2,823
Views
12
CrossRef citations to date
0
Altmetric
Article

Probabilistic modelling of wildfire occurrence based on logistic regression, Niassa Reserve, Mozambique

ORCID Icon, , &
Pages 1772-1792 | Received 04 Oct 2018, Accepted 28 Apr 2019, Published online: 10 Jul 2019

References

  • Allen RG, Pereira LS, Raes D, Smith M. 1998. Crop evapotranspiration -Guidelines for computing crop water requirements – FAO Irrigation and drainage paper 56. FAO; 6p.
  • Anderson LO, Aragão L, Gloor M, Arai E, Adami M, Saatchi S, Malhi Y, Shimabukuro YE, Barlow J, Berenguer E, Duarte V. 2015. Disentangling the contribution of multiple land covers to fire-mediated carbon emissions in Amazonia during the 2010 drought. Global Biogeochem Cycles. 29(10):1739–1753.
  • Archibald S, Roy D, Van Wilgen B, Scholes R. 2009. What limits fire? An examination of drivers of burnt area in Southern Africa. Global Chang. Biol. 15(3):613–630.
  • Archibald S, Scholes R, Roy D, Roberts G, Boschetti L. 2010. Southern African fire regimes as revealed by remote sensing. Int J Wildland Fire. 19(7):861–878.
  • Achard F, Eva HD, Mollicone D, Beuchle R. 2008. The effect of climate anomalies and human ignition factor on wildfires in Russian boreal forests. Phil. Trans. R. Soc. B Biol.Sci. 363:2331–2339.
  • Batista AC. 2000. Mapas de risco: uma alternativa para o planejamento de controle de incêndios florestais. Floresta. 30(1):45–54. v. n.
  • Berjak SG, Hearne JW. 2002. An improved cellular automaton model for simulating fire in a spatially heterogeneous Savanna system. Ecological Modelling. 148(2):133–151.
  • Bisquert MM, Sánchez JM, Caselles V. 2011. Fire danger estimation from MODIS enhanced vegetation index data: application to Galicia region (north-west Spain). Int J Wildland Fire. 20(3):465–473.
  • Bond WJ, Keeley JE. 2005. Fire as a global herbivore: the ecology and evolution of flammable ecosystems. Trends in Ecology and Evolution. 20(7):387–394. doi:10.1016/j.tree.2005.04.025
  • CENACARTA, Centro Nacional de Cartografia e Teledetecao 2008. Dados cartograficos de Moçambique. Accessed in 2018. www.cenacarta.com
  • Castro R, Chuvieco E. 1998. Modeling forest fire danger from geographic information systems. Geocarto Int. 13(1):15–23.
  • Catry FX, Rego FC, Bação FL, Moreira F. 2009. Modeling and mapping wildfire ignition risk in Portugal. Int J Wildland Fire. 18(8):921–931.
  • Catry FX. 2007. Modelação espacial do Risco de Ignição em Portugal continental. Dissertação de Mestrado em Ciência e Sistema de Informação Geográfica. Universidade Nova de Lisboa.
  • Chang Y, Zhu Z, Bu R, Chen H, Feng Y, Li Y, Hu Y, Wang Z. 2013. Predicting fire occurrence patterns with logistic regression in Heilongjiang Province, China. Landscape Ecol. 28(10):1989–2004.
  • Chang Y, He HS, Hu Y, Bu R, Li X. 2008. Historic and current fire regimes in the Great Xing’an Mountains, northeastern China: implications for long-term forest management. For Ecol Manag. 254(3):445–453.
  • Chidumayo EN. 1997. Miombo ecology and management: An introduction. Stockholm: Stockholm Environment Institute.
  • Chidumayo EN. 1988. A Re-Assessment of Effects of Fire on Miombo Regeneration in the Zambian Copperbelt. J Trop Ecol. 4(04):361–372.
  • Chuvieco E, Gonzalez I, Verdu F, Aguado I, Yebra M. 2009. Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem. Int J Wildland Fire. 18(4):430–441.
  • Chuvieco E, Aguado I, Jurdao S, Pettinari ML, Yebra M, Salas J, Hantson S, De La Riva J, Ibarra P, Rodrigues M, et al. 2012. Integrating geospatial information into fire risk assessment. Int J Wildland Fire. doi:10.1071/WF12052
  • Chuvieco E. 2003. Wildland fire danger estimation and mapping: the role of remote sensing data. River Edge, NJ: World Scientific Publishing Co.
  • Costafreda-Aumedes S, Cardil A, Molina DM, Daniel SN, Mavsar R, Vega-Garcia C. 2015. Analysis of factors influencing deployment of fire suppression resources in Spain using artificial neural networks. iForest. (early view).
