Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t a r t i c l e i n f o Recognition and classification of landslides is a critical requirement in pre-and post-disaster hazard analysis. This has been primarily done through field mapping or manual image interpretation. However, image interpretation can also be done semi-automatically by creating a routine in object-based classification using the spectral, spatial and morphometric properties of landslides, and by incorporating expert knowledge. This is a difficult task since a fresh landslide has spectral properties that are nearly identical to those of other natural objects, such as river sand and rocky outcrops, and they also do not have unique shapes. This paper investigates the use of a combination of spectral, shape and contextual information to detect landslides. The algorithm is tested with a 5.8 m multispectral data from Resourcesat-1 and a 10 m digital terrain model generated from 2.5 m Cartosat-1 imagery for an area in the rugged Himalayas in India. It uses objects derived from the segmentation of a multispectral image as classifying units for object-oriented analysis. Spectral information together with shape and morphometric characteristics was used initially to separate landslides from false positives. Objects recognised as landslides were subsequently classified based on material type and movement as debris slides, debris flows and rock slides, using adjacency and morphometric criteria. They were further classified for their failure mechanism using terrain curvature. The procedure was developed for a training catchment and then applied without further modification on an independent catchment. A total of five landslide types were detected by this method with 76.4% recognition and 69.1% classification accuracies. This method detects landslides relatively quickly, and hence has the potential to aid risk analysis, disaster management and decision making processes in the aftermath of an earthquake or an extreme rainfall event. Landslides are a major natural hazard, causing significant damage to properties, lives and engineering projects in all mountainous areas in the world. According to a recent world report, approximately four million people were affected by landslides in 2006 (OFDA/CRED, 2006). Landslide hazard and risk management begins with comprehensive landslide detection/mapping, which serves as a basis to understand their spatial …