Special Issue "Satellite remote sensing for landslide monitoring and mapping"

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Natural Hazards".

Deadline for manuscript submissions: closed (31 March 2020).

Special Issue Editors

Dr. Lorenzo Solari
Website
Guest Editor
Department of Earth Sciences, University of Firenze, Via La Pira 4, 50121 Firenze, Italy
Interests: remote sensing data interpretation; geohazards monitoring; landslide mapping; building monitoring; land subsidence
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Dr. Andrea Ciampalini
Website
Guest Editor
Department of Earth Sciences, University of Pisa, Via Santa Maria 53, Pisa, Italy
Interests: remote sensing data interpretation; geohazard monitoring; landslide mapping; building monitoring; land subsidence
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Dr. Federica Fiorucci
Website
Guest Editor
Consiglio Nazionale delle Ricerche (CNR), Istituto di Ricerca per la Protezione Idrogeologica (IRPI), Via Madonna Alta 126, 06128 Perugia, Italy
Interests: landslide mapping; landslide hazard and susceptibility; remote sensing data interpretation.
Dr. Anna Barra
Website
Guest Editor
Remote Sensing Department, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Avinguda Carl Friedrich Gauss, 7, 08860 Castelldefels, Barcelona, Spain
Interests: DInSAR; PSI; geohazards monitoring; landslide mapping and monitoring; remote sensing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Landslide monitoring and mapping are increasingly necessary due to the worldwide increasing impact of these geomorphological phenomena on populations and built-up areas. Landslides directly and indirectly impact a territory, causing fatalities and huge socio-economic losses, requiring correct land use policies and best practices for long-term risk mitigation and reduction. In this context, satellite remote sensing and image processing techniques offer effective support for mapping and monitoring the activity of landslides at both the local and regional scales. Optical and radar images are being successfully applied worldwide to achieve these tasks, providing valuable and useful products to landslide risk management actors. The decrease of image costs and the constant improvement of processing algorithms and computational capabilities have increased the use of remotely-sensed data for supporting civil protection activities. The launch of open constellations (ESA Sentinels) that grant free-to-use products, supported by open source image analysis software and easy-to-use cloud computing platforms have greatly expanded the number of potential users of satellite data, even in developing countries. Optical sensors can be profitably used for wide area landslide mapping after an extreme rainfall event or after an earthquake. They can provide useful information for single landslide multi-temporal characterization as well as integrating optical and radar remote sensing data. Scientists usually take advantage of semi-automatic and automatic image classification algorithms, sometimes based on machine learning classifiers to retrieve landslide information. Optical images are used to derive input data for landslide susceptibility models, such as landslide and land cover data. Researchers can take advantage of the dual nature of radar images in terms of amplitude and phase. Radar images can be successfully analyzed for post-event landslide mapping where cloud coverage does not allow for a timely acquisition of reliable optical images, especially in tropical areas. For example, amplitude can be employed for textural change and pixel offset analysis. Phase allows the reconstruction of ground motions occurring between two or more acquisitions of a radar satellite over the same place. Starting from the late 90s, several image processing algorithms were developed to extract phase information. Interferometric techniques have a wide range of applications, including landslides. Slope dynamics can be successfully monitored by means of interferometric techniques, providing long time series of deformations for single measurement points starting from 1992. Satellite interferometry has been demonstrated to be one of the most useful tools for the fast and wide area reconstruction of landslide behavior and for landslide state of activity mapping. In this Special Issue, we expect to gather new applications of optical or radar images for landslide mapping and monitoring at the local to regional scale. Landslide susceptibility and landslide impact approaches using optical or radar images as the input are welcome, as are case studies based on the use of open source software and cloud computing platforms. Accepted manuscripts may cover one of these topics:

  • Applications of radar or optical remote sensing data or a combination of both for landslide mapping and monitoring
  • Single landslide characterization based on remote sensing data. Validation with ground instrumentation is welcome;
  • Regional scale applications for landslide post-event rapid mapping and/or for long-term landslide monitoring;
  • Numerical and empirical models for landslide susceptibility based on remote sensing data;
  • Definition of risk scenarios and landslide impact analyses based on satellite monitoring data.

