An interpretation framework for fire events and post-fire dynamics in Ayora/Spain using time-series of Landsat-TM and -MSS data.

The weight of fire as an environmental concern significantly increased in the second half of the past century, often as a consequence of dramatic land-use changes experienced in many countries. While a variety of prevention and restoration initiatives have been taken, the difficulty to monitor their effects over long periods and for large areas has been noted. This suggests the utilisation of remote sensing data, which may be employed to perform retrospective studies and evaluate the impact of past management actions. Mapping fires and characterising post-fire dynamics have been the target of numerous studies. For global to regional scale, these are often based on small-scale sensor systems, such as NOAA-AVHRR, SPOT-Vegetation or MODIS (e.g., Barbosa et al., 1999), while local studies requiring higher levels of detail make use of medium-resolution data, such as Landsat-TM or SPOT (e.g., Garcia-Haro et al. 2001). Concerning target variables, Elmore et al. (2000) have demonstrated limitations of using NDVI in semi-arid areas, and suggested to employ Spectral Mixture Analysis (SMA) to derive quantitative vegetation estimates. In the current study, a long time series has been procured for a test site in the Ayora region (Eastern Spain). Based on geometrically corrected data, full radiometric processing has been applied, making use of a modified 5S Code (Tanre et al., 1990), and incorporating a correction accounting for topography-induced illumination variations (Hill et al., 1995). Subsequently, SMA has been applied, using a 3 endmember model to derive quantitative estimates of proportional vegetation cover, soil and bedrock background, and a shade component accounting for micro-shading effects. Making use of these information layers, an interpretation framework has been developed to support the creation of vegetation cover maps, the identification of fire events and perimeters, and the quantitative and qualitative assessment of vegetation recovery following the fires.