Exploitation of the Intermittent SBAS (ISBAS) algorithm with COSMO-SkyMed data for landslide inventory mapping in north-western Sicily, Italy

Novellino, Alessandro; Cigna, Francesca; Sowter, Andrew; Ramondini, Massimo; Calcaterra, Domenico

doi:10.1016/j.geomorph.2016.12.009

Cited by 49 publications

(37 citation statements)

References 57 publications

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“…Furthermore, the effective field-based monitoring of surface motion over global peatlands is impossible on account of their extent and inaccessibility. Satellite remote sensing, specifically Synthetic Aperture Radar (SAR), on the other hand, improves spatial and temporal coverage, is relatively inexpensive (often free) [23,24] and overcomes the frequent cloud cover of these peatland areas (e.g., [25]). In this paper, we advocate advanced Differential Interferometry Synthetic Aperture Radar (DInSAR) techniques, which can monitor the motion of the Earth's surface along the sensor Line-of-Sight (LOS) and measure its change [25], based on the signal phase difference observed in an interferogram formed between two co-registered SAR scenes acquired at different times over the same area of interest (AOI) e.g., [26].…”

Section: Introductionmentioning

confidence: 99%

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Long-Term Peatland Condition Assessment via Surface Motion Monitoring Using the ISBAS DInSAR Technique over the Flow Country, Scotland

et al. 2018

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Satellite Earth Observation (EO) is often used as a cost-effective method to report on the condition of remote and inaccessible peatland areas. Current EO techniques are primarily limited to reporting on the vegetation classes and properties of the immediate peat surface using optical data, which can be used to infer peatland condition. Another useful indicator of peatland condition is that of surface motion, which has the potential to report on mass accumulation and loss of peat. Interferometic SAR (InSAR) techniques can provide this using data from space. However, the most common InSAR techniques for information extraction, such as Persistent Scatterers' Interferometry (PSI), have seen limited application over peat as they are primarily tuned to work in areas of high coherence (i.e., on hard, non-vegetated surfaces only). A new InSAR technique, called the Intermittent Small BAseline Subset (ISBAS) method, has been recently developed to provide measurements over vegetated areas from SAR data acquired by satellite sensors. This paper examines the feasibility of the ISBAS technique for monitoring long-term surface motion over peatland areas of the Flow Country, in the northeast of Scotland. In particular, the surface motions estimated are compared with ground data over a small forested area (namely the Bad a Cheo forest Reserve). Two sets of satellite SAR data are used: ERS C-band images, covering the period 1992-2000, and Sentinel-1 C-band images, covering the period 2015-2016. We show that the ISBAS measurements are able to identify surface motion over peatland areas, where subsidence is a consequence of known land cover/land use. In particular, the ISBAS products agree with the trend of surface motion, but there are uncertainties with their magnitude and direction (vertical). It is concluded that there is a potential for the ISBAS method to be able to report on trends in subsidence and uplift over peatland areas, and this paper suggests avenues for further investigation, but this requires a well-resourced validation campaign.

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Section: Introductionmentioning

confidence: 99%

“…Previously, DInSAR has shown a clear capability to map and analyse the dynamics of surface motion associated with tectonic processes [28,29], land subsidence [30][31][32], slow landslides [23,[33][34][35] and volcanic activity [36,37]. Other studies have used DInSAR techniques to construct subsidence maps [38,39].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Peatland Condition Assessment via Surface Motion Monitoring Using the ISBAS DInSAR Technique over the Flow Country, Scotland

et al. 2018

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“…In recent years, landslide monitoring systems shifted from traditional on site instruments (e.g., conventional wire extensometers [17], inclinometers [18], GPS [19], levelling [20,21]) to remote sensing applications, including Light Detection and Ranging (LiDAR) [22,23], Advanced Differential Interferometry Synthetic Aperture Radar (A-DInSAR), Persistent Scatterers Interferometry (PSI) [24][25][26][27][28][29][30] and Small-BAseline Subset (SBAS) [31,32], Ground Based InSAR (GB-InSAR) [33], satellite and aerial imagery investigation [34][35][36], or Unnamed Aerial Vehicle (UAV) surveys e.g., [37][38][39]. Unlike ground-based monitoring systems, Earth-observation techniques allow measuring land motion of wide areas with millimetre to centimetre accuracy with a much higher frequency of acquisition and decreasing costs (with respect to reach similar precision with conventional techniques) [40].…”

