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Remote Sensing in Development of Rapid Landslide Detection and Mapping...
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Remote Sensing in Development of Rapid Landslide Detection and Mapping Scenarios

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 16955

Special Issue Editors

Dr. Sansar Raj Meena

E-Mail Website
Guest Editor
Department of Geosciences, University of Padua, 35122 Padua, Italy
Interests: natural hazards; detection and mapping of landslides; landslide susceptibility modeling; natural disasters; landslide hazard mapping; SAR interpretation for landslide analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Filippo Catani

E-Mail Website
Guest Editor
Department of Geosciences, University of Padova, Via Gradenigo, 35131 Padova, Italy
Interests: landslide hazard; monitoring and modelling of basin scale surface processes; natural hazards; applications of remote sensing to landslide studies; oil & gas environmental impact and risk; surface monitoring in open pit mines; scaling processes in geomorphology; machine learning applied to land surface processes
Special Issues, Collections and Topics in MDPI journals
Dr. Mario Floris

E-Mail Website
Guest Editor
Department of Geosciences, University of Padua, 35122 Padua, Italy
Interests: rainfall-induced landslides; GIS-based landslide hazard assessment; SAR interferometry applied to landslides and subsidence
Special Issues, Collections and Topics in MDPI journals
Dr. Yunus P. Ali

E-Mail Website
Guest Editor
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China
Interests: GIS; remote sensing; geomorphology; geoscience; geography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In this Special Issue, we present the opportunity to contribute towards alleviating the risks associated with landslides by tackling one of the most important components of landslide hazard studies: rapid mapping of landslides to generate event-based inventories. These inventories are crucial as they play a key role in modeling and assessing the susceptibility and hazard of landslides. A detailed and complete inventory of landslides is necessary to advance the quality and knowledge of landslide hazard assessment, as the lack of basic spatial distribution information hinders the opportunity for landslide susceptibility, hazard, and risk studies.

Now, more than ever, remote sensing data play a big role in detecting and mapping landslides over large areas.  With recent advancements in technologies such as UAVs, high-spatiotemporal resolution satellite images, microwave-based SAR images coupled with the state-of-the-art machine learning tools, the application of mapping landslides and generation of inventories has become convenient and easy.

We believe that with the constant improvement in quality research based on your submissions, the advancements in this field can be greatly boosted and that your contributions can bridge the existing research gaps. Therefore, we would like to invite you to submit one or more research and review articles to be published in this Special Issue. Submissions are encouraged to cover a range of topics on the applications of rapid landslide mapping with a diverse choice of remote sensing data.

Dr. Sansar Raj Meena
Prof. Dr. Filippo Catani
Prof. Dr. Mario Floris
Dr. Yunus P. Ali
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 inventory mapping
  • Landslide mapping
  • UAV, airborne and space-borne
  • Landslide modeling

Related Special Issue

Published Papers (5 papers)

