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Remote Sensing Application in Landslide Detection and Assessment

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A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 15943

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Dr. Candide Lissak

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Guest Editor

Unicaen, CNRS, IDEES-Caen, Normandie Univ., 14000 Caen, France
Interests: geomorphology; landslide; sediment transfers; hazard & risk assessment GIS; remote sensing; modeling
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Christopher Gomez

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Guest Editor

1. Laboratory of Sediment Hazards and Disaster Risk, Fukae-Campus, Kobe University, Higashinadaku, Minami-Fukae-Machi, 5-1-1, Kobe, Japan
2. PSBA Laboratory, Department of Geography, Adjunct at Universitas Gadjah Mada, Yogyakarta 55281, Daerah Istimewa Yogyakarta, Indonesia
Interests: geomorphology; mass movements (landslides and debris flows) hazards and disaster risk and sediment processes simulations
Special Issues, Collections and Topics in MDPI journals

Dr. Vittoria Vandelli

E-Mail Website
Guest Editor

Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, 41125 Modena, Italy
Interests: geomorphology; slope instability; landslide mapping; climate-related geomorphological processes; hazard and vulnerability assessment; GIS

Special Issue Information

Dear Colleagues,

The launch of the Landsat 1 satellite in 1972 has propelled half a century of frantic technological improvement, with ever-increasing imageryresolution, and so from a growing array of sensors (multispectral imaging, radar…). In addition to this, airborne and ground-based sensors have boomed, providing calibration opportunities as well as unrivaled spatial and temporal resolution. As a result, remote sensing technology has had a pervasive impact on civil society and its activities, particularly to tackle environmental issues and disaster risk management. Notably, these benefits have been substantial for research and monitoring of phenomena like landslides and other mass movements, which are often difficult to access and hidden under thick vegetation cover. Indeed, remote sensing techniques are now widely used for the early detection of ground deformation, the implementation of warning systems in case of imminent landslide triggering, and the medium- and long-term slope instability monitoring supporting prospective modeling. This is particularly important considering that landslide occurrences may be exacerbated by extreme weather events, which are growing in frequency due to ongoing climate change.

The present issue is thus timely to share and discuss the state of the art and the remaining challenges in the field of landslide hazard and disaster risk remote sensing. For the present issue, we invite articles dealing with (1) applications of existing methods; (2) technological developments; (3) processing developments, including new algorithms. If potential authors wish to discuss any additional topics, please feel free to contact the special issue editorial team.

Dr. Candide Lissak
Prof. Dr. Christopher Gomez
Dr. Vittoria Vandelli
Guest Editors

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Keywords

remote sensing for landslide monitoring and detection
landslide hazard and susceptibility assessment by means of remote sensing techniques
landslide mapping assisted by remotely sensed data (from spaceborne, UAV, ground-based sensors)
early warning systems based on remote sensing technology (e.g., GB-SAR, InSAR, LiDAR)
machine learning techniques for landslide hazard assessment
development in processing remotely sensed data for landslide studies
application of remotely sensed DEMs in the study of landslides
GIS-based landslide susceptibility and hazard assessment with remotely sensed data

Published Papers (9 papers)

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Research

20 pages, 12185 KiB

Open AccessArticle

Integrated PSInSAR and GNSS for 3D Displacement in the Wudongde Area

by Jiaxuan Huang, Weichao Du, Shaoxia Jin and Mowen Xie

Land 2024, 13(4), 429; https://doi.org/10.3390/land13040429 - 28 Mar 2024

Viewed by 574

Abstract

The major limitation of persistent scatterer interferometric synthetic aperture radar (PSInSAR) is that it detects only one- or two-dimensional displacements, such as those in the line of sight (LOS) and azimuth directions, by repeat-pass SAR observations. Three-dimensional (3D) displacement reflects the actual sliding surface and failure mechanism of a slope. To transform LOS deformation into a reliable 3D displacement, a new approach for obtaining the 3D displacement is proposed herein based on the slope deformation (

D_{s l o p e}

). First, the deformation value calculated using the Global Navigation Satellite System (GNSS) as a constraint is used to eliminate the residual deformation of PSInSAR. Then,

D_{s l o p e}

is obtained from the relationship between

D_{L O S}

and the slope angle extracted from the digital elevation model (DEM). Finally, according to the geometric relationship between

