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Integration of high- and low-resolution satellite data to estimate pine forest productivity in a Mediterranean coastal area

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2 Author(s)
Maselli, F. ; Inst. di Biometeorologia, Nat. Res. Council, Firenze, Italy ; Chiesi, M.

The estimation of vegetation primary productivity is particularly important in fragile Mediterranean environments that are vulnerable to both natural and human-induced perturbations. The current work was aimed at using remotely sensed data taken by various sensors to infer information about a protected coastal pine forest in Tuscany (Central Italy), which could serve for driving a simplified model of carbon fluxes, C-Fix. Being based on the direct relationship between normalized difference vegetation index (NDVI) and fraction of absorbed photosynthetically active radiation (FAPAR), C-Fix uses satellite and standard meteorological data to simulate gross (GPP) and net (NPP) primary productivity of forest ecosystems. Due to the limited size of the study area, a major difficulty was in creating an NDVI dataset with suitable spatial and temporal resolutions, which was essential for the model functioning. To reach this objective, eight Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images of two years (2000 and 2001) were merged to low-resolution NDVI estimates taken by both the Advanced Very High Resolution Radiometer (AVHRR) and VEGETATION (VGT) sensors. The C-Fix outputs for representative pine forest sites were evaluated by comparison to accurate estimates derived from a model of forest ecosystem processes previously calibrated in a similar environment (Forest-BGC). This analysis showed the potential of C-Fix for rapidly estimating GPP over wide forest areas when suitable NDVI inputs are provided. In particular, a slight superiority of VGT over AVHRR data was demonstrated, which could be reasonably attributed to the relevant higher radiometric and geometric properties. The estimation of NPP was instead quite inaccurate, due to the problematic simulation of forest respiration, which should necessarily rely on more complete modeling operations.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:43 ,  Issue: 1 )

Date of Publication:

Jan. 2005

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