Remotely sensed observations of seasonal greenness dynamics represent a valuable tool for studying vegetation phenology at regional and ecosystem-level scales. We investigated the seasonal variability of forests in Italy, examining the different mechanisms of phenological response to biophysical drivers. For each point of the Italian National Forests Inventory, we processed a multitemporal profile of the MODIS Enhanced Vegetation Index. Then we applied a multivariate approach for the purpose of (i) classifying the Italian forests into phenological clusters (i.e. pheno-clusters), (ii) identifying the main phenological characteristics and the forest compositions of each pheno-cluster and (iii) exploring the role of climate and physiographic variables in the phenological timing of each cluster. Results identified four pheno-clusters, following a clear elevation gradient and a distinct separation along the Mediterranean-to-temperate climatic transition of Italy. The “High-elevation coniferous” and the “High elevation deciduous” resulted mainly affected by elevation, with the former characterized by low annual productivity and the latter by high seasonality. To the contrary, the “Low elevation deciduous” showed to be mostly associated to moderate climate conditions and a prolonged growing season. Finally, summer drought was the main driving variable for the “Mediterranean evergreen”, characterized by low seasonality. The discrimination of vegetation phenology types can provide valuable information useful as a baseline framework for further studies on forests ecosystem and for management strategies.

Remotely-sensed phenology of Italian forests. Going beyond the species / Bajocco, S.; Ferrara, C.; Alivernini, A.; Bascietto, M.; Ricotta, C.. - In: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION. - ISSN 1569-8432. - 74:(2019), pp. 314-321. [10.1016/j.jag.2018.10.003]

Remotely-sensed phenology of Italian forests. Going beyond the species

Ricotta, C.
2019

Abstract

Remotely sensed observations of seasonal greenness dynamics represent a valuable tool for studying vegetation phenology at regional and ecosystem-level scales. We investigated the seasonal variability of forests in Italy, examining the different mechanisms of phenological response to biophysical drivers. For each point of the Italian National Forests Inventory, we processed a multitemporal profile of the MODIS Enhanced Vegetation Index. Then we applied a multivariate approach for the purpose of (i) classifying the Italian forests into phenological clusters (i.e. pheno-clusters), (ii) identifying the main phenological characteristics and the forest compositions of each pheno-cluster and (iii) exploring the role of climate and physiographic variables in the phenological timing of each cluster. Results identified four pheno-clusters, following a clear elevation gradient and a distinct separation along the Mediterranean-to-temperate climatic transition of Italy. The “High-elevation coniferous” and the “High elevation deciduous” resulted mainly affected by elevation, with the former characterized by low annual productivity and the latter by high seasonality. To the contrary, the “Low elevation deciduous” showed to be mostly associated to moderate climate conditions and a prolonged growing season. Finally, summer drought was the main driving variable for the “Mediterranean evergreen”, characterized by low seasonality. The discrimination of vegetation phenology types can provide valuable information useful as a baseline framework for further studies on forests ecosystem and for management strategies.
2019
discriminant analysis; MODIS EVI; pheno-clusters; time-series; vegetation phenology; global and planetary change; earth-surface processes; computers in earth sciences; management monitoring
01 Pubblicazione su rivista::01a Articolo in rivista
Remotely-sensed phenology of Italian forests. Going beyond the species / Bajocco, S.; Ferrara, C.; Alivernini, A.; Bascietto, M.; Ricotta, C.. - In: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION. - ISSN 1569-8432. - 74:(2019), pp. 314-321. [10.1016/j.jag.2018.10.003]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1272254
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