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29 pages, 5415 KiB  
Article
Predicting Landslides Susceptible Zones in the Lesser Himalayas by Ensemble of Per Pixel and Object-Based Models
by Ujjwal Sur, Prafull Singh, Sansar Raj Meena and Trilok Nath Singh
Remote Sens. 2022, 14(8), 1953; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081953 - 18 Apr 2022
Cited by 8 | Viewed by 2675
Abstract
Landslide susceptibility is a contemporary method for delineation of landslide hazard zones and holistically mitigating the future landslides risks for planning and decision-making. The significance of this study is that it would be the first instance when the ‘geon’ model will be attempted [...] Read more.
Landslide susceptibility is a contemporary method for delineation of landslide hazard zones and holistically mitigating the future landslides risks for planning and decision-making. The significance of this study is that it would be the first instance when the ‘geon’ model will be attempted to delineate landslide susceptibility map (LSM) for the complex lesser Himalayan topography as a contemporary LSM technique. This study adopted the per-pixel-based ensemble approaches through modified frequency ratio (MFR) and fuzzy analytical hierarchy process (FAHP) and compared it with the ‘geons’ (object-based) aggregation method to produce an LSM for the lesser Himalayan Kalsi-Chakrata road corridor. For the landslide susceptibility models, 14 landslide conditioning factors were carefully chosen; namely, slope, slope aspect, elevation, lithology, rainfall, seismicity, normalized differential vegetation index, stream power index, land use/land cover, soil, topographical wetness index, and proximity to drainage, road, and fault. The inventory data for the past landslides were derived from preceding satellite images, intensive field surveys, and validation surveys. These inventory data were divided into training and test datasets following the commonly accepted 70:30 ratio. The GIS-based statistical techniques were adopted to establish the correlation between landslide training sites and conditioning factors. To determine the accuracy of the model output, the LSMs accuracy was validated through statistical methods of receiver operating characteristics (ROC) and relative landslide density index (R-index). The accuracy results indicate that the object-based geon methods produced higher accuracy (geon FAHP: 0.934; geon MFR: 0.910) over the per-pixel approaches (FAHP: 0.887; MFR: 0.841). The results noticeably showed that the geon method constructs significant regional units for future mitigation strategies and development. The present study may significantly benefit the decision-makers and regional planners in selecting the appropriate risk mitigation procedures at a local scale to counter the potential damages and losses from landslides in the area. Full article
(This article belongs to the Special Issue Remote Sensing in Development of Rapid Landslide Detection and Mapping Scenarios)
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15 pages, 5538 KiB  
Article
Rapid Mapping of Landslides on SAR Data by Attention U-Net
by Lorenzo Nava, Kushanav Bhuyan, Sansar Raj Meena, Oriol Monserrat and Filippo Catani
Remote Sens. 2022, 14(6), 1449; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061449 - 17 Mar 2022
Cited by 33 | Viewed by 5812
Abstract
Multiple landslide events are common around the globe. They can cause severe damage to both human lives and infrastructures. Although a huge quantity of research has been shaped to address rapid mapping of landslides by optical Earth Observation (EO) data, various gaps and [...] Read more.
Multiple landslide events are common around the globe. They can cause severe damage to both human lives and infrastructures. Although a huge quantity of research has been shaped to address rapid mapping of landslides by optical Earth Observation (EO) data, various gaps and uncertainties are still present when dealing with cloud obscuration and 24/7 operativity. To address the issue, we explore the usage of SAR data over the eastern Iburi sub-prefecture of Hokkaido, Japan. In the area, about 8000 co-seismic landslides were triggered by an Mw 6.6 earthquake on 6 September 2018, at 03.08 local time (JST). In the following study, we modify a Deep Learning (DL) convolutional neural network (CNN) architecture suited for pixel-based classification purposes, the so-called Attention U-Net (Attn-U-Net) and we employ it to evaluate the potential of bi- and tri-temporal SAR amplitude data from the Sentinel-1 satellite and slope angle to map landslides even under thick cloud cover. Four different datasets, composed of two different band combinations per two satellite orbits (ascending and descending) are analyzed. Moreover, the impact of augmentations is evaluated independently for each dataset. The models’ predictions are compared against an accurate landslide inventory obtained by manual mapping on pre-and post-event PlanetScope imagery through F1-score and other common metrics. The best result was yielded by the augmented ascending tri-temporal SAR composite image (61% F1-score). Augmentations have a positive impact on the ascending Sentinel-1 orbit, while metrics decrease when augmentations are applied on descending path. Our findings demonstrate that combining SAR data with other data sources may help to map landslides quickly, even during storms and under deep cloud cover. However, further investigations and improvements are still needed, this being one of the first attempts in which the combination of SAR data and DL algorithms are employed for landslide mapping purposes. Full article
(This article belongs to the Special Issue Remote Sensing in Development of Rapid Landslide Detection and Mapping Scenarios)
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26 pages, 82609 KiB  
Article
Landslide Trail Extraction Using Fire Extinguishing Model
by Zhao Zhan, Wenzhong Shi, Min Zhang, Zhewei Liu, Linya Peng, Yue Yu and Yangjie Sun
Remote Sens. 2022, 14(2), 308; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020308 - 10 Jan 2022
Cited by 3 | Viewed by 1974
Abstract
Landslide trails are important elements of landslide inventory maps, providing valuable information for landslide risk and hazard assessment. Compared with traditional manual mapping, skeletonization methods offer a more cost-efficient way to map landslide trails, by automatically generating centerlines from landslide polygons. However, a [...] Read more.
