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Remote Sensing Analysis of Geologic Hazards

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 34382

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Special Issue Editors

Dr. Daniele Giordan

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Guest Editor

CNR IRPI, Turin, Italy
Interests: unmanned aerial vehicles; natural hazards; engineering geology
Special Issues, Collections and Topics in MDPI journals

Dr. Guido Luzi

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Remote Sensing department, Technological Centre of Telecommunications of Catalunya, Av. Carl Friedrich Gauss n 7, E-08860 Castelldefels, Spain
Interests: remotes sensing; SAR interferometry; microwave radiometry; geophysics
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Dr. Oriol Monserrat

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Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Remote Sensing Department, Division of Geomatics, Av. Gauss, 7 E-08860 Castelldefels, Barcelona, Spain
Interests: remote sensing data processing; SAR data; SAR interferometry; geohazards monitoring; landslide mapping; building monitoring; land subsidence
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Dr. Niccolò Dematteis

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Research Institute for Geo-Hydrological Protection, Italian National Research Council, Strada delle Cacce, 73, 10135 Torino, Italy
Interests: boundary layer meteorology; image processing; MATLAB; earth observation; deformation monitoring; remote sensing; data integration; glaciology; digital image correlation and tracking; advanced machine learning
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Special Issue Information

Dear Colleagues,

In recent decades, classical survey techniques (i.e., field measurements and aerial remote sensing) have evolved and with the advent of new technologies and platforms (e.g., terrestrial radar interferometry, UAV, digital time-lapse cameras, laser scanners), remote sensing systems became popular and widely used in geosciences.

Contactless devices are not invasive and allow measuring without access to the investigated area. This is an excellent advantage as earth surface processes often occur in remote areas and can be potentially dangerous or accessible with difficulty. Satellite and aerial remote sensing offers the possibility of using multi-band high-resolution images over large areas. While ground-based surveys usually have high-acquisition frequency compared to satellite systems and they are often able to observe the evolution of fast processes and their possible paroxysmal phase (e.g., volcanic eruptions, glaciers instabilities, landslides, floods). For their characteristics, proximal remote sensing applications are often used in high-frequency monitoring activities, as they can provide real-time or near-real-time information. Therefore, they can be of great support for early warning procedures and risk assessment and management. Combined with aerospace platforms, terrestrial contactless devices are particularly suitable for data integration techniques and multi-scale approaches.

In this Special Issue, we invite contributions focused on recent and upcoming advances in remote sensing applications in earth sciences. In particular, this Special Issue is dedicated to satellite, aerial and terrestrial contactless devices for monitoring, warning and risk and damage assessment, and new processing techniques and data integration approaches. Contributions presenting exemplar case studies of innovative uses of remote sensing will be welcome as well.

Specific contexts where remote sensing applications include but are not limited to the following:

Landslides;
Glacier dynamics and instabilities;
Debris flows;
Volcano eruptions;
Earthquakes;
River flows and floods;
Rock glaciers and periglacial processes;
Snow and ice avalanches.

Dr. Daniele Giordan
Dr. Guido Luzi
Dr. Oriol Monserrat
Dr. Niccolò Dematteis
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

Remote sensing
Landslides
Glacier dynamics and instabilities
Debris flows
Volcano eruptions
Earthquakes
River flows and floods
Rock glaciers and periglacial processes
Snow and ice avalanches

Published Papers (11 papers)

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Editorial

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6 pages, 1026 KiB

Open AccessEditorial

Remote Sensing Analysis of Geologic Hazards

by Daniele Giordan, Guido Luzi, Oriol Monserrat and Niccolò Dematteis

Remote Sens. 2022, 14(19), 4818; https://doi.org/10.3390/rs14194818 - 27 Sep 2022

Cited by 4 | Viewed by 1312

Abstract

In recent decades, classical survey techniques (i [...] Full article

(This article belongs to the Special Issue Remote Sensing Analysis of Geologic Hazards)

