abstracts
Bangkok, the capital city of Thailand, is one of the major cities in Asia and is a regional hub. The city has a high economic growth and every year many new constructions take place. It is situated on the low flat plain of Chao Phraya River, which extends to the Gulf of Thailand. Floods are the most frequent natural disasters in Bangkok affecting large number of population and causing huge economic damage every year. Although Bangkok is located in low seismic hazard area, there is a potential risk from distant earthquakes, due to the ability of underlying soft clay, to amplify ground motions (Warnitchai et al., 2000).

Disaster risk analysis is important not only to estimate the losses from future events but also to make recommendations for prevention, preparedness and response. Building inventories are essential for all types of disaster risk analysis models. With a slight difference in characterization of building types, all models require an estimate of number of buildings or total square footage (Eguchi et al., 2000). Land use information is very important for disaster risk analysis in urban areas. Traditional land surveying methods, such as field surveys, aerial photography, etc. are costly and time consuming. There is no single reliable source, which can be used for developing a unique database for building and infrastructures located in an urban area. Padermkul (1999) developed an inventory methodology for Bangkok metropolitan area using the multiple data sources. However, these sources contain old data and these files are not updated regularly. The data have been recorded for some specific period and data for other periods are not available. Some of the sources keep data only for some specific category of buildings. Another main problem is with the building type classification system. Such as, the building classification system of Department of Public Works does not match with that of Department of Policy and Planning. Padermkul (1999) used interpolation and extrapolation techniques to find the missing data, which does not reflect the actual feature of the region.

In order to rapidly derive detailed land use information in broad areas, it is necessary to use remote sensing techniques. Within these few years fine spatial resolution satellite imagery has become widely available. Such as the QuickBird satellite imagery and several new satellite sensors being developed are capable of generating imagery with spatial resolutions as fine as 0.6m in panchromatic mode and 2.8m in multi spectral mode. Many details such as buildings, roads, and other component elements of urban structures can be clearly identified from these high-resolution satellite images and this has opened a new window for urban land use information studies. A few studies, in the past years, were done to use satellite imagery to develop building and other infrastructures inventories for the selected areas. Yamazaki et al. (2000) investigated the capability of developing building inventory used for seismic risk analysis using satellite images from LANDSAT, IRS, JERS-1, ADEOS and IKONOS. They used principal component analysis and found this method as a possible solution to classify urban structures. However, Sande et al. (2003) proposed a segmentation and classification approach for IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment using object oriented image analysis technique.

The paper presents the methodology for development of an up-to-date building inventory using information obtained from remote sensing data analysis and existing databases. The main objectives of the study were; i) analysis of very high-resolution satellite remote sensing data for deriving urban building features, and ii) development of an inventory for buildings for an urban area for disaster risk analysis.