  • Dasgupta S, Qu JJ, Hao X, Bhoi S. 2007. Evaluating remotely sensed live fuel moisture estimations for fire behavior predictions in Georgia, USA. Remote Sens Environ. 108(2):138–150. doi:10.1016/j.rse.2006.06.023
  • de Vasconcelos MJP, Silva S, Tome M, Alvim M, Pereira JMC. 2001. Spatial predition of fire ignition probabilities comparing logistic regression and neural networks. Photogr Eng Remote Sensing. 67(1):73–81.
  • del Hoyo VL, Martın Isabel M, Vega MF. 2011. Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data. Eur J Res. 130:983–996.
  • Devisscher T, Anderson LO, Aragão LEOC, Galván L, Malhi Y. 2016. Increased wildfire risk driven by climate and development interactions in the Bolivian Chiquitania, Southern Amazonia. PLoS One. 11(9):e0161323. pone.0161323
  • Dolanc CR, Safford HD, Dobrowski SZ, Thorne JH. 2014. Twentieth century shifts in abundance and composition of vegetation types of the Sierra Nevada, CA, US. Appl Veg Sci. 17(3):442–455.
  • Eva H, Lambin EF. 1998. Burnt area mapping in central Africa using ATSR data. Int J Remote Sens. 19(18):3473–3497.
  • Fan Q, Wang C, Zhang D, Zang S. 2017. Environmental influences on forest fire regime in the Greater Hinggan Mountains, Northeast China. Forests. 8(10):372.
  • Ferraz SFB, Vettorazzi CA. 1998. Mapeamento de risco de incêndios florestais por meio de sistema de informações geográficas (SIG. Scientia Forestalis, v. 53:39–48. doi:10.1590/2179-8087.025615
  • Fielding, A H, Bell, J F. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Envir Conserv. 24(1):38–49. doi:10.1017/S0376892997000088.
  • Fried JJ, Gilless K, Riley W, Moody T, de Blas C, Hayhoe K, Moritz M, Stephens S, Torn M. 2008. Predicting the effect of climate change on wildfire behavior and initial attack success. Climat Change. 87(S1):251–264.
  • Galán C, López R. 2003. Sistemas de información geográfica. Madrid: RA-MA.
  • Garcia CV, Woodard P, Titus S, Adamowicz W, Lee B. 1995. A logit model for predicting the daily occurrence of human caused forest-fires. Int J Wildland Fire. 5(2):101–111.
  • Giglio L. 2015. MODIS Collection 6 Active Fire Product User’s Guide Revision A. University of Maryland, URL: http://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_A.pdf
  • Guo FT, Su ZW, Wang GY. 2015. Predition model human-caused fire occurrence in the boreal forest of northern China. Chin J Appl Ecol. 26:2009–2106.
  • Guo F, Zhang L, Jin S, Tigabu M, Su Z, Wang W. 2016a. Modeling anthropogenic fire occurrence in the boreal forest of China using logistic regression and random forests. Forests. 7(12):250.
  • Guo F, Su Z, Wang G, Sun L, Lin F, Liu A. 2016b. Wildfire ignition in the forests of southeast china: Identifying drivers and spatial distribution to predict wildfire likelihood. Appl Geogr. 66:12–21.
  • Glenn E, Huete A, Nagler P, Nelson S. 2008. Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: what vegetation indices can and cannot tell us about the landscape. Sensors (Basel). 8(4):2136–2160.
  • Gralewicz NJ, Nelson TA, Wulder MA. 2012. Factors in fluencing national scale wildfire susceptibility in Canada. For Ecol Manag. 265:20–29.
  • Hair JF. 1998. Multivariate data analysis. 5th ed. NJ: Prentice-Hall.
  • Hosmer D, Lemeshow S. 2000. Applied logistic regression. 2nd ed. New York: John Wiley.
  • Hosmer DW, Hosmer T, Le Cessie S, Lemeshow S. 1997. A comparison of goodness-of-fit tests for the logistic regression model. Stat Med. 16(9):965–980.
  • Jaiswal RK, Mukherjee S, Raju KD, Saxena R. 2002. Forest fire risk zone mapping from satellite imagery and GIS. Int J Appl Earth Observ Geoinf. 4(1):1–10. v. n.
  • Jaiswal RK, Krishnamurthy J, Mukherjee S. 2005. Regional study for mapping the natural resources prospect & problem zones using remote sensing and GIS. Geocarto Int. 20(3):21–31. doi:10.1080/10106040508542352
  • Jiménez-Valverde A. 2012. Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modeling. Glob Ecol Biogeogr. 21:498–507. doi:10.1111/j.1466-8238.2011.00683.x
  • Leblon B, Garcia PAF, Oldford S, MacLean DA, Flannigan M. 2007. Using cumulative NOAA-AVHRR spectral indices for estimating fire danger codes in northern boreal forests. Int J Appl Earth Observ Geoinf. 9(3):335–342. doi:10.1016/j.jag.2006.11.001
  • Leo-Smith K, Balson EW, Abacar A. 1997. Niassa game reserve. management and development plan 1997–006. vols. I & II. Maputo: Direção Nacional de Florestas e Fauna Bravia, Ministério da Agricultura; p. 120.