Section: Introductionmentioning

confidence: 99%

Landslide-Induced Damage Probability Estimation Coupling InSAR and Field Survey Data by Fragility Curves

et al. 2019

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Landslides are considered to be one of the main natural geohazards causing relevant economic damages and social effects worldwide. Italy is one of the countries worldwide most affected by landslides; in the Region of Tuscany alone, more than 100,000 phenomena are known and mapped. The possibility to recognize, investigate, and monitor these phenomena play a key role to avoid further occurrences and consequences. The number of applications of Advanced Differential Interferometric Synthetic Aperture Radar (A-DInSAR) analysis for landslides monitoring and mapping greatly increased in the last decades thanks to the technological advances and the development of advanced processing algorithms. In this work, landslide-induced damage on structures recognized and classified by field survey and velocity of displacement re-projected along the steepest slope were combined in order to extract fragility curves for the hamlets of Patigno and Coloretta, in the Zeri municipality (Tuscany, northern Italy). Images using ERS1/2, ENVISAT, COSMO-SkyMed (CSK) and Sentinel-1 SAR (Synthetic Aperture Radar) were employed to investigate an approximate 25 years of deformation affecting both hamlets. Three field surveys were conducted for recognizing, identifying, and classifying the landslide-induced damage on structures and infrastructures. At the end, the damage probability maps were designed by means of the use of the fragility curves between Sentinel-1 velocities and recorded levels of damage. The results were conceived to be useful for the local authorities and civil protection authorities to improve the land managing and, more generally, for planning mitigation strategies.

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“…Unfortunately, night-time and severe weather often limit the use of optical images in practice [12]. On the other hand, thanks to the unique characteristics of microwaves, SAR sensors can not only acquire periodic images regardless of weather and time, but can also provide valuable information on biophysical and geophysical parameters [13][14][15][16]. Although a number of methods have been proposed for single-channel SAR images [17][18][19][20], the interpretation of the backscattering changes of the land cover is limited [7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method of Change Detection in Bi-Temporal PolSAR Data Using a Joint-Classification Classifier Based on a Similarity Measure

Zhao

Lü

et al. 2017

Remote Sensing

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Accurate and timely change detection of the Earth's surface features is extremely important for understanding the relationships and interactions between people and natural phenomena. Owing to the all-weather response capability, polarimetric synthetic aperture radar (PolSAR) has become a key tool for change detection. Change detection includes both unsupervised and supervised methods. Unsupervised change detection is simple and effective, but cannot detect the type of land cover change. Supervised change detection can detect the type of land cover change, but is easily affected and depended by the human interventions. To solve these problems, a novel method of change detection using a joint-classification classifier (JCC) based on a similarity measure is introduced. The similarity measure is obtained by a test statistic and the Kittler and Illingworth (TSKI) minimum-error thresholding algorithm, which is used to automatically control the JCC. The efficiency of the proposed method is demonstrated by the use of bi-temporal PolSAR images acquired by RADARSAT-2 over Wuhan, China. The experimental results show that the proposed method can identify the different types of land cover change and can reduce both the false detection rate and false alarm rate in the change detection.

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Exploitation of the Intermittent SBAS (ISBAS) algorithm with COSMO-SkyMed data for landslide inventory mapping in north-western Sicily, Italy

Cited by 49 publications

References 57 publications

Long-Term Peatland Condition Assessment via Surface Motion Monitoring Using the ISBAS DInSAR Technique over the Flow Country, Scotland

Long-Term Peatland Condition Assessment via Surface Motion Monitoring Using the ISBAS DInSAR Technique over the Flow Country, Scotland

Landslide-Induced Damage Probability Estimation Coupling InSAR and Field Survey Data by Fragility Curves

A Novel Method of Change Detection in Bi-Temporal PolSAR Data Using a Joint-Classification Classifier Based on a Similarity Measure

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