D_{s l o p e}

and

D_{L O S}

, a novel approach for calculating 3D displacement is proposed. When comparing the 3D displacement extracted by the proposed method and that from GNSS data in Jinpingzi landslide, the root-mean-square error (RMSE) values were ±2.0 mm, ±2.8 mm, and ±2.6 mm in the vertical, north, and east directions, respectively. The proposed method shows high accuracy in 3D displacement calculation, which can help to determine the failure mechanism of a landslide. This method can be widely used in landslide monitoring in wide areas. Full article

(This article belongs to the Special Issue Remote Sensing Application in Landslide Detection and Assessment)

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19 pages, 11706 KiB

Open AccessArticle

SE-YOLOv7 Landslide Detection Algorithm Based on Attention Mechanism and Improved Loss Function

by Qing Liu, Tingting Wu, Yahong Deng and Zhiheng Liu

Land 2023, 12(8), 1522; https://doi.org/10.3390/land12081522 - 31 Jul 2023

Cited by 2 | Viewed by 1353

Abstract

With the continuous development of computer vision technology, more and more landslide identification detection tasks have started to shift from manual visual interpretation to automatic computer identification, and automatic landslide detection methods based on remote sensing satellite images and deep learning have been gradually developed. However, most existing algorithms often have the problem of low precision and weak generalization in landslide detection. Based on the Google Earth Engine platform, this study selected landslide image data from 24 study areas in China and established the DN landslide sample dataset, which contains a total of 1440 landslide samples. The original YOLOv7 algorithm model was improved and optimized by applying the SE squeezed attention mechanism and VariFocal loss function to construct the SE-YOLOv7 model to realize the automatic detection of landslides in remote sensing images. The experimental results show that the mAP, Precision value, Recall value, and F1-Score of the improved SE-YOLOv7 model for landslide identification are 91.15%, 93.35%, 94.54%, and 93.94%, respectively. At the same time, through a field investigation and verification study in Qianyang County, Baoji City, Shaanxi Province, comparing the detection results of SE-YOLOv7, it is concluded that the improved SE-YOLOv7 can locate the landslide location more accurately, detect the landslide range more accurately, and have fewer missed detections. The research results show that the algorithm model has strong detection accuracy for many types of landslide image data, which provides a technical reference for future research on landslide detection based on remote sensing images. Full article

(This article belongs to the Special Issue Remote Sensing Application in Landslide Detection and Assessment)

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34 pages, 18103 KiB

Open AccessArticle

Combining Soil Moisture and MT-InSAR Data to Evaluate Regional Landslide Susceptibility in Weining, China

by Qing Yang, Zhanqiang Chang, Chou Xie, Chaoyong Shen, Bangsen Tian, Haoran Fang, Yihong Guo, Yu Zhu, Daoqin Zhou, Xin Yao, Guanwen Chen and Tao Xie

Land 2023, 12(7), 1444; https://doi.org/10.3390/land12071444 - 20 Jul 2023

Cited by 2 | Viewed by 906

Abstract

Landslide susceptibility maps (LSMs) play an important role in landslide hazard risk assessments, urban planning, and land resource management. While states of motion and dynamic factors are critical in the landslide formation process, these factors have not received due attention in existing LSM-generation research. In this study, we proposed a valuable method for dynamically updating and refining LSMs by combining soil moisture products with Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data. Based on a landslide inventory, we used time-series soil moisture data to construct an index system for evaluating landslide susceptibility. MT-InSAR technology was applied to invert the displacement time series. Furthermore, the surface deformation rate was projected in the direction of the steepest slope, and the data was resampled to a spatial resolution consistent with that of the LSM to update the generated LSM. The results showed that varying soil moisture conditions were accompanied by dynamic landslide susceptibility. A total of 22% of the analyzed pixels underwent significant susceptibility changes (either increases or decreases) following the updating and refining processes incorporating soil moisture and MT-InSAR compared to the LSMs derived based only on static factors. The relative landslide density index obtained based on actual landslides and the analyses of Dongfeng, Haila town, and Dajie township confirmed the improved slow landslide prediction reliability resulting from the reduction of the false alarm and omission rates. Full article

(This article belongs to the Special Issue Remote Sensing Application in Landslide Detection and Assessment)

