Next Article in Journal
Next Article in Special Issue
Previous Article in Journal
Previous Article in Special Issue
Remote Sens. 2012, 4(5), 1310-1336; doi:10.3390/rs4051310
Article

A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories

1,* , 1
, 2,†
, 2,‡
, 2
 and 1
Received: 29 March 2012; in revised form: 27 April 2012 / Accepted: 28 April 2012 / Published: 7 May 2012
(This article belongs to the Special Issue Object-Based Image Analysis)
View Full-Text   |   Download PDF [8645 KB, uploaded 19 June 2014]
Abstract: Geoinformation derived from Earth observation (EO) plays a key role for detecting, analyzing and monitoring landslides to assist hazard and risk analysis. Within the framework of the EC-GMES-FP7 project SAFER (Services and Applications For Emergency Response) a semi-automated object-based approach for landslide detection and classification has been developed. The method was applied to a case study in North-Western Italy using SPOT-5 imagery and a digital elevation model (DEM), including its derivatives slope, aspect, curvature and plan curvature. For the classification in the object-based environment spectral, spatial and morphological properties as well as context information were used. In a first step, landslides were classified on a coarse segmentation level to separate them from other features with similar spectral characteristics. Thereafter, the classification was refined on a finer segmentation level, where two categories of mass movements were differentiated: flow-like landslides and other landslide types. In total, an area of 3.77 km² was detected as landslide-affected area, 1.68 km² were classified as flow-like landslides and 2.09 km² as other landslide types. The outcomes were compared to and validated by pre-existing landslide inventory data (IFFI and PAI) and an interpretation of PSI (Persistent Scatterer Interferometry) measures derived from ERS1/2, ENVISAT ASAR and RADARSAT-1 data. The spatial overlap of the detected landslides and existing landslide inventories revealed 44.8% (IFFI) and 50.4% (PAI), respectively. About 32% of the polygons identified through OBIA are covered by persistent scatterers data.
Keywords: object-based image analysis (OBIA); landslide mapping; persistent scatterers (PS); radar-interpretation; validation object-based image analysis (OBIA); landslide mapping; persistent scatterers (PS); radar-interpretation; validation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Hölbling, D.; Füreder, P.; Antolini, F.; Cigna, F.; Casagli, N.; Lang, S. A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories. Remote Sens. 2012, 4, 1310-1336.

AMA Style

Hölbling D, Füreder P, Antolini F, Cigna F, Casagli N, Lang S. A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories. Remote Sensing. 2012; 4(5):1310-1336.

Chicago/Turabian Style

Hölbling, Daniel; Füreder, Petra; Antolini, Francesco; Cigna, Francesca; Casagli, Nicola; Lang, Stefan. 2012. "A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories." Remote Sens. 4, no. 5: 1310-1336.


Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert