MAPPING OF HIGHLY HETEROGENEOUS URBAN STRUCTURE TYPE FOR FLOOD VULNERABILITY ASSESSMENT

Vulnerability plays an important role in risk assessment. For flood vulnerability assessment, the map and characteristics of elements-at-risk at different scales are strongly required depending on the risk and vulnerability assessment requirements. This study proposes a methodology to classify urban structure type by combining object-based image classification and different high resolution remote sensing data. In this study, a high resolution satellite image and LiDAR have been acquired over Kota Bharu, Kelantan which consists of highly heterogeneous urban structure type (UST) classes. The first stage is data pre-processing that includes orthorectification and pansharpening of Geoeye satellite image, image resampling for normalised Digital Surface Model (nDSM) and followed by image segmentation for creating meaningful objects. The second stage comprises of derivation of image features, generation of training and testing datasets, and classification of UST. The classification was based on three types of machine learning classifiers, i.e. Random Forest (RF), Support Vector Machine (SVM) and Classification and Regression Tree (CART). The results obtained from the classification processes were compared using individual omission and commission error, overcall accuracy and Kappa coefficient. The results show that Random Forest classifier with all image features achieved the highest overall accuracy (93.5%) and Kappa coefficient (0.94). This is followed by CART classifier with overall accuracy of 93.7% and Kappa coefficient of 0.92. Finally, SVM classifier produced the lowest overall accuracy and Kappa coefficient with 88.6% and 0.86, respectively. The UST classification result can be further used to assist detailed building characterisation for large scale flood vulnerability assessment

An indicator-based approach for micro-scale assessment of physical flood vulnerability of individual buildings

The current trends of floods event in many countries are alarming. Hence, managing flood and the associated risk are crucial in order to reduce the loss and to be well prepared for the combined impact of urbanization and climate changes. The best approach to manage flood activities is a risk-based approach, where the vulnerability of elements at risk is reduced to a minimum. There is a significant number of studies that use an indicator-based approach for flood vulnerability assessment with focus on the macro-scale. However, this paper assesses physical flood vulnerability of buildings at micro-scale using an indicator-based method in Kota Bharu, Malaysia. The region is one of the most flood affected regions in Malaysia. Micro-scale vulnerability assessment considers damages for individual buildings at risk, rather than in aggraded manner. In this study, the methodology adopted involve the use of 1D-2D SOBEK flood modelling, the selection and weightage of indicators, development of spatial based building index and, production of building vulnerability maps. The findings demonstrate the physical pattern of flood vulnerability of buildings at a micro-scale. The approach can assist in flood management planning and risk mitigation at a local scale