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20 pages, 4333 KiB

Open AccessArticle

Landslide Susceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm

by Ahmed Cemiloglu, Licai Zhu, Agab Bakheet Mohammednour, Mohammad Azarafza and Yaser Ahangari Nanehkaran

Land 2023, 12(7), 1397; https://doi.org/10.3390/land12071397 - 12 Jul 2023

Cited by 7 | Viewed by 1259

Abstract

Landslide susceptibility assessment is the globally approved procedure to prepare geo-hazard maps of landslide-prone areas, which are highly used in urban management and minimizing the possible disasters due to landslides. Multiple approaches to providing susceptibility maps for landslides have one specification. Logistic regression is a statistical-based model that investigates the probabilities of the events which is received extensive success in landslide susceptibility assessment. The presented study attempted to use a logistic regression application to prepare the Maragheh County hazard risk map. In this regard, several predisposing factors (e.g., elevation, slope aspect, slope angle, rainfall, land use, lithology, weathering, distance from faults, distance from the river, distance from the road, and distance from cities) are identified as main responsible for landslide occurrence and 20 historical sliding events which used to prepare hazard risk maps. As verification, the models were controlled by operating relative characteristics (ROC) curves which reported the overall accuracy for susceptibility assessment. According to the results, the region is located in a moderate to high-hazard risk zone. The north and northeast parts of Maragheh County show high suitability for landslides. Verification results of the model indicated that the AUC estimated for the training set is 0.885, and the AUC estimated for the testing set is 0.769. To justify the model, the results of the LR were comparatively checked with several benchmark learning models. Results indicated that LR model performance is reasonable. Full article

(This article belongs to the Special Issue Remote Sensing Application in Landslide Detection and Assessment)

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17 pages, 30989 KiB

Open AccessArticle

Applicability Assessment of Multi-Source DEM-Assisted InSAR Deformation Monitoring Considering Two Topographical Features

by Hui Liu, Bochen Zhou, Zechao Bai, Wenfei Zhao, Mengyuan Zhu, Ke Zheng, Shiji Yang and Geshuang Li

Land 2023, 12(7), 1284; https://doi.org/10.3390/land12071284 - 25 Jun 2023

Cited by 5 | Viewed by 987

Abstract

The high-precision digital elevation model (DEM) is of great significance for improving the accuracy of InSAR deformation monitoring. In today’s free opening of multi-source DEM, there is no consensus on how to select suitable DEMs to assist InSAR in deformation monitoring for different landforms. This article introduces five types of DEMs: ALOS12.5, SRTM-1, ASTER V3, AW3D30, and Copernicus 30, and uses SBAS-InSAR technology to analyze the applicability of deformation monitoring in the Qinghai Tibet Plateau and Central China Plain regions. The coverage, average value, standard deviation, and unwrapping efficiency of the phase unwrapping results, the temporal deformation rate curves of six random deformation points in the key deformation area, as well as the consistency with the second-level data and the comparative analysis of RMSE of all deformation points, show that in the Qinghai Tibet Plateau region, Copernicus 30 is the best, followed by ASTER V3, AW3D30, and SRTM-1 having low accuracy, and ALOS12.5 is the worst. In the Central China Plain region, AW3D30 is the best, followed by Copernicus 30, SRTM-1, and ASTER V3 having low accuracy, and ALOS12.5 is still the worst. Although ALOS12.5 has the highest resolution, it is not recommended for deformation monitoring based on its worst performance in plateau and plain areas. It is recommended to use Copernicus 30 in plateau areas and AW3D30 for deformation monitoring in plain areas. Full article

(This article belongs to the Special Issue Remote Sensing Application in Landslide Detection and Assessment)

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19 pages, 2442 KiB

Open AccessArticle

How Spatial Resolution of Remote Sensing Image Affects Earthquake Triggered Landslide Detection: An Example from 2022 Luding Earthquake, Sichuan, China

by Yu Huang, Jianqiang Zhang, Lili Zhang, Zaiyang Ming, Haiqing He, Rong Chen, Yonggang Ge and Rongkun Liu