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Research

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24 pages, 18073 KiB

Open AccessArticle

Spatiotemporal Evolution Pattern and Driving Mechanisms of Landslides in the Wenchuan Earthquake-Affected Region: A Case Study in the Bailong River Basin, China

by Linxin Lin, Guan Chen, Wei Shi, Jiacheng Jin, Jie Wu, Fengchun Huang, Yan Chong, Yang Meng, Yajun Li and Yi Zhang

Remote Sens. 2022, 14(10), 2339; https://doi.org/10.3390/rs14102339 - 12 May 2022

Cited by 5 | Viewed by 2336

Abstract

Understanding the spatiotemporal evolution and driving mechanisms of landslides following a mega-earthquake at the catchment scale can lead to improved landslide hazard assessment and reduced related risk. However, little effort has been made to undertake such research in the Wenchuan earthquake-affected region, outside Sichuan Province, China. In this study, we used the Goulinping valley in the Bailong River basin in southern Gansu Province, China, as an example. By examining the multitemporal inventory, we revealed various characteristics of the spatiotemporal evolution of landslides over the past 13 years (2007–2020). We evaluated the activity of landslides using multisource remote-sensing technology, analyzed the driving mechanisms of landslides, and further quantified the contribution of landslide evolution to debris flow in the catchment. Our results indicate that the number of landslides increased by nearly six times from 2007 to 2020, and the total volume of landslides approximately doubled. The evolution of landslides in the catchment can be divided into three stages: the earthquake driving stage (2008), the coupled driving stage of earthquake and rainfall (2008–2017), and the rainfall driving stage (2017–present). Landslides in the upstream limestone area were responsive to earthquakes, while the middle–lower loess–phyllite-dominated reaches were mainly controlled by rainfall. Thus, the current landslides in the upstream region remain stable, and those in the mid-downstream are vigorous. Small landslides and mid-downstream slope erosion can rapidly provide abundant debris flow and reduce its threshold, leading to an increase in the frequency and scale of debris flow. This study lays the foundation for studying landslide mechanisms in the Bailong River basin or similar regions. It also aids in engineering management and landslide risk mitigation under seismic activity and climate change conditions. Full article

(This article belongs to the Special Issue Remote Sensing Analysis of Geologic Hazards)

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25 pages, 14334 KiB

Open AccessArticle

Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape

by Erin Lindsay, Regula Frauenfelder, Denise Rüther, Lorenzo Nava, Lena Rubensdotter, James Strout and Steinar Nordal

Remote Sens. 2022, 14(10), 2301; https://doi.org/10.3390/rs14102301 - 10 May 2022

Cited by 22 | Viewed by 4867

Abstract

Regional early warning systems for landslides rely on historic data to forecast future events and to verify and improve alarms. However, databases of landslide events are often spatially biased towards roads or other infrastructure, with few reported in remote areas. In this study, we demonstrate how Google Earth Engine can be used to create multi-temporal change detection image composites with freely available Sentinel-1 and -2 satellite images, in order to improve landslide visibility and facilitate landslide detection. First, multispectral Sentinel-2 images were used to map landslides triggered by a summer rainstorm in Jølster (Norway), based on changes in the normalised difference vegetation index (NDVI) between pre- and post-event images. Pre- and post-event multi-temporal images were then created by reducing across all available images within one month before and after the landslide events, from which final change detection image composites were produced. We used the mean of backscatter intensity in co- (VV) and cross-polarisations (VH) for Sentinel-1 synthetic aperture radar (SAR) data and maximum NDVI for Sentinel-2. The NDVI-based mapping increased the number of registered events from 14 to 120, while spatial bias was decreased, from 100% of events located within 500 m of a road to 30% close to roads in the new inventory. Of the 120 landslides, 43% were also detectable in the multi-temporal SAR image composite in VV polarisation, while only the east-facing landslides were clearly visible in VH. Noise, from clouds and agriculture in Sentinel-2, and speckle in Sentinel-1, was reduced using the multi-temporal composite approaches, improving landslide visibility without compromising spatial resolution. Our results indicate that manual or automated landslide detection could be significantly improved with multi-temporal image composites using freely available earth observation images and Google Earth Engine, with valuable potential for improving spatial bias in landslide inventories. Using the multi-temporal satellite image composites, we observed significant improvements in landslide visibility in Jølster, compared with conventional bi-temporal change detection methods, and applied this for the first time using VV-polarised SAR data. The GEE scripts allow this procedure to be quickly repeated in new areas, which can be helpful for reducing spatial bias in landslide databases. Full article

