Remote Sensing and Urban Disaster Management Jie Chang Laurence Clinton 11/02/2006 One year ago Hurricane Katrina devastated the Eastern Coast of the US Alabama, Louisiana, Mississippi, Texas experienced property damages and loss of life US Katrina Disaster Statistics $81.2 billion in total damage 1,836 total deaths 80% of New Orleans flooded New Orleans: Before and After Katrina (IKONOS) Ikonos When Another Hurricane Hits What Can We Do? We are GIS analysts with the New Orleans GIS department responsible for: 1.Providing government bodies a quick and accurate assessment of damaged or affected areas 2.Providing Emergency and Rescue Units information regarding access points to affected areas Our GIS department needs to select the most appropriate satellite data, methodology and software to meet objectives So, a technological assessment is needed on both data and software requirements First, Technology Development Review Remote Sensing technology has been widely used for disaster management due to its ability to quickly, and accurately obtain spatial data over large areas at low costs (Jenson 2006). Feature extraction based on traditional pixel-based classification utilizing only spectral information from high resolution images resulted in less satisfactory results in urban areas (Schiewe et al 2001) 1)Was not effective in distinguishing objects with similar reflectance, e.g. streets and buildings reflectance, e.g. streets and buildings 2) Produced too much noise in classification results due to spectral 2) Produced too much noise in classification results due to spectral variability of urban areas variability of urban areas Feature extraction based on object-based classification, utilizing both spectral and spatial information, improved accuracy and interpretability of high resolution images (Aplin et al 1999) Lidar data, alternatively, has been applied in urban disaster management for accurate z-value obtainment and feature extraction. (Dash 2004, Steinle et al. 2001) Feature Extraction Pixel Based Classification Object Based Building extraction Object Based Bridge extraction Pixel Based Object Oriented Approach Quickbird Second, Functional Analysis Approach Functions Disaster ManagementDisaster Management Process Process Application Application Estimate urban development affected by flood Change Detection Determination of roadway accessibility after disaster Feature Extraction Development of thematic maps Thematic Map Production Third, Data Requirements For Change Detection High spatial resolution (