Ali P. Yunus

Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature optimization; and (3) detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87) demonstrates higher classification performance than the stand-alone OOIA (0.75) method for detecting landslides. The area under curve (AUC) value was also higher than that of the simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes.

Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner.

Automatic detection of sinkhole collapses at finer resolutions using a multi-component remote sensing approach

Abstract Sinkhole development is a typical geological disaster found in areas of carbonate bedrock. Compared with other geological disasters, sinkholes are considerably smaller and scattered according to scale and spatial distribution. Nevertheless, detecting and investigating sinkholes have become increasingly challenging. This study proposes a novel method by applying case-based reasoning (CBR) combined with object-based image analysis and genetic algorithms (GAs) to detect the sinkholes using high-resolution aerial images. This case study was performed in Paitan Town, Guangdong Province, China. The method comprises three major steps: (1) multi-image segmentation, (2) GA-based feature selection, and (3) application of CBR techniques. The detected sinkholes were categorized into three classes: buried, collapse type I, and collapse type II. The experiment demonstrated that the proposed method can obtain higher accuracy compared with the traditional supervised maximum likelihood classifier (MLC). The overall accuracy of CBR classification and MLC for the collapse area was 0.88 and 0.71, respectively. In addition, the kappa coefficient for CBR classification (0.81) was higher than that for MLC (0.5). A similar case library was also applied to another trial area for validation, the satisfactory results of which suggested that CBR is applicable for independently detecting sinkholes. The proposed method will be useful for preparing hazard maps that express the relative probability of a collapse in similar regions.

Geomorphic and lithologic control on bedrock channels in drainage basins of the Western Arabian Peninsula

Hypsometric curves, their integrals and channel profiles have long been used as effective indicators of stages in topographic evolution. This work presents the geomorphic and lithologic aspects of the drainage basins in the Western Arabian Peninsula by analysing basin hypsometry and channel longitudinal profile steepness and concavity indices in order to understand the erosion regimes. A total of 36 drainage basins in the region were analysed in a geographic information system (GIS) environment with a 30-m ASTER Global Digital Elevation Model. Hypsometric curves display shapes resembling from S-shaped to concave curves. Hypsometric integral (HI) shows generally low values in the study area and varies between 0.13 and 0.5, with a mean of 0.27. From the main set of 36 drainage basins defined, three sets of sub-basins were derived for each Strahler order ranging from 4 to 6. Their HI varied between 0.08 and 0.66 with an average HI of 0.33. Then, the HI for each basin order was plotted against the distance from the central location of each basin from south to north. Results reveal that basin hypsometry is independent of spatial variation and spatial scale. Also no clear relations were found between HI and basin area and shape. Further, HI and hypsometric curves were analysed in terms of lithological control on landforms. Results indicate that basin hypsometry is sensitive to lithological variation in the study area. At the largest scale, the HI values can be divided into two populations. More evenly distributed erosion in crystalline rocks and relatively uneven erosion in volcanic rocks are suggested from the results. Channel longitudinal profile steepness and concavity indices provide the distribution of anomalously steep channels and also reflect variations in the locus and rate of differential erosion. High steepness and low concavity in a basin underlain by volcanic rocks and a reverse trend in crystalline rocks confirm the observation from the results of basin hypsometric analysis.

Shoreline and Coastal Morphological Changes Induced by the 2004 Indian Ocean Tsunami in the Katchal Island, Andaman and Nicobar – A Study Using Archived Satellite Images

The December 24, 2004 Sumatra earthquake and Tsunami had caused large damage to the coastal environment in the Indian Ocean countries. Continuous monitoring of shorelines are needed to understand the causes and consequences of recent changes and to assess the long term impact of tsunami waves. Assessment of the shoreline and coastal morphological changes due to tsunami in Katchal Island have been lacking due to obstacles in the field data acquisition owing to their remote location. As access to the ground information is limited, the only possibility is the monitoring of shorelines from multi-temporal satellite images.

Uncertainties in Tidally Adjusted Estimates of Sea Level Rise Flooding (Bathtub Model) for the Greater London

Sea-level rise (SLR) from global warming may have severe consequences for coastal cities, particularly when combined with predicted increases in the strength of tidal surges. Predicting the regional impact of SLR flooding is strongly dependent on the modelling approach and accuracy of topographic data. Here, the areas under risk of sea water flooding for London boroughs were quantified based on the projected SLR scenarios reported in Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5) and UK climatic projections 2009 (UKCP09) using a tidally-adjusted bathtub modelling approach. Medium- to very high-resolution digital elevation models (DEMs) are used to evaluate inundation extents as well as uncertainties. Depending on the SLR scenario and DEMs used, it is estimated that 3%–8% of the area of Greater London could be inundated by 2100. The boroughs with the largest areas at risk of flooding are Newham, Southwark, and Greenwich. The differences in inundation areas estimated from a digital terrain model and a digital surface model are much greater than the root mean square error differences observed between the two data types, which may be attributed to processing levels. Flood models from SRTM data underestimate the inundation extent, so their results may not be reliable for constructing flood risk maps. This analysis provides a broad-scale estimate of the potential consequences of SLR and uncertainties in the DEM-based bathtub type flood inundation modelling for London boroughs.