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Remote Sensing
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Remote Sensing for Landslide Investigations: Mapping, Monitoring and Forecasting
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Remote Sensing for Landslide Investigations: Mapping, Monitoring and Forecasting

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 5664

Special Issue Editors

Dr. Xueling Wu

E-Mail Website
Guest Editor
School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Interests: landslide disaster monitoring and early warning; ecological environment quality assessment; geoscience statistics and spatial analysis
Dr. Faming Huang

E-Mail Website
Guest Editor
School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China
Interests: failure mechanism of geological hazards; landslide susceptibility, hazard and risk mapping; machine learning; numerical simulation; remote sensing; geographic information system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Diego Di Martire

E-Mail Website
Guest Editor
Department of Earth Sciences, Environment and Resources, University of Naples Federico II, 80126 Napoli, Italy
Interests: Landslides, hazard and risk assessment; interferometry SAR; GIS
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Mulas

E-Mail Website
Guest Editor
Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia, Modena, Italy
Interests: engineering geology; landslides; remote sensing; multitemporal InSAR; total station; GNSS; data analysis; early warning systems
Special Issues, Collections and Topics in MDPI journals
Dr. Massimo Conforti

E-Mail Website
Guest Editor
National Research Council of Italy, Research Institute for Geo-Hydrological Protection (CNR-IRPI), Via Cavour 4/6, 87036 Rende, CS, Italy
Interests: geographic information systems (GIS); elaboration of UAV data; digital; terrain analysis; spatial analysis; detection and mapping of landslides; landslide susceptibility modeling; geomorphometry; soil erosion; soil Vis-NIR spectroscopy

Special Issue Information

Dear Colleagues,

As the most common geological disaster, landslides are harmful and destructive, and can have a serious impact on human lives and the safety of public facilities. For the purpose of assessing and managing landslides, landslide mapping, forecasting, and monitoring are extremely crucial. By analysing and quantifying the relationship between landslides and landslide-influencing factors, landslide-prone areas can be predicted, therefore avoiding the deaths and economic losses caused by landslide disasters. Remote sensing has become one of the most often used techniques for landslide investigations due to the quick development of earth observation technology.

For landslide investigations, optical, multi/hyperspectral, and InSAR, etc., are common forms of remote sensing, and the utilisation of InSAR technology has been shown to provide a high accuracy of surface deformation for the purpose of early warning and prevention against landslide disasters. Landslide mapping and forecasting is evaluated via determining the combination of factors that have the greatest impact on the occurrence of landslides after a detailed analysis of the landslide generation conditions; consequently, the possibility of landslides occurring in a given area can be estimated.

This Special Issue aims to share any new research and advancements in the field of remote sensing applications for landslide investigations. We invite authors to submit research papers in the following categories of landslide research, as well as other relevant areas:

  • Mapping and forecasting landslide hazards;
  • Identification and inventory of landslides;
  • Monitoring of landslide deformation.

Dr. Xueling Wu
Dr. Faming Huang
Prof. Dr. Diego Di Martire
Dr. Marco Mulas
Dr. Massimo Conforti
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 susceptibility mapping
  • landslide monitoring
  • landslide forecasting
  • landslide hazard assessment
  • SBAS-InSAR
  • risk assessment

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Published Papers (4 papers)

