Phase and amplitude analyses of SAR data for landslide detection and monitoring in non-urban areas located in the North-Eastern Italian pre-Alps

Tessari, Giulia; Floris, Mario; Pasquali, Paolo

doi:10.1007/s12665-017-6403-5

Cited by 34 publications

(23 citation statements)

References 39 publications

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“…Mondini (2017) used continuous measures of SAR amplitude changes and spatial autocorrelation to intercept hundreds of landslides occurred in Myammar in 2015 [12]. Tessari et al (2017) measured the variation in the SAR amplitude between COSMO-SkyMed SAR acquisitions to detect the area affected by two landslides located in the North-Eastern Italian pre-Alps [55] while Konishi and Suga (2018) investigated the potential of the backscattering coefficient difference and the intensity correlation between pre-and post-disaster COSMO-SkyMed images for landslide detection in the Kii Peninsula [56].…”

Section: Introductionmentioning

confidence: 99%

Sentinel-1 SAR Amplitude Imagery for Rapid Landslide Detection

et al. 2019

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Despite landslides impact the society worldwide every day, landslide information is inhomogeneous and lacking. When landslides occur in remote areas or where the availability of optical images is rare due to cloud persistence, they might remain unknown, or unnoticed for long time, preventing studies and hampering civil protection operations. The unprecedented availability of SAR C-band images provided by the Sentinel-1 constellation offers the opportunity to propose new solutions to detect landslides events. In this work, we perform a systematic assessment of Sentinel-1 SAR C-band images acquired before and after known events. We present the results of a pilot study on 32 worldwide cases of rapid landslides entailing different types, sizes, slope expositions, as well as pre-existing land cover, triggering factors and climatic regimes. Results show that in about eighty-four percent of the cases, changes caused by landslides on SAR amplitudes are unambiguous, whereas only in about thirteen percent of the cases there is no evidence. On the other hand, the signal does not allow for a systematic use to produce inventories because only in 8 cases, a delineation of the landslide borders (i.e., mapping) can be manually attempted. In a few cases, cascade multi-hazard (e.g., floods caused by landslides) and evidences of extreme triggering factors (e.g., strong earthquakes or very rapid snow melting) were detected. The method promises to increase the availability of information on landslides at different spatial and temporal scales with benefits for event magnitude assessment during weather-related emergencies, model tuning, and landslide forecast model validation, in particular when accurate mapping is not required.

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Section: Introductionmentioning

confidence: 99%

Sentinel-1 SAR Amplitude Imagery for Rapid Landslide Detection

et al. 2019

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“…A successful example of the use of amplitude variations of the radar signal to analyze landslides is described by Zhao et al (2013), which inferred the Jiweishan rock slide in China using changes in SAR backscattering intensity in ALOS/PALSAR images. Tessari et al (2017) verified that when the phase information cannot be exploited, amplitude of the reflected signal is very useful to detect and map rapid-moving landslides that cause significant variations in the ground morphology and land cover. Mondini (2017) proved that both landslides and flooded areas can be detected by verifying changes in the spatial autocorrelation in a multitemporal series of SAR images.…”

Section: Introductionmentioning

confidence: 95%

“…The Differential Interferometric Synthetic Aperture Radar (DInSAR) (Gabriel et al, 1989) has been widely used to detect surface displacements over large areas with sub-centimeter accuracy. DInSAR is aimed to calculate phase differences between two or more multi-temporal images and has been successfully applied to analyze landslides (Calò et al, 2012;Zhao et al, 2012;Cigna et al, 2013;Calvello et al, 2017;Tessari et al, 2017), earthquakes, subsidence, soil consolidation, volcanoes and tectonic deformations (Plank, 2014 and references therein). Other techniques exploit the amplitude information contained in the pixels of the SAR images.…”

Section: Introductionmentioning

confidence: 99%

A semi-automatic procedure to support the detection of rapid-moving landslides using spaceborne SAR imagery

