for data acquisition and positioning, through different weight and size categories allowing different applications, including landslide mapping and surveying. Survey-grade UAVs can provide very precise flight and data but usually are very costly, and their use can be further bounded by many regulations. In this work, we have adopted a low-cost consumer-grade UAV to do multitemporal monitoring of an active landslide (Ruinon) in Northern Italy to evaluate the applicability of such setup in the landslide hazard domain. Moreover, for flight planning, photogrammetric reconstruction, and comparative analyses we have adopted free and open-source software solutions. The resulted dense point clouds and orthophotos yielded very satisfactory results from accuracies of few meters and even sub-meter level when reconstructed with field-surveyed ground control points. As a result, from the two surveys comparisons, July and October 2021, several displaced boulders and debris were detected where no significant reactivations were detected from our surveys. Such low-cost setup can be used also from non-professionals and in citizen science campaigns which can significantly contribute to additional landslide mapping and analyses by providing valuable datasets.

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“…Valtellina valley (Van Den Eeckhaut et al 2012)). Yordanov and Brovelli (2020) produced the landslide susceptibility map of the Val Tartano District (Southern Sondrio) using the SI, LR, and RF approaches. Val Tartano is in the right-hand side proximity of the area examined in the present work.…”

Section: Comparison With Literaturementioning

confidence: 99%

Landslide Susceptibility Zonation Using Statistical and Machine Learning Approaches in Northern Lecco, Italy

Mehrabi

2021

Preprint

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This study deals with landslide susceptibility mapping in the northern part of Lecco Province, Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen conditioning factors (including elevation, slope aspect, slope degree, plan curvature, profile curvature, distance to waterway, distance to road, distance to fault, soil type, land use, lithology, stream power index, and topographic wetness index) form the spatial database within geographic information system (GIS). The used evaluative models comprise a bivariate statistical approach called frequency ratio (FR) and two machine learning tools, namely multi-layer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS). These models first use landslide and non-landslide records for comprehending the relationship between the landslide occurrence and conditioning factors. Then landslide susceptibility values are predicted for the whole area. The accuracy of the produced susceptibility maps is measured using area under the curve (AUC) index, according to which, the MLPNN (AUC = 0.916) presented the most accurate map, followed by the FR (AUC = 0. 898) and ANFIS (AUC = 0.889). Visual interpretation of the susceptibility maps, FR-based correlation analysis, as well as the importance assessment of conditioning factors, all indicated the significant contribution of the road networks to the crucial susceptibility of landslide. Lastly, an explicit predictive formula is extracted from the implemented MLPNN model for a convenient approximation of landslide susceptibility value.

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Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy

Cited by 13 publications

References 43 publications

Estimating Landslide Surface Displacement by Combining Low-Cost UAV Setup, Topographic Visualization and Computer Vision Techniques

Estimating Landslide Surface Displacement by Combining Low-Cost UAV Setup, Topographic Visualization and Computer Vision Techniques

Landslide Surveys Using Low-Cost Uav and Foss Photogrammetric Workflow

Landslide Susceptibility Zonation Using Statistical and Machine Learning Approaches in Northern Lecco, Italy

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