Prediction of forest NPP in Italy by the combination of ground and remote sensing data
Author(s)
Date Issued
May 2015
Type
article
Abstract
Our research group has recently proposed a
strategy to simulate net forest carbon fluxes based on the coupling of a NDVI-driven parametric model, Modified C-Fix, and of a biogeochemical model, BIOME-BGC. The
outputs of the two models are combined through the use of
a proxy of ecosystem distance from equilibrium condition
which accounts for the occurred disturbances. This mod-
eling strategy is currently applied to all Italian forest areas
using an available set of NDVI images and ancillary data descriptive of an 8-year period (1999–2006). The obtained estimates of forest net primary production (NPP) are first analyzed in order to assess the importance of the main model drivers on relevant spatial variability. This analysis
indicates that growing stock is the most influential model
driver, followed by forest type and meteorological vari-
ables. In particular, the positive influence of growing stock on NPP can be constrained by thermal and water limitations, which are most evident in the upper mountain and most southern zones, respectively. Next, the NPP estimates, aggregated over seven main forest types and twenty administrative regions in Italy, are converted into current
annual increment of standing volume (CAI) by specific coefficients. The accuracy of these CAI estimates is finally
assessed by comparison with the ground data collected
during a recent national forest inventory. The results ob-
tained indicate that the modeling approach tends to overestimate the ground CAI for most forest types. In particular, the overestimation is notable for forest types which are mostly managed as coppice, while it is negligible
for high forests. The possible origins of these phenomena
are investigated by examining the main model drivers to-
gether with the results of previous studies and of older
forest inventories. The implications of using different NPP estimation methods are finally discussed in view of assessing the forest carbon budget on a national basis.
Citation
Chirici, G. et al. 2015. Prediction of forest NPP in Italy by the combination of ground and remote sensing data. "European Journal of Forest Research" 134 (3): 453-467
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