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Abstract
Disturbances spreading through the landscape, like wildfires, are essential processes in modeling landscape structure and dynamics. Like other disturbances, fire may spread from a local epicenter with a propagation rate enhanced or retarded by the spatial arrangement of fuel across the landscape. Therefore, fire ignition and spread are a direct consequence of the presence and arrangement of fire-prone habitats. Generalizing the concept of “habitat selection” to every spatially distributed ecological process, the resource selection functions used in zoology to summarize habitat use by wildlife can be also used to characterize the wildfire’s pattern across the landscape. The aim of this paper is thus to quantify the relationship between forest cover and burnt area in Canton Ticino (Switzerland) during 1980–2007 using a bootstrap test of significance: that is, to identify forest types that burn more (or less) than expected from a random null model based on the regional availability of the resource (forest type). The results show that fires behave selectively for most forest types; whereas chestnut stands and broad-leaved forests display overproportional burnt areas, coniferous forests typically burn less than expected by a random null model.
Abstract
Disturbances spreading through the landscape, like wildfires, are essential processes in modeling landscape structure and dynamics. Like other disturbances, fire may spread from a local epicenter with a propagation rate enhanced or retarded by the spatial arrangement of fuel across the landscape. Therefore, fire ignition and spread are a direct consequence of the presence and arrangement of fire-prone habitats. Generalizing the concept of “habitat selection” to every spatially distributed ecological process, the resource selection functions used in zoology to summarize habitat use by wildlife can be also used to characterize the wildfire’s pattern across the landscape. The aim of this paper is thus to quantify the relationship between forest cover and burnt area in Canton Ticino (Switzerland) during 1980–2007 using a bootstrap test of significance: that is, to identify forest types that burn more (or less) than expected from a random null model based on the regional availability of the resource (forest type). The results show that fires behave selectively for most forest types; whereas chestnut stands and broad-leaved forests display overproportional burnt areas, coniferous forests typically burn less than expected by a random null model.
Abstract
Satellite-based strategies for burned area mapping may rely on two types of remotely sensed data: postfire reflectance images and active fire detection. This study uses both methods in a synergistic way. In particular, burned area mapping is carried out using MCD43B4 [Moderate Resolution Imaging Spectrometer (MODIS); Terra + Aqua nadir bidirectional reflectance distribution function (BRDF); adjusted reflectance 16-day L3 global 1-km sinusoidal grid V005 (SIN)] postfire datasets and MODIS active fire products. The developed methodology was tested in Colombia, an area not covered by any known MODIS ground antenna, using data from 2004. The resulting burned area map was validated using a high-spatial-resolution Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image and compared to two global burned area products: L3JRC (terrestrial ecosystem monitoring global burnt area product) and MCD45A1 (MODIS Terra + Aqua burned area monthly global 500-m SIN grid V005). The results showed that this method would be of great interest at regional to national scales because it proved to be quick, accurate, and cost effective.
Abstract
Satellite-based strategies for burned area mapping may rely on two types of remotely sensed data: postfire reflectance images and active fire detection. This study uses both methods in a synergistic way. In particular, burned area mapping is carried out using MCD43B4 [Moderate Resolution Imaging Spectrometer (MODIS); Terra + Aqua nadir bidirectional reflectance distribution function (BRDF); adjusted reflectance 16-day L3 global 1-km sinusoidal grid V005 (SIN)] postfire datasets and MODIS active fire products. The developed methodology was tested in Colombia, an area not covered by any known MODIS ground antenna, using data from 2004. The resulting burned area map was validated using a high-spatial-resolution Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image and compared to two global burned area products: L3JRC (terrestrial ecosystem monitoring global burnt area product) and MCD45A1 (MODIS Terra + Aqua burned area monthly global 500-m SIN grid V005). The results showed that this method would be of great interest at regional to national scales because it proved to be quick, accurate, and cost effective.
Abstract
This article presents a new method for burned area mapping using high-resolution satellite images in the Mediterranean ecosystem. In such a complex environment, high-resolution satellite images represent an appropriate data source for identifying fire-affected areas, and single postfire data are often the only available source of information. The method proposed here integrates several spectral indices into a fuzzy synthetic indicator of likelihood of burn. The indices are interpreted through fuzzy membership functions that have been derived with a partially data-driven approach exploiting training data and expert knowledge. The final map of fire-affected areas is produced by applying a region growing algorithm on the basis of seed pixels selected on a conservative threshold of the synthetic fuzzy score. The algorithm has been developed and tested on a set of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes acquired over Southern Italy. Validation showed that the accuracy of the burned area maps is comparable or even better [overall accuracy (OA) > 90%, K > 0.76] than that obtained with approaches based on single index thresholds adapted to each image. The method described here provides an automatic approach for mapping fire-affected areas with very few false alarms (low commission error), whereas omission errors are mainly related to undetected small burned areas and are located in heterogeneous sparse vegetation cover.
Abstract
This article presents a new method for burned area mapping using high-resolution satellite images in the Mediterranean ecosystem. In such a complex environment, high-resolution satellite images represent an appropriate data source for identifying fire-affected areas, and single postfire data are often the only available source of information. The method proposed here integrates several spectral indices into a fuzzy synthetic indicator of likelihood of burn. The indices are interpreted through fuzzy membership functions that have been derived with a partially data-driven approach exploiting training data and expert knowledge. The final map of fire-affected areas is produced by applying a region growing algorithm on the basis of seed pixels selected on a conservative threshold of the synthetic fuzzy score. The algorithm has been developed and tested on a set of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes acquired over Southern Italy. Validation showed that the accuracy of the burned area maps is comparable or even better [overall accuracy (OA) > 90%, K > 0.76] than that obtained with approaches based on single index thresholds adapted to each image. The method described here provides an automatic approach for mapping fire-affected areas with very few false alarms (low commission error), whereas omission errors are mainly related to undetected small burned areas and are located in heterogeneous sparse vegetation cover.
