1. Remote Sensing and Urban Disaster Management Jie Chang Laurence Clinton 11/02/2006

2. One year ago  Hurricane Katrina devastated the Eastern Coast of the US  Alabama, Louisiana, Mississippi, Texas experienced property damages and loss of life

3. US Katrina Disaster Statistics  $81.2 billion in total damage  1,836 total deaths  80% of New Orleans flooded http://en.wikipedia.org/wiki/2005_Atlantic_hurricane_season

4. New Orleans: Before and After Katrina (IKONOS) Ikonos

5. When Another Hurricane Hits …

6. 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

7.  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

8. 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)

9. Feature Extraction Pixel Based Classification Object Based Building extraction Object Based Bridge extraction Pixel Based Object Oriented Approach Quickbird

10. 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

11. Third, Data Requirements  For Change Detection High spatial resolution (<5 meter) multi- spectral (VNIR) imageryHigh spatial resolution (<5 meter) multi- spectral (VNIR) imagery Before disaster imagery - updated yearlyBefore disaster imagery - updated yearly After disaster imagery - obtained < 5 days after hurricane hitsAfter disaster imagery - obtained < 5 days after hurricane hits  For Feature Extraction High spatial resolution (< 5 meter) multi- spectral resolution (VNIR) imageryHigh spatial resolution (< 5 meter) multi- spectral resolution (VNIR) imagery Alternatively, accurate Z-Value data obtained through active remote sensing sensorsAlternatively, accurate Z-Value data obtained through active remote sensing sensors Obtained within 5 days after hurricane hitsObtained within 5 days after hurricane hits

12. Remote Sensing Data Used in Urban Applications Data Vendor Spatial Resolution (meters) Swath Width SpectralResolution(micrometers) Temporal Resolution Accurate Z-Value Cost Meets Requirements IKONOS 1 m Pan, 4 m multi 11 KM.45 to.9 3 days No $59.00 / KM2 Yes QuickBird 0.61 m 20 - 40 KM.45 to.9 1 to 5 days No $41.00 / KM2 Yes OrbView 1m pan 4m multi 8 KM.45 to.9 3 days No $29.25 / KM2 Yes SPOT2.5m 60 KM 2 0.5 to 1.75 1 to 4 days Off Nadir, 22 revisit No $10,125 / 60KM2 Yes LandSat ETM+ 30 m RGBNIR / 60m TIR/ 15m pan 185 Km 0.45 to 0.9, 1.55 – 1.75, 10.4 -12.5 16 days No $425 / 180KM2 MAYBE FREE No ASTER 15m / 30m 60 KM 2 0.45 to 12.5 16 days No $80 granule / MAYBE FREE No Digital Ortho 12inch to 6 inch varies 6 inch Varies (flexible) No Varies – distributed between COGS etc. Yes RADAR / SAR Varies 50 km2 N/A 35 – 45 days No $4. 050 / 50 km2 No LIDAR +- 15cm variesN/A Varies (flexible) Yes Varies – distributed between COGS etc. Yes

13. Data that Meets Requirements Data Vendor Spatial Resolution (meters) Swath Width SpectralResolution(micrometers) Temporal Resolution Accurate Z-Value Cost Meets Requirements *IKONOS 1 m Pan, 4 m multi 11 KM.45 to.9 3 days No $59.00 / KM2 Yes QuickBird 0.61 m 20 - 40 KM.45 to.9 1 to 5 days No $41.00 /KM2 Yes *OrbView 1m pan 4m multi 8 KM.45 to.9 3 days No $29.25/ KM2 Yes SPOT2.5m 60 KM 2 0.5 to 1.75 1 to 4 days Off Nadir, 22 revisit No $10,125 /60KM2 Yes Digital Ortho RGB NIR varies 6 inch Varies (flexible) No Varies – distributed between COGS etc. Yes LIDAR +- 15cm variesN/A Varies (flexible) Yes between COGS etc. Varies – distributed between COGS etc. Yes *Orbimage (Orbview) acquired Space Imaging (IKONOS) http://www.space.com/spacenews/archive05/Orbimage_091905.html

14. High Spatial Resolution Imagery (QuickBird) http://www.digitalglobe.com/sample_imagery.shtml

15. High spatial resolution imagery (Orthophoto) http://www.image-america.com/katrina.htm

16. Lidar Data http://coastal.er.usgs.gov/lidar/ Technology ASCII data Results in ASCII LIDAR Data

17. Best Options Data Vendor Spatial Resolution (meters) Swath Width SpectralResolution(micrometers) Temporal Resolution Accurate Z- Value Cost Meets Requireme nts OrbView 1m pan 4m multi 8 KM.45 to.9 3 days No $29.25/ KM2 Yes Digital Ortho 12 inch to 6 inch varies 6 inch Varies (flexible) No Varies – distributed between COGS etc. Yes LIDAR +- 15cm variesN/A Varies (flexible) Yes Varies – distributed between COGS etc. Yes *Distinguishing factor was costs

