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Phenological variability drives the distribution of wildfires in Sardinia

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Abstract

Fuel characteristics play an important role in driving fire ignition and propagation; at the landscape scale fuel availability and flammability are closely related to vegetation phenology. In this view, the NDVI profiles obtained from high temporal resolution satellites, like MODIS, are an effective tool for monitoring the coarse-scale vegetation seasonal timing. The aim of this paper is twofold: our first objective consists in classifying by means of multitemporal NDVI profiles the coarse-scale vegetation of Sardinia into ‘phenological clusters’ in which fire incidence is higher (preferred) or lower (avoided) than expected from a random null model. If fires would burn unselectively, then fires would occur randomly across the landscape such that the number of fires in a given phenological cluster would be nearly proportional to the relative area of that land cover type in the analyzed landscape. Actually, certain vegetation types are more fire-prone than others. That is, they are burnt more frequently than others. In this framework, our second objective consists in investigating the temporal parameters of the remotely sensed NDVI profiles that best characterize the observed phenology–fire selectivity relationship. The results obtained show a good association between the NDVI temporal profiles and the spatio-temporal wildfire distribution in Sardinia, emphasizing the role of bioclimatic timing in driving fire regime characteristics.

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References

  • Ahl DE, Gower ST, Burrows SN, Shabanov NV, Myneni RB, Knyazikhin Y (2006) Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sens Environ 104:88–95

    Article  Google Scholar 

  • Akther MS, Hassan QK (2011) Remote sensing-based assessment of fire danger conditions over boreal forests. IEEE J Sel Top Appl Earth Obs 4:992–999

    Article  Google Scholar 

  • Allan G, Johnson A, Cridland S, Fitzgerald N (2003) Application of NDVI for predicting fuel curing at landscape scales in northern Australia: can remotely sensed data help schedule fire management operations? Int J Wildland Fire 12:299–308

    Article  Google Scholar 

  • Ambrosia VG, Buechel SW, Brass JA, Peterson JR, Davies RH, Kane RJ, Spain S (1998) An integration of remote sensing, GIS, and information distribution for wildfire detection and management. Photogramm Eng Remote Sens 64:977–985

    Google Scholar 

  • Anderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecol 26:32–46

    Google Scholar 

  • Bachelet D, Lenihan JM, Daly C, Neilson RP (2000) Interactions between fire, grazing and climate change at Wind Cave National Park, SD. Ecol Model 134:229–244

    Article  CAS  Google Scholar 

  • Bajocco S, Ricotta C (2008) Evidence of selective burning in Sardinia (Italy): which land cover classes do wildfires prefer? Landscape Ecol 23:241–248

    Article  Google Scholar 

  • Bajocco S, Pezzatti GB, Mazzoleni S, Ricotta C (2010a) Wildfire seasonality and land use: when do wildfires prefer to burn? Environ Monit Assess 164:445–452

    Article  PubMed  CAS  Google Scholar 

  • Bajocco S, Rosati L, Ricotta C (2010b) Knowing fire incidence through fuel phenology: a remotely sensed approach. Ecol Model 221:59–66

    Article  Google Scholar 

  • Bajocco S, Salvati L, Ricotta C (2011) Land degradation vs. fire: a spiral process? Prog Phys Geogr 35:3–18

    Article  Google Scholar 

  • Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical-theoretic approach. Springer, New York

    Google Scholar 

  • Caccamo G, Chisholm LA, Bradstock RA, Puotinen ML, Pippen BG (2011) Monitoring live fuel moisture content of heathland, shrubland and sclerophyll forest in south-eastern Australia using MODIS data. Int J Wildland Fire 21:257–269

    Article  Google Scholar 

  • Calcagno V, De Mazancourt C (2010) glmulti: an R package for easy automated model selection with (generalized) linear models. J Stat Softw 34:29. http://www.jstatsoft.org/v34/i12/paper

