1. Mapping Wildfire Disturbances in Southern California Using Machine Learning Algorithms John Rogan jrogan@clarku.edu Geography Department Clark University

2. Research Context Land cover/use change – Mapping and Monitoring Growing interest in Rapid Response Information Systems Empirical information about wildfire cause and behavior can inform wildfire risk analysis Paucity of historic burn severity information

3. Wildfire Monitoring Programs

4. Monitoring Fire Effects The physical environment and its response to fire Factors affecting fire behavior Ecosystem/watershed damage assessment Evaluating success of a management ignited fire Appraising the potential for future treatments

5. Existing Monitoring Efforts (BAER) Watershed Scale Source: California Department of Forestry and Fire Protection

6. Existing Monitoring Efforts Ecoregion Scale Source: California Department of Forestry and Fire Protection

7. A = Detected from satellite B = Conventional methods Existing Monitoring Efforts National Scale Source: Canadian Center for Remote Sensing

8. Existing Monitoring Efforts Global Scale Source: University of Maryland Global Fire Product

9. Research Objectives Test a new methodology to map fire severity in San Diego County (1985-2000) Employ machine learning to map severity, while integrating environmental variables with spectral variables (categorical and continuous) Examine the contribution of ancillary variables to burn map accuracy

10. Fire/Burn Severity Severity - A descriptive term that integrates various phenomenological characteristics of a fire-altered landscape Physical and biological manifestation of combustion on vegetation and soil Direct EffectsInfluenced by –Fuel consumption- Topography –Crown scorch- Disturbance history –Soil heating –Bole Charring

11. Previous Research Focus on mapping burn scars ( coarse resolution ) Recent emphasis on burn severity/mortality/damage levels ( fine-medium resolution ) for impact assessment Retrospective burn area mapping at medium resolution (e.g., Hudak and Brockett 2004—IJRS) BUT, challenges remain…………

12. Scene Model

13. Post-Fire IKONOS-2 Image

14. California Wildfire Threat Source: California Department of Forestry and Fire Protection

15. San Diego County

16. Study Area Significance Impacts of natural disturbance processes are increasing in severity Public lands began burning more frequently than private lands in the mid-1970s. This trend is increasing Population increase and peri-urban spread into fire-prone areas (WUI)

17. Landsat TM and ETM+ Data May August Signal DateMonth Time Since Last Fire 1985July3 1988August2 1990June1 1992June0 1996July0 1998July1 2000August0

18. Environmental Variables

19. Ground Reference Data SITE-Dominance Grassland(6) Chaparral(10) Conifer-Hardwood(5) Mixed(12) Composite Burn Index Key and Benson (2000)

20. Wildfire Effects (After Key and Benson)

21. Methods (Data Processing Flow)

22. (a)(b) Band 1 Band 2 Band 3 Bands 4,3,2 Haze Correction (Pre-)

23. (a)(b) Band 1 Band 2 Bands 4,3,2 Band 3 Haze Correction (Post-)

24. Spectral Mixture Analysis Decomposition of mixed pixel spectral response Production of fractional representation of sub- pixel proportions Biophysically-meaningful estimates of land cover components

25. Classification Tree Analysis A type of MLA used to predict membership of cases of a categorical dependent variable from their measurements on one or more predictor variables Hierarchical, non-linear recursive partitioning Structurally explicit

26. Desired Map Accuracy Source: Rogan and Franklin (2001)

27. Case Study (Pre-Fire)

28. Case Study – (Post-Fire)

29. Case Study Results - SMA ShadeGV BV Soil RMS Vegetation Map Hardwood Grassland Chaparral Conifer Scrub Fire perimeter

30. Case Study Results - Variable Selection Map ClassVariables Selected No Burn - VegetationGV, Soil, Veg No Burn - WaterShade, Slope No Burn - SoilSoil, Veg Severe BurnBV, Veg, Slope, Shade Moderate BurnBV, GV, Veg Light BurnGV, Soil, BV

31. Case Study Results – Burn Severity No burn – Vegetation90% No burn – Water100% Severe burn87% Moderate burn60% Light burn74% No burn – Bare Soil84% CLASS ACCURACY (kappa) 82.5%

32. County-Wide Results Variable Selection by Site(s) –GrassBurn, Soil Veg, Slope –CHPBurn, GV, Soil, Veg, Slope Veg, Slope –CON/HDWBurn, GV, Soil, Veg, Slope, Shade Slope, VegSlope, Aspect –Mixed Burn, GV, Slope, Veg, Shade, Slope, Aspect Mean Burn Map Accuracy by Site(s) –Grass87% (SD = 11%) –CHP81% (SD = 10%) –CON/HDW84% (SD = 7%) –Mixed 70% (SD = 16%)

33. County-Wide Results Time since fire (TSF) –Most problematic for grasslands, where TSF > 3 months –Least problematic for CHP, CON/HDW Map accuracy –Lowest for complex classes (e.g., mixed) –Highest for simple classes (e.g., grassland) Variable Selection –Many for complex classes (e.g., mixed) –Few for simple classes (e.g., grassland)

34. Implications Map accuracy –Range – 70-80%, depending on landscape type and TSF –Subtle burn classes are least accurate Variable Selection –Varied by landscape type (all used for complex areas) –Implication for fire risk mapping? –The larger the fire, the greater the potential for confusion caused by landscape heterogeneity

35. Wildland Fire Mapping Triangle Burn Severity Map Machine Learning Predictive Vegetation Modeling Image Processing and Enhancement “….search for standard methods for mapping fuels and fire regimes at high (spatial) resolutions over broad areas”. Rollins et al. (2004, p. 86)

36. Acknowledgements NASA Land Cover Land Use Change Program US Forest Service and CDF SDSU – Janet Franklin and Doug Stow UCSB – Dar Roberts and Alexandria Digital Library U of Arizona – Steve Yool