Esposito

Marchesini

Mondini

et al. 2020

Preprint

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Abstract. The increasing availability of free-access satellite data represents a relevant opportunity for the analysis and assessment of natural hazards. The systematic acquisition of spaceborne imagery allows monitoring areas prone to geo-hydrological disasters, providing relevant information for risk evaluation and management. In case of major landslide events, for example, spaceborne radar data can provide an innovative solution for the detection of slope failures, even in case of persistent cloud cover. The information about extension and location of the landslide-affected areas may support decision-making processes during the emergency responses. In this paper, we present a semi-automatic procedure, based on Sentinel-1 Synthetic Aperture Radar (SAR) images, aimed to facilitate the detection of rapid-moving landslides. The procedure evaluates changes of radar backscattered signals associated to land cover modifications, that may be also caused by mass movements. The procedure requires an initial manual selection of some parameters, and is able to execute automatically the download and pre-processing of images, the detection of SAR amplitude changes, and the identification of areas potentially affected by landslides, which are then displayed in a geo-referenced map. This map should help decision-makers and emergency managers to organize field investigations. The processes automatization is implemented with specific scripts running on a GNU/Linux operating system and exploiting modules of Open Source software. We tested the processing chain, in back analysis, on an area of about 3000 km2 in central Papua New Guinea that in February/March 2018 was struck by a severe seismic sequence that triggered numerous widespread landslides. In the area, we simulated a periodic survey of about seven months, from 12 November 2017 to 6 June 2018, downloading 36 Sentinel-1 images and performing 17 change detection analyses automatically. The procedure resulted in statistical and graphical evidences of widespread land cover changes occurred just after the most severe seismic events. Most of them can be interpreted as mass movements triggered by the main seismic shocks.

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“…Yun et al (2015) demonstrated that a damage proxy map based on ALOS-2/PALSAR-2 coherence correctly delineated the extent of the debris from reported landslide/avalanche events in Langtang valley. Tessari, Floris and Pasquali (2017) identified landslide areas using a stack of nine COSMO-SkyMed amplitude images. However, a detailed analysis of the intensity correlation or interferometric coherence for landslide detection using SAR imagery is generally not performed.…”

Section: Introductionmentioning

confidence: 99%

“…However, a detailed analysis of the intensity correlation or interferometric coherence for landslide detection using SAR imagery is generally not performed. Vegetation is one of the most common limitations in analysing landslide phenomena, because landslides generally occur on hilly and mountainous areas characterized by blooming vegetation (Tessari et al, 2017). There are large amounts of vegetation in mountainous areas of Japan.…”

Section: Introductionmentioning

confidence: 99%

Landslide detection using COSMO-SkyMed images: a case study of a landslide event on Kii Peninsula, Japan

Konishi

Suga

2018

European Journal of Remote Sensing

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The heavy rain induced by Typhoon Talas in 2011 caused deep-seated catastrophic landslides on Kii Peninsula in the central part of Japan. Remote-sensing techniques play a key role in natural disasters in terms of quick response and mitigation. We propose a quick response method for landslide mapping using high-resolution satellite images. For synthetic aperture radar analysis, pre-and post-disaster COSMO-SkyMed images were used to detect the landslide areas on Kii Peninsula. We investigated the potential for landslide detection using backscattering coefficient difference and intensity correlation. The difference in the backscattering coefficient or the intensity correlation reflects the land-cover change between preand post-disaster images. We also investigated the effects of speckle noise reduction filtering and window size. The detected landslide areas were compared with the area detected by EROS-B, which has a very high-resolution optical sensor. The optimal value of the F-measure (61.24%) was obtained within a 37 × 37 window size using the intensity correlation, with a 5 × 5 window size Frost filter. The precision and recall were 65.39% and 57.58%, respectively. The results confirm that rapid detection of a landslide at the initial stage could be effectively achieved using pre-and post-disaster COSMO-SkyMed images. ARTICLE HISTORY

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Phase and amplitude analyses of SAR data for landslide detection and monitoring in non-urban areas located in the North-Eastern Italian pre-Alps

Cited by 34 publications

References 39 publications

Sentinel-1 SAR Amplitude Imagery for Rapid Landslide Detection

Sentinel-1 SAR Amplitude Imagery for Rapid Landslide Detection

A semi-automatic procedure to support the detection of rapid-moving landslides using spaceborne SAR imagery

Landslide detection using COSMO-SkyMed images: a case study of a landslide event on Kii Peninsula, Japan

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