Special Issue "Applications of Lidar and Photogrammetry in Monitoring Natural Hazards"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2020).

Special Issue Editors

Dr. Fabio Matano
Website
Guest Editor
Consiglio Nazionale delle Ricerche, Istituto di Scienze Marine (CNR-ISMAR) [National Research Council, Institute of Marine Sciences]
Interests: geomorphology; geology; remote sensing; natural hazard; monitoring
Dr. Giuseppe Esposito
Website
Guest Editor
Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica (CNR-IRPI) [National Research Council, Research Institute for Geo-Hydrological Protection]
Interests: geomatics; geomorphology; engineering geology; active tectonics; risk management

Special Issue Information

Dear Colleagues,

Every year, natural hazards have catastrophic impacts on environmental and anthropic systems throughout the world, causing huge economic loss and countless victims. Moreover, people and infrastructures are exposed to increasing levels of risk due to the effects of global climate change.

Spatially and temporally detailed monitoring is an essential basis for all strategies aimed at reducing the consequences of natural hazards. In recent decades, high-resolution topography data acquired by different remote sensing systems are becoming of primary importance in assessing and monitoring a wide spectrum of natural hazards. Among the available techniques, LIDAR and photogrammetry play a relevant role. A significant advantage of these techniques consists in the acquisition of dense and accurate terrain data with sensors mounted on different fixed and mobile platforms, allowing the integrated monitoring of topographically complex areas. In addition, recent technological advances, algorithm developments, and processing techniques can attain tridimensional high-resolution topography data at low cost, promoting their widespread utilization for natural hazard monitoring among the scientific community.

The goal of this Special Issue is to collect original research articles about LIDAR and photogrammetry applications in monitoring several categories of natural hazards. Authors are encouraged to submit articles that may include monitoring applications related to slope failures, erosion, floods, coastal processes, subsidence, ground deformation, earthquakes, volcanoes, wildfires, and glacier processes. Multi-hazard monitoring applications are considered strongly intriguing. Review papers and case studies about the integrated employment of LIDAR and photogrammetry techniques are particularly welcome.

Dr. Fabio Matano
Dr. Giuseppe Esposito
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 papers will be 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 2200 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.

Published Papers (3 papers)

