http://http://dspace.unitus.it:80 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Wed, 22 May 2013 04:14:33 GMT 2013-05-22T04:14:33Z http://hdl.handle.net/2067/2098 Title: Spazializzazione di dati climatici a livello nazionale tramite modelli regressivi localizzati Authors: Blasi, Carlo; Chirici, Gherardo; Corona, Piermaria; Marchetti, Marco; Maselli, Fabio; Puletti, Nicola Abstract: The availability of spatialised climatic data is an essential pre-requisite for the implementation of GIS-based analysis in many application fields. Among the different methodologies for the spatialization of climatic data collected in weather-stations the most used are those based on geostatistical approaches, on parametric correlative models or on neural networks. Within the “Completamento delle Conoscenze Naturalistiche di Base” project, funded by the Italian Ministry for the Environment (Department of Nature Protection) a database of 403 weather-stations distributed across Italy with a time series of thirty years was collected. Data of mean monthly temperature (minimum and maximum) and rainfalls were spatialized by a local linear univariate regressive method based on elevation as independent variable. A total of 36 monthly maps with a geometric resolution of 250 m was generated. The present paper introduces the adopted methodology and the accuracy results estimated by leave-one-out cross validation. Description: L'articolo è disponibile sul sito dell'editore www.sisef.it Sun, 31 Dec 2006 23:00:00 GMT http://hdl.handle.net/2067/2098 2006-12-31T23:00:00Z http://hdl.handle.net/2067/2133 Title: Evaluating the effects of environmental changes on the gross primary production of italian forests Authors: Maselli, Fabio; Moriondo, Marco; Chiesi, Marta; Chirici, Gherardo; Puletti, Nicola; Barbati, Anna; Corona, Piermaria Abstract: A ten-year data-set descriptive of Italian forest gross primary production (GPP) has been recently constructed by the application of Modified C-Fix, a parametric model driven by remote sensing and ancillary data. That data-set is currently being used to develop multivariate regression models which link the inter-year GPP variations of five forest types (white fir, beech, chestnut, deciduous and evergreen oaks) to seasonal values of temperature and precipitation. The five models obtained, which explain from 52% to 88% of the interyear GPP variability, are then applied to predict the effects of expected environmental changes (+2 °C and increased CO2 concentration). The results show a variable response of forest GPP to the simulated climate change, depending on the main ecosystem features. In contrast, the effects of increasing CO2 concentration are always positive and similar to those given by a combination of the two environmental factors. These findings are analyzed with reference to previous studies on the subject, particularly concerning Mediterranean environments. The analysis confirms the plausibility of the scenarios obtained, which can cast light on the important issue of forest carbon pool variations under expected global changes. Wed, 31 Dec 2008 23:00:00 GMT http://hdl.handle.net/2067/2133 2008-12-31T23:00:00Z http://hdl.handle.net/2067/2088 Title: Combining remote sensing and ancillary data to monitor the gross productivity of water-limited forest ecosystems Authors: Maselli, Fabio; Papale, Dario; Puletti, Nicola; Chirici, Gherardo; Corona, Piermaria Abstract: This paper describes the development and testing of a procedure which combines remotely sensed and ancillary data to monitor forest productivity in Italy. The procedure is based on a straightforward parametric model (C-Fix) that uses the relationship between the fraction of photosynthetically active radiation absorbed by plant canopies (fAPAR) and relevant gross primary productivity (GPP). Estimates of forest fAPAR are derived from Spot-VGT NDVI images and are combined with spatially consistent data layers obtained by the elaboration of ground meteorological measurements. The original version of C-Fix is first applied to estimate monthly GPP of Italian forests during eight years (1999–2006). Next, a modification of the model is proposed in order to simulate the short-term effect of summer water stress more efficiently. The accuracy of the original and modified C-Fix versions is evaluated by comparison with GPP data taken at eight Italian eddy covariance flux tower sites. The experimental results confirm the capacity of C-Fix to monitor national forest GPP patterns and indicate the utility of considering the short-term effect of water stress during Mediterranean dry months. Description: L'articolo è disponibile sul sito dell'editore www.sciencedirect.com Wed, 31 Dec 2008 23:00:00 GMT http://hdl.handle.net/2067/2088 2008-12-31T23:00:00Z