Factors influencing the formation of unburned forest islands within the perimeter of a large forest fire

Román-Cuesta R.M., Gracia M., Retana J. (2009) Factors influencing the formation of unburned forest islands within the perimeter of a large forest fire. Forest Ecology and Management. 258: 71-80.
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Doi: 10.1016/j.foreco.2009.03.041

Resum:

Large forest fires have recently increased in frequency and severity in many ecosystems. Due to the heterogeneity in fuels, weather and topography, these large fires tend to form unburned islands of vegetation. This study focuses on a large forest fire that occurred in north-eastern Spain in 1998, which left large areas of unburned vegetation within its perimeter. Based on a satellite post-fire severity map we searched for the relative influence of biotic and abiotic factors leading to unburned island formation. We divided the area of the fire into individual units we called "slopes" which were meant to separate the differential microclimatic effects of contrasted aspects. The number of unburned islands and their areas were related to 12 variables that influence their formation (i.e. land cover composition, aspect, steepness, forest structure, two landscape indices and weather variables). We hypothesized that unburned vegetation islands would concentrate on northern aspects, in less flammable forests (i.e. broadleaf species) and higher fragmentation to interrupt the advance of fire. While north and western aspects did have a higher presence of unburned vegetation islands, our study suggests greater presence of islands in slopes that are larger (i.e. more continuous areas with relatively homogeneous aspect), with greater proportions of forest cover, with higher wood volumes and with lower proportions of broadleaf species. Climate also played a role, with relative humidity and wind speed positively and negatively correlated to island formation, respectively. Unburned vegetation was more frequent on slopes with lower diversity of land covers and higher dominance of one land cover in the slope. Since slopes with only one land cover (i.e. forests) had more islands than slopes with multiple cover types, we infer that under severe meteorological conditions, fragmented forests can be more affected by wind and by water stress, thus burning more readily than forests that are protected from this edge phenomenon. These results would reinforce forest management strategies that avoid linear features (fire-lines and fire-breaks), to enhance fuel treatments that focus on areas and minimize fragmentation. © 2009 Elsevier B.V. All rights reserved.

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A quantitative comparison of methods for classifying burned areas with LISS-III imagery

Román-Cuesta R.M., Retana J., Gracia M., Rodríguez R. (2005) A quantitative comparison of methods for classifying burned areas with LISS-III imagery. International Journal of Remote Sensing. 26: 1979-2003.
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Doi: 10.1080/01431160512331299315

Resum:

Environmental agencies frequently require tools for quick assessments of areas affected by large fires. Remote sensing techniques have been reported as efficient tools to evaluate the effects of fire. However, there exist few quantitative comparisons about the performance of the diverse methods. This study quantitatively evaluated the accuracy of five different techniques, a field survey and four satellite-based techniques, in order to quickly classify a large forest fire that occurred in 1998 in Solsonès (north-east Spain) by means of an IRS LISS-III image. Three pure classes were determined: burned area, unburned vegetation, and bare soil; along with a non-pure class that we called mixed area. These selected techniques were included into a tree classifier to investigate their partial contribution to the final classification. The most accurate methods when focusing on pure classes were those directly related to the spectral characteristics of the pixel: Reflectance Data and Spectral Unmixing (82% of overall accuracy), versus the poorer performances of Vegetation Indices (70%), Textural measures (72%) and the field survey (68.6%). Since no image processing technique was applied to the Raw Reflectance Data, it can be considered the most cost-effective method, and the tree classifier reinforces its importance. The results of this study reveal that time consuming and expensive methods are not necessarily the most accurate, especially when potentially easily distinguishable classes are involved. © 2005 Taylor & Francis Group Ltd.

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