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Environmental Modeling & Assessment

, Volume 19, Issue 6, pp 533–546 | Cite as

Assessment of Wildfire Hazards with a Semiparametric Spatial Approach

A Case Study of Wildfires in South America
  • Ricardo Acevedo-CabraEmail author
  • Yolanda Wiersma
  • Donna Ankerst
  • Thomas Knoke
Article

Abstract

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).

Keywords

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

Notes

Acknowledgments

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.

References

  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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  14. 14.
    CONAF (Corporacin Nacional Forestal de Chile) (2013). Regulacin legal. Web page http://www.conaf.cl/proteccion/seccion-regulacion.html, 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.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  19. 19.
    FAO (2011). State of the worlds forests 2011. Rome: Food and Agriculture Organization of the United Nations.Google Scholar
  20. 20.
    Fearnside, P.M. (2005). Deforestation in Brazilian Amazonia: History, rates, and consequences. Conservation Biology, 19, 680–688.CrossRefGoogle Scholar
  21. 21.
    Gill, A.M., Stephens, S.L., Cary, G.J. (2013). The worldwide “wildfire” problem. Ecological Applications, 23(2), 438454.Google Scholar
  22. 22.
    Hazelton, M.L. (2008). Kernel estimation of risk surfaces without the need for edge correction. Statistics in Medicine, 27 (12), 2269–2272.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
  25. 25.
    IBAMA (Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais) (2013). Queima Controlada. Web page http://www.ibama.gov.br/areas-tematicas/queima-controlada, 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.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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: http://www.umass.edu/landeco/research/fragstats/fragstats.html.
  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.CrossRefGoogle Scholar
  35. 35.
    NASA (National Aeronautics and Space Administration) (2012). NRT Global MODIS flood mapping. Web page: http://oas.gsfc.nasa.gov/floodmap/home.html, 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
  43. 43.
    Rosenblatt, M. (1956). Remarks on some non-parametric estimates of a density function. Annals of Mathematical Statistics, 27, 832–7.CrossRefGoogle 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.CrossRefGoogle Scholar
  45. 45.
    Scott, D.W. (1992). Multivariate density estimation. Theory practice and visualization. New York: Wiley. Chapter 6.CrossRefGoogle 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.CrossRefGoogle Scholar
  47. 47.
    Schoenberg, F.P., & Tranbarger, K.E. (2008). Description of earthquake aftershock sequences using prototype point patterns. Environmetrics, 19, 271–286.CrossRefGoogle Scholar
  48. 48.
    Sheather, S.J. (2004). Density estimation. Statistical Science, 19 (4), 588–597.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
  52. 52.
    The World Bank (2011). Weather index insurance for agriculture. Guidance for development practitioners. Agriculture and rural development discussion paper 50.Google Scholar
  53. 53.
    UNFCCC (UN Framework Convention on Climate Change) (2010). The Cancun agreements. New York, NY: UN. http://unfccc.int/resource/docs/2010/cop16/eng/07a01.pdf. Viewed 6 Oct 2012.
  54. 54.
    UN-REDD (2014). About REDD+. Accessed online at http://www.un-redd.org/aboutredd/tabid/102614/default.aspx. 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.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ricardo Acevedo-Cabra
    • 1
    Email author
  • Yolanda Wiersma
    • 2
  • Donna Ankerst
    • 3
  • Thomas Knoke
    • 1
  1. 1.Institute of Forest ManagementTechnische Universität MünchenFreisingGermany
  2. 2.Department of BiologyMemorial UniversitySt. John’sCanada
  3. 3.Chair of Mathematical StatisticsTechnische Universität MünchenMunichGermany

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