    Google Scholar 

  • Catry FX, Rego FC, Silva JS, Moreira F, Camia A, Ricotta C, Conedera M (2010) Fire starts and human activities. In: Silva JS, Rego F, Fernandes P, Rigolot E (eds) Towards integrated fire management—outcomes of the European project fire paradox. European Forest Institute, Joensuu, pp 9–22

    Google Scholar 

  • Chéret V, Denux JP (2007) Mapping wildfire danger at regional scale with an index model integrating coarse spatial resolution remote sensing data. J Geophys Res 112:G02006

    Article  Google Scholar 

  • Chuvieco E, Riaño D, Van Wagtendok J, Morsdof F (2003) Fuel loads and fuel type mapping. In: Chuvieco E (ed) Wildland fire danger estimation and mapping. The role of remote sensing data. World Scientific Publishing, Singapore, pp 119–142

    Chapter  Google Scholar 

  • Conedera M, Torriani D, Neff C, Ricotta C, Bajocco S, Pezzatti GB (2011) Using Monte Carlo simulations to estimate relative fire ignition danger in a low-to-medium fire-prone region. For Ecol Manag 261:2179–2187

    Article  Google Scholar 

  • Conti F, Abbate G, Alessandrini A, Blasi C (2005) An annotated checklist of the Italian vascular flora. Palombi Editore, Roma

    Google Scholar 

  • Cumming SG (2001) Forest type and wildfire in the Alberta boreal mixedwood: what do fires burn? Ecol Appl 11:97–110

    Article  Google Scholar 

  • Dennison PE, Roberts DA, Peterson SH, Rechel J (2005) Use of normalized difference water index for monitoring live fuel moisture. Int J Remote Sens 26:1035–1042

    Article  Google Scholar 

  • Desbois N, Vidal A (1996) Real-time monitoring of vegetation flammability using NOAA-AVHRR thermal infrared data. EARSeL Adv Remote Sens 4:25–32

    Google Scholar 

  • Elmore AJ, Asner GP (2005) Satellite monitoring of vegetation phenology and fire fuel conditions in Hawaiian drylands. Earth Interact 9:1–21

    Article  Google Scholar 

  • Gabban A, San-Miguel-Ayanz J, Barbosa P, Libertá G (2006) Analysis of NOAA-AVHRR NDVI inter-annual variability for forest fire risk estimation. Int J Remote Sens 27:1725–1732

    Article  Google Scholar 

  • Guglietta D, Conedera M, Mazzoleni S, Ricotta C (2011) Mapping fire ignition risk in a complex anthropogenic landscape. Remote Sens Lett 2:213–219

    Article  Google Scholar 

  • Gute BD, Basak SC, Mills D, Hawkins DM (2002) Tailored similarity spaces for the prediction of physicochemical properties. Internet Electron J Mol Des 1:374–387

    CAS  Google Scholar 

  • Halkidi M, Batistakis Y, Vazirgiannis M (2001) On clustering validation techniques. J Intell Inf Syst 17:107–145

    Article  Google Scholar 

  • Hassan QK, Bourque CPA, Meng FR, Cox RM (2007) A wetness index using terrain-corrected surface temperature and normalized difference vegetation index derived from standard MODIS products: an evaluation of its use in a humid forest-dominated region of eastern Canada. Sensors 7:2028–2048

    Article  Google Scholar 

  • Hèly C, Flannigan MD, Bergeron Y, McRae DJ (2001) Role of vegetation and weather on fire behavior in the Canadian mixedwood boreal forest using two fire behavior prediction systems. Can J For Res 31:430–441

    Article  Google Scholar 

  • Hicke JA, Asner GP, Kasischke ES, French NHF, Randerson JT, Collatz GJ, Stocks BJ, Tucker CJ, Los SO, Field CB (2003) Postfire response of North American boreal forest net primary productivity analyzed with satellite observations. Glob Change Biol 9:1145–1157

