Assessment of inter-annual forest production variations in Italy by the use of remote-sensing and ancillary data

The application of Monteith’s models to estimate the gross primary production (GPP) of forest ecosystems is a common and efficient practice. A modified version of one of these parametric models, C-Fix, has been widely tested in Italy at daily to annual temporal scales. The current paper is a step fo...

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Bibliographic Details
Main Authors: Marta Chiesi, Luca Fibbi, Roberto Tognetti, Fabio Lombardi, Gherardo Chirici, Maurizio Teobaldelli, Paolo Cherubini, Fabio Maselli
Format: Article
Language:English
Published: Taylor & Francis Group 2017-01-01
Series:European Journal of Remote Sensing
Subjects:
Online Access:http://dx.doi.org/10.1080/22797254.2017.1374215
Description
Summary:The application of Monteith’s models to estimate the gross primary production (GPP) of forest ecosystems is a common and efficient practice. A modified version of one of these parametric models, C-Fix, has been widely tested in Italy at daily to annual temporal scales. The current paper is a step forward demonstrating the capability of this model to reproduce also inter-annual forest GPP variations. To this aim, dendrochronological measurements collected in two study areas were utilized to validate the model. The first study area is a coastal Mediterranean pine forest and the second is a more temperate ecosystem dominated by the presence of European beech. The experiment was organized into the following main steps. Modified C-Fix was applied to simulate annual GPP over a 15-year period for both sites. Both the main driving factors used by the model and the estimated GPP were then analysed and assessed against the detrended tree-ring width series of the collected cores. The study yields indications on the factors most influential on GPP for the two forest ecosystems and supports the capability of the proposed modelling approach to reproduce relevant inter-annual GPP variations, which is useful for predicting the impacts of ongoing climatic changes.
ISSN:2279-7254