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Research

Open AccessArticle
Decoding Complex Erosion Responses for the Mitigation of Coastal Rockfall Hazards Using Repeat Terrestrial LiDAR
Remote Sens. 2020, 12(16), 2620; https://doi.org/10.3390/rs12162620 - 13 Aug 2020
Cited by 1
Abstract
A key factor limiting our understanding of rock slope behavior and associated geohazards is the interaction between internal and external system controls on the nature, rates, and timing of rockfall activity. We use high-resolution, monthly terrestrial light detection and ranging (LiDAR) surveys over [...] Read more.
A key factor limiting our understanding of rock slope behavior and associated geohazards is the interaction between internal and external system controls on the nature, rates, and timing of rockfall activity. We use high-resolution, monthly terrestrial light detection and ranging (LiDAR) surveys over a 2 year monitoring period to quantify rockfall patterns across a 0.6 km-long (15.3 × 103 m2) section of a limestone rock cliff on the northeast coast of England, where uncertainty in rates of change threaten the effective planning and operational management of a key coastal cliff top road. Internal system controls, such as cliff material characteristics and foreshore geometry, dictate rockfall characteristics and background patterns of activity and demonstrate that layer-specific analyses of rockfall inventories and sequencing patterns are essential to better understand the timing and nature of rockfall risks. The influence of external environmental controls, notably storm activity, is also evaluated, and increased storminess corresponds to detectable rises in both total and mean rockfall volume and the volumetric contribution of large (>10 m3) rockfalls at the cliff top during these periods. Transient convergence of the cumulative magnitude–frequency power law scaling exponent (ɑ) during high magnitude events signals a uniform erosion response across the wider cliff system that applies to all lithologies. The tracking of rockfall distribution metrics from repeat terrestrial LiDAR in this way demonstrably improves the ability to identify, monitor, and forecast short-term variations in rockfall hazards, and, as such, provides a powerful new approach for mitigating the threats and impacts of coastal erosion. Full article
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Open AccessArticle
Assessment of Tuff Sea Cliff Stability Integrating Geological Surveys and Remote Sensing. Case History from Ventotene Island (Southern Italy)
Remote Sens. 2020, 12(12), 2006; https://doi.org/10.3390/rs12122006 - 22 Jun 2020
Cited by 1
Abstract
This study provides a detailed integrated analysis of the erosional processes affecting the volcanoclastic headlands of a pocket beach, of a typical Tyrrhenian volcanic island (Ventotene, south Italy). It compares the survey carried out in 2012 and the recent landslides that occurred in [...] Read more.
This study provides a detailed integrated analysis of the erosional processes affecting the volcanoclastic headlands of a pocket beach, of a typical Tyrrhenian volcanic island (Ventotene, south Italy). It compares the survey carried out in 2012 and the recent landslides that occurred in 2018–2020. The studied tuff cliff is characterised by steep, up to overhanging walls affected by a fracture network, which locally isolates blocks in precarious equilibrium. The stability conditions of the southern Cala Nave Bay sea cliff were evaluated by integrating a geological field survey, structural analysis of discontinuities, and a detailed topographic survey consisting of a terrestrial laser scanner (TLS) and photogrammetry data acquisition and processing, providing a three-dimensional (3D) model of the sea cliff. The 3D model of the area affected by the recent landslides was created using proximity photogrammetry, the Structure for Motion (SfM) methodology. The fracture network was represented by using high-resolution digital models and projected to realize geostructural vertical mapping of the cliff. The data acquired in 2012 were more recently compared with further surveys carried out, following rock failures that occurred in winter 2019–2020. The detachment planes and failure modalities coincide perfectly with the ones previously assessed. The applied techniques and the comparison with the recent rock failures have proven to be important in defining these conditions to address risk mitigation interventions. Full article
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Open AccessArticle
Machine Learning-Based and 3D Kinematic Models for Rockfall Hazard Assessment Using LiDAR Data and GIS
Remote Sens. 2020, 12(11), 1755; https://doi.org/10.3390/rs12111755 - 29 May 2020
Cited by 1
Abstract
Rockfall is one of the most hazardous phenomena in mountainous and hilly regions with high and steep terrain. Such incidents can cause massive damage to people, properties, and infrastructure. Therefore, proper rockfall hazard assessment methods are required to save lives and provide a [...] Read more.
Rockfall is one of the most hazardous phenomena in mountainous and hilly regions with high and steep terrain. Such incidents can cause massive damage to people, properties, and infrastructure. Therefore, proper rockfall hazard assessment methods are required to save lives and provide a guide for the development of an area. The aim of this research is to develop a method for rockfall hazard assessment at two different scales (regional and local). A high-resolution airborne laser scanning (ALS) technique was utilized to derive an accurate digital terrain model (DTM); next, a terrestrial laser scanner (TLS) was used to capture the topography of the two most critical areas within the study area. A staking machine-learning model based on different classifiers, namely logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (KNN), was optimized and employed to determine rockfall probability by utilizing various rockfall conditioning factors. A developed 3D rockfall kinematic model was used to obtain rockfall trajectories, velocity, frequency, bouncing height, kinetic energy, and impact location. Next, a spatial model combined with a fuzzy analytical hierarchy process (fuzzy-AHP) integrated in the Geographic Information System (GIS) was developed to assess rockfall hazard in two different areas in Ipoh, Malaysia. Additionally, mitigation processes were suggested and assessed to provide a comprehensive information for urban planning management. The results show that, the stacking random forest–k-nearest neighbor (RF-KNN) model is the best hybrid model compared to other tested models with an accuracy of 89%, 86%, and 87% based on training, validation, and cross-validation datasets, respectively. The three-dimension rockfall kinematic model was calibrated with an accuracy of 93% and 95% for the two study areas and subsequently the rockfall trajectories and their characteristics were derived. The assessment of the suggested mitigation processes proves that the proposed methods can reduce or eliminate rockfall hazard in these areas. According to the results, the proposed method can be generalized and applied in other geographical places to provide decision-makers with a comprehensive rockfall hazard assessment. Full article
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