    Article  Google Scholar 

  • ISPRA—Istituto Superiore per la Protezione e la Ricerca Ambientale (2010) La realizzazione in Italia del Progetto Corine Land Cover 2006. Rapporto 131/2010. ISPRA, Rome

  • Keane RE, Mincemoyer SA, Schmindt KM, Garner JL, Long DG (2000) Mapping vegetation and fuels for fire management on the Gila National Forest Complex, New Mexico. USDA Forest Service, Rocky Mountain Research Station, Ogden. General Technical Report RMRS-GTR-46-CD

  • Keeley JE, Fotheringham CJ (2003) Impact of past, present, and future fire regimes on North American Mediterranean shrublands. In: Veblen TT, Baker WL, Montenegro G, Swetnam TW (eds) Fire and climatic change in temperate ecosystems of the western Americas. Springer, New York, pp 218–262

    Chapter  Google Scholar 

  • Leblon B (2005) Monitoring forest fire danger with remote sensing. Nat Hazards 35:343–359

    Article  Google Scholar 

  • Leblon B, Chen J, Alexander ME, White S (2001) Fire danger monitoring using NOAA-AVHRR NDVI images in the case of northern boreal forests. Int J Remote Sens 22:2839–2846

    Google Scholar 

  • Legendre P, Legendre L (1998) Numerical ecology. Elsevier, Amsterdam

    Google Scholar 

  • Lloret F, Calvo E, Pons X, Diàz-Delgado R (2002) Wildfires and landscape patterns in the Eastern Iberian Peninsula. Landscape Ecol 17:745–759

    Article  Google Scholar 

  • Lozano FJ, Suárez-Seoane S, de Luis E (2007) Assessment of several spectral indices derived from multi-temporal Landsat data for fire occurrence probability modelling. Remote Sens Environ 107:533–544

    Article  Google Scholar 

  • Mermoz M, Kitzberger T, Veblen TT (2005) Landscape influences on occurrence and spread of wildfires in Patagonian forests and shrublands. Ecology 86:2705–2715

    Article  Google Scholar 

  • Moreira F, Rego FC, Ferriera PG (2001) Temporal (1958–1995) pattern of change in a cultural landscape of northwestern Portugal: implications for fire occurrence. Landscape Ecol 16:557–567

    Article  Google Scholar 

  • Mouillot F, Ratte J, Joffre R, Moreno MJ, Rambal S (2003) Some determinants of the spatio-temporal fire cycle in a Mediterranean landscape (Corsica, France). Landscape Ecol 18:665–674

    Article  Google Scholar 

  • Newnham GJ, Verbesselt J, Grant IF, Anderson SAJ (2011) Relative greenness index for assessing curing of grassland fuel. Remote Sens Environ 115:1456–1463

    Article  Google Scholar 

  • Nunes MCS, Vasconcelos MJ, Pereira JMC, Dasgupta N, Alldredge RJ, Rego FC (2005) Land-cover type and fire in Portugal: do fires burn land cover selectively? Landscape Ecol 20:661–673

    Article  Google Scholar 

  • Oswald BP, Fancher JT, Kulhavy DL, Reeves HC (1999) Classifying fuels with aerial photography in East Texas. Int J Wildland Fire 9:301–319

    Article  Google Scholar 

  • Pausas JG (2004) Changes in fire and climate in the eastern Iberian Peninsula (Mediterranean basin). Clim Change 63:337–350

    Article  Google Scholar 

  • Pezzatti GB, Bajocco S, Torriani D, Conedera M (2009) Selective burning of forest vegetation in Canton Ticino (Southern Switzerland). Plant Biosyst 143:609–620

    Article  Google Scholar 

  • Podur JJ, Martell DL (2009) The influence of weather and fuel type on the fuel composition of the area burned by forest fires in Ontario, 1996–2006. Ecol Appl 19:1246–1252