(This article belongs to the Special Issue Remote Sensing Analysis of Geologic Hazards)

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20 pages, 3803 KiB

Open AccessArticle

A Landslide Numerical Factor Derived from CHIRPS for Shallow Rainfall Triggered Landslides in Colombia

by Cheila Avalon Cullen, Rafea Al Suhili and Edier Aristizabal

Remote Sens. 2022, 14(9), 2239; https://doi.org/10.3390/rs14092239 - 07 May 2022

Cited by 8 | Viewed by 2487

Abstract

Despite great advances in remote sensing technologies, accurate satellite information is sometimes challenged in tropical regions where dense vegetation prevents the instruments from retrieving reliable readings. In this work, we introduce a satellite-based landslide rainfall threshold for the country of Colombia by studying 4 years of rainfall measurements from The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) for 346 rainfall-triggered landslide events (the dataset). We isolate the two successive rainy/dry periods leading to each landslide to create variables that simulate the dynamics of antecedent wetness and dryness. We test the performance of the derived variables (Rainfall Period 1 (PR1), Rainfall Sum 1 (RS1), Rainfall Period 2 (PR2), Rainfall Sum 2 (RS2), and Dry Period (DT)) in a logistic regression that includes three (3) static parameters (Soil Type (ST), Landcover (LC), and Slope angle). Results from the logistic model describe the influence of each variable in landslide occurrence with an accuracy of 73%. Subsequently, we use these dynamic variables to model a landslide threshold that, in the absence of satellite antecedent soil moisture data, helps describe the interactions between the dynamic variables and the slope angle. We name it the Landslide Triggering Factor—LTF. Subsequently, with a training dataset (65%) and one for testing (35%) we evaluate the LTF threshold performance and compare it to the well-known event duration (E-D) threshold. Results demonstrate that The LTF performs better than the E-D threshold for the training and testing datasets at 71% and 81% respectively. Full article

(This article belongs to the Special Issue Remote Sensing Analysis of Geologic Hazards)

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22 pages, 6586 KiB

Open AccessArticle

Assessing the Potential of Long, Multi-Temporal SAR Interferometry Time Series for Slope Instability Monitoring: Two Case Studies in Southern Italy

by Fabio Bovenga, Ilenia Argentiero, Alberto Refice, Raffaele Nutricato, Davide O. Nitti, Guido Pasquariello and Giuseppe Spilotro

Remote Sens. 2022, 14(7), 1677; https://doi.org/10.3390/rs14071677 - 31 Mar 2022

Cited by 6 | Viewed by 1927

Abstract

Multi-temporal SAR interferometry (MTInSAR), by providing both mean displacement maps and displacement time series over coherent objects on the Earth’s surface, allows analyzing wide areas, identifying ground displacements, and studying the phenomenon evolution at a long time scale. This technique has also been proven to be very useful for detecting and monitoring slope instabilities. For this type of hazard, detection of velocity variations over short time intervals should be useful for early warning of damaging events. In this work, we present the results obtained by using both COSMO-SkyMed (CSK) and Sentinel-1 (S1) data for investigating the ground stability of two hilly villages located in the Southern Italian Apennines (Basilicata region), namely the towns of Montescaglioso and Pomarico. In these two municipalities, landslides occurred in the recent past (in Montescaglioso in 2013) and more recently (in Pomarico in 2019), causing damage to houses, commercial buildings, and infrastructures. SAR datasets acquired by CSK and S1 from both ascending and descending orbits were processed using the SPINUA MTInSAR algorithm. Mean velocity maps and displacement time series were analyzed, also by means of innovative ad hoc procedures, looking, in particular, for non-linear trends. Results evidenced the presence of nonlinear displacements in correspondence of some key infrastructures. In particular, the analysis of accelerations and decelerations of PS objects corresponding to structures affected by recent stabilization measures helps to shed new light in relation to known events that occurred in the area of interest. Full article

