Relation between land cover and landslide susceptibility in Val d'Aran, Pyrenees (Spain): Historical aspects, present situation and forward prediction

Shu, Heping; Hürlimann, Marcel; Molowny‐Horas, Roberto; González, Marta González; Pinyol, Jordi; Abancó, Clàudia; Ma, Jinzhu

doi:10.1016/j.scitotenv.2019.07.363

Cited by 81 publications

(51 citation statements)

References 57 publications

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“…1) mostly consist of diorite and diorite porphyry (DENR-MGB, 1995). The study area can also be described as seismically active (Su, 1988). The whole area is covered by surficial formations consisting of loam and clays and undifferentiated mountain soils.…”

Section: Geological and Geomorphological Settingmentioning

confidence: 99%

The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines

Abancó

Bennett

Matthews

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. In 2018 Typhoon Mangkhut (locally known as Typhoon Ompong) triggered thousands of landslides in the Itogon region of the Philippines. A landslide inventory of the affected region is compiled for the first time, comprising 1101 landslides over a 570 km2 area. The inventory is used to study the geomorphological characteristics and land cover more prone to landsliding as well as the hydrometeorological conditions that led to widespread failure. The results showed that landslides mostly occurred on grassland and wooded slopes of clay superficial geology, predominantly facing east-southeast. Rainfall (Integrated Multi-satellitE Retrievals for Global Precipitation Measurement, IMERG GPM) associated with Typhoon Mangkhut is compared with 33 high-intensity rainfall events that did not trigger regional landslide events in 2018. Results show that landslides occurred during high-intensity rainfall that coincided with the highest soil moisture values (estimated clays saturation point), according to Soil Moisture Active Passive level 4 (SMAP-L4) data. Our results demonstrate the potential of SMAP-L4 and GPM IMERG data for landslide hazard assessment and early warning where ground-based data are scarce. However, other rainfall events in the months leading up to Typhoon Mangkhut that had similar or higher rainfall intensities and also occurred when soils were saturated did not trigger widespread landsliding, highlighting the need for further research into the conditions that trigger landslides in typhoons.

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Section: Geological and Geomorphological Settingmentioning

confidence: 99%

The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines

Abancó

Bennett

Matthews

et al. 2021

Nat. Hazards Earth Syst. Sci.

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“…In the remote sensing domain, historical aerial photographs were captured for various parts of the world starting in the early 1930s (Luman et al, 1997). Historical aerial black and white photographs have served as a key input for topics as different as for instance, landslide inventory and distribution analysis (Martha et al, 2012;Shu et al, 2019), vegetation dynamic studies (Carter et al, 2011;Hudak and Wessman, 1998;Jeter Jr and Carter, 2016;Kadmon and Harari-kremer, 1999;Lucas et al, 2010;Miller, 1999), landcover mapping (Caridade et al, 2008;Pinto et al, 2019;Robertson, 2012) and the evolution of river morphology (Abate et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Fully convolutional networks for land cover classification from historical panchromatic aerial photographs

Mboga

Grippa

Georganos

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

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Historical aerial photographs provide salient information on the historical state of the landscape. The exploitation of these archives is often limited by accessibility and the timeconsuming process of digitizing the analogue copies at a high resolution and processing them with a proper photogrammetric workflow. Furthermore, these data are characterised by limited spectral information since it occurs very often in a single band. Our work presents a first application of deep learning for the extraction of land cover from historical aerial panchromatic photographs of the African cities of Goma, Bukavu and Bujumbura. We evaluate the suitability of deep learning for land cover generation from a challenging dataset of photographs from the 1940s and 1950s that covers large geographical extents and is characterised by radiometric variations between dates and locations. A fully convolutional approach is investigated by considering two network architectures with different strategies of exploiting contextual information: one used atrous convolutional layers without downsampling, whereas the second network has both downsampling and learned upsampling convolutional layers (U-NET). The networks are trained to detect three main classes namely, buildings, high vegetation and a mixed class of bare land and low vegetation class. High overall accuracies of > 90% in Goma-Gisenyi and Bukavu, and >85 % in Bujumbura are obtained. This work provides a novel methodology that outperforms a baseline standard machine learning classifier for the exploitation of the vast archives of historical aerial photographs that can aid long-term environmental baseline studies. Future work will entail developing domain adaptation strategies in order to make the trained network robust for different image mosaics.

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“…Conversely, in local-scale applications, high-resolution DEMs can be obtained using LiDAR (Light Detection And Ranging) [29]. Another series of remotely sensed products, which are widely used in LSM, are indicators of the vegetation cover (e.g., the NDVI -Normalized Difference Vegetation Index) [30,31] and land-use or land-cover thematic maps [22,32,33].…”

Section: Introductionmentioning

confidence: 99%

Integration of Remotely Sensed Soil Sealing Data in Landslide Susceptibility Mapping

et al. 2020

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Soil sealing is the destruction or covering of natural soils by totally or partially impermeable artificial material. ISPRA (Italian Institute for Environmental Protection Research) uses different remote sensing techniques to monitor this process and updates yearly a national-scale soil sealing map of Italy. In this work, for the first time, we tried to combine soil sealing indicators as additional parameters within a landslide susceptibility assessment. Four new parameters were derived from the raw soil sealing map: Soil sealing aggregation (percentage of sealed soil within each mapping unit), soil sealing (categorical variable expressing if a mapping unit is mainly natural or sealed), urbanization (categorical variable subdividing each unit into natural, semi-urbanized, or urbanized), and roads (expressing the road network disturbance). These parameters were integrated with a set of well-established explanatory variables in a random forest landslide susceptibility model and different configurations were tested: Without the proposed soil-sealing-derived variables, with all of them contemporarily, and with each of them separately. Results were compared in terms of AUC ((area under receiver operating characteristics curve, expressing the overall effectiveness of each configuration) and out-of-bag-error (estimating the relative importance of each variable). We found that the parameter “soil sealing aggregation” significantly enhanced the model performances. The results highlight the potential relevance of using soil sealing maps on landslide hazard assessment procedures.

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Relation between land cover and landslide susceptibility in Val d'Aran, Pyrenees (Spain): Historical aspects, present situation and forward prediction

Cited by 81 publications

References 57 publications

The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines

The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines

Fully convolutional networks for land cover classification from historical panchromatic aerial photographs

Integration of Remotely Sensed Soil Sealing Data in Landslide Susceptibility Mapping

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