18. Fourth Software Requirements  GIS Software ArcGIS has been employed in departmentArcGIS has been employed in department  Remote Sensing Software Basic Image ProcessingBasic Image Processing Change DetectionChange Detection Feature ExtractionFeature Extraction Lidar Processing CapabilityLidar Processing Capability Interoperability with ArcGISInteroperability with ArcGIS Low PriceLow Price NOTE: Since ArcGIS already has map processing capabilities it was not selected as a criteria selected as a criteria

19. Remote Sensing Software Assessment  ERDAS Imagine and ENVI Leading Remote Sensing image processing software Leading Remote Sensing image processing software ERDAS ImagineERDAS Imagine Highly integrated with Arc/Info data structure Highly integrated with Arc/Info data structure Powerful Radar and orthophoto processing capability with add-on modules Powerful Radar and orthophoto processing capability with add-on modules ENVIENVI Strong hyperspectral image processing capability Strong hyperspectral image processing capability Capable of processing Radar and orthophoto within its standard package Capable of processing Radar and orthophoto within its standard package  ArcGIS extensions: Image Analysis/Feature Analyst/Lidar Analyst Image processing within ArcGIS environment Image processing within ArcGIS environment

20. Remote Sensing Software Assessment (Cont.) Image AnalysisImage Analysis Simplified version of ERDAS Imagine with main image processing functions Simplified version of ERDAS Imagine with main image processing functions Change Detection Example

21. Remote Sensing Software Assessment (Cont.) Feature AnalystFeature Analyst Automatic extraction of features from satellite and aerial imagery based on object oriented classification Automatic extraction of features from satellite and aerial imagery based on object oriented classification linklinklink Example of Building Extraction From Digital Image

22. Remote Sensing Software Assessment (Cont.) Lidar AnalystLidar Analyst Powerful Lidar processing capabilities Powerful Lidar processing capabilities Automatic extraction of features from Lidar data Automatic extraction of features from Lidar data linklinklink Example of Building Extraction Using Lidar data

23. Comparison Matrix Software Application Basic Image ProcessingChangeDetection Feature Extraction Lidar Processing PriceArcGISInteroperability (1 to 5) ENVIYESYESNONO$8,4003 ENVI Spatial Feature Extraction Module NONOYESNO ? New 3 ERDAS Imagine YESYESNONO$7,7004 Image Analysis for ArcGIS YESYESNONO$1,0005 Feature Analyst for ERDAS / ArcGIS NOYESYESNO$3,3365 Lidar Analyst for ArcGIS NONOYESYES$3,3365 Software Requirements

24. Software Selection  Image Analysis for ArcGIS + Feature Analyst / or Lidar Analyst for ArcGIS Low CostsLow Costs Meets all software requirementsMeets all software requirements Same software platform (ArcGIS)Same software platform (ArcGIS)  Familiarity for users Extensions to ArcGISExtensions to ArcGIS  Interoperability with ArcGIS Vendor Basic Image ProcessingChangeDetection Feature Extraction Lidar Processing PriceArcGISInteroperability (1 to 5) Image Analysis for ArcGIS YESYESNONO$1,0005 Feature Analyst for ArcGIS NOYESYESNO$3,3365 Lidar Analyst for ArcGIS NONOYESYES$3,3365

25. Overall Recommendations  For Change Detection Data: Orthophoto/OrbviewData: Orthophoto/Orbview Software: Image Analysis for ArcGIS and Feature Analyst for ArcGISSoftware: Image Analysis for ArcGIS and Feature Analyst for ArcGIS  For Feature Extraction Data: Orthophoto/Orbview or Lidar DataData: Orthophoto/Orbview or Lidar Data Software: Image Analysis with Feature Analyst using Orthophoto/Orbview or Lidar Analyst with Lidar dataSoftware: Image Analysis with Feature Analyst using Orthophoto/Orbview or Lidar Analyst with Lidar data  Selection of combination depends on data availability If we have access to Lidar data plus digital imagesIf we have access to Lidar data plus digital images  Image Analysis for ArcGIS + Lidar Analyst If we have access to only digital images then:If we have access to only digital images then:  Image Analysis for ArcGIS + Feature Analyst

26. Wave of the Future Urban AnalystUrban Analyst Urban application Urban application Seamless integration of Feature Analyst and Lidar Analyst Seamless integration of Feature Analyst and Lidar Analyst Scheduled to be released in November 2006 Scheduled to be released in November 2006 Urban Analyst can deliver advanced analytical capabilities for disaster-stricken area such as New Orleans http://www.featureanalyst.com/assets/images/UA_ad01.pdf

27. References  Adams, Beverley, et al (2006). Collecting Data for a Multi-Hazard Situation, Geospatial Solutions, p. 11.  Apline et al (1999) per-field classification of land use using the forthcoming very fine spatial resolution satellite sensors: problems and potential solutions. Advances in remote sensing and GIS Analysis. John Wiley & Sons, Chichester, p. 219 -239  Dash, Steinle, Singh, Bahr (2004). Automatic building extraction from laser scanning data; an input tool for disaster management. Advances in Space Research 33  Schiew, J., Tufte, L. and Ehlers, M. (2001). Potential and problems of multi-scale segmentation methods in remote sensing, Proceedings of GIS-Zeitschrift fur Geoinformationsystemse 6/2001 pp.34-39