The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. http://http://dspace.unitus.it:80 2013-05-20T10:43:35Z 2013-05-20T10:43:35Z Blasi, Carlo Chirici, Gherardo Corona, Piermaria Marchetti, Marco Maselli, Fabio Puletti, Nicola http://hdl.handle.net/2067/2098 2011-06-07T00:30:53Z 2006-12-31T23:00:00Z

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

2006-12-31T23:00:00Z Maselli, Fabio Barbati, Anna Chiesi, Marta Chirici, Gherardo Corona, Piermaria http://hdl.handle.net/2067/2080 2011-06-03T00:30:38Z 2005-12-31T23:00:00Z

Title: Use of remotely sensed and ancillary data for estimating forest gross primary productivity in Italy Authors: Maselli, Fabio; Barbati, Anna; Chiesi, Marta; Chirici, Gherardo; Corona, Piermaria Abstract: The current paper describes the development and testing of a procedure which can use widely available remotely sensed and ancillary data to assess large-scale patterns of forest productivity in Italy. To reach this objective a straightforward model (C-Fix) was applied which is based on the relationship between photosynthetically active radiation absorbed by plant canopies and relevant gross primary productivity (GPP). The original C-Fix methodology was improved by using more abundant ancillary information and more efficient techniques for NDVI data processing. In particular, two extraction methods were applied to NDVI data, derived from two sensors (NOAA-AVHRR and SPOT-VGT) to feed C-Fix. The accuracy of the model outputs was assessed through comparison with annual and monthly values of forest GPP derived from eight eddy covariance flux towers. The results obtained indicated the superiority of SPOT-VGT over NOAA-AVHRR data and a higher efficiency of the more advanced NDVI extraction method. Globally, the procedure was proved to be of easy and objective implementation and allowed the evaluation of mean productivity levels of existing forests on the national scale. Description: L'articolo è disponibile sul sito dell'editore www.sciencedirect.com

2005-12-31T23:00:00Z Maselli, Fabio Moriondo, Marco Chiesi, Marta Chirici, Gherardo Puletti, Nicola Barbati, Anna Corona, Piermaria http://hdl.handle.net/2067/2133 2011-06-14T00:30:55Z 2008-12-31T23:00:00Z

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.

2008-12-31T23:00:00Z Maselli, Fabio Chirici, Gherardo Bottai, Lorenzo Corona, Piermaria Marchetti, Marco http://hdl.handle.net/2067/2135 2011-06-14T00:30:55Z 2004-12-31T23:00:00Z

Title: Estimation of Mediterranean forest attributes by the application of k-NN procedures to multitemporal Landsat ETM+ images Authors: Maselli, Fabio; Chirici, Gherardo; Bottai, Lorenzo; Corona, Piermaria; Marchetti, Marco Abstract: Routinely, applications of nonparametric estimation methods to satellite data for assisting the creation of forest inventories in Northern European countries are stimulating interest in the possible extension of these methods to more complex Mediterranean areas. This is the subject of the current work, which presents an experiment based on the integration of remotely sensed images and sample field measurements aimed at producing forest attribute maps in central Italy. Testing was carried out in an area where 370 geocoded field plots, sampled on a singlestage cluster design, were collected to characterize wood and non-wood forest attributes. These ground data served to apply various k-Nearest Neighbour (k- NN) estimation procedures to multitemporal Landsat 7 ETM+ images in order to map major forest attributes (basal area and simulated leaf area index, LAI). More specifically, the investigation focused on evaluating the effects of using satellite images from different periods of the growing season and spectral metrics of increasing complexity. The results achieved by the examined methods are finally discussed in order to provide guidelines for possible operational utilization. Description: L'articolo è disponibile sul sito dell'editore http://www.tandf.co.uk/journals/

2004-12-31T23:00:00Z Maselli, Fabio Chiesi, Marta Barbati, Anna Corona, Piermaria http://hdl.handle.net/2067/2132 2011-06-14T00:30:54Z 2009-12-31T23:00:00Z

