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

Abstract

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.

Introduction

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 and temporal occurrences (Carrara and Merenda, 1976, Guzzetti et al., 2000, Brardinoni et al., 2003). Detection of landslides includes recognition and classification (Mantovani et al., 1996), frequently done using the systematic classification of landslides based on type of material and type of movement proposed by Varnes (1978). In Varnes' classification, the types of material are rock, debris and earth, with falls, topples, slides, spreads and flows constituting movement types (Cruden and Varnes, 1996). The classification proposed by Varnes, and consistent with the UNESCO Working Party on the World Landslide Inventory (UNESCO-WP/WLI, 1993), is essentially a field based method, conceptualised and illustrated using block diagrams, without reference to their surrounding morphometry and contextual relationship. However, Earth observation data are increasingly used for landslide mapping, with automatic methods being preferable over manual approaches for obtaining quicker results over a large area, whereby the use of spectral, spatial, morphometric and contextual properties is essential to their success (Barlow et al., 2006, Borghuis et al., 2007). A comprehensive characterisation of landslides from an automatic detection perspective is required for the extraction of fast and accurate results that will help decision makers in implementing disaster management strategies.

Visual interpretation of aerial photographs, combined with field investigations, remained the major source for landslide inventory map preparation until recently (Kääb, 2002, Casson et al., 2003, van Westen and Lulie Getahun, 2003). Although aerial photographs accurately depict details of a landslide, they are often not available in a timely manner for the majority of landslide prone areas in the world. Satellite imagery has become an alternative data source since it allows a more economic assessment of larger landslide affected areas, as well as a synoptic appreciation of the context within which landslides occur, especially in terms of land cover dynamics. Limited initially by low spatial resolution, early studies focused on pure detection of large landslides. However, recent studies have increasingly made use of very high resolution imagery (e.g. QuickBird, Ikonos, WorldView-1, Cartosat-1 and 2, SPOT-5 and ALOS-PRISM) for landslide mapping, and the number of operational sensors with similar characteristics is growing year by year (van Westen et al., 2008). Other remote sensing approaches of landslide inventory mapping, though infrequent, include shaded relief images produced from Light Detection and Ranging (LiDAR) and Synthetic Aperture Radar (SAR) interferometry based digital elevation models (DEMs) (Singhroy et al., 1998, Van Den Eeckhaut et al., 2007).

Preparation of landslide inventory maps using automatic methods has been attempted by previous researchers. Borghuis et al. (2007) showed how unsupervised classification could detect 63% of all landslides mapped manually. Other familiar automatic methods of landslide mapping are change detection and image fusion. Nichol and Wong (2005) showed the use of change detection technique to successfully differentiate landslides from spectrally similar features such as bare rock and soil. However, the automatic methods described above are pixel-based methods, and pixels are ill-suited to represent a geomorphic process such as a landslide. Therefore, the output gives ‘salt and pepper’ appearance, and are mostly not verifiable on the ground. These methods also rely only on the spectral signature, a property not unique for landslides. In addition to spectral signature, landslide diagnostic features can include vegetation, slope angle, slope morphology, drainage, tension cracks, presence of man-made features such as retaining walls, or artificial surface drainage. Previous researchers have attempted to quantify some of these landslide diagnostic features. Pike (1988) calculated the geometric signature from a DEM for a set of topographic variables that separates a landslide from its surroundings. Similarly, Iwahashi and Pike (2007) used slope gradient, terrain texture and local convexity derived from a DEM for automatic classification of topography. Previous works have also shown that an integration of remote sensing data and DEM derivatives produces a better result than the standalone approach (McDermid and Franklin, 1994, Florinsky, 1998). Object-oriented analysis (OOA), a platform for integration of different types of data (spectral, elevation and thematic), has already proven its ability for successful automatic classification of landforms (Dragut and Blaschke, 2006, van Asselen and Seijmonsbergen, 2006). It has a potential to detect landslides automatically in a better way than the pixel-based methods, by incorporating a multitude of landslide diagnostic features.

Object-oriented image classification is a knowledge driven method, whereby spectral, morphometric and contextual landslide diagnostic features can be integrated based on expert knowledge to accurately detect landslides (Barlow et al., 2003, Barlow et al., 2006). Since landslides occur in diverse geomorphic settings, it is crucial to address a landslide as an object embedded in its surroundings. Image segmentation, a mandatory step prior to OOA does this by grouping spectrally homogenous pixels into an object (Baatz and Schape, 2000). The significant advantage of OOA is the realistic outputs that can be easily verified on the ground. However, to make effective use of OOA, we need a comprehensive understanding of all potentially useful landslide characteristics, and specifically from a segmentation-based perspective. We also need to update and synthesize the criteria for the detection of landslides as per Varnes' classification scheme, using newer means of landslide inventory preparation, such as high resolution satellite data and DEMs. There have been limited attempts to detect landslides using OOA (e.g. Barlow et al., 2006). However, while they differentiated landslide types such as debris slides, debris flows and rock slides using OOA, their characterisation of different landslide types is essentially data driven by considering a very limited set of parameters. In another recent study, Moine et al. (2009) used shape, spectral, texture and neighbourhood features, but no morphometric parameters, to detect landslides from aerial and satellite images using OOA. This clearly shows that the potential of OOA for automatic landslide detection has so far not been fully exploited. Using geomorphometry tools implemented in modern GIS softwares, and with the possibility of extracting many spectral, spatial and some morphometric parameters in image processing softwares, landslide characterisation can be done efficiently in comparison to tools available to previous researchers (e.g. Pike, 1988, McDermid and Franklin, 1994, Barlow et al., 2006), also creating possibilities for less data driven approaches.

The purpose of this paper is to update and synthesize the diagnostic features for semi-automatic detection (recognition and classification) of landslides, to provide an effective basis for researchers to develop object-based landslide mapping routines. The potential of spectral landslide diagnostic features such as normalised difference vegetation index (NDVI), shape features such as length/width ratios, asymmetry, texture, and morphometric features such as slope, terrain curvature and flow direction, derived from high resolution satellite data and a DEM, respectively, is discussed in this paper. OOA is effectively a combination of segmentation to derive image primitives, and their subsequent classification based on characteristics calculated from the extracted objects. This paper focuses primarily on object classification. In a separate study we address the segmentation and achievement of complex landslide shapes. Segmentation and extraction of spectral and texture characteristics were carried out using Definiens Developer software, while ArcGIS was used to derive additional morphometric indices. A complex analysis routine was then built in Definiens Developer to test how well all available spectral, textural, morphometric and contextual information can be used to detect landslides unambiguously. We test this routine in part of the High Himalayas that suffers extensively from landslides, and where efficient remote sensing data based techniques provide a real potential for improved landslide hazard and risk analysis.