Journal of Geology & Geophysics
Open Access

Landslide Susceptibility Mapping Using Remote Sensing and GIS

Elhoucine Essefi^{* *}Correspondence: Elhoucine Essefi, Department of Geophysics, Higher Institute of Applied Sciences and Technology of Gabes, University of Gabes, Zrig Eddakhlania, Tunisia, Email:

Author info »

Description

Landslides are a major geological hazard that can cause significant damage to both human lives and infrastructure. To mitigate the risk posed by landslides, accurate landslide susceptibility mapping is crucial. Remote sensing and Geographic Information Systems (GIS) have revolutionized this field by providing valuable tools for data acquisition, analysis, and mapping. This article explores the integration of remote sensing and GIS in landslide susceptibility mapping, highlighting its benefits and the advancements it brings to the field.

Advantages of remote sensing

Remote sensing, the acquisition of information about the Earth's surface from a distance, offers several advantages in landslide susceptibility mapping. Satellite imagery, aerial photographs, and Light Detection and Ranging (LiDAR) data are commonly used remote sensing techniques.

Firstly, remote sensing enables large-scale coverage, allowing for the identification and monitoring of extensive areas prone to landslides. This is especially useful in regions with challenging terrain or limited accessibility. Furthermore, remote sensing data can be collected periodically, facilitating the detection of temporal changes in land cover and topography that may contribute to landslide occurrence.

Secondly, remote sensing provides valuable information on various landslide-related factors, including slope gradient, aspect, land cover, and geological characteristics. High-resolution satellite imagery and LiDAR data allow for the extraction of accurate topographic information, which is crucial for landslide susceptibility analysis. By integrating remote sensing data with GIS, researchers can create detailed, multi-layered maps that highlight areas at high risk of landslides.

GIS-based analysis and mapping

Geographic Information Systems (GIS) play a pivotal role in landslide susceptibility mapping. GIS facilitates the integration, analysis, and visualization of diverse data layers, allowing researchers to identify patterns and relationships between landslide occurrences and contributing factors.

GIS-based analysis involves overlaying and combining spatial datasets to create susceptibility models. By incorporating remote sensing data on topography, land cover, rainfall patterns, and geological characteristics, GIS helps identify areas susceptible to landslides based on their similarity to known landslide-prone regions. Statistical models, such as logistic regression or decision tree analysis, can be employed to assign probabilities or classes of susceptibility to different areas.

The spatial visualization capabilities of GIS allow for the creation of comprehensive landslide susceptibility maps. These maps can assist decision-makers, urban planners, and emergency management agencies in implementing preventive measures, such as land-use planning, early warning systems, and evacuation plans. Moreover, the continuous updating of GIS databases with new remote sensing data enables dynamic monitoring of landslide-prone areas and the assessment of the effectiveness of mitigation strategies over time.

Recent advancements and future prospects

Advancements in remote sensing and GIS technology have further enhanced landslide susceptibility mapping. Highresolution satellite imagery, LiDAR, and Synthetic Aperture Radar (SAR) have provided finer details on topography, surface deformation, and soil moisture content. This increased resolution aids in the identification of subtle features that contribute to landslide occurrence.

Machine learning algorithms, such as Random Forest and Support Vector Machines, have gained popularity in landslide susceptibility mapping. These algorithms enable automated analysis and classification of large datasets, improving the accuracy and efficiency of mapping. Additionally, the integration of multi-temporal remote sensing data allows for the detection of temporal changes, aiding in the prediction of landslide events.

In the future, advancements in remote sensing technologies, such as the deployment of microsatellites and the use of Unmanned Aerial Vehicles (UAVs), will further improve the accuracy and accessibility of data acquisition. This will enable real-time monitoring and faster response to landslide events.

Remote sensing and GIS-based landslide susceptibility mapping offer significant advantages in terms of coverage, data integration, and visualization. The combination of remote sensing data, including satellite imagery and LiDAR, with GIS facilitates comprehensive analysis and mapping of landslide-prone areas. Recent advancements in technology and the integration of machine learning algorithms have further improved the accuracy and efficiency of mapping. As these technologies continue to evolve, remote sensing and GIS will play a pivotal role in mitigating the risks associated with landslides, protecting lives, and ensuring sustainable land-use planning.