Landslide features identification and morphology investigation using high-resolution DEM derivatives

Pawłuszek, Kamila

doi:10.1007/s11069-018-3543-1

Cited by 32 publications

(16 citation statements)

References 33 publications

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“…are some of the risks associated with a landslide event that can translate into major social impacts and economic loss. Expansion of human settlement into geologically sensitive areas, infrastructure development, and increased agricultural practices result in land use changes that further aggravate the problem of landslides and associated risks [5][6][7]. Cutting slopes for infrastructure development, particularly during road construction, is a major triggering factor for most landslides.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

Pasang

Kubíček

2020

Geosciences

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In areas prone to frequent landslides, the use of landslide susceptibility maps can greatly aid in the decision-making process of the socio-economic development plans of the area. Landslide susceptibility maps are generally developed using statistical methods and geographic information systems. In the present study, landslide susceptibility along road corridors was considered, since the anthropogenic impacts along a road in a mountainous country remain uniform and are mainly due to road construction. Therefore, we generated landslide susceptibility maps along 80.9 km of the Asian Highway (AH48) in Bhutan using the information value, weight of evidence, and logistic regression methods. These methods have been used independently by some researchers to produce landslide susceptibility maps, but no comparative analysis of these methods with a focus on road corridors is available. The factors contributing to landslides considered in the study are land cover, lithology, elevation, proximity to roads, drainage, and fault lines, aspect, and slope angle. The validation of the method performance was carried out by using the area under the curve of the receiver operating characteristic on training and control samples. The area under the curve values of the control samples were 0.883, 0.882, and 0.88 for the information value, weight of evidence, and logistic regression models, respectively, which indicates that all models were capable of producing reliable landslide susceptibility maps. In addition, when overlaid on the generated landslide susceptibility maps, 89.3%, 85.6%, and 72.2% of the control landslide samples were found to be in higher-susceptibility areas for the information value, weight of evidence, and logistic regression methods, respectively. From these findings, we conclude that the information value method has a better predictive performance than the other methods used in the present study. The landslide susceptibility maps produced in the study could be useful to road engineers in planning landslide prevention and mitigation works along the highway.

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Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

Pasang

Kubíček

2020

Geosciences

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“…The identification of the landslides was based on the interpretation of their specific morphological features that are noticeable in high-resolution imagery, including the crown, main scarp, flanks, body, and toe (Pawluszek, 2019). Other features include the presence of flow materials along gullies, streams with different erosional features, flow tracks, scars along the cliff face, and block deposits on the cliff base (Epifânio et al, 2013;Elkadiri et al, 2014).…”

Section: Landslide Inventorymentioning

confidence: 99%

Landslide susceptibility assessment in the rocky coast subsystem of Essaouira, Morocco

Khouz

Trindade

Oliveira

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. In recent decades, multiple researchers have produced landslide susceptibility maps using different techniques and models, including the information value method, which is a statistical model that is widely applied to various coastal environments. This study aimed to evaluate susceptibility to landslides in the Essaouira coastal area using bivariate statistical methods. In this study, 588 distinct landslides were identified, inventoried, and mapped. Landslides are performed by means of observation and interpretation of different data sources, namely high-resolution satellite images, aerial photographs, topographic maps, and extensive field surveys. The rocky coastal system of Essaouira is located in the middle of the Atlantic coast of Morocco. The study area was split into 1534 cliff terrain units that were 50 m in width. For training and validation purposes, the landslide inventory was divided into two independent groups: 70 % for training and 30 % for validation. Twenty-two layers of landslide conditioning factors were prepared – namely, elevation, slope angle, slope aspect, plan curvature, profile curvature, cliff height, topographic wetness index, topographic position index, slope over area ratio, solar radiation, presence of faulting, lithological units, toe lithology, presence and type of cliff toe protection, layer tilt, rainfall, streams, land-use patterns, normalised difference vegetation index, lithological material grain size, and presence of springs. The statistical relationship between the conditioning factors and the different landslide types was calculated using the bivariate information value method in a pixel-based model and in the elementary terrain units-based model. Coastal landside susceptibility maps were validated using landslide training group partitions. The receiver operating characteristic curve and area under the curve were used to assess the accuracy and prediction capacity of the different coastal landslide susceptibility models. Two methodologies, considering a pixel-based approach and using coastal terrain units, were adopted to evaluate coastal landslide susceptibility. The results allowed for the classification of 38 % of the rocky coast subsystem as having high susceptibility to landslides, which were mostly located in the southern part of the Essaouira coastal area. These susceptibility maps will be useful for future planned development activities as well as for environmental protection.

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“…Automatic methods for landslide mapping include analyses of RS data, such as optical images [8,10,40,41], synthetic aperture radar (SAR) data [42,43], and Light Detection and Ranging (LiDAR) delivered digital elevation models (DEMs) [14,15,17,18,44,45]. The diversity of data and their resolution provide opportunities for various types of investigations.…”

Section: Related Studiesmentioning

confidence: 99%

On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification

Pawłuszek-Filipiak

Borkowski

2020

Remote Sensing

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Many automatic landslide detection algorithms are based on supervised classification of various remote sensing (RS) data, particularly satellite images and digital elevation models (DEMs) delivered by Light Detection and Ranging (LiDAR). Machine learning methods require the collection of both training and testing data to produce and evaluate the classification results. The collection of good quality landslide ground truths to train classifiers and detect landslides in other regions is a challenge, with a significant impact on classification accuracy. Taking this into account, the following research question arises: What is the appropriate training–testing dataset split ratio in supervised classification to effectively detect landslides in a testing area based on DEMs? We investigated this issue for both the pixel-based approach (PBA) and object-based image analysis (OBIA). In both approaches, the random forest (RF) classification was implemented. The experiments were performed in the most landslide-affected area in Poland in the Outer Carpathians-Rożnów Lake vicinity. Based on the accuracy assessment, we found that the training area should be of a similar size to the testing area. We also found that the OBIA approach performs slightly better than PBA when the quantity of training samples is significantly lower than the testing samples. To increase detection performance, the intersection of the OBIA and PBA results together with median filtering and the removal of small elongated objects were performed. This allowed an overall accuracy (OA) = 80% and F1 Score = 0.50 to be achieved. The achieved results are compared and discussed with other landslide detection-related studies.

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Landslide features identification and morphology investigation using high-resolution DEM derivatives

Cited by 32 publications

References 33 publications

Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

Landslide susceptibility assessment in the rocky coast subsystem of Essaouira, Morocco

On the Importance of Train–Test Split Ratio of Datasets in Automatic Landslide Detection by Supervised Classification

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