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Landslide Monitoring, Susceptibility, Hazard Assessment and Prediction with Remotely Sensed Big Data

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 44008

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Special Issue Editors

Dr. Romy Schlögel

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Guest Editor

UNOSAT-Division for Satellite Analysis and Applied Research, United Nations Institute for Training and Research (UNITAR),7 bis, avenue de la Paix, CH-1202 Geneva 2, Switzerland
Interests: optical and radar remote sensing; landslide monitoring; natural hazard assessment; susceptibility zonation; geomorphology mapping; climate change; AI4EO
Special Issues, Collections and Topics in MDPI journals

Dr. Mauro Rossi

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Guest Editor

Istituto di Ricerca per la Protezione Idrogeologica, Consiglio Nazionale delle Ricerche, Via della Madonna Alta 126, 06128 Perugia, Italia
Interests: landslide; erosion; hazard; susceptibility; early-warning systems; landslide & erosion forecast; societal risk; geo-hydrological modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Landslides are frequent and occur every year, causing severe damages to structures, infrastructures and the population worldwide. They can be triggered by rainfall, earthquakes, snowmelt, and their spatial occurrence can be conditioned by a variety of geo-environmental factors and human-induced disturbances in the landscape. In particular, changes in climate and related factors may affect significantly landslide susceptibility and hazard assessment.

Information on landslides, debris flows, or mud flows and rock falls in remote mountainous areas is often either lacking or inhomogeneous, especially when these events do not affect people or settlements. They can be either water- or cryosphere-related and since they may occur at different velocities, they leave identifiable but different footprints on the landscape. Satellite data can be used to identify, detect or map these events and can complement aerial and human observations to understand the natural hazard-triggering mechanisms as well as to identify and possibly quantify the processes involved and their location and magnitude. Earth observation data can be exploited to complete existing natural hazard inventories or to investigate the influence of climate perturbation on potentially dangerous natural phenomena in order to improve susceptibility and hazard models.

Diversified approaches have been proposed for landslide susceptibility and hazard modelling with associated terrain zonations. Among these, physically and statistically based approaches have been largely used in the scientific and technical literature. Earth Observation data have been proved to be useful in constructing landslide susceptibility models and maps in different physiographical and climatic settings.

This Special Issue welcomes all publications related to innovative researches using Big Remotely Sensed data for identifying, detecting and monitoring landslide, for susceptibility zonation or hazard assessment and for improving disaster risk management. Physical and statistically based models involving large close- and far-range remote sensing datasets are accepted. Fast and slow landslide hazards are of interest, and cascading effects with other natural hazards may be considered. Research papers are encouraged to cover a wide range of subjects related to mountainous hazards monitoring, susceptibility zonation, hazard and risk assessment with statistical and physical approaches, vulnerability and risk studies, including the following topics:

innovative processing of Big remotely sensed data;
landslide and/or climate change monitoring from far-, close-range and ground-based products;
evaluating changing hazard occurrence in relation to the changing climate;
improvement of landslide susceptibility and hazard models with EO data;
machine learning and artificial intelligence in mountainous hazard and risk assessment;
integration of large geospatial data for disaster risk management.

Dr. Romy Schlögel
Dr. Mauro Rossi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Landslide
Monitoring
Earth observation
Susceptibility
Hazard assessment
Big data
Machine learning
Physically based

Published Papers (6 papers)

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Research

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23 pages, 7090 KiB

Open AccessArticle

Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data

by Bahareh Kalantar, Naonori Ueda, Vahideh Saeidi, Kourosh Ahmadi, Alfian Abdul Halin and Farzin Shabani

Remote Sens. 2020, 12(11), 1737; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111737 - 28 May 2020

Cited by 89 | Viewed by 5577

Abstract

Predicting landslide occurrences can be difficult. However, failure to do so can be catastrophic, causing unwanted tragedies such as property damage, community displacement, and human casualties. Research into landslide susceptibility mapping (LSM) attempts to alleviate such catastrophes through the identification of landslide prone areas. Computational modelling techniques have been successful in related disaster scenarios, which motivate this work to explore such modelling for LSM. In this research, the potential of supervised machine learning and ensemble learning is investigated. Firstly, the Flexible Discriminant Analysis (FDA) supervised learning algorithm is trained for LSM and compared against other algorithms that have been widely used for the same purpose, namely Generalized Logistic Models (GLM), Boosted Regression Trees (BRT or GBM), and Random Forest (RF). Next, an ensemble model consisting of all four algorithms is implemented to examine possible performance improvements. The dataset used to train and test all the algorithms consists of a landslide inventory map of 227 landslide locations. From these sources, 13 conditioning factors are extracted to be used in the models. Experimental evaluations are made based on True Skill Statistic (TSS), the Receiver Operation characteristic (ROC) curve and kappa index. The results show that the best TSS (0.6986), ROC (0.904) and kappa (0.6915) were obtained by the ensemble model. FDA on its own seems effective at modelling landslide susceptibility from multiple data sources, with performance comparable to GLM. However, it slightly underperforms when compared to GBM (BRT) and RF. RF seems most capable compared to GBM, GLM, and FDA, when dealing with all conditioning factors. Full article