(This article belongs to the Special Issue Remote Sensing Analysis of Geologic Hazards)

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31 pages, 12732 KiB

Open AccessArticle

Detailed Mapping of Lava and Ash Deposits at Indonesian Volcanoes by Means of VHR PlanetScope Change Detection

by Moritz Rösch and Simon Plank

Remote Sens. 2022, 14(5), 1168; https://doi.org/10.3390/rs14051168 - 26 Feb 2022

Cited by 8 | Viewed by 4494

Abstract

Mapping of lava flows in unvegetated areas of active volcanoes using optical satellite data is challenging due to spectral similarities of volcanic deposits and the surrounding background. Using very high-resolution PlanetScope data, this study introduces a novel object-oriented classification approach for mapping lava flows in both vegetated and unvegetated areas during several eruptive phases of three Indonesian volcanoes (Karangetang 2018/2019, Agung 2017, Krakatau 2018/2019). For this, change detection analysis based on PlanetScope imagery for mapping loss of vegetation due to volcanic activity (e.g., lava flows) is combined with the analysis of changes in texture and brightness, with hydrological runoff modelling and with analysis of thermal anomalies derived from Sentinel-2 or Landsat-8. Qualitative comparison of the mapped lava flows showed good agreement with multispectral false color time series (Sentinel-2 and Landsat-8). Reports of the Global Volcanism Program support the findings, indicating the developed lava mapping approach produces valuable results for monitoring volcanic hazards. Despite the lack of bands in infrared wavelengths, PlanetScope proves beneficial for the assessment of risk and near-real-time monitoring of active volcanoes due to its high spatial (3 m) and temporal resolution (mapping of all subaerial volcanoes on a daily basis). Full article

(This article belongs to the Special Issue Remote Sensing Analysis of Geologic Hazards)

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24 pages, 7550 KiB

Open AccessArticle

Threshold Definition for Monitoring Gapa Landslide under Large Variations in Reservoir Level Using GNSS

by Shuangshuang Wu, Xinli Hu, Wenbo Zheng, Matteo Berti, Zhitian Qiao and Wei Shen

Remote Sens. 2021, 13(24), 4977; https://doi.org/10.3390/rs13244977 - 08 Dec 2021

Cited by 7 | Viewed by 2575

Abstract

The triggering threshold is one of the most important parameters for landslide early warning systems (EWSs) at the slope scale. In the present work, a velocity threshold is recommended for an early warning system of the Gapa landslide in Southwest China, which was reactivated by the impoundment of a large reservoir behind Jinping’s first dam. Based on GNSS monitoring data over the last five years, the velocity threshold is defined by a novel method, which is implemented by the forward and reverse double moving average of time series. As the landslide deformation is strongly related to the fluctuations in reservoir water levels, a crucial water level is also defined to reduce false warnings from the velocity threshold alone. In recognition of the importance of geological evolution, the evolution process of the Gapa landslide from topping to sliding is described in this study to help to understand its behavior and predict its potential trends. Moreover, based on the improved Saito’s three-stage deformation model, the warning level is set as “attention level”, because the current deformation stage of the landslide is considered to be between the initial and constant stages. At present, the early warning system mainly consists of six surface displacement monitoring sites and one water level observation site. If the daily recorded velocity in each monitoring site exceeds 4 mm/d and, meanwhile, the water level is below 1820 m above sea level (asl), a warning of likely landslide deformation accelerations will be released by relevant monitoring sites. The thresholds are always discretely exceeded on about 3% of annual monitoring days, and they are most frequently exceeded in June (especially in mid-June). The thresholds provide an efficient and effective way for judging accelerations of this landslide and are verified by the current application. The work presented provides critical insights into the development of early warning systems for reservoir-induced large-scale landslides. Full article

