Mediterranean vegetation is strongly subjected to the risk of wild res, which can become a major cause of land degradation. The knowledge of the spatial variations of this risk is essential, therefore, for forest resource management. Relying on the fact that diOE erent vegetation types can be associated with diOE erent risk levels, a classi cation approach based on the use of Landsat Thematic Mapper (TM) scenes is currently proposed for the generation of maps related to re risk. Hard and fuzzy classi cations were tested for this purpose on Elba island (central Italy), taking into account the eOE ects of the use of scenes from diOE erent periods (spring and summer) and of ancillary data. The re risk images obtained were evaluated by comparison with the re events that occurred on the island during the last decade. The results show that, while the acquisition period has only minor eOE ects, classi cation accuracy is strongly dependent on the inclusion of ancillary data. Moreover, the fuzzy approach better exploits the information of the integrated datasets, producing maps which are temporally stable and highly indicative of the re risk in the study area.

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The evaluation offorest fire risk is an important issue in Mediterranean areas where the long arid season often creates favourable conditions for the occurrence of fires. In this Letter three indices related to this risk have been produced and compared for the western part of the Elba Island (Central Italy). The first index is based on the analysis of environmental information layers (topography, vegetation and soil type) within a Land Information System, while the other two are derived from a summer Landsat Thematic Mapper (TM) scene processed by unsupervised and supervised procedures, respectively. The results show the effectiveness of all these approaches, and, in particular, a greater accuracy of the supervised spectral index.

A debate is in progress concerning the possible effects of climate changes on the primary production of both natural and artificial ecosystems. The current investigation builds on the hypothesis that trends of increasing air temperature observed in several Italian regions should positively affect productivity of mountain forest ecosystems. Temperature rise in the Mugello valley (central Italy) in the period 1986–2001 was first confirmed by the analysis of data from a local station. The effects of this rise on the productivity of deciduous forest ecosystems (dominated by beech, Fagus sylvatica L.) were then analysed through estimates of the fraction of absorbed photosynthetically active radiation (FAPAR) derived from the US National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer satellite normalized difference vegetation index (NDVI) data. The use of a simplified parametric model (C-Fix) then allowed the combination of these FAPAR estimates with meteorological data (temperature and radiation) to produce annual values of forest gross primary productivity (GPP). Finally, validation of these GPP estimates was carried out by a comparison with dendrochronological measurements taken in the study forests. Because tree measurements were affected by external factors not exclusively related to forest GPP (stand aging, management practices, etc.), the comparison gave positive results only after applying a detrending operation to both series of annual GPP estimates and dendrochronological data. These results are a first indication that the rise in temperature that has occurred in Italy in the last decades has positively affected the productivity of mountain forest ecosystems.

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