Evaluating the Effects of Environmental Changes on the Gross Primary Production of Italian Forests
AbstractA ten-year data-set descriptive of Italian forest gross primary production (GPP) has been recently constructed by the application of Modified C-Fix, a parametric model driven by remote sensing and ancillary data. That data-set is currently being used to develop multivariate regression models which link the inter-year GPP variations of five forest types (white fir, beech, chestnut, deciduous and evergreen oaks) to seasonal values of temperature and precipitation. The five models obtained, which explain from 52% to 88% of the inter-year GPP variability, are then applied to predict the effects of expected environmental changes (+2 °C and increased CO2 concentration). The results show a variable response of forest GPP to the simulated climate change, depending on the main ecosystem features. In contrast, the effects of increasing CO2 concentration are always positive and similar to those given by a combination of the two environmental factors. These findings are analyzed with reference to previous studies on the subject, particularly concerning Mediterranean environments. The analysis confirms the plausibility of the scenarios obtained, which can cast light on the important issue of forest carbon pool variations under expected global changes.
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Maselli, F.; Moriondo, M.; Chiesi, M.; Chirici, G.; Puletti, N.; Barbati, A.; Corona, P. Evaluating the Effects of Environmental Changes on the Gross Primary Production of Italian Forests. Remote Sens. 2009, 1, 1108-1124.
Maselli F, Moriondo M, Chiesi M, Chirici G, Puletti N, Barbati A, Corona P. Evaluating the Effects of Environmental Changes on the Gross Primary Production of Italian Forests. Remote Sensing. 2009; 1(4):1108-1124.Chicago/Turabian Style
Maselli, Fabio; Moriondo, Marco; Chiesi, Marta; Chirici, Gherardo; Puletti, Nicola; Barbati, Anna; Corona, Piermaria. 2009. "Evaluating the Effects of Environmental Changes on the Gross Primary Production of Italian Forests." Remote Sens. 1, no. 4: 1108-1124.