PhD Defence Mr Tapas Ranjan Martha
Dept. of Earth Systems Analysis
Title of Defence
Detection of Landslides by Object-Oriented Image Analysis
A comprehensive landslide inventory is a prerequisite for planning immediate landslide disaster response, and for quantitative landslide hazard and risk assessment. Interpretation of remote sensing data (aerial photographs and satellite images) and field mapping have traditionally been the most widely used techniques for generating landslide inventories. Several research attempts have been made to automate this process in order to save time. Since pixel-based methods have not produced sufficiently accurate results for detection and classification of landslides, object-oriented analysis (OOA) which imitates the human interpretation process in identification of landslides, has emerged as a good alternative recently due to the inherent ability of OOA to incorporate additional information layers such as digital terrain models (DTMs) and thematic maps in the analysis. Furthermore, as landslides are geomorphic processes, their characterisation in different types, following a classification such as the one by Varnes (1984) mostly relies on contextual criteria, which can best be described by objects obtained from segmenting the digital image into spatially cohesive regions / objects rather than pixel values.
This research outlines the development of semi-automatic image analysis algorithms that combine spectral, shape, texture, morphometric and contextual information derived from high resolution satellite data and DTMs for the preparation of new as well as historical landslide inventories. The main innovative aspect of the research lies in the selection of landslide diagnostic parameters and their use in the comprehensive characterisation of different types of landslides, a concept which is addressed for the first time for detection of landslides in an object-based environment.
DTM accuracy is an important factor since its morphometric derivatives, such as terrain curvature, slope, and flow direction, contribute to the successful detection and classification of landslides. New generation Cartosat-1 alongtrack stereoscopic data, which are provided with RPCs for block triangulation, were used to create a digital surface model (DSM) with 10 m grid size. Alongtrack satellite data have advantages for DSM generation, due to improved correlation between image pairs and high B/H ratio. However, difficulties arise in very steep areas, particularly in valleys oriented across the satellite track direction. Use of control points obtained from DGPS survey improved the absolute accuracy and resulted in vertical and planimetric RMS errors of 2.31 and <1 m, respectively, which are acceptable given the spatial resolution of Cartosat-1 only 2.5 m. However, for deriving accurate morphometric information, spatial accuracy is more important than the absolute accuracy. Therefore, drainage lines were used as a proxy to measure the spatial accuracy of the DSM, which showed that for valleys perpendicular to the satellite track, the DSM extracted from a low sun elevation angle data had 45 % higher spatial accuracy than the DSM extracted from high sun elevation angle data. However, in other areas these data proved to be a good source for elevation information. The research showed that sun elevation angle and local valley orientation can have a pronounced effect on the accuracy of a DSM. Conversion of a DSM to a DTM is necessary for calculating landslide volume and terrain morphometric parameters. This was done by subtracting vegetation height from the DSM. The landslide volume extracted from pre- and post-landslide DTMs without control points matched well with volume extracted from the DTMs with control points, indicating that a field survey for control points is not a strict requirement. It also showed that landslide volume information can be derived only with RPCs, if both pre- and post-image pairs can be brought into the same relative reference framework.
A set of approaches was developed that exploit the object properties extracted using a region growing segmentation of multispectral Resourcesat- 1 LISS-IV Mx (5.8 m) image. Okhimath town, an area in the rugged Indian Himalayas frequently affected by landslides, was selected for developing the methodology. Landslides were characterised from an object-based detection perspective, and an algorithm comprising 45 individual routines, such as controlled segmentation, merging and classification was developed using eCognition software, which detected 42 major and minor landslides in an 80 km2 area. The algorithm, consisting of three sub-modules, initially extracts landslide candidates using an NDVI threshold, and subsequently false positives were eliminated from the landslide candidates using spectral, texture, shape and contextual criteria. Landslide classification was done using terrain curvature and contextual criteria, and five different types of landslides were identified. The object-based classification when compared with a landslide inventory map prepared by stereoscopic photo-interpretation and detailed field check resulted in a detection accuracy of 76.4%, while 69.1% of the landslides were correctly classified in different landslide types. The results are considered to be good, since landslides are detected in an area dominated by false positives such as rocky barren land, uncultivated agricultural terraces and river sands. The minimum landslide size detected by the method was 774 m2, which indicates that the algorithm is not sensitive to sizes.
