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|Title:||Modelling Carbon Budget of Mediterranean Forests Using Ground and Remote Sensing Measurements|
|Authors:||CHIESI M.; MASELLI M.; BINDI M.; FIBBI L.; CHERUBINI P.; ARLOTTA E.; TIRONE G.; MATTEUCCI Giorgio; SEUFERT GUENTHER|
|Citation:||AGRICULTURAL AND FOREST METEOROLOGY vol. 135 p. 22-34|
|JRC Publication N°:||JRC32511|
|Type:||Articles in Journals|
|Abstract:||The current work illustrates a method to operationally apply the model FOREST-BGC for the estimation of forest carbon fluxes in Mediterranean environments. The work was carried out in a pine forest stand within the coastal area of San Rossore (Central Italy) using both conventionally collected and remotely sensed data. The calibration of the model performed using estimates of Net Primary Productivity (NPP) derived from the carbon accumulated in the forest stems during the last four decades. These estimates were obtained by transforming dendrochronological measurements collected in the stand into annual increments of woody biomass and carbon matter. Next, the model performance was validated against values of Net Ecosystem Exchange (NEE) and Gross Primary Productivity (GPP) collected in the last four years (1999-2002) by an eddy covariance flux tower. A method based on deriving FAPAR from remotely sensed NDVI data was also calibrated and validated in order to more directly assess forest GPP. The results achieved indicate that the multi-year calibration against past carbon accumulation was essential in properly configuring the model in terms of respiration and allocation functions. Due to the importance of these functions, only the calibrated model was in fact able to correctly simulate the forest carbon fluxes, giving monthly estimates of both NEE and GPP quite close to those measured by the flux tower. These estimates were further improved by the proper integration of remotely sensed GPP evaluation and model carbon partitioning, which could be particularly useful for operational monitoring applications on a regional scale.|
|JRC Institute:||Institute for Environment and Sustainability|
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