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Proceedings Paper

Estimation of tree biomass volume in alpine forest areas using multireturn lidar data and support vector regression
Author(s): Michele Dalponte; Lorenzo Bruzzone; Damiano Gianelle
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Paper Abstract

In this paper we present a system for the estimation of forest biomass at individual tree level, which is based on multireturn LIDAR data and on Support Vector Regression (SVR). In particular, we estimate stem diameters that, combined with tree heights (provided by the Digital Canopy Model extracted from LIDAR data), allow one to retrieve tree biomass. The system proposed is made up of a preprocessing module, a LIDAR segmentation algorithm (aimed at retrieving tree crowns), a variable extraction and selection procedure and an estimation technique. The variables derived from LIDAR data, which are computed from both the intensity and elevation channels of all available returns, are representative of the characteristics of tree crowns. Four different methods of variable selection have been analyzed, and the sets of variables obtained have been used in the estimation phase based on the Support Vector Regression (SVR) technique. Experimental results show that the system proposed is effective for the estimation of stem diameters and tree biomass.

Paper Details

Date Published: 10 October 2008
PDF: 12 pages
Proc. SPIE 7109, Image and Signal Processing for Remote Sensing XIV, 71090C (10 October 2008); doi: 10.1117/12.801669
Show Author Affiliations
Michele Dalponte, Univ. of Trento (Italy)
Lorenzo Bruzzone, Univ. of Trento (Italy)
Damiano Gianelle, Ctr. di Ecologia Alpina (Italy)

Published in SPIE Proceedings Vol. 7109:
Image and Signal Processing for Remote Sensing XIV
Lorenzo Bruzzone; Claudia Notarnicola; Francesco Posa, Editor(s)

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