Journal of Geographical Systems

, Volume 18, Issue 2, pp 159–181 | Cite as

What governs the presence of residual vegetation in boreal wildfires?

  • Yikalo H. ArayaEmail author
  • Tarmo K. Remmel
  • Ajith H. Perera
Original Article


Wildfires are frequent boreal forest disturbances in Canada, and emulating their patterns with forest harvesting has emerged as a common forest management goal. Wildfires contain many patches of residual vegetation of various size, shape, and composition; understanding their characteristics provides insights for improved emulation criteria. We studied the occurrence of residual vegetation within eleven boreal wildfire events in a natural setting; fires ignited by lightning, no suppression efforts, and no prior anthropogenic disturbances. Relative importance of the measurable geo-environmental factors and their marginal effects on residual presence are studied using Random Forests. These factors included distance from natural firebreaks (wetland, bedrock and non-vegetated areas, and water), land cover, and topographic variables (elevation, slope, and ruggedness index). We present results at spatial resolutions ranging from four to 64 m while emphasizing four and 32 m since they mimic IKONOS- and Landsat-type images. Natural firebreak features, especially the proximity to wetlands, are among the most important variables that explain the likelihood residual occurrence. The majority of residual vegetation areas are concentrated within 100 m of wetlands. Topographic variables, typically important in rugged terrain, are less important in explaining the presence of residuals within our study fires.


Residuals Random Forests Predictor variables Variable importance Partial dependency Spatial resolution 