(This article belongs to the Special Issue Remote Sensing Analysis of Geologic Hazards)

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21 pages, 14318 KiB

Open AccessArticle

Use of Multiplatform SAR Imagery in Mining Deformation Monitoring with Dense Vegetation Coverage: A Case Study in the Fengfeng Mining Area, China

by Bochen Zhang, Songbo Wu, Xiaoli Ding, Chisheng Wang, Jiasong Zhu and Qingquan Li

Remote Sens. 2021, 13(16), 3091; https://doi.org/10.3390/rs13163091 - 05 Aug 2021

Cited by 11 | Viewed by 2597

Abstract

Ground deformation related to mining activities may occur immediately or many years later, leading to a series of mine geological disasters, such as ground fissures, collapses, and even mining earthquakes. Deformation monitoring has been carried out with techniques, such as multitemporal interferometric synthetic aperture radar (MTInSAR). Over the past decade, MTInSAR has been widely used in monitoring mining deformation, and it is still difficult to retrieve mining deformation over dense vegetation areas. In this study, we use multiple-platform SAR images to retrieve mining deformation over dense vegetation areas. The high-quality interferograms are selected by the coherence map, and the mining deformation is retrieved by the MSBAS-InSAR technique. SAR images from TerraSAR-X, Sentinel-1A, Radarsat-2, and PALSAR-2 over the Fengfeng mining area, Heibei, China, are used to retrieve the deformation of mining activities covered with dense vegetation. The results show that the subsidence in the Fengfeng mining area reaches up to 90 cm over the period from July 2015 to April 2016. The root-mean-square error (RMSE) between the results from InSAR and leveling is 83.5 mm/yr at two mining sites, i.e., Wannian and Jiulong Mines. Full article

(This article belongs to the Special Issue Remote Sensing Analysis of Geologic Hazards)

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22 pages, 9092 KiB

Open AccessArticle

Targeted Rock Slope Assessment Using Voxels and Object-Oriented Classification

by Ioannis Farmakis, David Bonneau, D. Jean Hutchinson and Nicholas Vlachopoulos

Remote Sens. 2021, 13(7), 1354; https://doi.org/10.3390/rs13071354 - 01 Apr 2021

Cited by 5 | Viewed by 3367

Abstract

Reality capture technologies, also known as close-range sensing, have been increasingly popular within the field of engineering geology and particularly rock slope management. Such technologies provide accurate and high-resolution n-dimensional spatial representations of our physical world, known as 3D point clouds, that are mainly used for visualization and monitoring purposes. To extract knowledge from point clouds and inform decision-making within rock slope management systems, semantic injection through automated processes is necessary. In this paper, we propose a model that utilizes a segmentation procedure which delivers segments ready to classify and be retained or rejected according to complementary knowledge-based filter criteria. First, we provide relevant voxel-based features based on the local dimensionality, orientation, and topology and partition them in an assembly of homogenous segments. Subsequently, we build a decision tree that utilizes geometrical, topological, and contextual information and enables the classification of a multi-hazard railway rock slope section in British Columbia, Canada into classes involved in landslide risk management. Finally, the approach is compared to machine learning integrating recent featuring strategies for rock slope classification with limited training data (which is usually the case). This alternative to machine learning semantic segmentation approaches reduces substantially the model size and complexity and provides an adaptable framework for tailored decision-making systems leveraging rock slope semantics. Full article

(This article belongs to the Special Issue Remote Sensing Analysis of Geologic Hazards)

