The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. http://http://dspace.unitus.it:80 2013-05-23T05:02:01Z 2013-05-23T05:02:01Z Bertini, Roberta Chirici, Gherardo Corona, Piermaria Travaglini, Davide http://hdl.handle.net/2067/2084 2011-06-03T00:30:37Z 2006-12-31T23:00:00Z

Title: Confronto di metodi parametrici e non-parametrici per la spazializzazione della provvigione legnosa tramite integrazione di misure a terra, dati telerilevati e informazioni ancillari Authors: Bertini, Roberta; Chirici, Gherardo; Corona, Piermaria; Travaglini, Davide Abstract: The use of remotely sensed data for forest inventory and monitoring of natural resources is ever increasing. Distinctively, remotely sensed data, integrated with ancillary data, can be exploited for the spazialization of biophysical attributes measured by forest inventories or management plans. Such applications are based on the relationships between the considered attributes and the spectral information measured by multispectral satellite images. Operative applications are commonly based on parametric or, more frequently, non-parametric approaches. The final aim of the present contribution is the spazialization of forest standing volume of various tree species in a study site in northern Italy by parametric (multiregressive) and non-parametric algorithms (k-Nearest Neighbors). The project is based on field data measured in productive forest stands dominated by Abies alba Mill. and/or Picea abies L. in the Provincia Autonoma di Trento (eastern Alpine Region of Italy). Remotely sensed images were acquired by the Landsat 7 ETM+ sensor while ancillary information is given by the altitude obtained from DEM and the site fertility from the GIS of the management plans. The contribution compares spazialization performance of several operative configurations of the tested methods in order to provide guidelines for the operative application of such techniques on vast areas. The study results emphasize the higher suitability of the tested non-parametric methods. Description: L'articolo è disponibile sul sito dell'editore www.sisef.it

2006-12-31T23:00:00Z Chirici, Gherardo Balsi, Marco Bertini, Roberta Bonora, Nico Chiavetta, Ugo Ottaviano, Marco Corona, Piermaria Lamonaca, Andrea Giuliarelli, Diego Mastronardi, Alessandro Nardinocchi, Giovanni Sambucini, Valter Tonti, Daniela Marchetti, Marco http://hdl.handle.net/2067/2117 2011-06-10T00:30:45Z 2007-12-31T23:00:00Z

Title: Post fire natural regeneration monitoring with the integrated use of high resolution remotely sensed images: the case study of the Pineta di Castel Fusano Authors: Chirici, Gherardo; Balsi, Marco; Bertini, Roberta; Bonora, Nico; Chiavetta, Ugo; Ottaviano, Marco; Corona, Piermaria; Lamonaca, Andrea; Giuliarelli, Diego; Mastronardi, Alessandro; Nardinocchi, Giovanni; Sambucini, Valter; Tonti, Daniela; Marchetti, Marco Abstract: La pineta di Castel Fusano (Roma) è stata colpita il 4 luglio del 2000 da un importante incendio boschivo in seguito al quale si è avviata nell’area una intensa rinnovazione naturale sia per via gamica che agamica. Ai fini di monitoraggio della suddetta rinnovazione sono stati realizzati una serie di rilievi a terra in aree campione nel 2003 e nel 2006. Negli stessi anni è stata acquisita la copertura di immagini telerilevate multispettrali ad altissima risoluzione Ikonos e Quick Bird. Scopo del presente lavoro è la sperimentazione di diverse metodologie finalizzate alla modellizzazione delle relazioni esistenti tra i dati telerilevati acquisiti e le misure realizzate a terra per la stima e la mappatura dei fenomeni di rinnovazione gamica e agamica. Sono stati per questo sperimentati metodi sia tradizionali di analisi regressiva multivariata, sia di tipo non parametrico, con algoritmi basati su reti neurali (Relevance Vector Machine e Multi-Layer Perceptron) e k-Nearest Neighbors. Le attività si inquadrano nell’ambito del progetto GRINFOMED - MEDIFIRE per il quale è stato realizzato un apposito software denominato Spatial Forest Modeller (SFM) capace di analizzare le relazioni tra variabili telerilevate e misurate a terra e di individuare i modelli predittivi migliori in modo da derivare mappe tematiche delle variabili acquisite mediante campionamento a terra. Il contributo illustra i dati acquisiti, le metodologie di analisi e di modellizzazione e i risultati ottenuti. Viene inoltre illustrato il funzionamento del software SFM.; Stone pine stand of Castel Fusano (Rome) burnt on July the 4th 2000 during a huge wildfire. As a consequence of the fire an intensive natural sexual and asexual regeneration began. In order to monitor such a regeneration field surveys were carried out in 2003 and 2006 in sample plots. Remotely sensed high resolution images from Ikonos and Quick Bird were acquired for the same years. The purpose of this work is to test different methodologies for modeling existing relationships between remotely sensed images and ground collected data in order to estimate and to map both sexual and asexual regeneration. For such a purpose different methodologies were tested: step-wise Muliple Linear Regression, Neural Networks (Relevance-Vector-Machine and the Multi-Layered-Perceptron) and the k-Nearest-Neighbors. These activities were carried out within the framework of the GRINFOMED- MEDIFIRE also developing a specific software named Spatial Forest Modeler (SFM) able to analyze existing relationships between remotely sensed variables and data collected in the field in order to identify the best available models to map and estimate the studied variables acquired on the basis of a field sampling design. The present paper presents data collected in the field, analysis and modeling methods and achieved results. The SFM software is also presented.

2007-12-31T23:00:00Z 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 Rondeux, Jacques Bertini, Roberta Bastrup-Birk, Annemarie Corona, Piermaria Latte, Nicolas McRoberts, Ronald E. Ståhl, Göran Winter, Susanne Chirici, Gherardo http://hdl.handle.net/2067/2312 2012-08-06T23:05:33Z 2011-12-31T23:00:00Z

Title: Assessing Deadwood Using Harmonized National Forest Inventory Data Authors: Rondeux, Jacques; Bertini, Roberta; Bastrup-Birk, Annemarie; Corona, Piermaria; Latte, Nicolas; McRoberts, Ronald E.; Ståhl, Göran; Winter, Susanne; Chirici, Gherardo Abstract: Deadwood plays an important role in forest ecological processes and is fundamental for the maintenance of biological diversity. Further, it is a forest carbon pool whose assessment must be reported for international agreements dealing with protection and forest management sustainability. Despite wide agreement on deadwood monitoring by national forest inventories (NFIs), much work is still necessary to clarify definitions so that estimates can be directly compared or aggregated for international reporting. There is an urgent need for an international consensus on definitions and agreement on harmonization methods. The study addresses two main objectives: to analyze the feasibility of harmonization procedures for deadwood estimates and to evaluate the impact of the harmonization process based on different definitions on final deadwood estimates. Results are reported for an experimental harmonization test using NFI deadwood data from 9,208 sample plots measured in nine European countries and the United States. Harmonization methods were investigated for volume by spatial position (lying or standing), decay classes, and woody species accompanied by accuracy assessments. Estimates of mean plot volume based on harmonized definitions with minimum length/height of 1 m and minimum diameter thresholds of 10, 12, and 20 cm were on average 3, 8, and 30% smaller, respectively, than estimates based on national definitions. Volume differences were less when estimated for various deadwood categories. An accuracy assessment demonstrated that, on average, the harmonization procedures did not substantially alter deadwood observations (root mean square error 23.17%). Description: L'articolo è disponibile sul sito dell'editore www.safnet.org

2011-12-31T23:00:00Z