Integrating satellite imagery with simulation modeling to improve burn severity mapping

Environmental Management

Volumn 54, Issue 1, 2014, Pages 98-111

  Karau, Eva C ^a   Sikkink, Pamela G ^a   Keane, Robert E ^a   Dillon, Gregory K ^a  

^a ROCKY MOUNTAIN RESEARCH STATION   (United States)

Author keywords

Burn severity mapping; Fire effects; Modeling; RdNBR

Indexed keywords

COMPUTER SIMULATION; FIRES; MAPPING; MODELS; RISK ASSESSMENT;

BURN SEVERITY; COMPOSITE BURN INDICES; FIRE EFFECT; IMAGE-BASED METHODS; INTEGRATED MODELING; RDNBR; SIMULATION-BASED MODELS; STATISTICAL MODELING;

SATELLITE IMAGERY;

ACCURACY ASSESSMENT; BURNING; ENVIRONMENTAL DISTURBANCE; FIRE MANAGEMENT; FOREST FIRE; INDEX METHOD; MAPPING METHOD; NUMERICAL MODEL; SATELLITE IMAGERY;

ARTICLE; COMPOSITE BURN INDEX; CONTROLLED STUDY; ECOSYSTEM FIRE HISTORY; FIRE; FOREST; HAZARD RATIO; IMAGE PROCESSING; LANDSCAPE; MEASUREMENT ACCURACY; RATING SCALE; RELATIVE DIFFERENCED NORMALIZED BURN RATIO; RESPONSE VARIABLE; RISK ASSESSMENT; SATELLITE IMAGERY; SIMULATION; STATISTICAL MODEL; UNITED STATES; VEGETATION;

MONTANA; UNITED STATES; WASHINGTON [UNITED STATES];

COMPUTER SIMULATION; FIRES; GEOGRAPHIC MAPPING; MODELS, THEORETICAL; MONTANA; SATELLITE IMAGERY; WASHINGTON;

EID: 84902551871     PISSN: 0364152X     EISSN: 14321009     Source Type: Journal    
DOI: 10.1007/s00267-014-0279-x     Document Type: Article

References (50)