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24 pages, 6137 KiB

Open AccessFeature PaperArticle

Comparison of Digital Image Correlation Methods and the Impact of Noise in Geoscience Applications

by Niccolò Dematteis and Daniele Giordan

Remote Sens. 2021, 13(2), 327; https://doi.org/10.3390/rs13020327 - 19 Jan 2021

Cited by 21 | Viewed by 3722

Abstract

Digital image correlation (DIC) is a commonly-adopted technique in geoscience and natural hazard studies to measure the surface deformation of various geophysical phenomena. In the last decades, several different correlation functions have been developed. Additionally, some authors have proposed applying DIC to other image representations, such as image gradients or orientation. Many works have shown the reliability of specific methods, but they have been rarely compared. In particular, a formal analysis of the impact of different sources of noise is missing. Using synthetic images, we analysed 15 different combinations of correlation functions and image representations and we investigated their performances with respect to the presence of 13 noise sources. Besides, we evaluated the influence of the size of the correlation template. We conducted the analysis also on terrestrial photographs of the Planpincieux Glacier (Italy) and Sentinel 2B images of the Bodélé Depression (Chad). We observed that frequency-based methods are in general less robust against noise, in particular against blurring and speckling, and they tend to underestimate the displacement value. Zero-mean normalised cross-correlation applied to image intensity showed high-quality results. However, it suffers variations of the shadow pattern. Finally, we developed an original similarity function (DOT) that proved to be quite resistant to every noise source. Full article

(This article belongs to the Special Issue Remote Sensing Analysis of Geologic Hazards)

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21 pages, 22771 KiB

Open AccessArticle

Multi-Temporal Satellite Interferometry for Fast-Motion Detection: An Application to Salt Solution Mining

by Lorenzo Solari, Roberto Montalti, Anna Barra, Oriol Monserrat, Silvia Bianchini and Michele Crosetto

Remote Sens. 2020, 12(23), 3919; https://doi.org/10.3390/rs12233919 - 29 Nov 2020

Cited by 9 | Viewed by 2460

Abstract

Underground mining is one of the human activities with the highest impact in terms of induced ground motion. The excavation of the mining levels creates pillars, rooms and cavities that can evolve in chimney collapses and sinkholes. This is a major threat where the mining activity is carried out in an urban context. Thus, there is a clear need for tools and instruments able to precisely quantify mining-induced deformation. Topographic measurements certainly offer very high spatial accuracy and temporal repeatability, but they lack in spatial distribution of measurement points. In the past decades, Multi-Temporal Satellite Interferometry (MTInSAR) has become one of the most reliable techniques for monitoring ground motion, including mining-induced deformation. Although with well-known limitations when high deformation rates and frequently changing land surfaces are involved, MTInSAR has been exploited to evaluate the surface motion in several mining area worldwide. In this paper, a detailed scale MTInSAR approach was designed to characterize ground deformation in the salt solution mining area of Saline di Volterra (Tuscany Region, central Italy). This mining activity has a relevant environmental impact, depleting the water resource and inducing ground motion; sinkholes are a common consequence. The MTInSAR processing approach is based on the direct integration of interferograms derived from Sentinel-1 images and on the phase splitting between low (LF) and high (HF) frequency components. Phase unwrapping is performed for the LF and HF components on a set of points selected through a “triplets closure” method. The final deformation map is derived by combining again the components to avoid error accumulation and by applying a classical atmospheric phase filtering to remove the remaining low frequency signal. The results obtained reveal the presence of several subsidence bowls, sometimes corresponding to sinkholes formed in the recent past. Very high deformation rates, up to −250 mm/yr, and time series with clear trend changes are registered. In addition, the spatial and temporal distribution of velocities and time series is analyzed, with a focus on the correlation with sinkhole occurrence. Full article

(This article belongs to the Special Issue Remote Sensing Analysis of Geologic Hazards)

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