Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers

Ping Lu,Shibiao Bai,Veronica Tofani,Nicola Casagli

doi:10.1016/j.isprsjprs.2019.08.004

Abstract

Long-term InSAR techniques, such as Persistent Scatterer Interferometry and Distributed Scatterer Interferometry, are effective approaches able to detect slow-moving landslides with millimeter precision. This study presents a novel approach of optimized hot spot analysis (OHSA) on persistent scatterers (PS) and distributed scatterers (DS), and evaluates its performance on detection of landslides across the Volterra area in central Tuscany region of Italy. 1625 ascending and 2536 descending PS processed from eight years (2003–2010) of ENVISAT images were produced by the PS-InSAR technique. In addition, 16,493 ascending and 9746 descending PS/DS measurement points (MP) processed from four years (2011–2014 for ascending orbits and 2010–2013 for descending orbits) of COSMO-SkyMed images were collected by the SqueeSAR approach. The OHSA approach was then implemented on the derived PS and DS through the analysis of incremental spatial autocorrelation and the Getis-Ord Gi* statistics. As a result of OHSA, PS and DS MP that are statistically significant with velocity >|±2| mm/year, p-value < 0.01 and z-score >|±2.58| were recognized as hot spots (HS). Meanwhile, a landslide inventory covering the Volterra area was manually prepared as the reference data for accuracy assessment of landslide detection. The results indicate that, in terms of OHSA-derived ENVISAT HS, the detection accuracy can be improved from 23.3% to 25.3% and from 50.7% to 66.4%, with decreased redundancy from 5.3% to 3.7% and from 5.3% to 2.4%, for ascending and descending orbits, respectively. In addition, for OHSA-derived Cosmo-SkyMed HS, the detection accuracy can be improved from 57.7% to 70.3% and from 73.8% to 81.5%, with decreased redundancy from 3.1% to 1.7% and from 3.4% to 2.1%, for ascending and descending orbits, respectively. Compared to traditional HS analysis such as Persistent Scatterers Interferometry Hot Spot and Cluster Analysis (PSI-HCA), OHSA has the significant advantage that the scale distance used for the Getis-Ord Gi* statistics can be automatically determined by the analysis of incremental spatial autocorrelation and accordingly no manual intervention or additional digital terrain model (DTM) is further needed. The proposed method is very succinct and can be easily implemented in diverse geographic information system (GIS) platforms. To the best of our knowledge, this is the first time that OHSA has been applied to PS and DS.

Highlights

Landslide is one of the major types of natural hazards around the world
This study proposed a novel approach of optimized hot spot analysis (OHSA) on persistent scatterers (PS) and distributed scatterers (DS) datasets and subsequently evaluated its potential in landslide detection across the Volterra area
PS processed from eight years (2003–2010) of ENVISAT images were acquired by PS-InSAR, and PS/ DS measurement points (MP) processed from four years (2011–2014 for ascending and 2010–2013 for descending orbit) of COSMO-SkyMed images were obtained by SqueeSAR

Summary

Introduction

Landslide is one of the major types of natural hazards around the world. Is a country notably susceptible to landslide hazards. Due to landslide hazards, more than 5800 deaths and 700,000 homeless people have been caused in Italy (Guzzetti, 2000; Guzzetti and Tonelli, 2004). It has been estimated that an annual direct economic loss of approximate 12 billion Euros was brought by landslides, accounting for about 0.15% of the national gross domestic product (GDP) throughout the whole country of. (Canuti et al, 2002). At the beginning stage, such small ground displacement was primarily estimated through differential InSAR (DInSAR). DInSAR is often restrained by temporal decorrelation and atmospheric disturbances, both of which are difficult to be thoroughly eliminated when

Results

Discussion

Conclusion