1. Anderson HE (1982) Aids to determining fuel models for estimating fire behavior. General Technical Report INT-122. U.S. Department of Agriculture, Intermountain Forest and Range Experimental Station, Ogden, UT
2. Bradshaw LS, Tirmenstein D (2010) FireFamilyPlus users guide, version 4.1 (Draft). In: U.S. Department of Agriculture Forest Service, Fire Sciences Laboratory, Missoula
3. Cansler CA (2011) Drivers of burn severity in the northern Cascade Range, Washington. Master of Science, University of Washington, Washington
4. Cansler CA, McKenzie D (2012) How robust are burn severity indices when applied in a new region? Evaluation of alternate field-based and remote-sensing methods. Remote Sens 4:456-483
5. Carlson JD, Bradshaw LS, Nelson RM, Bensch RR, Jabrzemski R (2007) Application of the Nelson model to four timelag fuel classes using Oklahoma field observations: model evaluation and comparison with National Fire Danger Rating System algorithms. Int J Wildland Fire 16:204-216 (Pubitemid 46697878)
6. Chuvieco E, Riano D, Danson FM, Martin P (2006) Use of a radiative transfer model to simulate the postfire spectral response to burn severity. J Geophys Res 111:G04S09
7. Cocke AE, Fule PZ, Crouse JE (2005) Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data. Int J Wildland Fire 14:189-198 (Pubitemid 40938624)
8. Collins BM, Kelly M, van Wagtendonk JW, Stephens SL (2007) Spatial patterns of large natural fires in Sierra Nevada wilderness areas. Landsc Ecol 22:545-557 (Pubitemid 46731561)
9. Congalton RG, Green K (1999) Assessing the accuracy of remotely sensed data: principles and practices. CRC/Lewis Press, New York
10. De Santis A, Chuvieco E, Vaughan PJ (2009) Short-term assessment of burn severity using the inversion of PROSPECT and GeoSail models. Remote Sens Environ 113:126-136
11. De Santis A, Asner GP, Vaughan PJ, Knapp DE (2010) Mapping burn severity and burning efficiency in California using simulation models and Landsat imagery. Remote Sens Environ 114:1535-1545
12. Dillon GK, Holden ZA, Morgan P, Crimmins MA, Heyerdahl EK, and Luce CH, (2011a) Both topography and climate affected forest and woodland burn severity in two regions of the western US, 1984 to 2006. Ecosphere 212: article 130
13. Dillon G, Morgan P, Holden Z (2011b) Mapping the potential for high severity wildfire in the western United States. Fire Manag Today 71:25-28
14. Drury SA, JM Herynk (2011) The national tree-list layer. General Technical Report RMRS-GTR 254. U.S. Department of Agriculture Forest Service, Rocky Mountain Research Station, Fort Collins, CO
15. Eidenshink J, Schwind B, Brewer K, Zhu Z, Quayle B, Howard S (2007) A project for monitoring trends in burn severity. Fire Ecol 3:3-21
16. Epting J, Verbyla D (2004) Landscape-level interactions of prefire vegetation, burn severity, and postfire vegetation over a 16-year period in interior Alaska. Can J For Res 35:1367-1377 (Pubitemid 41483385)
17. French NH, Kasischke ES, Hall RJ, Murphy KA, Verbyla DL, Hoy EE, Allen JL (2008) Using Landsat data to assess fire and burn severity in the North American Boreal forest region: and overview and summary of results. Int J Wildland Fire 17:443-462
18. Haire SL, McGarigal K (2009) Changes in fire severity across gradients of climate, fire size, and topography: a landscape ecological perspective. Fire Ecol 5:86-103
19. Holden ZA, Morgan PA, Evans JS (2009) A predictive model of burn severity based on 20-years of burn severity data in a large Southwestern US wilderness area. For Ecol Manag 258:2399-2406
20. Hudak AT, Morgan PA, Bobbit MJ, Smith AMS, Lewis SA, Lentile LB, Robichaud PR, Clark JT, McKinley RA (2007) The relationship of multispectral satellite imagery to immediate fire effects. Fire Ecol 3:64-90
21. Jensen JR (1983) Biophysical remote sensing. Ann Assoc Am Geogr 73:111-132
22. Karau EC, Keane RE (2010) Burn severity mapping using simulation modeling and satellite imagery. Int J Wildland Fire 19:710-724
23. Keane RE, Frescino TL, Reeves MC and Long J (2006) Mapping wildland fuels across large regions for the LANDFIRE prototype project. In: Rollins MG, Frame C (eds) The LANDFIRE prototype project: nationally consistent and locally relevant geospatial data for wildland fire management. General Technical Report RMRS-GTR-175. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO
24. Keane RE, Drury SA, Karau EC, Hessburg PF, Reynolds KM (2010) A method for mapping fire hazard and risk across multiple scales and its application in fire management. Ecol Model 221:2-18
25. Keane R, Gray K, Bacciu V, Leirfallom S (2012) Spatial scaling of wildland fuels for six forest and rangeland ecosystems of the northern Rocky Mountains, USA. Landsc Ecol 27:1213-1234