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Research

25 pages, 12169 KiB  
Article
Assessment of Landslide Susceptibility Based on the Two-Layer Stacking Model—A Case Study of Jiacha County, China
by Zhihan Wang, Tao Wen, Ningsheng Chen and Ruixuan Tang
Remote Sens. 2025, 17(7), 1177; https://doi.org/10.3390/rs17071177 - 26 Mar 2025
Viewed by 204
Abstract
The challenge of obtaining landslide susceptibility zoning in Tibet is compounded by the high altitude, extensive range, and difficult exploration of the region. To address this issue, a novel evaluation approach based on Stacking ensemble machine learning is proposed. This study focuses on [...] Read more.
The challenge of obtaining landslide susceptibility zoning in Tibet is compounded by the high altitude, extensive range, and difficult exploration of the region. To address this issue, a novel evaluation approach based on Stacking ensemble machine learning is proposed. This study focuses on Jiacha County, adopts the slope unit as the evaluation unit, and picks up 14 evaluation factors that symbolize the topography and geomorphology, environmental and hydrological features, and basic geological features. These landslide conditioning factors were integrated into a total of 4660 Stacking ensemble learning models, randomly combined by 10 base-algorithms, including AdaBoost, Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), k-Nearest Neighbors (kNNs), LightGBM, Multilayer Perceptron (MLP), Random Forest (RF), Ridge Regression, Support Vector Machine (SVM), and XGBoost. All models were trained, using the natural discontinuity method to classify landslide susceptibility, and the AUC value, the area under the ROC curve, was taken to evaluate the model. The results show that the maximum AUC values in the 9 models performing better reach 0.78 and 0.99 over the test set and the train set. Most of the areas identified as high susceptibility and above show consistency with the interpretation of the existing geological field data. Thus, the Stacking ensemble method is applicable to the landslide susceptibility situation in Jiacha County, Tibet, and can provide theoretical support for disaster prevention and mitigation work in the Qinghai–Tibet Plateau area. Full article
(This article belongs to the Special Issue Remote Sensing for Landslide Investigations: Mapping, Monitoring and Forecasting)
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24 pages, 11584 KiB  
Article
Method for Landslide Area Detection with RVI Data Which Indicates Base Soil Areas Changed from Vegetated Areas
by Kohei Arai, Yushin Nakaoka and Hiroshi Okumura
Remote Sens. 2025, 17(4), 628; https://doi.org/10.3390/rs17040628 - 12 Feb 2025
Viewed by 592
Abstract
This study investigates the use of the radar vegetation index (RVI) derived from Sentinel-1 synthetic aperture radar (SAR) data for landslide detection. Traditional landslide detection methods often rely on the Normalized Difference Vegetation Index (NDVI) derived from optical imagery, which is susceptible to [...] Read more.
This study investigates the use of the radar vegetation index (RVI) derived from Sentinel-1 synthetic aperture radar (SAR) data for landslide detection. Traditional landslide detection methods often rely on the Normalized Difference Vegetation Index (NDVI) derived from optical imagery, which is susceptible to limitations imposed by weather conditions (clouds, rain) and nighttime. In contrast, SAR data, acquired by Sentinel-1, provides all-weather, day-and-night coverage. To leverage this advantage, we propose a novel approach utilizing RVI, a vegetation index calculated from SAR data, to identify non-vegetated areas, which often indicate potential landslide zones. To enhance the accuracy of non-vegetated area classification, we employ the high-performing EfficientNetV2 deep learning model. We evaluated the classification performance of EfficientNetV2 using RVI derived from Sentinel-1 SAR data with VV and VH polarizations. Experiments were conducted on SAR imagery of the Iburi district in Hokkaido, Japan, severely impacted by an earthquake in 2018. Our findings demonstrate that the classification performance using RVI with both VV and VH polarizations significantly surpasses that of using VV and VH polarizations alone. These results highlight the effectiveness of RVI for identifying non-vegetated areas, particularly in landslide detection scenarios. The proposed RVI-based method has broader applications beyond landslide detection, including other disaster area assessments, agricultural field monitoring, and forest inventory. Full article
(This article belongs to the Special Issue Remote Sensing for Landslide Investigations: Mapping, Monitoring and Forecasting)
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20 pages, 22339 KiB  
Article
Evaluation of Rainfall-Induced Accumulation Landslide Susceptibility Based on Remote Sensing Interpretation
by Zhen Wu, Runqing Ye, Jue Huang, Xiaolin Fu and Yao Chen
Remote Sens. 2025, 17(2), 339; https://doi.org/10.3390/rs17020339 - 20 Jan 2025
Viewed by 773
Abstract
Landslide susceptibility evaluation is an indispensable part of disaster prevention and mitigation work. Selecting effective evaluation methods and models for landslide susceptibility assessment is of significant importance. This study focuses on selected areas in Yunyang County, Chongqing City. By interpreting high-resolution satellite remote [...] Read more.