Dr. Lorenzo Solari
Dr. Andrea Ciampalini
Dr. Federica Fiorucci
Dr. Anna Barra
Guest Editors

Manuscript Submission Information

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Keywords

  • landslide mapping
  • landslide monitoring
  • optical remote sensing
  • radar remote sensing
  • radar interferometry
  • landslide susceptibility
  • landslide impact

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Published Papers (4 papers)

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Research

Open AccessArticle
Monitoring the Recent Activity of Landslides in the Mailuu-Suu Valley (Kyrgyzstan) Using Radar and Optical Remote Sensing Techniques
Geosciences 2020, 10(5), 164; https://doi.org/10.3390/geosciences10050164 - 01 May 2020
Abstract
Central Asian mountain regions are prone to multiple types of natural hazards, often causing damage due to the impact of mass movements. In spring 2017, Kyrgyzstan suffered significant losses from a massive landslide activation event, during which also two of the largest deep-seated [...] Read more.
Central Asian mountain regions are prone to multiple types of natural hazards, often causing damage due to the impact of mass movements. In spring 2017, Kyrgyzstan suffered significant losses from a massive landslide activation event, during which also two of the largest deep-seated mass movements of the former mining area of Mailuu-Suu—the Koytash and Tektonik landslides—were reactivated. This study consists of the use of optical and radar satellite data to highlight deformation zones and identify displacements prior to the collapse of Koytash and to the more superficial deformation on Tektonik. Especially for the first one, the comparison of Digital Elevation Models of 2011 and 2017 (respectively, satellite and unmanned aerial vehicle (UAV) imagery-based) highlights areas of depletion and accumulation, in the scarp and near the toe, respectively. The Differential Synthetic Aperture Radar Interferometry analysis identified slow displacements during the months preceding the reactivation in April 2017, indicating the long-term sliding activity of Koytash and Tektonik. This was confirmed by the computation of deformation time series, showing a positive velocity anomaly on the upper part of both landslides. Furthermore, the analysis of the Normalized Difference Vegetation Index revealed land cover changes associated with the sliding process between June 2016 and October 2017. In addition, in situ data from a local meteorological station highlighted the important contribution of precipitation as a trigger of the collapse. The multidirectional approach used in this study demonstrated the efficiency of applying multiple remote sensing techniques, combined with a meteorological analysis, to identify triggering factors and monitor the activity of landslides. Full article
(This article belongs to the Special Issue Satellite remote sensing for landslide monitoring and mapping)
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Open AccessArticle
Ground Deformation in The Ciloto Landslides Area Revealed by Multi-Temporal InSAR
Geosciences 2020, 10(5), 156; https://doi.org/10.3390/geosciences10050156 - 27 Apr 2020
Abstract
Landslides are one of the natural hazards that occur annually in Indonesia. A continuous geodetic observation in the landslide prone area is essential to support the precautionary measures. Because of its hilly topography, torrential rainfall and landslide history, the Ciloto district in Indonesia [...] Read more.
Landslides are one of the natural hazards that occur annually in Indonesia. A continuous geodetic observation in the landslide prone area is essential to support the precautionary measures. Because of its hilly topography, torrential rainfall and landslide history, the Ciloto district in Indonesia has been affected by ground deformation for an extended period of time. The purpose of our study is to detect significant movement and quantify the kinematics of its motion using the Interferometric synthetic aperture radar (InSAR) time series analysis and multi-band SAR images. We utilized the small baseline SDFP technique for processing multi-temporal SAR data, comprising ERS1/2 (1998–1999), ALOS PALSAR (2007–2009), and Sentinel-1 (2014–2018). Based on the detected deformation signal in the Ciloto area, the displacement rates are categorized as very slow movements. Two active main landslide zones; the Puncak Pass and the Puncak Highway area, which show the trend of slow movement progressively increasing or descreasing, were detected. The integration of the velocity rate between InSAR results and ground observations (e.g., terrestrial and GPS) was conducted at the Puncak Highway area from the temporal perspective. Using the polynomial model, we estimated that the area had cumulatively displaced up to −42 cm for 25 years and the type of movements varied from single compound to multiple rotational and compound. Full article