Title: Assessment of forest net primary production through the elaboration of multisource ground and remote sensing data Authors: Maselli, Fabio; Chiesi, Marta; Barbati, Anna; Corona, Piermaria Abstract: This paper builds on previous work by our research group which demonstrated the applicability of a parametric model, Modified C-Fix, for the monitoring of Mediterranean forests. Specifically, the model is capable of combining ground and remote sensing data to estimate forest gross primary production (GPP) on various spatial and temporal scales. Modified C-Fix is currently applied to all Italian forest areas using a previously produced data set of meteorological data and NDVI imagery descriptive of a ten-year period (1999–2008). The obtained GPP estimates are further elaborated to derive forest net primary production (NPP) averages for 20 Italian Regions. Such estimates, converted into current annual increment of standing volume (CAI) through the use of specific coefficients, are compared to the data of a recent national forest inventory (INFC). The results obtained indicate that the modelling approach tends to overestimate the ground CAI values for all forest types. The correction of a drawback in the current model implementation leads to reduce this overestimation to about 9% of the INFC increments. The possible origins of this overestimation are investigated by examining 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.

2009-12-31T23:00:00Z Maselli, Fabio Papale, Dario Puletti, Nicola Chirici, Gherardo Corona, Piermaria http://hdl.handle.net/2067/2088 2011-06-07T00:30:49Z 2008-12-31T23:00:00Z

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

2008-12-31T23:00:00Z Chirici, Gherardo Barbati, Anna Corona, Piermaria Marchetti, Marco Maselli, Fabio Bertini, Roberta http://hdl.handle.net/2067/2086 2011-06-07T00:30:50Z 2007-12-31T23:00:00Z

Title: Non-parametric and parametric methods using satellite images for estimating growing stock volume in alpine and Mediterranean forest ecosystems Authors: Chirici, Gherardo; Barbati, Anna; Corona, Piermaria; Marchetti, Marco; Maselli, Fabio; Bertini, Roberta Abstract: This paper describes applications of non-parametric and parametric methods for estimating forest growing stock volume using Landsat images on the basis of data measured in the field, integrated with ancillary information. Several k-Nearest Neighbors (k-NN) algorithm configurations were tested in two study areas in Italy belonging to Mediterranean and Alpine ecosystems. Field data were acquired by the regional forest inventory and forest management plans, and satellite images are from Landsat 5 TM and Landsat 7 ETM+. The paper describes the data used, the methodologies adopted and the results achieved in terms of pixel level accuracy of forest growing stock volume estimates. The results show that several factors affect estimation accuracy when using the k-NN method. For the two test areas a total of 3500 different configurations of the k-NN algorithm were systematically tested by changing the number and type of spectral and ancillary input variables, type of multidimensional distance measures, number of nearest neighbors and methods for spectral feature extraction using the leave-one-out (LOO) procedure. The best k-NN configurations were then used for pixel level estimation; the accuracy was estimated with a bootstrapping procedure; and the results were compared to estimates obtained using parametric regression methods implemented on the same data set. The best k-NN growing stock volume pixel level estimates in the Alpine area have a Root Mean Square Error (RMSE) ranging between 74 and 96 m3 ha− 1 (respectively, 22% and 28% of the mean measured value) and between 106 and 135 m3 ha− 1 (respectively, 44% and 63% of the mean measured value) in the Mediterranean area. On the whole, the results cast a promising light on the use of non-parametric techniques for forest attribute estimation and mapping with accuracy high enough to support forest planning activities in such complex landscapes. The results of the LOO analyses also highlight the importance of a local empirical optimization phase of the k-NN procedure before defining the best algorithm configuration. In the tests performed the pixel level accuracy increased, depending on the k-NN configuration, as much as 100%. Description: L'articolo è disponibile sul sito dell'editore www.sciencedirect.com

2007-12-31T23:00:00Z Chiesi, Marta Chirici, Gherardo Corona, Piermaria Duce, Pierpaolo Salvati, Riccardo Spano, Donatella Vaccari, Francesco Maselli, Fabio http://hdl.handle.net/2067/2308 2012-07-26T23:05:46Z 2011-12-31T23:00:00Z

