Assessment of Wildfire Hazards with a Semiparametric Spatial Approach

A Case Study of Wildfires in South America


Rural households in agricultural economies are vulnerable to several environmental risks such as fires, floods, and droughts that may affect their productivity in whole or in part. These hazards are especially relevant in developing countries where farmers have few or no access to traditional risk-transfer techniques, such as insurance and finance, and where low governmental investments in rural infrastructure, risk assessment techniques, or early warning systems makes the aftermath of such hazards more expensive and results in slower recovery for those who are affected. In this paper, we use historical satellite data (Terra) of burned areas in South America to fit a semiparametric spatial model, based on kernel smoothing and on a nonlinear relationship between average time between events and damage, to assess the environmental hazard affecting the land’s productivity. The results were twofold: first, we were able to develop a spatial assessment of fire hazard, and second, we were able to evaluate how much a farmer may lose in terms of productivity per hectare due to the environmental hazard. The methodology may be easily adapted to other world regions; to different environmental hazards such as floods, windbreak, windthrow, or related land-use changes; or to integrate various environmental hazards simultaneously, as long as they can be monitored via remote sensing (e.g., satellite imagery, aerial photographs, etc).

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  1. 1.

    Agresti, A., & Coull, B.A. (1998). Approximate is better than “exact” for interval estimation of binomial proportions. American Statistician, 52 (2), 119–126.

    Google Scholar 

  2. 2.

    Aragão, L.E.O., & Shimabukuro, Y.E (2010). The incidence of fire in Amazonian forests with implications for REDD. Science, 328 (5983), 1275–1278.

    Article  Google Scholar 

  3. 3.

    Baddeley, A., Mller, J., Pakes, A.G. (2008). Properties of residuals for spatial point processes. Annals of the Institute of Statistical Mathematics, 60, 627–649.

    Article  Google Scholar 

  4. 4.

    Baddeley, A., Mller, J., Waagepetersen, R. (2000). Non- and semiparametric estimation of interaction in inhomogeneous point patterns. Statistica Neerlandica, 54 (3), 329–350.

    Article  Google Scholar 

  5. 5.

    Balch, J. K., Nepstad, D. C., Brando, P. M., Curran, L. M., Portela, O., de Carvalho Jr., O., Lefebre, P. (2008). Negative fire feedback in a transitional forest of southeastern Amazonia. Global Change Biology, 14, 2276–2287.

    Article  Google Scholar 

  6. 6.

    Balch, J. K., Nepstad, D. C., Curran, L. M., Brando, P. M., Portela, O., Guilherme, P., Reuning-Schere, J.D., de Carvalho Jr., O. (2011). Size, species, and fire behaviour predict tree and liana mortality from experimental burns in the Brazilian Amazon. Forest Ecology and Management, 261, 68–77.

    Article  Google Scholar 

  7. 7.

    Barlow, J., Peres, C. A., Lagan, B. O., Haugaasen, T. (2003). Large tree mortality and the decline of forest biomass following Amazonian wildfires. Ecology Letters, 6, 6–8.

    Article  Google Scholar 

  8. 8.

    Barnett., B. J., Barrett., C.B., Skees, J.R. (2008). Poverty traps and index-based risk transfer products. World Development, 36 (10), 1766–1785.

    Article  Google Scholar 

  9. 9.

    Boschetti, L., Roy, D., Hoffmann, A.A. (2009). MODIS Collection 5 Burned Area Product - MCD45 User’s Guide Version 2.0, November 2009.

  10. 10.

    Bush, M. B., Silan, M. R., McMichael, C., Saatchi, S. (2008). Fire, climate change and biodiversity in Amazonia; a late-Holocene perspective. Philosophical Transactions of the Royal Society B, 363, 1795–1802.

    CAS  Article  Google Scholar 

  11. 11.

    Castro, L.M., Calvas, B., Hildebrandt, P., Knoke, T. (2013). Avoiding the loss of shade coffee plantations: How to derive conservation payments for risk-averse land-users. Agroforestry Systems, 87, 331–347.

    Article  Google Scholar 

  12. 12.

    Caldarelli, G., Frondoni, R., Gabrielli, A., Montuori, M., Retzlaff, R., Ricotta, C. (2001). Percolation in real wildfires. Europhysics Letters, 56, 510–516.

    CAS  Article  Google Scholar 

  13. 13.

    Calkin, D.E., Ager, A.A., Thompson, M.P. (2011). A comparative risk assessment framework for wildland fire management: the 2010 cohesive strategy science report. Gen. Tech. Rep. RMRS-GTR-262. U.S. Department of Agriculture, Forest Service, p. 63.

  14. 14.

    CONAF (Corporacin Nacional Forestal de Chile) (2013). Regulacin legal. Web page, accessed the 21.03.13.

  15. 15.

    Diggle, P.J. (1985). A kernel method for smoothing point process data. Applied Statistics, 34 (2), 138–147.

    Article  Google Scholar 

  16. 16.

    Diggle, P.J. (2003). Statistical analysis of spatial point patterns, 2nd edn. London: Arnold.

    Google Scholar 

  17. 17.

