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Article << Previous     |     Next >>   Contents Vol 21(1)

Fire frequency analysis in Portugal (1975–2005), using Landsat-based burnt area maps

Sofia L. J. Oliveira A C , José M. C. Pereira A and João M. B. Carreiras B

A Forest Research Centre, School of Agriculture, Technical University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal.
B Geoinformation for Development Centre, Department of Natural Sciences, Tropical Research Institute, R. Joao de Barros 27, 1300-319 Lisbon, Portugal.
C Corresponding author. Email address: sloliveira@isa.utl.pt

International Journal of Wildland Fire 21(1) 48-60 http://dx.doi.org/10.1071/WF10131
Submitted: 23 October 2010  Accepted: 24 April 2011   Published: 24 October 2011


 
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Abstract

Fire frequency in 21 forest planning regions of Portugal during the period 1975–2005 was estimated from historical burnt area maps generated with semi-automatic classification of Landsat Thematic Mapper (TM) satellite imagery. Fire return interval distributions were modelled with the Weibull function and the estimated parameters were used to calculate regional mean, median and modal fire return intervals, as well as regional hazard functions. Arrangement of the available data into three different time series allowed for assessment of the effects of minimum mapping unit, time series length and use of censored data on the Weibull function parameter estimates. Varying the minimum mapping unit between 5 and 35 ha had a negligible effect on parameter estimates, whereas changing the time series length from 22 to 31 years substantially affected the estimates. However, the strongest effect was caused by censored data. Its exclusion led to substantial overestimation of fire frequency and of burning probability dependence on fuel age. We estimated a country-wide mean fire interval of 36 years and an annual burnt area of 1.2%. Regional variations in fire frequency descriptors were interpreted in terms of land cover and land use practices that affect the contemporary fire regime in Portugal.

Additional keywords: censored data, Weibull model.


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