  • Legendre P, Legendre L. 1998. Numerical ecology. Amsterdam: Elsevier.
  • Lozano FJ, Suarez-Seoane S, Kelly M, Luis E. 2008. A multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: a case study in a mountainous Mediterranean region. Remote Sens Environ. 112(3):708–719. doi:10.1016/j.rse.2007.06.006
  • MODIS Active Fire Detections extracted from MCD14ML distributed by NASA FIRMS. Available on-line https://earthdata.nasa.gov/firms
  • Millar CI, Westfall RD, Delany DL, King JC, Graumlich LJ. 2004. Response of subalpine conifers in the Sierra Nevada, California, USA, to 20th-century warming and decadal climate variability. Arctic Ant Alpine Res. 36(2):181–200.2.0.CO;2]
  • Martínez J, Vega-Garcıa C, Chuvieco E. 2009. Human-caused wildfire risk rating for prevention planning in Spain. J Environ Manage. 90(2):1241–1252.
  • Martınez-Fernandez J, Chuvieco E, Koutsias N. 2013. Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression. Nat Hazards Earth Syst Sci. 13:311–327.
  • Mohammadi F, Bavaghar MP, Shabanian N. 2014. Forest fire risk zone modeling using logistic regression and GIS: an Iranian case study. Small Scale For. 13(1):117–125.
  • Magnussen S, Taylor SW. 2012. Prediction of daily lightning- and human-caused fires in British Columbia. Int J Wildland Fire. 21(4):342–356.
  • Menard SW. 2010. Logistic regression: from introductory to advanced concepts and applications, London: Sage; p. 377.
  • McCune B, Grace JB, Urban DL. 2002. Analysis of ecological communities. MjM software design, 28, Oregon: Gleneden Beach. ISSN 0022–0981
  • Norusis MJ. 2002. SPSS 11.0 guide to data analysis. NJ: Prentice Hall.
  • Nhongo EJS, Fontana D, Guasselli LA, Esquerdo J. 2017. Caracterização fenológica da cobertura vegetal com base em série temporal NDVI/MODIS na reserva do Niassa – Moçambique. Revista Brasilera de Cartografia. 69(6):1175–1187. No /
  • Oliveras I, Anderson LO, Malhi Y. 2014. Application of remote sensing to understanding fire regimes and bio- mass burning emissions of the tropical Andes. Global Biogeochem Cycles. 28(4):480–496.
  • Oliveira S, Oehler F, San-Miguel-Ayanz J, Camia A, Pereira J. 2012. Modeling spatial patterns of fire occurrence in Mediterranean Europe using multiple regression and random forest. For Ecol Manag. 275:117–129. doi:10.1016/j.foreco.2012.03.003
  • Padilla M, Vega-Garcıa C. 2011. On the comparative importance of fire danger rating indices and their integration with spatial and temporal variables for predicting daily human-caused fire occurrences in Spain. Int J Wildland Fire. 20(1):46–58.
  • Parisien MA, Moritz MA. 2009. Environmental controls on the distribution of wildfire at multiple spatial scales. Ecol Monogr. 79(1):127–154.
  • Prestemon JP, Chas-Amil ML, Touza JM, Goodrick SL. 2012. Forecasting intentional wildfires using temporal and spatiotemporal autocorrelations. Int J Wildland Fire. 21(6):743–754.
  • Renard Q, Pélissier R, Ramesh BR, Kodandapani N. 2012. Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. Int J Wildland Fire. 21(4):368–379. doi:10.1071/WF10109
  • Ribeiro NS, Shugart HH, Washington-Allen R. 2008. The effects of fire and elephants on species composition and structure of the Niassa Reserve, northern Mozambique. For Ecol Manage. 225:1626–1636. doi:10.1016/j.foreco.2007.11.033
  • Ribeiro NS, Cangela A, Chauque A, Bandeira RR, Ribeiro-Barros AI. 2017. Characterisation of Spatial and temporal distribution of the fire regime in Niassa National Reserve, northern Mozambique. Int J Wildland Fire . 26(12):1021–1029.
  • Ronde C, Goldammer JG, Wade DD, Soares RV. 1990. Prescribe fire in industrial plantation. In: Goldammer JG, editor. Fire in the tropical biota. Berlin: Spring-Verlag; pp. 216–278.
