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Application of Remote Sensing Approaches in Geohazard Risk (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation for Emergency Management".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 995

Special Issue Editors

School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
Interests: landslide risk analysis; InSAR; artificial intelligence; landslide early earning and prediction
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Guest Editor
Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
Interests: landslide motoring and early warning; landslide risk analysis; reservoir landslide

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Guest Editor
Department of Earth Sciences, University of Florence, Via La Pira, 4, 50121 Firenze, Italy
Interests: landslide; subsidence; risk analysis; monitoring; InSAR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the overwhelming support and interest in the previous Special Issue (SI), we are introducing a second edition of “Application of Remote Sensing Approaches in Geohazard Risk”. We would like to thank all the authors and co-authors who contributed to the success of the first edition of this SI.

Under the influence of global climate change, rapid urban expansion, and drastic human activities, geological hazards, including landslides, debris flow, and subsidence, occur frequently every year around the world. Numerous geological disasters pose a great threat to human life and property safety, especially in less developed regions. Carrying out a risk study of geological disasters is considered an effective method to reduce losses.

Accurate observation of the geohazard phenomena from initiation to failure is the premise for risk analysis and prediction. Traditional ground-based monitoring techniques can directly observe various phenomena of geological hazards, but the high cost and sparse spatial distribution limit their application on the regional scale and in fine evaluations. In recent years, remote sensing methods, such as radar interferometry, UAVs, and LiDAR, have been widely used. These advanced approaches make significant contributions to various steps of geological disaster risk prevention, including detection, monitoring, and early warning.

The purpose of this Special Issue is to publish studies covering various applications of remote sensing in risk prevention for geohazards. We invite authors to submit research papers and technical notes on topics including but not limited to the following:

  • Advanced remote sensing methods for geohazard observation;
  • Detection and monitoring of geological hazards;
  • Multi-scale risk analysis and mapping.

Dr. Chao Zhou
Prof. Dr. Kunlong Yin
Dr. Federico Raspini
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

  • satellite-, UAV-, and ground-based remote sensing approaches
  • landslides, debris flow, rockfall, subsidence, etc.
  • susceptibility and hazard mapping
  • geohazard detection, monitoring, and early warning
  • risk analysis and prediction
  • mechanisms of geohazards
  • srtificial intelligence

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Published Papers (1 paper)

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Research

27 pages, 30746 KB  
Article
An Ensemble Learning Approach for Landslide Susceptibility Assessment Considering Spatial Heterogeneity Partitioning and Feature Selection
by Xiangchao Jiang, Zhen Yang, Hongbo Mei, Meinan Zheng, Jiajia Yuan and Lei Wang
Remote Sens. 2025, 17(16), 2875; https://doi.org/10.3390/rs17162875 - 18 Aug 2025
Viewed by 659
Abstract
Traditional landslide susceptibility assessment (LSA) methods typically adopt a global modeling strategy, which struggles to account for the pronounced spatial heterogeneity arising from variations in topography, geology, and vegetation conditions within a region. Furthermore, model predictive performance is often undermined by feature redundancy. [...] Read more.
Traditional landslide susceptibility assessment (LSA) methods typically adopt a global modeling strategy, which struggles to account for the pronounced spatial heterogeneity arising from variations in topography, geology, and vegetation conditions within a region. Furthermore, model predictive performance is often undermined by feature redundancy. To address these limitations, this study focuses on the landslide disaster early-warning demonstration area in Honghe Prefecture, Yunnan Province. It proposes an ensemble learning model termed heterogeneity feature optimized stacking (HF-stacking), which integrates spatial heterogeneity partitioning (SHP) with feature selection to improve the scientific rigor of LSA. This method initially establishes an LSA system comprising 15 static landslide conditioning factors (LCFs) and two dynamic factors representing the average annual deformation rates derived from interferometric synthetic aperture radar (InSAR) technology. Based on landslide inventory data, an SHP method combining t-distributed stochastic neighbor embedding (t-SNE) and iterative self-organizing (ISO) clustering was developed to divide the study area into subregions. Within each subregion, a tailored feature selection strategy was applied to determine the optimal feature subset. The final LSA was performed using the stacking ensemble learning approach. The results show that the HF-stacking model achieved the best overall performance, with an average AUC of 95.90% across subregions, 4.23% higher than the traditional stacking model. Other evaluation metrics also demonstrated comprehensive improvements. This study confirms that constructing an SHP framework and implementing feature selection strategies can effectively reduce the impact of spatial heterogeneity and feature redundancy, thereby significantly enhancing the predictive performance of LSA models. The proposed method contributes to improving the reliability of regional landslide risk assessments. Full article
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