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Landslide Susceptibility Analysis for GIS and Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 2871

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Prof. Dr. Stephan van Gasselt

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Geomatics Group, National Chengchi University, Taipei 11605, Taiwan
Interests: cartographic models; temporal topology and data models; geographic information sciences and technology; remote sensing data analysis; planetary geology and geomorphology; planetary resources and exploration
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Prof. Dr. Shih-Yuan Lin

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Department of Land Economics, National Chengchi University, Taipei 11605, Taiwan
Interests: InSAR; planetary mapping; error regulation of planetary topography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Landslides are natural hazards that are challenging to model. The associated risks are difficult to assess due to their complexity and a large number of uncertainties related to their development. They occur as a result of over-steepened slopes, and their formation and development are mainly influenced by the underlying geology, climate factors, erosional regime, or anthropogenic stressors such as construction or mining. An increasing world population, the need for finding additional space for people, even in challenging environments, as well as the accumulating effects of climate change, all demand a better understanding of landslide formation in order to work on better decision-making for mitigating the impact of landslides. This paradigm shift from disaster management towards understanding risks has been emphasized by the Sendai Framework for Disaster Risk Reduction (DRR) 2015–2030.

Landslide susceptibility analyses are approaches to identifying areas that are more susceptible to landslides based on various geological, environmental, and anthropogenic factors. Such analyses might be based on traditional mapping by analyzing spatial distributions of landslides and their triggering factors. Other analysis approaches may cover classical statistical analysis, advanced multi-variable regression models, and also contemporary and innovative machine learning-based tools. Such analyses are therefore considered to be an important tool for DRR as they help to implement effective decision-making and improve urban/rural planning for areas prone to landslides.

This Special Issue invites contributions in all fields of landslide susceptibility mapping and analyses using remote-sensing data and GIS-based analysis approaches. We especially invite contributions in the field of multitemporal data analyses as well as satellite image time series (SITS) analysis. In addition, submissions presenting in situ fieldwork data and their contribution to validating remote-sensing data are highly encouraged. We would also like to invite contributions that establish a crosslink between policy and decision-making in local or national governments, as well as reports on collaborative approaches for risk mitigation. We encourage contributions with a link to the Sustainable Development Goals (SDG) and their indicator metrics, in particular SDG 11 (Sustainable Cities and Communities, 13 (Climate Action), or 15 (Life on Land). Landslides have several connections to Environmental, Social, and Governance (ESG) factors, and we would therefore like to invite contributions in this field as well.

Prof. Dr. Stephan van Gasselt
Prof. Dr. Shih-Yuan Lin
Guest Editors

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Keywords

landslides
landslide susceptibility
decision-making
remote sensing data analyses
spatial data analyses and GIS
satellite image time series
multitemporal analysis
regression analysis

Published Papers (3 papers)

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Research

22 pages, 46891 KiB

Open AccessArticle

Seismic Landslide Susceptibility Assessment Using Newmark Displacement Based on a Dual-Channel Convolutional Neural Network

by Yan Li, Dongping Ming, Liang Zhang, Yunyun Niu and Yangyang Chen

Remote Sens. 2024, 16(3), 566; https://doi.org/10.3390/rs16030566 - 01 Feb 2024

Viewed by 746

Abstract

Landslide susceptibility assessment (LSA) is an essential tool for landslide hazard warning. The selection of earthquake-related factors is pivotal for seismic LSA. In this study, Newmark displacement (D_n) is employed as the earthquake-related factor, providing a detailed representation of seismic characteristics. On the algorithmic side, a dual-channel convolutional neural network (CNN) model is built, and the last classification layer is replaced with two machine learning (ML) models to facilitate the extraction of deeper features related to landslide development. This research focuses on Beichuan County in Sichuan Province, China. Fifteen landslide predisposing factors, including hydrological, geomorphic, geological, vegetation cover, anthropogenic, and earthquake-related features, were extensively collected. The results demonstrate some specific issues. D_n outperforms conventional earthquake-related factors such as peak ground acceleration (PGA) and Arias intensity (I_a) in capturing seismic influence on landslide development. Under the same conditions, the OA improved by 5.55% and AUC improved by 0.055 compared to the PGA; the OA improved by 3.2% and AUC improved by 0.0327 compared to the Ia. The improved CNN outperforms ML models. Under the same conditions, the OA improved by 4.69% and AUC improved by 0.0467 compared to RF; the OA improved by 4.47% and AUC improved by 0.0447 compared to SVM. Additionally, historical landslides validate the reasonableness of the landslide susceptibility maps. The proposed method exhibits a high rate of overlap with the historical landslide inventory. The proportion of historical landslides in the very high and high susceptibility zones exceeds 87%. The method not only enhances accuracy but also produces a more fine-grained susceptibility map, providing a reliable basis for early warning of seismic landslides. Full article

