Landslide Monitoring and the Inventory Map Validation by Ensemble DInSAR Processing of ASAR and PALSAR Images (Case Stud
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ORIGINAL PAPER
Landslide Monitoring and the Inventory Map Validation by Ensemble DInSAR Processing of ASAR and PALSAR Images (Case Study: Doab-Samsami Basin in Chaharmahal and Bakhtiari Province, Iran) Kourosh Shirani
. Mehrdad Pasandi
Received: 31 May 2020 / Accepted: 3 September 2020 Ó Springer Nature Switzerland AG 2020
Abstract Applications of Synthetic Aperture Sensors (SAR) and particularly Differential Interferometric Synthetic Aperture Radar (DInSAR) have provided new opportunities for detecting and monitoring of slow and even fast land deformations such as landslides and also updating their inventory maps. Employing this technique has made possible continuous detection and monitoring of small land movements with high precision over a wide spread area. In this study, two image series including 12 radar images with descending orbit acquired by ASAR sensor of ENVISAT satellite and 10 radar images with ascending orbit collected by PALAR sensor of ALOS satellite were selected and processed by the DInSAR method in order to detect landslides in the DoabSamsami basin in Chaharmahal and Bakhtiari province, Iran. Landslides detected in the study area cover over 5959 hec according to the processing of ASAR and PALSAR images collected between 2003 and 2011, whereas landslides detected by field studies cover over 5056 hec. Based on the results of the radar processing technique for detecting and mapping of K. Shirani (&) Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources, Research and Education Center, AREEO, Isfahan, Iran e-mail: [email protected] M. Pasandi Department of Geology, University of Isfahan, Isfahan, Iran
landslides, the ASAR images can provide more details of slides due to their shorter wavelengths but the PALSAR images have comparatively greater penetration and lower incoherence due to the longer wavelengths. Results of the receiver operator characteristic (ROC) method show a well agreement between the landslides map provided by the DInSAR approach and field study. The area under the curve of receiver operator characteristic (ROC) curve was estimated to be 0.95 with a standard deviation of 0.02 at 95% confidence level. The Cohen’s Kappa of 0.61 indicate relatively good conformity between classification of the detected landslide distribution in the study area based on the DInSAR method and field survey. Keywords Radar remote sensing PALSAR and ASAR sensors Differential SAR interferometry Landslide
1 Introduction Landslide is a widespread geohazard all over the world causing significant damages to human lives, structures and infrastructures (D’Elia and Rossi-Doria 2000; Picarelli and Russo 2004; Spizzichino et al. 2004; Bonnard et al. 2008; Urciuoli and Picarelli 2008; Wang et al. 2008; Mansour et al. 2011; Garcı´aDavalillo et al. 2014; Ferlisi et al. 2015; Peduto et al.
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2016; Calvello et al. 2017). In addition to the destructive role of this phenomenon on forests,
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