The algorithm developed required user defined segmentation criteria to control the object size, which was considered a drawback in applying a fast and generic method for landslide detection and classification. Therefore, an objective method to optimise segments was developed subsequently. Using spatial autocorrelation and intrasegment variance, a new plateau objective function (POF) was developed, which was used to determine the segmentation criteria for multi-scale analysis, essential for the detection of landslides and elimination of false positives. Another drawback of the originally developed algorithm was the use of manual thresholds for the elimination of false positives from landslide candidates. This was adjusted using a K-means clustering method. The improved algorithm, comprising of four sub-modules, resulted in a detection accuracy of 76.9% for the training area and 77.7% accuracy for a geomorphologically distinct validation area. It not only increased the accuracy of detection but also reduced the overall error of commission. The objective determination of the scale factor and unsupervised selection of thresholds for landslide diagnostic parameters made an important contribution for making this method transferable to other areas.
In the previous algorithm, NDVI derived from multispectral satellite data was used in landslide detection. However, in several cases, particularly while preparing a historical landslide inventory from archived high resolution images, only panchromatic data are available. To use these data, a third algorithm, which is a modified version of the second one, was developed using a brightness threshold instead of NDVI to extract landslide candidates. Local thresholds using contextual criteria show better results than global thresholds, and allowed to identify small translational landslides within barren rocky land that are generally bright. To eliminate false positives, more texture measures, such as GLCM homogeneity and standard deviation, were used along with shape and contextual criteria. Finally, a multi-temporal annual landslide inventory for 13 years was prepared and used for the generation of a landslide susceptibility map with the help of a bivariate model (weights of evidence). The spatial probability was determined from the density of landslide for each observation period within each susceptibility class, and the temporal probability was calculated using a Gumbel frequency distribution analysis. These data were used together with the susceptibility map and a road and building map to produce a risk curve for the Okhimath area, which indicates the likely loss due to future landslide occurrences. The final algorithm for the detection of landslides, developed in this study is generic and requires two primary inputs (a satellite image and a DTM), while a priori knowledge about the terrain is not mandatory. The semi-automatic approach is flexible enough to address the spatial and spectral variability of landslides and false positives. The knowledge-based method shows considerable improvement over previous pixel- and object-based methods of landslide detection in terms of the location, size and type of landslide. The method has increased the potential to rapidly generate event-based landslide inventories after major triggering events, within a short period of time, and without fieldwork. The method developed in this research has proven its value in several areas in the Indian Himalayas and could potentially contribute to the rapid detection of landslides in other susceptible areas.
Tapas Ranjan Martha was born on 05 August 1977 in Begunia, Khurda district, Odisha state, India. He is a gold medalist from the Khallikote (Autonomous) college, Berhampur, Odisha, India, where he completed his bachelor degree in Geology (Hons.) during 1993 to 1996 period. He worked on his MSc. Tech degree in applied geology from 1996 to 1999 at the Indian School of Mines (ISM), Dhanbad, India (www.ismdhanbad.ac.in), and continued there as a researcher from 1999 to 2001. In 2001 he joined the National Remote Sensing Centre (NRSC) of the Indian Space Research Organisation (ISRO), Department of Space, Government of India (www.nrsc.gov.in) in Hyderabad, India as a scientist. He has worked mainly on content extraction from satellite images and aerial photographs, particularly those useful for remote-sensing-based geological applications and subsequent integration of those layers in geographic information systems for several geotechnical, mineral and oil exploration, and geo-environmental projects. He has contributed to landslide hazard zonation (LHZ) program of ISRO. Now he is involved in the National Geomorphological and Lineament mapping project (NGM), which aims at preparation of geomorphological and lineament maps of India on 1:50,000 scale. In 2007, he was deputed to ITC, The Netherlands to carry out advanced research on rapid detection of landslides from Earth Observation (EO) data under a GSI-NRSC-ITC joint agreement.
Martha, T.R., Jetten, V.G. (Promotor) , van Westen, C.J. (assistant promotor) and Kerle, N. (assistant promotor) (2011) Detection of landslides by object - oriented image analysis : e-book. PhD thesis University of Twente; summaries in Dutch and English. ITC Dissertation 189, ISBN: 978-90-6164-309-8.
|Event starts:||Monday 04 July 2011 at 13:30|
|City where event takes place:||Enschede|
|Country where event takes place:||Netherlands|