(This article belongs to the Special Issue Landslide Monitoring, Susceptibility, Hazard Assessment and Prediction with Remotely Sensed Big Data)

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22 pages, 30065 KiB

Open AccessEditor’s ChoiceArticle

Sentinel-1 DInSAR for Monitoring Active Landslides in Critical Infrastructures: The Case of the Rules Reservoir (Southern Spain)

by Cristina Reyes-Carmona, Anna Barra, Jorge Pedro Galve, Oriol Monserrat, José Vicente Pérez-Peña, Rosa María Mateos, Davide Notti, Patricia Ruano, Agustín Millares, Juan López-Vinielles and José Miguel Azañón

Remote Sens. 2020, 12(5), 809; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050809 - 03 Mar 2020

Cited by 47 | Viewed by 7456

Abstract

Landslides in reservoir contexts are a well-recognised hazard that may lead to dangerous situations regarding infrastructures and people’s safety. Satellite-based radar interferometry is proving to be a reliable method to monitor the activity of landslides in such contexts. Here, we present a DInSAR (Differential Interferometric Synthetic Aperture Radar) analysis of Sentinel-1 images that exemplifies the usefulness of the technique to recognize and monitor landslides in the Rules Reservoir (Southern Spain). The integration of DInSAR results with a comprehensive geomorphological study allowed us to understand the typology, evolution and triggering factors of three active landslides: Lorenzo-1, Rules Viaduct and El Arrecife. We could distinguish between rotational and translational landslides and, thus, we evaluated the potential hazards related to these typologies, i.e., retrogression (Lorenzo-1 and Rules Viaduct landslides) or catastrophic slope failure (El Arrecife Landslide), respectively. We also observed how changes in the water level of the reservoir influence the landslide’s behaviour. Additionally, we were able to monitor the stability of the Rules Dam as well as detect the deformation of a highway viaduct that crosses a branch of the reservoir. Overall, we consider that other techniques must be applied to continue monitoring the movements, especially in the El Arrecife Landslide, in order to avoid future structural damages and fatalities. Full article

(This article belongs to the Special Issue Landslide Monitoring, Susceptibility, Hazard Assessment and Prediction with Remotely Sensed Big Data)

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29 pages, 5056 KiB

Open AccessArticle

Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran

by Alireza Arabameri, Sunil Saha, Jagabandhu Roy, Wei Chen, Thomas Blaschke and Dieu Tien Bui

Remote Sens. 2020, 12(3), 475; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030475 - 03 Feb 2020

Cited by 131 | Viewed by 6285

Abstract

This analysis aims to generate landslide susceptibility maps (LSMs) using various machine learning methods, namely random forest (RF), alternative decision tree (ADTree) and Fisher’s Linear Discriminant Function (FLDA). The results of the FLDA, RF and ADTree models were compared with regard to their applicability for creating an LSM of the Gallicash river watershed in the northern part of Iran close to the Caspian Sea. A landslide inventory map was created using GPS points obtained in a field analysis, high-resolution satellite images, topographic maps and historical records. A total of 249 landslide sites have been identified to date and were used in this study to model and validate the LSMs of the study region. Of the 249 landslide locations, 70% were used as training data and 30% for the validation of the resulting LSMs. Sixteen factors related to topographical, hydrological, soil type, geological and environmental conditions were used and a multi-collinearity test of the landslide conditioning factors (LCFs) was performed. Using the natural break method (NBM) in a geographic information system (GIS), the LSMs generated by the RF, FLDA, and ADTree models were categorized into five classes, namely very low, low, medium, high and very high landslide susceptibility (LS) zones. The very high susceptibility zones cover 15.37% (ADTree), 16.10% (FLDA) and 11.36% (RF) of the total catchment area. The results of the different models (FLDA, RF, and ADTree) were explained and compared using the area under receiver operating characteristics (AUROC) curve, seed cell area index (SCAI), efficiency and true skill statistic (TSS). The accuracy of models was calculated considering both the training and validation data. The results revealed that the AUROC success rates are 0.89 (ADTree), 0.92 (FLDA) and 0.97 (RF) and predication rates are 0.82 (ADTree), 0.79 (FLDA) and 0.98 (RF), which justifies the approach and indicates a reasonably good landslide prediction. The results of the SCAI, efficiency and TSS methods showed that all models have an excellent modeling capability. In a comparison of the models, the RF model outperforms the boosted regression tree (BRT) and ADTree models. The results of the landslide susceptibility modeling could be useful for land-use planning and decision-makers, for managing and controlling the current and future landslides, as well as for the protection of society and the ecosystem. Full article

(This article belongs to the Special Issue Landslide Monitoring, Susceptibility, Hazard Assessment and Prediction with Remotely Sensed Big Data)

