Landslide Mapping in Vegetated Areas Using Change Detection Based on Optical and Polarimetric SAR Data

Simon Plank,André Twele,Sandro Martinis

doi:10.3390/rs8040307

Abstract

Mapping of landslides, quickly providing information about the extent of the affected area and type and grade of damage, is crucial to enable fast crisis response, i.e., to support rescue and humanitarian operations. Most synthetic aperture radar (SAR) data-based landslide detection approaches reported in the literature use change detection techniques, requiring very high resolution (VHR) SAR imagery acquired shortly before the landslide event, which is commonly not available. Modern VHR SAR missions, e.g., Radarsat-2, TerraSAR-X, or COSMO-SkyMed, do not systematically cover the entire world, due to limitations in onboard disk space and downlink transmission rates. Here, we present a fast and transferable procedure for mapping of landslides, based on change detection between pre-event optical imagery and the polarimetric entropy derived from post-event VHR polarimetric SAR data. Pre-event information is derived from high resolution optical imagery of Landsat-8 or Sentinel-2, which are freely available and systematically acquired over the entire Earth’s landmass. The landslide mapping is refined by slope information from a digital elevation model generated from bi-static TanDEM-X imagery. The methodology was successfully applied to two landslide events of different characteristics: A rotational slide near Charleston, West Virginia, USA and a mining waste earthflow near Bolshaya Talda, Russia.

Highlights

Large landslides are a global phenomenon, causing damage and casualties [1]
Difference Vegetation Index (NDVI) of the multispectral pre-event imagery (e.g., Landsat-8 or Sentinel-2). (II) after polarimetric speckle filtering using the edge-preserving refined Lee filter, (III) the H/α decomposition is applied to the post-event polarimetric synthetic aperture radar (SAR) image to detect, within the pre-selected areas, regions characterized by low entropy (H) values, i.e., evidence of bare soil or rock. (IV) assuming a minimum slope value δ ě 20 ̋
At the SAR image acquired after the second slope failure the landslide detection procedure was able to detect the main part of the landslide

Summary

Introduction

Large landslides are a global phenomenon, causing damage and casualties [1]. Landslides arouse emergency situations when urban areas or man-made constructions, such as buildings, bridges, railroads, and roads, are affected. Rapid mapping of landslides is crucial to detect the extent of the affected area, including grade and type of damage. Rapid mapping is a key element of fast crisis response, e.g., to support rescue, humanitarian, and reconstruction operations in the crisis area [2]. Earth Observation (EO) based on satellite remote sensing plays a key role due to its fast response, wide field of view, and relatively low cost [3]. (Semi)-automatic landslide mapping based on satellite EO data provides an important information source to support field surveys. During rapid to very rapid events, i.e., deformation rates in the order of several meters per hour [4], access to the landslide area may be too difficult, making field surveys too dangerous [5] Earth Observation (EO) based on satellite remote sensing plays a key role due to its fast response, wide field of view, and relatively low cost [3]. (Semi)-automatic landslide mapping based on satellite EO data provides an important information source to support field surveys.

Methods

Results

Discussion

Conclusion