(This article belongs to the Special Issue Satellite remote sensing for landslide monitoring and mapping)
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Open AccessArticle
Topographic Analysis of Landslide Distribution Using AW3D30 Data
Geosciences 2020, 10(4), 115; https://doi.org/10.3390/geosciences10040115 - 25 Mar 2020
Abstract
Landslides cause serious damage to society, and some occur as reactivations of old landslides in response to earthquakes and/or rainfall. Landslide distributions are therefore useful when siting engineering projects such as road and tunnel constructions. Although several methods have been proposed to extract [...] Read more.
Landslides cause serious damage to society, and some occur as reactivations of old landslides in response to earthquakes and/or rainfall. Landslide distributions are therefore useful when siting engineering projects such as road and tunnel constructions. Although several methods have been proposed to extract landslides from topographic data on the basis of their morphological features (crown, main scarp, and main body), such morphological features are gradually eroded by heavy precipitation or landslide recurrence. Therefore, conventional methods cannot always identify areas influenced by recurrent landslides. In this study, we investigated the relationship between ridgeline continuity and landslide distribution using AW3D30, which is a global digital surface model (DSM) dataset produced from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS) launched by the Japan Aerospace Exploration Agency (JAXA) in 2013. The relationship between the area of landslides and the number of ridge pixels was analyzed, and we propose a method for estimating the upper bound distribution of landslide topographies based on extracted ridgelines data using the Data Envelopment Analysis (DEA) function on the R statistical software packages. The upper bound on the area of landslides decreases as the number of ridge pixels increases. The same trend was seen in all the five sites, and the upper bound derived from one site is hardly exceeded by those derived from all other sites. By using the upper bound distribution function, the landslide distribution will not be missed. Full article
(This article belongs to the Special Issue Satellite remote sensing for landslide monitoring and mapping)
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Open AccessArticle
Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A Case Study in Kullu Valley, Himalayas
Geosciences 2019, 9(8), 360; https://doi.org/10.3390/geosciences9080360 - 17 Aug 2019
Cited by 1
Abstract
Landslides are one of the most damaging geological hazards in mountainous regions such as the Himalayas. The Himalayan region is, tectonically, the most active region in the world that is highly vulnerable to landslides and associated hazards. Landslide susceptibility mapping (LSM) is a [...] Read more.
Landslides are one of the most damaging geological hazards in mountainous regions such as the Himalayas. The Himalayan region is, tectonically, the most active region in the world that is highly vulnerable to landslides and associated hazards. Landslide susceptibility mapping (LSM) is a useful tool for understanding the probability of the spatial distribution of future landslide regions. In this research, the landslide inventory datasets were collected during the field study of the Kullu valley in July 2018, and 149 landslide locations were collected as global positioning system (GPS) points. The present study evaluates the LSM using three different spatial resolution of the digital elevation model (DEM) derived from three different sources. The data-driven traditional frequency ratio (FR) model was used for this study. The FR model was used for this research to assess the impact of the different spatial resolution of DEMs on the LSM. DEM data was derived from Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) ALOS-PALSAR for 12.5 m, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global for 30 m, and the Shuttle Radar Topography Mission (SRTM) for 90 m. As an input, we used eight landslide conditioning factors based on the study area and topographic features of the Kullu valley in the Himalayas. The ASTER-Global 30m DEM showed higher accuracy of 0.910 compared to 0.839 for 12.5 m and 0.824 for 90 m DEM resolution. This study shows that that 30 m resolution is better suited for LSM for the Kullu valley region in the Himalayas. The LSM can be used for mitigation and future planning for spatial planners and developmental authorities in the region. Full article
(This article belongs to the Special Issue Satellite remote sensing for landslide monitoring and mapping)
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