Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/2624
Title: Combination of optical and LiDAR satellite imagery with forest inventory data to improve walltowall assessment of growing stock in Italy
Authors: Maselli, Fabio
Chiesi, Marta
Mura, Matteo
Marchetti, Marco
Corona, Piermaria
Chirici, Gherardo
Keywords: Forest inventory;Locally weighted regression;CORINE land cover;GLAS;MODIS
Issue Date: 2014
Publisher: Elsevier
Source: Maselli, F. et al. 2014. Combination of optical and LiDAR satellite imagery with forest inventory data to improve walltowall assessment of growing stock in Italy. "International Journal of Applied Earth Observation and Geoinformation" 26: 377–386
Abstract: 
The acquisition of information about growing stock is a fundamental step in the framework of forest management planning and scenario modeling, besides being essential for assessing the amount of carbon stored within forest ecosystems. Gallaun et al. (2010) produced a panEuropean map of forest growing
stock by the combination of ground and remotely sensed data. The first objective of the current paper is to assess the accuracy of this map versus the ground data collected during the latest Italian National Forest
Inventory (INFC). Next, a new walltowall
estimation of growing stock is obtained by combining ground measurements of four regional forest inventories with the CORINE land cover map of Italy and the global canopy height map derived from Geoscience Laser Altimeter System (GLAS) and Moderate Resolution Imaging Spectroradiometer (MODIS) data. More particularly, the growing stock measurements of the four inventories are stratified by ecosystem type and extended over all Italian forest areas through the
application of locally weighted regressions to the GLAS/MODIS canopy height map. When compared to the INFC measurements, the new map shows higher accuracy than that by Gallaun et al., particularly for high growing stock values. The coefficient of determination between estimated and INFC growing stocksis improved by about 0.5, whilst the mean square error is reduced from 90 to 48 m3 ha−1.
Description: 
L'articolo è disponibile sul sito dell'editore www.elsevier.com/locate/jag
URI: http://hdl.handle.net/2067/2624
ISSN: 0303-2434
Appears in Collections:DiSAFRi - Archivio della produzione scientifica

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