Landslide Susceptibility Mapping

Dear Colleagues,

The main objective of this Special Issue on “Landslide Susceptibility Mapping” is to provide a scientific forum for solving the challenges and advancing the successful implementation of remote sensing technologies (RS), geographic information systems (GIS), and machine learning methods in landslide susceptibility mapping (LSM).

Given the increase in intensity and frequency of extreme weather events (heavy precipitation, earthquakes, etc.) and frequently major anthropogenic activities, landslides are a type of catastrophic geological disaster leading to loss of life, damage to properties, and economic disruption around the world. Hence, to reduce the damage and the negative impacts, it is necessary to predict the spatial distribution of potential landslides based on the recorded landslide inventory and related conditioning factors. However, how to accurately predict the potential locations of future landslides still remains a great challenge which plays an important role in the policy of land use and physical environment protection. The LSM is an effective method to identify and delineate landslide-prone areas for the visual probability distribution of potential landslides, and further for the landslide hazard and risk assessment.

Consequently, with the development of RS, GIS, and machine learning methods, some state-of-the-art techniques and different quantitative approaches have been developed for LSM. Broadly, there are four main types of LSM approach: physical-based models, opinion-driven (i.e., heuristic) models, statistical models, and more recently machine learning models. Each of these individual approaches has been shown to have its own advantages and limitations. Although a large number of encouraging results has been obtained, there are still some issues of LSM which need to be solved and researched further. For example, how to select the appropriate mapping unit and conditioning factors at different scale with different resolutions remains a difficult and uncertain task; the evaluation of the predictive performances and the interpretability of the landslide susceptibility models needs to be thoroughly researched. Moreover, landslide susceptibility may change due to meteorological and environmental changes driven or conditioned by the predicted climate changes.

This Special Issue aims to provide an outlet for peer-reviewed publications that implement state-of-the-art methods and techniques incorporating RS, GIS, and machine learning methods, so as to improve the performance accuracy, application, and interpretability of landslide susceptibility. This Special Issue aims to cover, without being limited to, the following areas:

• Landslide susceptibility mapping;
• Landslide susceptibility modelling using machine learning methods;
• Uncertainty analysis of landslide susceptibility;
• Interpretability of the landslide susceptibility models and mapping;

• Landslide susceptibility mapping with the climate change

I also encourage you to send me a short abstract outlining the purpose of the research and the principal results obtained, in order to verify at an early stage if the contribution you intend to submit fits with the objectives of the Special Issue.

Dr. Faming Huang
Prof. Dr. Filippo Catani
Dr. Zhilu Chang
Dr. Sansar Raj Meena
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. Geosciences is an international peer-reviewed open access monthly 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 1800 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.

• landslide susceptibility mapping
• remote sensing
• geographic information systems
• machine learning
• early warning
• climate change