Land 2023, 12(3), 681; https://doi.org/10.3390/land12030681 - 14 Mar 2023

Cited by 4 | Viewed by 1461

Abstract

The magnitude 6.8 Luding earthquake that occurred on 5 September 2022, triggered multiple large-scale landslides and caused a heavy loss of life and property. The investigation of earthquake-triggered landslides (ETLs) facilitates earthquake disaster assessments, rescue, reconstruction, and other post-disaster recovery efforts. Therefore, it is important to obtain landslide inventories in a timely manner. At present, landslide detection is mainly conducted manually, which is time-consuming and laborious, while a machine-assisted approach helps improve the efficiency and accuracy of landslide detection. This study uses a fully convolutional neural network algorithm with the Adam optimizer to automatically interpret the aerial and satellite data of landslides. However, due to the different resolutions of the remote sensing images, the detected landslides vary in boundary and quantity. In this study, we conducted an assessment in the study area of Wandong village in the earthquake-affected area of Luding. UAV images, GF-6 satellite images, and Landsat 8 satellite images, with a resolution of 0.2 m, 2 m, and 15 m, respectively, were selected to detect ETLs. Then, the accuracy of the results was compared and verified with visual detection results and field survey data. The study indicates that as the resolution decreases, the accuracy of landslide detection also decreases. The overall landslide area detection rate of UAV imagery can reach 82.17%, while that of GF-6 and Landsat 8 imagery is only 52.26% and 48.71%. The landslide quantity detection rate of UAV imagery can reach 99.07%, while that of GF-6 and Landsat 8 images is only 48.71% and 61.05%. In addition, for each landslide detected, little difference is found in large-scale landslides, and it becomes more difficult to correctly detect small-scale landslides as the resolution decreases. For example, landslides under 100 m² could not be detected from a Landsat 8 satellite image. Full article

(This article belongs to the Special Issue Remote Sensing Application in Landslide Detection and Assessment)

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29 pages, 13313 KiB

Open AccessArticle

Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps

by Sheela Bhuvanendran Bhagya, Anita Saji Sumi, Sankaran Balaji, Jean Homian Danumah, Romulus Costache, Ambujendran Rajaneesh, Ajayakumar Gokul, Chandini Padmanabhapanicker Chandrasenan, Renata Pacheco Quevedo, Alfred Johny, Kochappi Sathyan Sajinkumar, Sunil Saha, Rajendran Shobha Ajin, Pratheesh Chacko Mammen, Kamal Abdelrahman, Mohammed S. Fnais and Mohamed Abioui

Land 2023, 12(2), 468; https://doi.org/10.3390/land12020468 - 13 Feb 2023

Cited by 10 | Viewed by 3728

Abstract

Landslides are prevalent in the Western Ghats, and the incidences that happened in 2021 in the Koottickal area of the Kottayam district (Western Ghats) resulted in the loss of 10 lives. The objectives of this study are to assess the landslide susceptibility of the high-range local self-governments (LSGs) in the Kottayam district using the analytical hierarchy process (AHP) and fuzzy-AHP (F-AHP) models and to compare the performance of existing landslide susceptible maps. This area never witnessed any massive landslides of this dimension, which warrants the necessity of relooking into the existing landslide-susceptible models. For AHP and F-AHP modeling, ten conditioning factors were selected: slope, soil texture, land use/land cover (LULC), geomorphology, road buffer, lithology, and satellite image-derived indices such as the normalized difference road landslide index (NDRLI), the normalized difference water index (NDWI), the normalized burn ratio (NBR), and the soil-adjusted vegetation index (SAVI). The landslide-susceptible zones were categorized into three: low, moderate, and high. The validation of the maps created using the receiver operating characteristic (ROC) technique ascertained the performances of the AHP, F-AHP, and TISSA maps as excellent, with an area under the ROC curve (AUC) value above 0.80, and the NCESS map as acceptable, with an AUC value above 0.70. Though the difference is negligible, the map prepared using the TISSA model has better performance (AUC = 0.889) than the F-AHP (AUC = 0.872), AHP (AUC = 0.867), and NCESS (AUC = 0.789) models. The validation of maps employing other matrices such as accuracy, mean absolute error (MAE), and root mean square error (RMSE) also confirmed that the TISSA model (0.869, 0.226, and 0.122, respectively) has better performance, followed by the F-AHP (0.856, 0.243, and 0.147, respectively), AHP (0.855, 0.249, and 0.159, respectively), and NCESS (0.770, 0.309, and 0.177, respectively) models. The most landslide-inducing factors in this area that were identified through this study are slope, soil texture, LULC, geomorphology, and NDRLI. Koottickal, Poonjar-Thekkekara, Moonnilavu, Thalanad, and Koruthodu are the LSGs that are highly susceptible to landslides. The identification of landslide-susceptible areas using diversified techniques will aid decision-makers in identifying critical infrastructure at risk and alternate routes for emergency evacuation of people to safer terrain during an exigency. Full article

