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Remote Sensing
Special Issues
Multiplatform and Multisensor Applications for Landslide Characterization and Monitoring
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Multiplatform and Multisensor Applications for Landslide Characterization and Monitoring

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 (28 February 2026) | Viewed by 3230

Special Issue Editors

Dr. Stratis Karantanellis

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Guest Editor
Department of Geological Sciences, California State University, Fullerton, CA, USA
Interests: landslides; engineering geology; remote sensing; UAV photogrammetry; 3D analysis
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: landslides; GNSS; remote sensing; UAV; monitoring
Dr. Giuseppe Ciccarese

E-Mail Website
Guest Editor
Department of Biological, Geological, and Environmental Sciences, University of Bologna, Bologna, Italy
Interests: landslides; remote sensing; UAV; monitoring; hazard mapping; rainfall thresholds
Prof. Dr. Paolo Mazzanti

E-Mail Website
Guest Editor
Department of Earth Sciences, University of Rome “Sapienza”, Rome, Italy
Interests: landslide monitoring; photomonitoring; interferometry; geological risks; geological hazards; satellite images; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, active landslides can be identified and monitored via several imaging platforms, ranging from terrestrial to crewed/uncrewed aerial vehicles or spaceborne satellites. Despite the imaging sensing method adopted, the scientific community has extensive options in terms of image processing algorithms, which have been developed to detect changes and/or derive spatially distributed displacements over time. The vast number of combinations of sensors and platforms, coupled with the significant range of geometric and temporal resolution, can lead to countless applications. Such tools, when integrated with ground truth datasets, increasingly provide new solutions for landslide monitoring and interpretation. Moreover, integrating these high-tech imaging and processing tools with rigorous ground truth datasets has revolutionized the methods via which landslides can be monitored and interpreted. Ground truth data provide a crucial baseline for validating remote sensing observations, ensuring both the precision and reliability of interpretations made using satellite and aerial imagery. The emerging insights gained from these integrated systems not only enhance our understanding of landslide dynamics but also substantially contribute to risk mitigation efforts. By providing early warning signals and facilitating proactive disaster management strategies, such tools are invaluable assets for guarding against the devastation commonly wrought by landslides. In this Special Issue, papers dealing with landslide characterization and monitoring and/or technical papers presenting innovative image processing algorithms applied to ground displacement analysis/observation are welcome. If potential authors wish to discuss any proposal, feel free to get in touch with this Special Issue’s Editorial Team.

Dr. Stratis Karantanellis
Dr. Marco Mulas
Dr. Giuseppe Ciccarese
Prof. Dr. Paolo Mazzanti
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 250 words) can be sent to the Editorial Office for assessment.

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 detection
  • landslide characterization
  • landslide monitoring
  • data fusion
  • remote sensing

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Published Papers (2 papers)

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36 pages, 39268 KB  
Article
Spectral Feature Integration and Ensemble Learning Optimization for Regional-Scale Landslide Susceptibility Mapping in Mountainous Areas
by Yun Tian, Taorui Zeng, Linfeng Wang, Gang Chen, Sihang Yang, Hao Chen and Ligang Wang
Remote Sens. 2026, 18(3), 382; https://doi.org/10.3390/rs18030382 - 23 Jan 2026
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Abstract
Current research on landslide susceptibility modeling is often constrained by reliance on conventional topographic and geological features, potentially overlooking the discriminative power of surface material properties derived from multi-source remote sensing. This study aims to enhance the accuracy and reliability of susceptibility assessment [...] Read more.
Current research on landslide susceptibility modeling is often constrained by reliance on conventional topographic and geological features, potentially overlooking the discriminative power of surface material properties derived from multi-source remote sensing. This study aims to enhance the accuracy and reliability of susceptibility assessment by innovatively integrating spectral information and advanced machine learning techniques. Focusing on Chongqing, a landslide-prone mountainous region in China, this work conducted three innovative investigations: it (i) introduced 12 spectral features into the feature set; (ii) systematically evaluated spectral features contribution, redundancy, and set completeness through feature engineering; and (iii) implemented a comprehensive Stacking ensemble framework with multiple meta-learners and enhancement strategies (Bagging and Cross-Training) to identify the optimal integration scheme. The key results show that spectral features provided a significant positive impact, boosting the AUC of tree-based ensemble models by up to 4.52%. The optimal model, a Stacking ensemble with Bagging_XGBoost as the meta-learner, achieved a superior test AUC of 0.8611, outperforming all individual base learners. Furthermore, the spatial analysis revealed a concentration of high and very high susceptibility areas in Engineering Geological Zone I, which represents approximately 38% of such areas. This study provides a replicable framework for enhancing landslide susceptibility mapping through the integration of spectral features and ensemble learning, offering a scientific basis for targeted risk management and mitigation planning in complex mountainous terrains. Full article
(This article belongs to the Special Issue Multiplatform and Multisensor Applications for Landslide Characterization and Monitoring)
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18 pages, 11202 KB  
Technical Note
Multi-Technique 3D Modelling of Narrow Gorges to Assess Stability: Case Study of Caminito Del Rey (Spain)
by José Luis Pérez-García, Antonio Tomás Mozas-Calvache, José Miguel Gómez-López, Diego Vico-García and Jorge Delgado-García
Remote Sens. 2025, 17(22), 3702; https://doi.org/10.3390/rs17223702 - 13 Nov 2025
Cited by 1 | Viewed by 1576
Abstract
The use of digital photogrammetry and laser data acquisition systems, along with the ability to mount these sensors on unmanned aerial vehicles (UAVs), has revolutionized rockfall assessment. While these techniques have facilitated numerous studies across diverse scenarios, complex environments like narrow gorges necessitate [...] Read more.
The use of digital photogrammetry and laser data acquisition systems, along with the ability to mount these sensors on unmanned aerial vehicles (UAVs), has revolutionized rockfall assessment. While these techniques have facilitated numerous studies across diverse scenarios, complex environments like narrow gorges necessitate the integration of various geomatic techniques to achieve complete and accurate spatial products. To address the critical gap in the literature regarding standardized multi-sensor integration in narrow gorges, this study presents a novel methodology for the cohesive integration of data from these techniques, leveraging their respective strengths to generate reliable products for rockfalls risk assessment. To validate the methodology, we applied this approach to a challenging rockfall susceptibility study at the Caminito del Rey in Málaga, Spain. The site presented significant complexities, including vertical walls hundreds of meters high with abundant overhangs, and canyons as narrow as 10 m, severely limiting single-technique approaches. The successful integration of these diverse datasets yielded a comprehensive, very high-resolution point cloud (1–10 cm density), among other products, covering the entire study area, making it ideal for detailed rockfall assessment and simulation. The approach has demonstrated that data fusion from multiple techniques supposes an advantage because one supports the other both in data coverage and in processing. Although processing the extensive acquired information presented a significant challenge, a successful balance between data volume and processing capacity was achieved, ensuring the outputs met the specific requirements for these studies. Full article
(This article belongs to the Special Issue Multiplatform and Multisensor Applications for Landslide Characterization and Monitoring)
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