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[1] This analysis concerns an estimation of burned area and fire severity levels in an area affected by a large wildfire that took place in the south of Spain in July 2004. Fire severity is defined in this work as the impact of fire on the vegetation. The objective was to find an efficient method for quick fire severity mapping based on remote sensing techniques that can be useful for postfire forest management. Several methods for image analysis (Linear Spectral Unmixing, Matched Filtering and Normalized Burn Ratio Index) were applied to postfire Landsat 5-TM, Envisat-MERIS, and Terra-MODIS images. Maps depicting fire severity of three levels of an acceptable reliability were obtained using a small amount of field data and following a simple method of processing. Linear spectral unmixing produced the best classifications for MERIS and MODIS images, while the matched filtering technique produced the most accurate classification for the TM image. These preliminary results show that short-term fire severity maps can be obtained by means of high-to medium-resolution postfire remote sensing data, in order to evaluate the situation after a forest fire and plan forest restoration works.Citation: Roldán-Zamarrón, A., S. Merino-de-Miguel, F. González-Alonso, S. García-Gigorro, and J. M. Cuevas (2006), Minas de Riotinto (south Spain) forest fire: Burned area assessment and fire severity mapping using Landsat 5-TM, Envisat-MERIS, and Terra-MODIS postfire images,

Remotely sensed data with different spatial resolutions are being used as the primary information source for the analysis of forest fragmentation. However, there is currently a lack of appropriate methods that allow for the comparison of forest fragmentation estimates across various spatial scales. To provide insights into this problem we analyzed a forested study area in central Spain and a set of 10 widely used fragmentation indices. Forests were mapped from two simultaneously gathered satellite images with different spatial resolutions, 30 m (Landsat-TM) and 188 m (IRS-WiFS). TM forest data were transferred to WiFS resolution through different aggregation rules and compared with actual WiFS data. We found that incorporating sensor point spread function (which replicates the real way in which remote sensors acquire radiation from the ground) greatly improved comparability of forest fragmentation indices. We found a poor performance of power scaling laws for estimating forest fragmentation at finer spatial resolutions, and suggest that the true accuracy and practical utility of these scaling functions may have been overestimated in previous literature. Finally, we report an unstable behavior of three cell-based fragmentation indices (clumpiness, aggregation, and patch cohesion indices), for which spuriously high values can be obtained by resampling forest data to finer spatial resolutions. We believe that the results and guidelines provided may significantly contribute to an adequate analysis and comparison across scales of forest fragmentation estimations. FOR. SCI. 51(1):51–63.

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