Multitemporal landslides inventory map updating using spaceborne SAR analysis

Ventisette, Chiara Del; Righini, Gaia; Moretti, Sandro; Casagli, Nicola

doi:10.1016/j.jag.2014.02.008

Cited by 63 publications

(31 citation statements)

References 29 publications

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“…from COSMO-SkyMed constellation) and subsequently to properly link them. This makes possible the generation of high quality map products at various levels of specificity (Bianchini et al 2016;Ciampalini et al 2016b;Raspini et al 2015;Tofani et al 2014;Del Ventisette et al 2014).…”

Section: Wcoe (2014-2017) Activities Research Activitiesmentioning

confidence: 99%

Advanced Technologies for Landslides (WCoE 2014–2017, IPL-196, IPL-198)

Tofani

Catani

Moretti

et al. 2017

Advancing Culture of Living With Landslides

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Section: Wcoe (2014-2017) Activities Research Activitiesmentioning

confidence: 99%

Advanced Technologies for Landslides (WCoE 2014–2017, IPL-196, IPL-198)

Tofani

Catani

Moretti

et al. 2017

Advancing Culture of Living With Landslides

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show abstract

“…Persistent Scatterer Interferometry (PSI) is another approach of interferometry which may contribute in subsidence mapping with effective results with millimeter accuracy (Crosetto, Monserrat, Cuevas-González, Devanthéry, & Crippa, 2016;Rosi, Agostini, Tofani, & Casagli, 2014;Strozzi et al, 2001;Righini, Raspini, Moretti, & Cigna, 2011;Tofani, Raspini, Catani, & Casagli, 2013). PSI using ERS-1/2 and Envisat data has been applied for landslide monitoring in the Italian Alps (Del Ventisette, Righini, Moretti, & Casagli, 2014). The same approach was exploited different SAR data and the results were associated with GNSS data (Bovenga, Refice, Pasquariello, Nitti, & Nutricato, 2014;Colesanti & Wasowski, 2006;Crosetto et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Preliminary results from active landslide monitoring using multidisciplinary surveys

Nikolakopoulos

Kavoura

Depountis

et al. 2017

European Journal of Remote Sensing

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This study presents the synergy of multidisciplinary surveys for the monitoring of an active landslide in Western Greece. The aim of this paper is to highlight and validate a methodology based on multiple sensors data integration which can successfully be used to manage natural disasters or to improve the knowledge of a specific phenomenon in order to prevent and mitigate the risk. Photogrammetric and interferometric processing has been applied to a complex set of remote sensing data such as high resolution satellite images, digital airphotos, aerial photos acquired from an Unmanned Aerial Vehicle and radar data. Global Navigation Satellite System measurements and continuous inclinometer measurements are being performed. The multifunctional technology of Geographic Information Systems is used in order to collect, storage, manage, process, analyze and cartographically represent the previously described complex geoscientific information. ARTICLE HISTORY

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“…Generally, landslide identification can be performed by applying of different approaches including geomorphological field reconnaissance (Ardizzone et al, 2007), interpretation of stereoscopic aerial photographs (Li et al, 2016), surface and sub-surface monitoring and innovative remote sensing technologies such as the interpretation of synthetic aperture radar (SAR) images (Zhao et al, 2012, Del Ventisette et al, 2014, the interpretation of high resolution multispectral images (Cheng et al, 2004) or the analysis of high quality digital elevation models (DEMs) obtained from space or airborne sensors (Booth et al, 2009, Ardizzone et al, 2007, Van Den Eeckhaut et al, 2005, Tarolli et al, 2012, Tarolli., 2014. However, many of them are time-consuming (geomorphological field reconnaissance, surface and sub-surface monitoring) or are not applicable in forested regions (e.g.…”

Section: Introductionmentioning

confidence: 99%

Towards the Optimal Pixel Size of Dem for Automatic Mapping of Landslide Areas

Pawłuszek

Borkowski

Tarolli

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification.

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Multitemporal landslides inventory map updating using spaceborne SAR analysis

Cited by 63 publications

References 29 publications

Advanced Technologies for Landslides (WCoE 2014–2017, IPL-196, IPL-198)

Advanced Technologies for Landslides (WCoE 2014–2017, IPL-196, IPL-198)

Preliminary results from active landslide monitoring using multidisciplinary surveys

Towards the Optimal Pixel Size of Dem for Automatic Mapping of Landslide Areas

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