Title: Use of BIOME-BGC to simulate water and carbon fluxes within Mediterranean macchia Authors: Chiesi, Marta; Chirici, Gherardo; Corona, Piermaria; Duce, Pierpaolo; Salvati, Riccardo; Spano, Donatella; Vaccari, Francesco; Maselli, Fabio Abstract: The biogeochemical model BIOME-BGC is capable to estimate the main ecophysiological processes characterising all terrestrial ecosystems. To this aim it needs to be properly adapted to reproduce the behaviour of each biome type through a calibration phase. The aim of this paper is to adapt BIOME-BGC to reproduce the evapotranspiration (ET) and photosynthesis (GPP) of Mediterranean macchia spread all over Italy. Ten different sites were selected in the Centre-South of Italy and their gross primary production (GPP) was estimated by applying a parametric model, C-Fix, based on remotely sensed data for ten years (1999-2008). These monthly data were then used to calibrate BIOME-BGC through an iterative process which led to reproduce the spatial and temporal GPP variations found by C-Fix. The calibrated model was then applied to simulate the ET and GPP of two Italian sites characterised by the presence of an eddy flux tower; its performances were evaluated against ground data by common statistics. The results obtained indicate that, after a proper calibration phase, BIOME-BGC can be applied to estimate the evapotranspiration and photosynthesis of Mediterranean macchia with a good accuracy, strictly dependent on the input data utilised. Description: L'articolo è disponibile sul sito dell'editore www.sisef.it

2011-12-31T23:00:00Z Chirici, Gherardo Chiesi, Marta Pasqui, Massimiliano Corona, Piermaria Salvati, Riccardo Barbati, Anna Lombardi, Fabio Maselli, Fabio http://hdl.handle.net/2067/2294 2012-08-01T07:42:46Z 2010-12-31T23:00:00Z

Title: Stima dei flussi di carbonio degli ecosistemi forestali italiani attraverso dati telerilevati ed ancillari Authors: Chirici, Gherardo; Chiesi, Marta; Pasqui, Massimiliano; Corona, Piermaria; Salvati, Riccardo; Barbati, Anna; Lombardi, Fabio; Maselli, Fabio Abstract: Le foreste rivestono un ruolo fondamentale nell’ambito dei cicli bio-geo-chimici di molti elementi quali, tra gli altri, azoto e carbonio. In particolare possono svolgere l’importante funzione di assorbitori di carbonio, sottraendo CO2 dall’atmosfera. Per questo, ed in vista dei cambiamenti climatici in atto sul nostro pianeta, un obiettivo importante è quello di quantificare l’effettivo accumulo di carbonio stoccato nelle foreste italiane. A questo ambisce il progetto FIRB C_FORSAT finanziato dal MIUR fino al 2013. Tra le metodologie proposte per raggiungere tale scopo (tecniche di eddy covariance, immagini da satellite e modelli bio-geochimici), quelle basate sull’impiego di modelli di simulazione dell’ecosistema unite all’utilizzo di dati telerilevati risultano le più promettenti. Esse infatti uniscono la possibilità offerta dai modelli di stimare tutti i processi dell’ecosistema (GPP, NPP ed NEE) basandosi sulla conoscenza delle specie analizzate e dell’ambiente in cui si trovano con quella di ottenere informazioni su vasta scala spaziale e con alto grado di ripetizione grazie all’uso di dati tele rilevati. A questo scopo il modello bio-geochimico BIOME-BGC opportunamente calibrato e validato per le principali classi forestali italiane appare particolarmente utile. L’utilizzo del modello in forma spazializzata su base nazionale richiede però la disponibilità di una vasta disponibilità di strati informativi. Tra questi i dati meteorologici giornalieri sono particolarmente critici, in quanto non risultano ancora disponibili sul territorio nazionale. Il contributo richiama brevemente la metodologia utilizzata nel progetto e si sofferma in particolare sull’approccio individuato per la generazione della banca dati meteo spazializzata ed il suo utilizzo per simulare il comportamento della macchia mediterranea.; Forests play an important role within numerous bio-geo-chemical cycles among which those of nitrogen and carbon. In particular, forests can behave as carbon sink by removing CO2 from the atmosphere. For this reason, and in view of global climate changes, it is important to quantify the amount of carbon stocked within Italian forest ecosystems. This is the objective of the FIRB project C_FORSAT financed by MIUR up to 2013. Among the available methodologies (eddy-covariance, remote sensing and bio-geo-chemical models), those based on the combined use of ecosystem simulation model and remotely sensed data are the most promising. They in fact enable to estimate all ecosystem processes (GPP, NPP and NEE) based on the knowledge of the species and the environment in which these live. Moreover, they offer the possibility to obtain spatial information with a high temporal frequency. The model BIOME-BGC is particularly useful to this aim after proper calibration and validation for the main Italian forest types. It requires numerous data layers, among which daily meteorological data are the most difficult to obtain for the whole national territory. This contribution summirezes the main methodological steps and focuses on the creation of a daily meteorological database, which is utilized to drive the simulation of Mediterranean macchia. Description: La pubblicazione è disponibile all'indirizzo http://www.attiasita.it/ASITA2011/indice_atti.html

