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This paper investigates the use of an ecosystem simulation model, FOREST-BGC, to estimate the main ecophysiological processes (transpiration and photosynthesis) of Mediterranean coastal forest areas using remotely sensed data. Model testing was carried out at two protected forest sites in central Italy, one of which was covered by Turkey oak (Circeo National Park) and the other by holm-oak (Castelporziano Estate). At both sites, transpiration and photosynthesis measurements were collected in the field during the growing seasons over a four-year period (1999 and 2001 for the Turkey oak; 1997, 1999 and 2000 for the holm-oak). Calibration of the model was obtained through combining information derived from ground measurements and remotely sensed data. In particular, remote sensing estimates of the Leaf Area Index derived from 1 × 1-km NOAA AVHRR Normalized Difference Vegetation Index data were used to improve the adaptation of the model to local forest conditions. The results indicated different strategies regarding water use efficiency, ‘water spending’ for Turkey oak and ‘water saving’ for holm-oak. The water use efficiency for the holm-oak was consistently higher than that for the Turkey oak and the relationship between VPD and WUE for the holm-oak showed a higher coefficient of determination ( R2 = 0.9238). Comparisons made between the field measurements of transpiration and photosynthesis and the model estimates showed that the integration procedure used for the deciduous oak forest was effective, but that there is a need for further studies regarding the sclerophyllous evergreen forest. In particular, for Turkey oak the simulations of transpiration yielded very good results, with errors lower than 0.3 mm H2O/day, while the simulation accuracy for photosynthesis was lower. In the case of holm-oak, transpiration was markedly overestimated for all days considered, while the simulations of photosynthesis were very accurate. Overall, the approach offers interesting operational possibilities for the monitoring of Mediterranean forest ecosystems, particularly in view of the availability of new satellite sensors with a higher spatial and temporal resolution, which have been launched in recent years. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim To understand how vegetation mediates the interplay between fire and climate. Specifically, we predict that neither the switching of climatic conditions to high flammability nor the sensitivity of fire to such conditions are universal, but rather depend on fuel (vegetation) structure, which in turn changes with productivity. Location An aridity/productivity gradient on the Iberian Peninsula (Mediterranean Basin). Methods We defined 13 regions distributed along an aridity gradient, which thus differ in productivity and fuel structure. We then assessed the changes in the temporal fire-climate relationship across regions. Specifically, for each region we estimated three variables: the aridity level for switching to flammable conditions (i.e. climatic conditions conducive to fire), the frequency of these flammable conditions and the area burnt under such conditions. These variables were then related to regional aridity and fuel structure indicators. Results In mediterranean ecosystems, the aridity level for switching to flammable conditions increased along the aridity gradient. Differences in fire activity between regions were not explained by the frequency of flammable conditions but by the sensitivity of fire to such conditions, which was higher in wetter and more productive regions. Main conclusions Under mediterranean climatic conditions, fuel structure is more relevant in driving fire activity than the frequency of climatic conditions conducive to fire. At a global scale, fuel also drives the fire-climate relationship because it determines the climatic (aridity) threshold for switching to flammable conditions. Our results emphasize the role of landscape structure in shaping current and future fire-climate relationships at a regional scale, and suggest that future changes in the fire regime (i.e. under global warming) might be different from what it is predicted by climate alone. [ABSTRACT FROM AUTHOR]