26. Keane RE, Herynk JM, Toney C, Urbanski SP, Lutes DC, Ottmar RD (2013) Evaluating the performance and mapping of three fuel classification systems using Forest Inventory Analysis surface fuel measurements. For Ecol Manag 305:248-263
27. Keeley JE, Brennan T, Pfaff AH (2008) Fire severity and ecosystem responses following crown fires in California shrublands. Ecol Appl 18:1530-1546
28. Key CH, Benson NC (2006a) Landscape assessment: remote sensing of severity, the Composite Burn Index. In: Lutes DC, Keane RE, Caratti JF, Key CH, Benson NC Ganji LJ (eds) 'FIREMON': Fire effects monitoring and inventory system. General Technical Report RMRS-GTR-164. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ogden, UT
29. Key CH, Benson NC (2006b) Landscape assessment: remote sensing of severity, the Normalized Burn Ratio. In: Lutes DC, Keane RE, Caratti JF, Key CH, Benson NC Ganji LJ (eds) 'FIREMON': Fire effects monitoring and inventory system. General Technical Report RMRS-GTR-164. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ogden, UT
30. Kopper K (2012) Frequency and severity of a mixed-severity fire regime: the Douglas fir/Ponderosa pine forests of Stehekin, Washington, USA. Dissertation, University of Washington
31. Krasnow K, Schoennagel T, Veblen TT (2009) Forest fuel mapping and evaluation of the LANDFIRE fuel maps of Boulder County, Colorado, USA. For Ecol Manag 257:1603-1612
32. Lutes DC, Keane RE, Caratti JF (2009) A surface fuels classification for estimating fire effects. Int J Wildland Fire 18:802-814
33. McNab WH, Avers PE (1994). Ecological subregions of the United States: section descriptions. Administrative Publication WO-WSA-5, USDA Forest Service, Washington, DC
34. Miller JD, Thode AE (2007) Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sens Environ 109:66-80 (Pubitemid 46776741)
35. Miller JD, Knapp EE, Key CH, Skinner CN, Isbell CJ, Creasy RM, Sherlock JW (2009a) Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sens Environ 113:645-656
36. Miller JD, Safford HD, Crimmins M, Thode AE (2009b) Quantitative evidence for increasing forest fire severity in the Sierra Nevada and Southern Cascade mountains, California and Nevada, USA. Ecosystems 12:16-32
37. Morgan PA, Keane RE, Dillon GK, Karau EC, Sikkink P, Silverstein R. The problem with fire severity and its application in fire management. Int J Wildland Fire (in review)
38. Nelson RM (2000) Prediction of diurnal change in 10-h fuel stick moisture content. Can J For Res 30:1071-1087
39. Prichard S, Peterson DL, Jacobson K (2010) Fuel treatments reduce the severity of wildfire effects in dry mixed conifer forests, Washington, USA. Can J For Res 40:1615-1626
40. Reeves MC, Kost JR, Ryan KC (2006) Fuels products of the LANDFIRE project. In: Andrews PL, Butler BW (eds) Fuels management-how to measure success. Proceedings RMRS-P-41. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO
41. Reeves MC, Ryan KC, Rollins MC, Thompson TG (2009) Spatial fuel data products of the LANDFIRE project. Int J Wildland Fire 18:250-267
42. Reinhardt E, Keane RE, Brown JK (1997) First Order Fire Effects Model: FOFEM 4.0 User's Guide. General Technical Report INT-GTR-344. US Department of Agriculture, Forest Service, Intermountain Research Station, Ogden, UT
43. Rollins MG (2009) LANDFIRE: a nationally consistent vegetation, wildland fire, and fuel assessment. Int J Wildland Fire 18:235-249
44. Sikkink P, Keane RE, Lutes DC (2009). Field guide for identifying fuel loading models. General Technical Report RMRS-GTR-225. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO
45. Soverel NO, Perrakis DDB, Coops NC (2010) Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada. Remote Sens Environ 114:1896-1909
46. R Development Core Team (2010) R: A language and environment for statistical computing; R foundation for statistical computing: Vienna, Austria. http://www.R-project.org/. Accessed Feb 2014
47. Van Wagner CE, Pickett TL (1985) Equations and FORTRAN program for the Canadian Forest Fire Weather Index System. Forestry Technical Report 33. Canadian Forestry Service, Ottawa, Ontario
48. Van Wagtendonk JW, Root RR, Key CH (2004) Comparison of AVIRIS and Landsat ETM? detection capabilities for burn severity. Remote Sens Environ 92:397-408 (Pubitemid 40047463)
49. Western Regional Climate Center (2012) RAWS USA Climate Archive. Desert Research Institute, Reno, NV. http://www.raws.dri.edu/index.html. Accessed Feb 2014
50. Zhu Z, Key C, Ohlen D, Benson N (2006) Evaluate sensitivities of burn-severity mapping algorithms for different ecosystems and fire histories in the United States. Final Report JFSP 01-1-4-12. Joint Fire Sciences Program