JEL Classification

C8 C14 Q5 


  1. Andison DW (2012) The influence of wildfire boundary delineation on our understanding of burning patterns in the Alberta foothills. Can J For Res 42(7):1253–1263CrossRefGoogle Scholar
  2. Araya YH, Remmel TK, Perera AH (2015) Residual vegetation patches within natural boreal wildfires: characterizing by pattern metrics, land cover expectations, and proximity to firebreak features. Geomatica 69(4):3237–3338CrossRefGoogle Scholar
  3. Arseneault D (2001) Impact of fire behaviour on post-fire forest development in a homogeneous boreal landscape. Can J For Res 31(8):1367–1374CrossRefGoogle Scholar
  4. Breiman L (2001) Random forests. J Mach Learn 45(1):5–31CrossRefGoogle Scholar
  5. Burton PJ, Parisien M, Hicke J, Hall RJ, Freeburn JT (2008) Large fires as agents of ecological diversity in the North American boreal forest. Int J Wildland Fire 17(6):754–767CrossRefGoogle Scholar
  6. Cuesta RM, Garcia M, Retana J (2009) Factors influencing the formation of unburned forest islands within the perimeter of a large forest fire. For Ecol Manag 258(2):71–80CrossRefGoogle Scholar
  7. Cullinane-Anthony BL, Seefelt NE, Corace RG, Kashian DM, Gehring TM (2014) Influence of residual forest patches on post-fire bird diversity patterns in jack pine-dominated ecosystems of northern Lower Michigan. For Ecol Manag 331:93–103CrossRefGoogle Scholar
  8. Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random forest for classification in ecology. Ecology 88(11):2783–2792CrossRefGoogle Scholar
  9. De’ath G, Fabricius K (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. J Ecol 81(11):3178–3192CrossRefGoogle Scholar
  10. Delong SC, Tanner D (1996) Managing the pattern of forest harvest: lessons from wildfire. Biodivers Conserv 5(10):1191–1205CrossRefGoogle Scholar
  11. Dragotescu L, Kneeshaw DD (2012) A comparison of residual forest following fires and harvesting in boreal forests in Quebec, Canada. Silva Fenn 46(3):365–376CrossRefGoogle Scholar
  12. Epting J, Verbyla D (2005) Landscape-level interactions of prefire vegetation burn severity, and postfire vegetation over a 16-year period in interior Alaska. Can J For Res 35(6):1367–1377CrossRefGoogle Scholar
  13. Evans JE, Cushman SA (2009) Gradient modelling of conifer species using random forests. Landsc Ecol 24(5):673–683CrossRefGoogle Scholar
  14. Friedman J (2009) Tutorial: getting started with MART in R.
  15. Genuer R, Poggi J, Tuleau-Malot C (2010) Variable selection using random forests. Pattern Recogn 31(14):2225–2236CrossRefGoogle Scholar
  16. Gislason PO, Benedictsson JA, Sveinsson KR (2006) Random forests for land cover classification. Pattern Recogn Lett 27(4):294–300CrossRefGoogle Scholar
  17. Guisan A, Zimmerman NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135(2–3):147–186CrossRefGoogle Scholar
  18. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer series in statistics. Springer, Berlin, pp 337–384Google Scholar
  19. Hills GA (1961) The ecological basis for land-use planning. Research report 46, Research Branch, Ontario Department of Lands and Forests, MapleGoogle Scholar
  20. Iverson LR, Prasad AM, Liaw A (2004) New machine learning tools for predictive vegetation mapping after climate change: bagging and random forest perform better than regression tree analysis. In: Richard S (ed) Landscape ecology of trees and forests: proceedings of the twelfth annual IALE (UK) conference, International Association for Landscape Ecology, Cirencester, pp 317–320Google Scholar
  21. Johnson EA (1995) Fire and vegetation dynamics: studies from the North American boreal forest. Cambridge University Press, Cambridge, pp 22–58Google Scholar
  22. Johnson EA, Miyanishi K, Weir JH (1998) Wildfires in the western Canadian boreal forest: landscape patterns and ecosystem management. J Veg Sci 9(4):603–610CrossRefGoogle Scholar
  23. Kafka V, Gauthier S, Bergeron Y (2001) Fire impacts and crowing in the boreal forest: study a large wildfire in western Quebec. Int J Wildland Fire 10:119–127CrossRefGoogle Scholar
  24. Liaw A, Wiener M (2002) Classification and regression by Random Forests. R News 2(3):18–22Google Scholar
  25. Madoui A, Leduc A, Gauthier S, Bergeron Y (2009) Spatial pattern analyses of post-fire residual stands in the black spruce boreal forest of western Quebec. Int J Wildland Fire 19(8):1110–1126CrossRefGoogle Scholar
  26. Morlon H, Chuyong G, Condit R, Hubbel S, Kenfack D, Valencia R, Green JL (2008) A general framework for the distance-decay of similarity in ecological communities. Ecol Lett 11(9):904–917CrossRefGoogle Scholar
  27. Munoz J, Felicisimo AM (2004) Comparison of statistical methods commonly used in predictive modelling. J Veg Sci 15(2):285–292CrossRefGoogle Scholar
  28. [OMNR] Ontario Ministry of Natural Resources (2010) Forest management guide for conservation biodiversity at the stand and site scales: background and rationale for direction. Queen’s printer for Ontario, TorontoGoogle Scholar
  29. [OMNR] Ontario Ministry of Natural Resources (2014) Forest management guide for boreal landscapes. Queen’s printer for Ontario, TorontoGoogle Scholar
  30. Pearce J, Ferrier S (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model 133(3):225–245CrossRefGoogle Scholar
  31. Perera AH, Buse LJ (2014) Ecology of wildfire residuals in boreal forests. Wiley Blackwell, Hoboken, pp 18–80Google Scholar
  32. Perera AH, Buse LJ, Weber MG, Crow TR (2004) Emulating natural forest landscape disturbances: a synthesis. In: Perera AH, Buse LJ, Weber MG (eds) Emulating natural forest landscape disturbance: concepts and applications. Columbia University Press, New York, pp 265–282Google Scholar
  33. Perera AH, Dalziel BD, Buse LJ, Routledge RG (2009a) Spatial variability of stand-scale residuals in Ontario’s boreal forest fires. Can J For Res 39(5):945–961CrossRefGoogle Scholar
  34. Perera AH, Remmel TK, Buse LJ, Ouellete MR (2009b) An assessment of residual patches in boreal fires, in relation to Ontario’s policy directions for emulating natural forest disturbance. Ontario Ministry of Natural Resources, Ontario Forest Research Institute, Saulte Ste. Marie, Ontario. Forest research report no. 169Google Scholar
  35. Peters J, Baets BD, Verhoest NE, Samson R, Degroeve S, Becker PD, Huybrechts W (2007) Random forests as a tool for ecohydrological distribution modelling. Ecol Model 207(2–4):304–318CrossRefGoogle Scholar
  36. Prasad AM, Iverson LR, Liaw A (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. J Ecosyst 9(2):181–199CrossRefGoogle Scholar
  37. R Core Team (2014) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria.
  38. Remmel TK, Perera AH (2009) Mapping natural phenomena: boreal forest fires with non-discrete boundaries. Cartographica 44(4):274–288CrossRefGoogle Scholar
  39. Riley S, DeGloria SD, Elliot R (1999) A terrain ruggedness index that quantifies topographic heterogeneity. Int J Sci 1(4):23–27Google Scholar
  40. Ryan KC (2002) Dynamic interactions between forest structure and fire behaviour in boreal ecosystems. Silva Fenn 36(1):13–39CrossRefGoogle Scholar
  41. Schroff F, Criminisi A, Zisserman A (2008) Object class segmentation using random forests. In: Everingham M, Needham C (eds) Proceedings of the 19th British machine vision conference, BMVA Press, Leeds, pp 54.1–54.10Google Scholar
  42. Spectranalysis (2005) IKONOS land cover classification for the assessment of forest burns. Internal report prepared for forest landscape ecology program, Ontario Forest Research Institute, Ministry of Natural ResourcesGoogle Scholar
  43. Strobl C, Hothorn T, Zeileis A (2009) Party on! A new, conditional variable importance measure for random forests available in the party package. R J 1(2):14–17Google Scholar
  44. Turner MG, Romme WH, Gardner RH, Hargrove WW (1997) Effects of fire size and pattern on early succession in Yellowstone National Park. Ecol Monogr 67(4):411–433CrossRefGoogle Scholar
  45. van Wagtendonk JW (2004) Fire and landscapes: patterns and processes. USDA Forest Service Gen. Technical report, PSW-GTR-193Google Scholar
  46. William JC, Gray PA, Uhling PW, Wester MC (2009) The ecosystems of Ontario, part 1: ecozones and ecoregions. Science and information branch: inventory, monitoring, and assessment section, technical report SIB TER IMA TR-01Google Scholar
  47. Wong DW (2009) The modifiable areal unit problem (MAUP). In: Fotheringham AS, Rogerson PA (eds) The SAGE handbook of spatial analysis. Sage Publications, London, pp 105–122Google Scholar
  48. Wu J (1999) Hierarchy and scaling: extrapolating information along a scaling ladder. Can J Remote Sens 25(4):367–380CrossRefGoogle Scholar
  49. Wu J, Shen W, Sun W, Tueller PT (2002) Empirical patterns of the effects of changing scale on landscape metrics. Landsc Ecol 17(8):761–782CrossRefGoogle Scholar
  50. Zaniewski AE, Lehmann A, Overton MJ (2002) Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecol Model 157(2-3):261–280CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Yikalo H. Araya
    • 1
    Email author
  • Tarmo K. Remmel
    • 1
  • Ajith H. Perera
    • 2
  1. 1.Department of GeographyYork UniversityTorontoCanada
  2. 2.Ontario Ministry of Natural ResourcesOntario Forest Research InstituteSault Ste. MarieCanada

Personalised recommendations