Landslide trails are important elements of landslide inventory maps, providing valuable information for landslide risk and hazard assessment. Compared with traditional manual mapping, skeletonization methods offer a more cost-efficient way to map landslide trails, by automatically generating centerlines from landslide polygons. However, a challenge to existing skeletonization methods is that expert knowledge and manual intervention are required to obtain a branchless skeleton, which limits the applicability of these methods. To address this problem, a new workflow for landslide trail extraction (LTE) is proposed in this study. To avoid generating redundant branches and to improve the degree of automation, two endpoints, i.e., the crown point and the toe point, of the trail were determined first, with reference to the digital elevation model. Thus, a fire extinguishing model (FEM) is proposed to generate skeletons without redundant branches. Finally, the effectiveness of the proposed method is verified, by extracting landslide trails from landslide polygons of various shapes and sizes, in two study areas. Experimental results show that, compared with the traditional grassfire model-based skeletonization method, the proposed FEM is capable of obtaining landslide trails without spurious branches. More importantly, compared with the baseline method in our previous work, the proposed LTE workflow can avoid problems including incompleteness, low centrality, and direction errors. This method requires no parameter tuning and yields excellent performance, and is thus highly valuable for practical landslide mapping. Full article
(This article belongs to the Special Issue Remote Sensing in Development of Rapid Landslide Detection and Mapping Scenarios)
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22 pages, 8456 KiB  
Article
Susceptibility Assessment for Landslide Initiated along Power Transmission Lines
by Shuhao Liu, Kunlong Yin, Chao Zhou, Lei Gui, Xin Liang, Wei Lin and Binbin Zhao
Remote Sens. 2021, 13(24), 5068; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245068 - 14 Dec 2021
Cited by 12 | Viewed by 2566
Abstract
The power network has a long transmission span and passes through wide areas with complex topography setting and various human engineering activities. They lead to frequent landslide hazards, which cause serious threats to the safe operation of the power transmission system. Thus, it [...] Read more.
The power network has a long transmission span and passes through wide areas with complex topography setting and various human engineering activities. They lead to frequent landslide hazards, which cause serious threats to the safe operation of the power transmission system. Thus, it is of great significance to carry out landslide susceptibility assessment for disaster prevention and mitigation of power network. We, therefore, undertake an extensive analysis and comparison study between different data-driven methods using a case study from China. Several susceptibility mapping results were generated by applying a multivariate statistical method (logistic regression (LR)) and a machine learning technique (random forest (RF)) separately with two different mapping-units and predictor sets of differing configurations. The models’ accuracies, advantages and limitations are summarized and discussed using a range of evaluation criteria, including the confusion matrix, statistical indexes, and the estimation of the area under the receiver operating characteristic curve (AUROC). The outcome showed that machine learning method is well suitable for the landslide susceptibility assessment along transmission network over grid cell units, and the accuracy of susceptibility models is evolving rapidly from statistical-based models toward machine learning techniques. However, the multivariate statistical logistic regression methods perform better when computed over heterogeneous slope terrain units, probably because the number of units is significantly reduced. Besides, the high model predictive performances cannot guarantee a high plausibility and applicability of subsequent landslide susceptibility maps. The selection of mapping unit can produce greater differences on the generated susceptibility maps than that resulting from the selection of modeling methods. The study also provided a practical example for landslide susceptibility assessment along the power transmission network and its potential application in hazard early warning, prevention, and mitigation. Full article
(This article belongs to the Special Issue Remote Sensing in Development of Rapid Landslide Detection and Mapping Scenarios)
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13 pages, 3473 KiB  
Technical Note
A Novel Weakly Supervised Remote Sensing Landslide Semantic Segmentation Method: Combining CAM and cycleGAN Algorithms
by Yongxiu Zhou, Honghui Wang, Ronghao Yang, Guangle Yao, Qiang Xu and Xiaojuan Zhang
Remote Sens. 2022, 14(15), 3650; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153650 - 29 Jul 2022
Cited by 16 | Viewed by 1954
Abstract
With the development of deep learning algorithms, more and more deep learning algorithms are being applied to remote sensing image classification, detection, and semantic segmentation. The landslide semantic segmentation of a remote sensing image based on deep learning mainly uses supervised learning, the [...] Read more.
With the development of deep learning algorithms, more and more deep learning algorithms are being applied to remote sensing image classification, detection, and semantic segmentation. The landslide semantic segmentation of a remote sensing image based on deep learning mainly uses supervised learning, the accuracy of which depends on a large number of training data and high-quality data annotation. At this stage, high-quality data annotation often requires the investment of significant human effort. Therefore, the high cost of remote sensing landslide image data annotation greatly restricts the development of a landslide semantic segmentation algorithm. Aiming to resolve the problem of the high labeling cost of landslide semantic segmentation with a supervised learning method, we proposed a remote sensing landslide semantic segmentation with weakly supervised learning method combing class activation maps (CAMs) and cycle generative adversarial network (cycleGAN). In this method, we used the image level annotation data to replace pixel level annotation data as the training data. Firstly, the CAM method was used to determine the approximate position of the landslide area. Then, the cycleGAN method was used to generate the fake image without a landslide, and to make the difference with the real image to obtain the accurate segmentation of the landslide area. Finally, the pixel-level segmentation of the landslide area on remote sensing image was realized. We used mean intersection-over-union (mIOU) to evaluate the proposed method, and compared it with the method based on CAM, whose mIOU was 0.157, and we obtain better result with mIOU 0.237 on the same test dataset. Furthermore, we made a comparative experiment using the supervised learning method of a u-net network, and the mIOU result was 0.408. The experimental results show that it is feasible to realize landslide semantic segmentation in a remote sensing image by using weakly supervised learning. This method can greatly reduce the workload of data annotation. Full article
(This article belongs to the Special Issue Remote Sensing in Development of Rapid Landslide Detection and Mapping Scenarios)
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