Advanced Image Analysis in Disaster Response

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1 Advanced Image Analysis in Disaster Response Creating Geographic Knowledge Thomas Harris ITT The information contained in this document pertains to software products and services that are subject to the controls of the Export Administration Regulations (EAR). All products and generic services described have been classified as EAR99 under U.S. Export Control laws and regulations, and may be re-transferred to any destination other than those expressly prohibited by U.S. laws and regulations. The recipient is responsible for ensuring compliance to all applicable U.S. Export Control laws and regulations.

2 Imagery in the GIS: It Is About Creating Richer Geospatial Information A Framework for Understanding and Managing Our Earth Imagery, along with tools like ENVI, support the rapid and timely y creation of Geographic Knowledge Geographic Knowledge Creating Measuring Organizing Analyzing Modeling Applying Planning Designing Deciding Managing Acting Multi-layered Multi-Source Multi-Dimensional Greater Accuracy More Timely Widely Accessible Analytic Visual 2

3 Imagery and Natural Disasters: Applications in Response, Recovery and Impact Analysis In the immediate aftermath of Hurricane Katrina, and the 2004 Indian Ocean Tsunami, remote sensing technology played a key role in assessing urban damage caused by windstorm, flood and storm surge. Assessments were done at the broad area level, region of interest level and per structure level. Adams (ImageCat UK), Womble (Texas Tech), Ghosh (ImageCat US),Friedland (Louisiana State University) 3

4 Aiding in Rapid First Response When natural disasters occur, such as the 2004 tsunami in the Indian Ocean, relief and recovery efforts need to be swift. Using imagery enabled rescuers to cover a wide area very rapidly. 4

5 Real Time Needs Required Rapid, Repeatable Analysis Using before and after imagery with automated workflows designed to quickly produce change maps, analysts were able to map the extent and location of damages zones. These maps were of tremendous aid to first responders looking for survivors 5

6 Integration With The GIS Identified changes were automatically extracted as vectors for further analysis in GIS applications to understand how damage occurred relative to population centers, agricultural zones and transportation routes. 6

7 Visual (Qualitative) Analysis With ArcGIS Assessment of flooded areas: Used available LiDAR and Quickbird data Analyzed city and river elevations to predict flooding Extracted house locations to outlines to aid in rescue efforts New Orleans, LA: Hurricane Katrina floods the city 7

8 Quantitative Analysis With ArcGIS The extracted coastal damage assessment information, along with the wide array of attribute data extracted from the imagery, was then available to GIS users in their applications to quantify and classify the extent of the damage. 8

9 Qualitative and Quantitative Analysis Combined Further analysis of imagery derived information within ArcGIS: View extracted building outlines and locations Plan evacuation routes Identified most distressed or flooded areas requiring immediate assistance Highlighted areas show buildings and flooded areas below 10 feet 9

10 Integrated Data Sharing For Maximum Efficiency & Progressively Richer Geographic Context The imagery analysis results were also written directly to central geodatabases for sharing with other users across the enterprise. This helps reduce redundant analyses and provides more a priori information for subsequent analysis based on updated imagery. 10

11 Creating A Multilayered Data Set From Imagery Analysts were ultimately able to develop a content rich, multidimensional view of the impacted geography. 11

12 Agricultural Analysis: Ensuring Sustainable Food Supplies Across the Globe The Food and Agriculture Organization of the UN focuses on the reduction of food insecurity in the world. The FAO uses imagery to monitor crops and vegetation; analyze and predict drought conditions and analyze soils to plan crop rotations to enhance yields. Penn State University is Conducting Research on Applications of Land Remote Sensing to Human Welfare Remotely sensed land use and land cover data could profoundly influence decisions on human welfare, such as those related to subsistence and precision agricultural techniques, and related environmental variables such as biodiversity. Satellite data has the potential to reveal human, agricultural, and environmental interactions on a range of scales. 12

13 Factors Influencing Sustainable Food Supplies Weather phenomenon Climate change Biodiversity Geo Political factors Education demographics Economic factors 13

14 A Recent Example: Cyclone Nargis in Myanmar May 2008 Data from the MODIS and Landsat satellites was used to produce a map of the cyclone-damaged rice production regions of Myanmar. 14

15 Cyclone Nargis After the storm, the US Foreign Agricultural Service used satellite imagery to create maps of the effected areas and produce a series of commodity intelligence reports to provide updated forecasts for global crop conditions and yields. Satellite imagery obtained from NASA s moderate-resolution imaging spectroradiometer (MODIS) satellite was used to delineate the post-cyclone flooding region. Combined with rice land-cover classification data from the Landsat satellite, and the tools in ArcGIS, maps of the damaged rice production regions of Myanmar were easily created. 15

16 Attribute Based Classification for Agriculture Image pixels are classified and mapped according to crop types based on their similarities in their spatial, textural or spectral attributes. 16

17 Attribute Based Classification for Agriculture Results are then exported to ArcGIS so that further analytic, cartographic or geo-processing models can be applied to the data. 17

18 Conclusions Image analysis and GIS software used together can provide rich information for both qualitative and quantitative analysis. Users can not only visualize the effects of natural disasters but use the imagery derived data to perform advanced analytics. The rapidly growing availability of imagery and the growing ease of use in image analysis tools makes it much easier for GIS users to more regularly update their geodatabases. This in turn gives analysts much richer and current data with which to make decisions when events such as Katrina, the Indian Ocean tsunami or Cylcone Nargis occur. It also aid greatly in the speed of recovery and reconstruction efforts as the data is much more current and much more accurate. 18

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