(This article belongs to the Special Issue Remote Sensing Application in Landslide Detection and Assessment)

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25 pages, 9451 KiB

Open AccessArticle

Multilayer Perceptron and Their Comparison with Two Nature-Inspired Hybrid Techniques of Biogeography-Based Optimization (BBO) and Backtracking Search Algorithm (BSA) for Assessment of Landslide Susceptibility

by Hossein Moayedi, Peren Jerfi Canatalay, Atefeh Ahmadi Dehrashid, Mehmet Akif Cifci, Marjan Salari and Binh Nguyen Le

Land 2023, 12(1), 242; https://doi.org/10.3390/land12010242 - 12 Jan 2023

Cited by 13 | Viewed by 2075

Abstract

Regarding evaluating disaster risks in Iran’s West Kurdistan area, the multi-layer perceptron (MLP) neural network was upgraded with two novel techniques: backtracking search algorithm (BSA) and biogeography-based optimization (BBO). Utilizing 16 landslide conditioning elements such as elevation (aspect), plan (curve), profile (curvature), geology, NDVI (land use), slope (degree), stream power index (SPI), topographic wetness index (TWI), rainfall, and sediment transport index (STI), and 504 landslides as target variables, a large geographic database is constructed. Applying the techniques mentioned above to the synthesis of the MLP results in the suggested BBO-MLP and BSA-MLP ensembles. As accuracy standards, we benefit from mean absolute error, mean square error, and area under the receiving operating characteristic curve to assess the utilized models, we have also designed a scoring system. The MLP’s accuracy increases thanks to the application of the BBO and BSA algorithms. Comparing the BBO with the BSA, we find that the former achieves higher average MLP optimization ranks (20, 15, and 14). A further finding showed that the BBO is superior to the BSA at maximizing the MLP. Full article

(This article belongs to the Special Issue Remote Sensing Application in Landslide Detection and Assessment)

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27 pages, 9378 KiB

Open AccessFeature PaperArticle

Integrated Geomechanical and Digital Photogrammetric Survey in the Study of Slope Instability Processes of a Flysch Sea Cliff (Debeli Rtič Promontory, Slovenia)

by Stefano Furlani, Alberto Bolla, Linley Hastewell, Matteo Mantovani and Stefano Devoto

Land 2022, 11(12), 2255; https://doi.org/10.3390/land11122255 - 10 Dec 2022

Cited by 4 | Viewed by 1679

Abstract

This work presents an integrated study approach that combines the results of a geomechanical survey with data obtained using digital photogrammetry (DP), to assess slope instability processes affecting a sea cliff at the Debeli Rtič promontory (Slovenia). The investigated cliff is 4–18 m-high and is made up of an alternation of sandstones and marlstones belonging to the Flysch Formation of Trieste, which is Eocene in age. The studied cliff was subjected to localized slope failures that occurred in the past and is currently subject to frequent rock collapses, thus resulting in its partial and episodic retreat. Field evidence acquired through a traditional survey was integrated with outputs of the DP technique based on 1399 images that were collected using both a commercial unmanned aerial vehicle (UAV) and a mobile phone (MP). UAV-derived images were useful for performing rock mass structure analysis in the upper part of the investigated cliff, where the traditional survey was not possible due to hazardous operating conditions. In addition, the use of a MP was observed to be a useful tool for the rapid collection of images at the toe of unsafe marine cliff environments. This study highlights that UAV-DP and MP-DP techniques can only be effective if the outcomes obtained from the 3D model reconstruction are validated by direct measurements acquired by means of the traditional field survey, thus avoiding improper or even erroneous results while enlarging the amount of data and the area of investigation. The study approach presented herein allowed for the assessment of slope instabilities affecting the Flysch Sea cliff, whose retreat is caused by the combined action of marine erosion and slope gravitational processes. Full article

(This article belongs to the Special Issue Remote Sensing Application in Landslide Detection and Assessment)

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