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Modelling the phenological niche of large fires with remotely sensed NDVI profiles

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  • De Angelis, Antonella
  • Bajocco, Sofia
  • Ricotta, Carlo

Abstract

Although the area burned by single wildfires may range over many orders of magnitude, only a handful papers has explored the factors that contribute to drive fire ignitions into large burns. While in southern Europe most fire ignitions are of human origin, fire propagation is mostly influenced by weather conditions, topography and fuel type. In this framework, the phenological status of vegetation represents the primary driver influencing fuel availability and moisture content. Therefore, any investigation on fire behavior requires the capability of capturing spatio-temporal changes in coarse-scale vegetation phenology that are descriptive of changes in fuel conditions. The aim of this study is thus to apply a habitat suitability modelling tool originally developed in wildlife science for reconstructing the phenological niche of the ignition points of wildfires from remotely-sensed multitemporal NDVI profiles in Sardinia (Italy) in the period of 2000–2008. Overall, our findings indicate that wildfires occur preferentially in remotely-sensed phenological conditions that considerably differ from the mean phenological conditions in Sardinia. When compared to the entire set of ignition points (irrespective of the resulting fire size), ignitions resulting in burned areas larger than 20ha are preferentially associated to a longer and later fire season showing also marked preference for higher fuel loads.

Suggested Citation

  • De Angelis, Antonella & Bajocco, Sofia & Ricotta, Carlo, 2012. "Modelling the phenological niche of large fires with remotely sensed NDVI profiles," Ecological Modelling, Elsevier, vol. 228(C), pages 106-111.
  • Handle: RePEc:eee:ecomod:v:228:y:2012:i:c:p:106-111
    DOI: 10.1016/j.ecolmodel.2012.01.003
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    1. Chuvieco, Emilio & Aguado, Inmaculada & Yebra, Marta & Nieto, Héctor & Salas, Javier & Martín, M. Pilar & Vilar, Lara & Martínez, Javier & Martín, Susana & Ibarra, Paloma & de la Riva, Juan & Baeza, J, 2010. "Development of a framework for fire risk assessment using remote sensing and geographic information system technologies," Ecological Modelling, Elsevier, vol. 221(1), pages 46-58.
    2. Bajocco, S. & Rosati, L. & Ricotta, C., 2010. "Knowing fire incidence through fuel phenology: A remotely sensed approach," Ecological Modelling, Elsevier, vol. 221(1), pages 59-66.
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    1. Xiaowei Li & Gang Zhao & Xiubo Yu & Qiang Yu, 2014. "A comparison of forest fire indices for predicting fire risk in contrasting climates in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 70(2), pages 1339-1356, January.

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