    Diggle, P.J., & Marron, J.S. (1988). Equivalence of smoothing parameter selectors in density and intensity estimation. Journal of the American Statistical Association, 83, 793–800.

    Article  Google Scholar 

  18. 18.

    FAO (2010). Global forest resources assessment 2010. Main report. Rome: Food and Agriculture Organization of the United Nations. FAO Forestry paper 163.

  19. 19.

    FAO (2011). State of the worlds forests 2011. Rome: Food and Agriculture Organization of the United Nations.

  20. 20.

    Fearnside, P.M. (2005). Deforestation in Brazilian Amazonia: History, rates, and consequences. Conservation Biology, 19, 680–688.

    Article  Google Scholar 

  21. 21.

    Gill, A.M., Stephens, S.L., Cary, G.J. (2013). The worldwide “wildfire” problem. Ecological Applications, 23(2), 438454.

  22. 22.

    Hazelton, M.L. (2008). Kernel estimation of risk surfaces without the need for edge correction. Statistics in Medicine, 27 (12), 2269–2272.

    Article  Google Scholar 

  23. 23.

    Hildebrandt, P., & Knoke, T. (2011). Investment decisions under uncertainty—a methodological review on forest science studies. Forest Policy and Economics, 13, 1–15.

    Article  Google Scholar 

  24. 24.

    Hoffmann, W. A., Adasme, R., Haridasan, M., deCarvalho, M. T., Geiger, E. L., Pereira, M. A. B., Gotsch, S. G., Franco, A. C. (2009). Tree topkill, not mortality, governs the dynamics of savanna-forest boundaries under frequent fire in central Brazil. Ecology, 90 (5), 1326–1337.

    Article  Google Scholar 

  25. 25.

    IBAMA (Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais) (2013). Queima Controlada. Web page, accessed the 21.03.13.

  26. 26.

    Justice, C.O., Giglio, L., Korontzi, S., Owens, J., Morisette, J.T., Roy, D., Descloitres, J., Alleaume, S., Petitcolin, F., Kaufman, Y. (2002). The MODIS fire products. Remote Sensing of Environment, 83, 244–262.

    Article  Google Scholar 

  27. 27.

    Keane, R.E., Cary, G.J., Davies, I.D., Flannigan, M.D., Gardner, R.H., Lavorel, S., Lenihan, J.M., Li, Chao, Rupp, T.S. (2004). A classification of landscape fire succession models: Spatial simulations of fire and vegetation dynamics. Ecological Modelling, 179 (1), 3–27.

    Article  Google Scholar 

  28. 28.

    Keeley, J.E., Safford, H., Fotheringham, C.J., Franklin, J., Moritz, M. (2009). The 2007 southern California wildfires: Lessons in complexity. Journal of Forestry, 287–296.

  29. 29.

    Knoke, T., Steinbeis, O. E., Bsch, M., Romn-Cuesta, R.M., Burkhardt, T. (2011). Cost-effective compensation to avoid carbon emissions from forest loss: an approach to consider pricequantity effects and risk-aversion. Ecological Economics, 70, 1139–1153.

    Article  Google Scholar 

  30. 30.

    Knoke, T. (2013). Uncertainties and REDD+: Implications of applying the conservativeness principle to carbon stock estimates. Climatic Change, Springboard Commentary, 119, 261–267.

    Article  Google Scholar 

  31. 31.

    Malamud, B.D., Millington, J.D.A., Perry, G.L.W. (2005). Characterizing wildfire regimes in the United States. Proceedings of the National Academy of Sciences of the USA, 102 (13), 4694–4699.

    CAS  Article  Google Scholar 

  32. 32.

    McGarigal, K., Cushman, S. A., Ene, E. (2012). FRAGSTATS v4: Spatial pattern analysis program for categorical and continuous maps. Amherst: University of Massachusetts. Available at:

  33. 33.

    Miranda, H.S., Bustamante, M.M.C., Miranda, A.C. (2002). The fire factor. In Olivera, P.S., & Marquis, R.J. (Eds.), The cerrados of Brazil: Ecology and natural history of a neotropical savanna, (pp. 51–68). New York: Columbia University Press.

    Google Scholar 

  34. 34.

    Moritz, M.A., Morais, M.E., Summerell, L.A., Carlson, J. M., Doyle, J. (2005). Wildfires, complexity, and highly optimized tolerance. PNAS, 102 (5), 17912–17917.

    CAS  Article  Google Scholar 

  35. 35.

    NASA (National Aeronautics and Space Administration) (2012). NRT Global MODIS flood mapping. Web page:, accessed: 19th March 2013.

  36. 36.

    Nepstad, D. C., Verissimo, A., Alencar, A., Nobre, C., Lima, E., Lefebre, P., Schlesinger, P., Potter, C., Moutinho, P., Mendoza, E., Cochrane, M., Brooks, V. (1999). Large-scale impoverishment of Amazonian forests by logging and fire. Nature, 398, 505–508.

    CAS  Article  Google Scholar 

  37. 37.

    Nichols, K., Schoenberg, F.P., Keeley, J., Diez, D. (2011). The application of prototype point processes for the summary and description of California wildfires. Journal of Time Series Analysis, 32 (4), 420–429.

