Regional approaches to estimate the carbon budget of Italian forest ecosystems using Process-Based Models (PBMs), have been applied by several national institutions and researchers. Gross and net primary productivity (GPP and NPP) have been estimated through the PBMs simulations of carbon, water, and elemental cycles driven by remotely sensed data set and ancillary data. In particular the results of the GPP and NPP estimations provided by the implementation of two hybrid models are presented. The first modeling approach, based on the integration of two widely used models (C-fix and BIOME-BGC), has been applied to simulate monthly GPP and NPP values of all Italian forests for the decade 1999–2008. The approach, driven by remotely sensed SPOT-VEGETATION tenday Normalized Difference Vegetation Index (NDVI) images and meteorological data, provided a NPP map of Italian forests reaching maximum values of about 900 g C m−2 year−1. The second modeling approach is based on the implementation of a modified version of the 3-PG model running on a daily time step to produce daily estimates of GPP and NPP. The model is driven by MODIS remotely sensed vegetation indexes and meteorological data, and parameterized for specific soil and land cover characteristics. Average annual GPP and NPP maps of Italian forests and average annual values for different forest types according to Corine Land Cover 2000 classification are reported.
Nolé, A., Collalti, A., Borghetti, M., Chiesi, M., Chirici, G., Magnani, F., et al. (2015). The role of managed forest ecosystems: A modeling based approach. Berlin : Springer-Verlag [10.1007/978-3-642-32424-6_5].
The role of managed forest ecosystems: A modeling based approach
MAGNANI, FEDERICO;
2015
Abstract
Regional approaches to estimate the carbon budget of Italian forest ecosystems using Process-Based Models (PBMs), have been applied by several national institutions and researchers. Gross and net primary productivity (GPP and NPP) have been estimated through the PBMs simulations of carbon, water, and elemental cycles driven by remotely sensed data set and ancillary data. In particular the results of the GPP and NPP estimations provided by the implementation of two hybrid models are presented. The first modeling approach, based on the integration of two widely used models (C-fix and BIOME-BGC), has been applied to simulate monthly GPP and NPP values of all Italian forests for the decade 1999–2008. The approach, driven by remotely sensed SPOT-VEGETATION tenday Normalized Difference Vegetation Index (NDVI) images and meteorological data, provided a NPP map of Italian forests reaching maximum values of about 900 g C m−2 year−1. The second modeling approach is based on the implementation of a modified version of the 3-PG model running on a daily time step to produce daily estimates of GPP and NPP. The model is driven by MODIS remotely sensed vegetation indexes and meteorological data, and parameterized for specific soil and land cover characteristics. Average annual GPP and NPP maps of Italian forests and average annual values for different forest types according to Corine Land Cover 2000 classification are reported.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