    Article  PubMed  Google Scholar 

  • Pollard KS, van der Laan MJ (2008) Supervised distance matrices. Stat Appl Genet Mol Biol 7:Article 33

    Google Scholar 

  • Reed BC, Brown JF, Vandeer Zee D, Loveland TR, Merchant JW, Ohlen DO (1994) Measuring the phenological variability from satellite imagery. J Veg Sci 5:703–714

    Article  Google Scholar 

  • Riaño D, Chuvieco E, Salas J, Palacios-Orueta A, Bastarrika A (2002) Generation of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems. Can J For Res 32:1301–1315

    Article  Google Scholar 

  • Ricotta C, Moretti M (2010) Assessing the functional turnover of species assemblages with tailored dissimilarity matrices. Oikos 119:1089–1098

    Article  Google Scholar 

  • Roberts DA, Dennison PE, Gardner ME, Hetzel Y, Ustin SL, Lee CT (2003) Evaluation of the potential of Hyperion for fire danger assessment by comparison to the Airborne Visible/Infrared Imaging Spectrometer. IEEE Trans Geosci Remote Sens 41:1297–1310

    Article  Google Scholar 

  • Roberts DA, Dennison PE, Peterson S, Sweeney S, Rechel J (2006) Evaluation of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Moderate Resolution Imaging Spectrometer (MODIS) measures of live fuel moisture and fuel condition in a shrubland ecosystem in southern California. J Geophys Res 111:G04S02

    Article  Google Scholar 

  • Schneider P, Roberts DA, Kyriakidis PC (2008) A VARI-based relative greenness from MODIS data for computing the fire potential index. Remote Sens Environ 112:1151–1167

    Article  Google Scholar 

  • Sharples JJ, McRae RHD, Weber RO (2010) Wind characteristics over complex terrain with implications for bushfire risk management. Environ Modell Softw 25:1099–1120

    Article  Google Scholar 

  • Stolle F, Chomitz KM, Lambin EF, Tomich TP (2003) Land use and vegetation fires in Jambi Province, Sumatra, Indonesia. For Ecol Manag 179:277–292

    Article  Google Scholar 

  • Stow D, Niphadkar M, Kaiser J (2005) MODIS-derived visible atmospherically resistant index for monitoring chaparral moisture content. Int J Remote Sens 26:3867–3873

    Article  Google Scholar 

  • Vàzquez A, Pèrez B, Fernandèz-Gonzàlez F, Moreno JM (2002) Recent fire regime characteristics and potential natural vegetation relationships in Spain. J Veg Sci 13:663–676

    Article  Google Scholar 

  • Verbesselt J, Somers B, Lhermitte S, Jonckheere I, van Aardt J, Coppin P (2007) Monitoring herbaceous fuel moisture content with SPOT VEGETATION time-series for fire risk prediction in savanna ecosystems. Remote Sens Environ 108:357–368

    Article  Google Scholar 

  • Vidal A, Devaux-Ros C (1995) Evaluating forest fire hazard with a Landsat TM derived water stress index. Agr For Meteorol 77:207–224

    Article  Google Scholar 

  • Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, Gao F, Reed BC, Huete A (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84:471–475

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Environmental Biology, University of Rome “La Sapienza”, Piazzale Aldo Moro 5, 00185, Rome, Italy

    Antonella De Angelis & Carlo Ricotta

  2. National Council for Research in Agriculture, Unit of Climatology and Meteorology Applied to Agriculture (CRA-CMA), Via del Caravita 7a, 00186, Rome, Italy

    Sofia Bajocco

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  1. Antonella De Angelis
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  2. Sofia Bajocco
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  3. Carlo Ricotta
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Correspondence to Carlo Ricotta.

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De Angelis, A., Bajocco, S. & Ricotta, C. Phenological variability drives the distribution of wildfires in Sardinia. Landscape Ecol 27, 1535–1545 (2012). https://doi.org/10.1007/s10980-012-9808-2

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