    Article  Google Scholar 

  38. 38.

    Pausas, J.G., & Keeley, J.E. (2009). A burning story: the role of fire in the history of life. BioScience, 59 (7), 593–601.

    Article  Google Scholar 

  39. 39.

    Peng, R. D., Schoenberg, F. P., Woods, J. (2005). A space-time conditional intensity model for evaluating a wildfire hazard index. Journal of the American Statistical Association, 100 (469), 26–35.

    CAS  Article  Google Scholar 

  40. 40.

    Pivello, V. R. (2011). The use of fire in the Cerrado and Amazonian rainforests of Brazil: Past and present. Fire Ecology, 7 (1), 24–39.

    Article  Google Scholar 

  41. 41.

    Pyne, S., Andrews, P., Laren, R. (1996). Introduction to wildland fire. New York: Wiley.

    Google Scholar 

  42. 42.

    Roman-Cuesta, R. M., Salinas, N., Asbjornsen, H., Oliveras, I., Huaman, V., Gutirrez, Y., Puelles, L., Kala, J., Yabar, D., Rojas, M., Astete, R., Jordn, D. Y., Silman, M., Mosandl, R., Weber, M., Stimm, B., Gnter, S., Knoke, T., Malhi, Y. (2011). Implications of fires on carbon budgets in Andean cloud montane forest: the importance of peat soils and tree resprouting. Forest Ecology and Management, 261, 1987–1997.

    Article  Google Scholar 

  43. 43.

    Rosenblatt, M. (1956). Remarks on some non-parametric estimates of a density function. Annals of Mathematical Statistics, 27, 832–7.

    Article  Google Scholar 

  44. 44.

    Roy, D.P., Jin, Y., Lewis, P.E., Justice, C.O. (2005). Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data. Remote Sensing of Environment, 97, 137–162.

    Article  Google Scholar 

  45. 45.

    Scott, D.W. (1992). Multivariate density estimation. Theory practice and visualization. New York: Wiley. Chapter 6.

    Google Scholar 

  46. 46.

    Scott, D.W. (2012). Multivariate density estimation and visualization. In Gentle, J.E., Haerdle, W., Mori, Y. (Eds.), Handbook of computational statistics: Concepts and methods, 2nd edn., (pp. 549–570). New York: Springer.

    Google Scholar 

  47. 47.

    Schoenberg, F.P., & Tranbarger, K.E. (2008). Description of earthquake aftershock sequences using prototype point patterns. Environmetrics, 19, 271–286.

    Article  Google Scholar 

  48. 48.

    Sheather, S.J. (2004). Density estimation. Statistical Science, 19 (4), 588–597.

    Article  Google Scholar 

  49. 49.

    Simon, M.F., Grether, R., Queiroz, L.P., Skema, C., Pennington, R.T., Hughes, C.E. (2009). Recent assembly of the Cerrado, a neotropical plant diversity hotspot, by in situ evolution of adaptations to fire. Proceedings of the National Academy of Science, 106, 20359–30364.

    CAS  Article  Google Scholar 

  50. 50.

    Simmons, C.S., Walker, R.T., Wood, C.H., Arima, E. (2004). Wildfires in Amazonia: a pilot study examining the role of farming systems, social capital, and fire contagion. Journal of Latin American Geography, 3 (1), 81–95.

    Article  Google Scholar 

  51. 51.

    Tan, F., San Lim, H., Abdullah, K. (2012). Relationship between orography and the wind-cloud systems of tropical cyclones. Optical Engineering, 51 (10), 101712.

    Article  Google Scholar 

  52. 52.

    The World Bank (2011). Weather index insurance for agriculture. Guidance for development practitioners. Agriculture and rural development discussion paper 50.

  53. 53.

    UNFCCC (UN Framework Convention on Climate Change) (2010). The Cancun agreements. New York, NY: UN. Viewed 6 Oct 2012.

  54. 54.

    UN-REDD (2014). About REDD+. Accessed online at 07.04.14.

  55. 55.

    Uriarte, M., Pinedo-Vasquez, M., DeFriesa, R.S., Fernandes, K., Gutierrez-Veleza, V., Baethgenc, W.E., Padochd, C. (2012). Depopulation of rural landscapes exacerbates fire activity in the western Amazon. PNAS, 109, 21546–21550.

    CAS  Article  Google Scholar 

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We are most thankful for the financial support of the “Deutsche Forschungsgemeinschaft” (DFG), project KN 586/5-2 and to the members of the research group “FOR 816” whose research initiative and support made this study possible. We are grateful to Professor Claudia Klueppelberg for her valuable comments and to Christian Schemm for the preparation of the dataset with ArcGIS.

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Correspondence to Ricardo Acevedo-Cabra.

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Acevedo-Cabra, R., Wiersma, Y., Ankerst, D. et al. Assessment of Wildfire Hazards with a Semiparametric Spatial Approach. Environ Model Assess 19, 533–546 (2014).

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  • Environmental risk assessment
  • Kernel smoothing
  • Semiparametric
  • Average time between events
  • Fire risk
  • Satellite imagery