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23 pages, 27978 KiB

Open AccessEditor’s ChoiceArticle

Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models

by Nikhil Prakash, Andrea Manconi and Simon Loew

Remote Sens. 2020, 12(3), 346; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030346 - 21 Jan 2020

Cited by 148 | Viewed by 10873

Abstract

Mapping landslides using automated methods is a challenging task, which is still largely done using human efforts. Today, the availability of high-resolution EO data products is increasing exponentially, and one of the targets is to exploit this data source for the rapid generation of landslide inventory. Conventional methods like pixel-based and object-based machine learning strategies have been studied extensively in the last decade. In addition, recent advances in CNN (convolutional neural network), a type of deep-learning method, has been widely successful in extracting information from images and have outperformed other conventional learning methods. In the last few years, there have been only a few attempts to adapt CNN for landslide mapping. In this study, we introduce a modified U-Net model for semantic segmentation of landslides at a regional scale from EO data using ResNet34 blocks for feature extraction. We also compare this with conventional pixel-based and object-based methods. The experiment was done in Douglas County, a study area selected in the south of Portland in Oregon, USA, and landslide inventory extracted from SLIDO (Statewide Landslide Information Database of Oregon) was considered as the ground truth. Landslide mapping is an imbalanced learning problem with very limited availability of training data. Our network was trained on a combination of focal Tversky loss and cross-entropy loss functions using augmented image tiles sampled from a selected training area. The deep-learning method was observed to have a better performance than the conventional methods with an MCC (Matthews correlation coefficient) score of

0.495

and a POD (probability of detection) rate of

0.72

. Full article

(This article belongs to the Special Issue Landslide Monitoring, Susceptibility, Hazard Assessment and Prediction with Remotely Sensed Big Data)

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Other

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14 pages, 10663 KiB

Open AccessLetter

Automatic Mapping of Landslides by the ResU-Net

by Wenwen Qi, Mengfei Wei, Wentao Yang, Chong Xu and Chao Ma

Remote Sens. 2020, 12(15), 2487; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152487 - 03 Aug 2020

Cited by 66 | Viewed by 6352

Abstract

Massive landslides over large regions can be triggered by heavy rainfalls or major seismic events. Mapping regional landslides quickly is important for disaster mitigation. In recent years, deep learning methods have been successfully applied in many fields, including landslide automatic identification. In this work, we proposed a deep learning approach, the ResU-Net, to map regional landslides automatically. This method and a baseline model (U-Net) were collectively tested in Tianshui city, Gansu province, where a heavy rainfall triggered more than 10,000 landslides in July 2013. All models were performed on a 3-band (near infrared, red, and green) GeoEye-1 image with a spatial resolution of 0.5 m. At such a fine spatial resolution, the study area is spatially heterogeneous. The tested study area is 128 km², 80% of which was used to train models and the remaining 20% was used to validate accuracy of the models. This proposed ResU-Net achieved higher accuracy than the baseline U-Net model in this mountain region, where F1 improved by 0.09. Compared with the U-Net model, this proposed model (ResU-Net) performs better in discriminating landslides from bare floodplains along river valleys and unplanted terraces. By incorporating environmental information, this ResU-Net may also be applied to other landslide mapping, such as landslide susceptibility and hazard assessment. Full article

(This article belongs to the Special Issue Landslide Monitoring, Susceptibility, Hazard Assessment and Prediction with Remotely Sensed Big Data)

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13 pages, 7241 KiB

Open AccessLetter

Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model

by Peng Liu, Yongming Wei, Qinjun Wang, Yu Chen and Jingjing Xie

Remote Sens. 2020, 12(5), 894; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050894 - 10 Mar 2020

Cited by 83 | Viewed by 5876

Abstract

Seismic landslides are the most common and highly destructive earthquake-triggered geological hazards. They are large in scale and occur simultaneously in many places. Therefore, obtaining landslide information quickly after an earthquake is the key to disaster mitigation and relief. The survey results show that most of the landslide-information extraction methods involve too much manual participation, resulting in a low degree of automation and the inability to provide effective information for earthquake rescue in time. In order to solve the abovementioned problems and improve the efficiency of landslide identification, this paper proposes an automatic landslide identification method named improved U-Net model. The intelligent extraction of post-earthquake landslide information is realized through the automatic extraction of hierarchical features. The main innovations of this paper include the following: (1) On the basis of the three RGB bands, three new bands, DSM, slope, and aspect, with spatial information are added, and the number of feature parameters of the training samples is increased. (2) The U-Net model structure is rebuilt by adding residual learning units during the up-sampling and down-sampling processes, to solve the problem that the traditional U-Net model cannot fully extract the characteristics of the six-channel landslide for its shallow structure. At the end of the paper, the new method is used in Jiuzhaigou County, Sichuan Province, China. The results show that the accuracy of the new method is 91.3%, which is 13.8% higher than the traditional U-Net model. It is proved that the new method is effective and feasible for the automatic extraction of post-earthquake landslides. Full article

(This article belongs to the Special Issue Landslide Monitoring, Susceptibility, Hazard Assessment and Prediction with Remotely Sensed Big Data)

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