  • Sass O, Sarcletti S. 2017. Patterns of long-term regeneration of forest fire slopes in the Northern European Alps – a logistic regression approach. Geografiska Annaler: Series A Phys Geogr. 99(1):56–71. doi:10.1080/04353676.2016.1263131
  • San-Miguel-Ayanz J, Carlson JD, Alexander M, Tolhurst K, Morgan G, Sneeuwjagt R. 2003. Current methods to assess fire danger potential. In: Chuvieco E, editor. Wildland fire danger estimation and mapping. The role of remote sensing data. Singapore: World Scientific; pp. 21–61.
  • Schwartz MW, Butt N, Dolanc CR, Holguin A, Moritz MA, North MP, Safford HD, Stephenson NL, Thorne JH, van Mantgem PJ. 2015. Increasing elevation of fire in the Sierra Nevada and implications for forest change. Ecosphere. 6(7):121. doi:10.1890/ES15-00003.1
  • Syphard AD, Radeloff VC, Keuler NS, Taylor RS, Hawbaker TJ, Stewart SI, Clayton MK. 2008. Predicting spatial patterns of fire on a southern California landscape. Int J Wildland Fire. 17(5):602–613.
  • Swets JA. 1988. Measuring the accuracy of diagnostic systems. Science. 240(4857):1285–1293.
  • Shlisky A, Waugh J, Gonzalez P, Gonzalez M, Manta H, Santoso H, Alvarado A, Ainuddin Nuruddin DA, Rodríguez-Trejo R, Swaty D, Schmidt M, Kaufmann R, Myers R, et al. 2007. Fire, ecosystems and people: threats and strategies for global biodiversity conservation. GFI Technical Report 2007-2. The Nature Conservancy. Arlington, VA.
  • Schroeder W, Prins E, Giglio L, Csiszar I, Schmidt C, Morisette J, Morton D. 2008. Validation of GOES and MODIS active fire detection products using ASTER and ETM + data. Rem Sens Environ. 112(5):2711–2726. doi:10.1016/j.rse.2008.01.005
  • Scholes RJ, Pickett G, Ellery WN, Blackmore AC. 1997. Plant functional types in African Savannas and grasslands. In Smith TM, Shugart HH, and Woodward FI, editors. Plant functional types. IGBP Book Series No. 1. Cambridge, UK: Cambridge University Press; pp. 255–268.
  • Timberlake J, Golding J, Clarke P. 2004. Niassa Botanical Expedition June 2003. Prepared for Sociedade para a Gestão e Desenvolvimento da Reserva do Niassa Moçambique, Biodiversity, N. 12.
  • Taylor SW, Woolford DG, Dean CB, Martell DL. 2013. Wildfire prediction to inform fire management: statistical science challenges. Statist Sci. 28(4):586–615.
  • Trapnell CG. 1959. Ecological results of woodland and burning experiments in Northern Rhodisia. J Ecol. 47(1):129–168. Vol.
  • Turner JA, Lillywhite JWW, Pieslak Z. 1961. Forecasting for forest fire services. Technical note No.42. Geneva, Switzerland: Word Metereological Organization (WMO).
  • Trollope WS, Trollope LA. 2004. Fire effects and management in Africa grasslands and savannas. In: Range and animal sciences and resource management.
  • Wotton BM, Nock CA, Flannigan MD. 2010. Forest fire occurrence and climate change in Canada. Int J Wildland Fire. 19(3):253–271.
  • Villagarcía T. 2006. “Regresión”, Curso de Metodología de Investigación Cuantitativa. Técnicas Estadísticas. CSIC.
  • Yakubu I, Mireku-Gyimah D, Duker AA. 2015. Review of methods for modelling forest fire risk and hazard. Afr J Environ Sci Technol. 9(3):155–165.
  • Ye T, Wang Y, Guo Z, Li Y. 2017. Factor contribution to fire occurrence, size, and burn probability in a subtropical coniferous forest in East China. PLoS One. 12(2):e0172110.
  • Zhang Y, Lim S, Sharples JJ. 2016. Modelling spatial patterns of wildfire occurrence in South-Eastern Australia. Geomat Nat Hazards Risk. 7(6):1800–1815. doi:10.1080/19475705.2016.1155501
  • Zhang HJ, Han XY, Dai S. 2013. Fire occurrence probability mapping of Northeast China with binary logistic regression model. IEEE J Sel Top Appl Earth Observations Remote Sensing. 6(1):121–127.
  • Zarco-Tejada PJ, Rueda CA, Ustin SL. 2003. Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing Environ. 85(1):109–124.
  • Zumbrunnen T, Pezzatti G, Menendez P, Bugmann H, Bürgi M, Conedera M. 2011. Weather and human impacts on forest fires: 100 years of fire history in two climatic regions of Switzerland. For Ecol Manag. 261(12):2188–2199.