(This article belongs to the Special Issue Landslide Susceptibility Analysis for GIS and Remote Sensing)

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18 pages, 16783 KiB

Open AccessArticle

Combined Methodology for Rockfall Susceptibility Mapping Using UAV Imagery Data

by Svetlana Gantimurova and Alexander Parshin

Remote Sens. 2024, 16(1), 177; https://doi.org/10.3390/rs16010177 - 31 Dec 2023

Viewed by 760

Abstract

Gravitational processes on cut slopes located close to infrastructure are a high concern in mountainous regions. There are many techniques for survey, assessment, and prognosis of hazardous exogenous geological processes. The given research describes using UAV data and GIS morphometric analysis for delineation of hazardous rockfall zones and 3D modelling to obtain an enhanced, detailed evaluation of slope characteristics. Besides the slope geomorphometric data, we integrated discontinuity layers, including rock plains orientation and fracture network density. Cloud Compare software 2.12 was utilised for facet extraction. Fracture discontinuity analysis was performed in QGIS using the Network GT plugin. The presented research uses an Analytical Hierarchy Process (AHP) to determine the weight of each contributing factor. GIS overlay of weighted factors is applied for rockfall susceptibility mapping. This integrated approach allows for a more comprehensive GIS-based rockfall susceptibility mapping by considering both the structural characteristics of the outcrop and the geomorphological features of the slope. By combining UAV data, GIS-based morphometric analysis, and discontinuity analysis, we are able to delineate hazardous rockfall zones effectively. Full article

(This article belongs to the Special Issue Landslide Susceptibility Analysis for GIS and Remote Sensing)

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19 pages, 14284 KiB

Open AccessArticle

Modeling Landslide Susceptibility in Forest-Covered Areas in Lin’an, China, Using Logistical Regression, a Decision Tree, and Random Forests

by Chongzhi Chen, Zhangquan Shen, Yuhui Weng, Shixue You, Jingya Lin, Sinan Li and Ke Wang

Remote Sens. 2023, 15(18), 4378; https://doi.org/10.3390/rs15184378 - 06 Sep 2023

Cited by 3 | Viewed by 1033

Abstract

Landslides are a common geodynamic phenomenon that cause substantial life and property damage worldwide. In the present study, we developed models to evaluate landslide susceptibility in forest-covered areas in Lin’an, southeastern China using logistic regression (LR), decision tree (DT), and random forest (RF) techniques. In addition to conventional landslide-related natural and human disturbance factors, factors describing forest cover, including forest type (two plantations (hickory and bamboo) and four natural forests (conifer, hardwood, shrub, and moso bamboo) and understory vegetation conditions, were included as predictors. Model performance was evaluated based on true-positive rate, Kappa value, and area under the ROC curve using a 10-fold cross-validation method. All models exhibited good performance with measures of ≥0.70, although the LR model was relatively inferior. The key predictors were forest type, understory vegetation height (UVH), normalized differential vegetation index (NDVI) in summer, distance to road (DTRD), and maximum daily rainfall (MDR). Hickory plantations yielded the highest landslide probability, while conifer and hardwood forests had the lowest values. Bamboo plantations had probability results comparable to those of natural forests. Using the RF model, areas with a shorter UVH (<1.2 m), a lower NDVI (<0.70), a heavier MDR (>115 mm), or a shorter DTRD (<500 m) were predicted to be landslide-prone. Information on forest cover is essential for predicting landslides in areas with rich forest cover, and conversion from natural forests to plantations could increase landslide risk. Across the study areas, the northwestern part was the most landslide-prone. In terms of landslide prevention, the RF model-based map produced the most accurate predictions for the “very high” category of landslide. These results will help us better understand landslide occurrences in forest-covered areas and provide valuable information for governments in designing disaster mitigation. Full article

(This article belongs to the Special Issue Landslide Susceptibility Analysis for GIS and Remote Sensing)

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