Environmental Engineering Reference
In-Depth Information
1999-2008. The approach, driven by remotely sensed SPOT-VEGETATION ten-
day 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 implementa-
tion of a modified version of the 3-PG model running on a daily time step to pro-
duce 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.
5.1 Introduction
Simulation models of forest ecosystems answer two needs: first to clarify the
relationship between key ecosystem components, for a deeper understanding
of their functioning (Kimmins
2008
), and second to predict how the state varia-
bles of a dynamic system change due to processes in a forest stand or landscape
(Brang et al.
2002
). In recent years, modeling has undergone significant develop-
ments especially in forestry. Modeling tools are increasingly used by both for-
est ecologists, who face the challenge of transferring knowledge to stakeholders
and the general community, and forest managers, who benefit from the develop-
ment of scenario-based supports for decision-making (Vacchiano et al.
2012
).
From a general point of view, modeling means trying to capture the essence of
a system, deconstructing complex interactions between system components until
only the most essential structures and processes remain (Haefner
2005
). From
stochastic and empirical models, developed over the past 50 years, the increased
availability of the data has led to a significant enhancement in the knowledge
of the processes that regulate the tree eco-physiology. The difficulties to apply
empirical models in sites other than those they were calibrated for, which do not
reflect the changes occurred in site conditions or related to management opera-
tions since they were developed, have switched to using models able to predict
changes in growth and productivity of forests also subject to climate changes,
often taking into consideration some factors relating to anthropogenic disturbance.
Depending on the modeling purpose, in the last three decades a series of mod-
eling approaches were developed in order to capture forest processes for a wide
spatial and temporal resolution scale. The most used approaches are: gap models
(Bugmann
2001
), landscape models (He
2008
), process-based models (PBMs)
(Makela et al.
2000
) and hybrid models (Zhang et al.
2008
). The former of this
series explicitly includes site and climate drivers for predicting forest composition,
structure and biomass. Small-area or gap models reproduce the growth of single
trees within forest patches (e.g., 100 m
2
) in relation to the prevailing growth con-
ditions at the site level (Botkin et al.
1972
; Shugart
1984
; Leemans and Prentice
1989
; Pacala et al.
1993
). Recent modeling approaches as for the 3D-CMCC