2010-12-31T23:00:00Z Chirici, Gherardo Corona, Piermaria Marchetti, Marco Mastronardi, Alessandro Maselli, Fabio Bottai, Lorenzo Travaglini, Davide http://hdl.handle.net/2067/2333 2013-01-11T00:05:09Z 2011-12-31T23:00:00Z

Title: K-NN FOREST: a software for the non-parametric prediction and mapping of environmental variables by the k-Nearest Neighbors algorithm Authors: Chirici, Gherardo; Corona, Piermaria; Marchetti, Marco; Mastronardi, Alessandro; Maselli, Fabio; Bottai, Lorenzo; Travaglini, Davide Abstract: In the last decades researchers investigated the possibility of extending the information collected in sampling units during a field survey to wider geographical areas through the use of remotely sensed images. One of the most widely adopted approaches is based on the non-parametric k-Nearest Neighbors (k-NN) algorithm. This contribution describes the software K-NN FOREST we developed to provide a complete tool for the implementation of the k-NN technique to generate spatially explicit estimations (maps) of a response variable acquired in the field by sampling units through the use of remotely sensed data or other ancillary variables. K-NN FOREST is designed to guide the user through a graphic user interface in the different phases of the process. K-NN FOREST is freely available for download and it is designed to run under Windows environment in conjunction with the GIS software IDRISI.

2011-12-31T23:00:00Z Maselli, Fabio Pasqui, Massimiliano Chirici, Gherardo Chiesi, Marta Fibbi, Luca Salvati, Riccardo Corona, Piermaria http://hdl.handle.net/2067/2349 2013-03-01T00:05:48Z 2011-12-31T23:00:00Z

Title: Modeling primary production using a 1 km daily meteorological data set Authors: Maselli, Fabio; Pasqui, Massimiliano; Chirici, Gherardo; Chiesi, Marta; Fibbi, Luca; Salvati, Riccardo; Corona, Piermaria Abstract: The availability of daily meteorological data extended over wide areas is a common requirement for modeling vegetation processes on regional scales. The present paper investigates the applicability of a pan-European data set of daily minimum and maximum temperatures and precipitation, E-OBS, to drive models of ecosystem processes over Italy. Daily meteorological data from a 10 yr period (2000 to 2009) were first downscaled to 1 km spatial resolution by applying locally calibrated regressions to a digital elevation model. The original and downscaled E-OBS maps were compared with meteorological data collected at 10 ground stations representative of different eco-climatic conditions. Additional tests were performed for the same sites to evaluate the effects of driving a model of vegetation processes, BIOME-BGC, with measured and estimated weather data. The tests were carried out using 10 BIOME-BGC versions characteristic for local vegetation types (Holm oak, other oaks, chestnut, beech, plain/hilly conifers, mountain conifers, Mediterranean macchia, olive trees, and C3 and C4 grasses). The experimental results indicate that the applied downscaling performs best for maximum temperatures, which is the most decisive factor for driving BIOME-BGC simulation of vegetation production. The downscaled data set is particularly suitable for the modeling of forest ecosystem processes, which could be further improved by the use of information obtained from remote sensing imagery. Description: L'articolo è disponibile sul sito dell'editore www.int-res.com. Periodo di embargo: 5 anni.

2011-12-31T23:00:00Z