Landslide susceptibility evaluation is an indispensable part of disaster prevention and mitigation work. Selecting effective evaluation methods and models for landslide susceptibility assessment is of significant importance. This study focuses on selected areas in Yunyang County, Chongqing City. By interpreting high-resolution satellite remote sensing images from before and after heavy rainfall on 31 August 2014, the distribution of rainfall-induced accumulation landslides was obtained. To evaluate the susceptibility of accumulation landslides, we have equated evaluation factors to accumulation distribution prediction factors. Eight evaluation factors were extracted using multi-source data, including lithology, elevation, slope, remote sensing image texture features, and the normalized difference vegetation index (NDVI). Various machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and BP Neural Network models, were employed to assess the susceptibility of rainfall-induced accumulation landslides in the study area. Subsequently, the accuracy of the evaluation models was compared and verified using the Receiver Operating Characteristic (ROC) curve, and the evaluation results were analyzed. Finally, the developed Random Forest model was applied to Gongping Town in Fengjie County to verify its applicability in other regions. The findings indicate that the complex geological conditions and the unique tectonic erosion landform patterns in the northeastern region of Chongqing not only make this area a center of heavy rainfall but also lead to frequent and recurrent rainfall-induced landslides. The Random Forest model effectively reflects the development characteristics of accumulation landslides in the study area. High and very high susceptibility zones are concentrated in the northern and central regions of the study area, while low and moderate susceptibility zones predominantly occupy the mountainous and riverside areas. Landslide susceptibility mapping in the study area shows that the Random Forest model yields reasonably graded results. Elevation, remote sensing image texture features, and lithology are highly significant factors in the evaluation system, indicating that the development factors of slope geological disasters in the study area are mainly related to topography, geomorphology, and lithology. The landslide susceptibility evaluation results in Gongping Town, Fengjie County, validate the applicability of the Random Forest model developed in this study to other regions. Full article
(This article belongs to the Special Issue Remote Sensing for Landslide Investigations: Mapping, Monitoring and Forecasting)
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20 pages, 18214 KiB  
Article
Optimized Landslide Susceptibility Mapping and Modelling Using the SBAS-InSAR Coupling Model
by Xueling Wu, Xiaoshuai Qi, Bo Peng and Junyang Wang
Remote Sens. 2024, 16(16), 2873; https://doi.org/10.3390/rs16162873 - 6 Aug 2024
Cited by 5 | Viewed by 3273
Abstract
Landslide susceptibility mapping (LSM) can accurately estimate the location and probability of landslides. An effective approach for precise LSM is crucial for minimizing casualties and damage. The existing LSM methods primarily rely on static indicators, such as geomorphology and hydrology, which are closely [...] Read more.
Landslide susceptibility mapping (LSM) can accurately estimate the location and probability of landslides. An effective approach for precise LSM is crucial for minimizing casualties and damage. The existing LSM methods primarily rely on static indicators, such as geomorphology and hydrology, which are closely associated with geo-environmental conditions. However, landslide hazards are often characterized by significant surface deformation. The Small Baseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology plays a pivotal role in detecting and characterizing surface deformation. This work endeavors to assess the accuracy of SBAS-InSAR coupled with ensemble learning for LSM. Within this research, the study area was Shiyan City, and 12 static evaluation factors were selected as input variables for the ensemble learning models to compute landslide susceptibility. The Random Forest (RF) model demonstrates superior accuracy compared to other ensemble learning models, including eXtreme Gradient Boosting, Logistic Regression, Gradient Boosting Decision Tree, and K-Nearest Neighbor. Furthermore, SBAS-InSAR was utilized to obtain surface deformation rates both in the vertical direction and along the line of sight of the satellite. The former is used as a dynamic characteristic factor, while the latter is combined with the evaluation results of the RF model to create a landslide susceptibility optimization matrix. Comparing the precision of two methods for refining LSM results, it was found that the method integrating static and dynamic factors produced a more rational and accurate landslide susceptibility map. Full article
(This article belongs to the Special Issue Remote Sensing for Landslide Investigations: Mapping, Monitoring and Forecasting)
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