Remote Sensing for Assessing Landslides and Associated Hazards

Abstract

Multi-platform remote sensing using space-, airborne and ground-based sensors has become essential tools for landslide assessment and disaster-risk prevention. Over the last 30 years, the multiplicity of Earth Observation satellites mission ensures uninterrupted optical and radar imagery archives. With the popularization of Unmanned Aerial Vehicles, free optical and radar imagery with high revisiting time, ground and aerial possibilities to perform high-resolution 3D point clouds and derived digital elevation models, it can make it difficult to choose the appropriate method for risk assessment. The aim of this paper is to review the mainstream remote-sensing methods commonly employed for landslide assessment, as well as processing. The purpose is to understand how remote-sensing techniques can be useful for landslide hazard detection and monitoring taking into consideration several constraints such as field location or costs of surveys. First we focus on the suitability of terrestrial, aerial and spaceborne systems that have been widely used for landslide assessment to underline their benefits and drawbacks for data acquisition, processing and interpretation. Several examples of application are presented such as Interferometry Synthetic Aperture Radar (InSAR), lasergrammetry, Terrestrial Optical Photogrammetry. Some of these techniques are unsuitable for slow moving landslides, others limited to large areas and others to local investigations. It can be complicated to select the most appropriate system. Today, the key for understanding landslides is the complementarity of methods and the automation of the data processing. All the mentioned approaches can be coupled (from field monitoring to satellite images analysis) to improve risk management, and the real challenge is to improve automatic solution for landslide recognition and monitoring for the implementation of near real-time emergency systems.

A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan)

“…Remote Sensing (RS) images play an essential role in gaining a deeper and more complete understanding of the precise locations, boundaries, extents, and distributions of landslides 10 – 12 . Therefore, landslide inventory maps are usually prepared by extracting the landslide information from RS images, including optical satellite images and synthetic aperture radar (SAR) data, because of the relatively low cost associated with obtaining RS images and their wide coverage area 13 , 14 . There is a range of common landslide inventory mapping approaches using optical satellite images, such as the manual extraction of landslide areas based on an expert’s visual interpretation, rule-based image classification approaches carried out by an experienced analyst 1 , 15 , analyzing of the multi-temporal SAR interferometry techniques 16 , applying optical or LiDAR data from unmanned aerial vehicles 17 , 18 and the semi-automatic/ automatic image classification using Machine Learning (ML) models in both pixel- and object-based working environments 19 , 20 .…”

When image correlation is needed: Unravelling the complex dynamics of a slow-moving landslide in the tropics with dense radar and optical time series

“…Modern remote sensing techniques, such as satellite-based radar or optical imagery, provide measurements of ground surface change with mm-to cm-scale accuracy and can be used to identify and monitor landslides over much larger areas. The growing availability and quality of short revisit satellite sensors (Elliott et al, 2016(Elliott et al, , 2020, now enable us to monitor landslides at weekly timescales almost anywhere on earth (Lissak et al, 2020).…”

Recognition and Mapping of Landslide Using a Fully Convolutional DenseNet and Influencing Factors

“…Some existing image segmentation algorithms cannot process complicated, largescale remote sensing data, resulting in a low RML efficiency and a poor RML performance [16]. In recent years, deep learning has developed rapidly, and many deep learning methods based on pixels and objects are used for RML development [17][18][19][20]. In contrast to traditional methods, the multilayer feedforward perceptron of convolutional neural networks (CNN) can automatically acquire effective feature representations of images, which allows these networks to identify the semantic features of landslide without having to manually compute complex landslide features [21].…”