Estimation of tree biomass volume in alpine forest areas using multireturn lidar data and support vector regression

Michele Dalponte; Lorenzo Bruzzone; Damiano Gianelle

doi:10.1117/12.801669

10 October 2008 Estimation of tree biomass volume in alpine forest areas using multireturn lidar data and support vector regression

Michele Dalponte, Lorenzo Bruzzone, Damiano Gianelle

Author Affiliations +

Proceedings Volume 7109, Image and Signal Processing for Remote Sensing XIV; 71090C (2008) https://doi.org/10.1117/12.801669
Event: SPIE Remote Sensing, 2008, Cardiff, Wales, United Kingdom

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.

Citation Download Citation

Michele Dalponte, Lorenzo Bruzzone, and Damiano Gianelle "Estimation of tree biomass volume in alpine forest areas using multireturn lidar data and support vector regression", Proc. SPIE 7109, Image and Signal Processing for Remote Sensing XIV, 71090C (10 October 2008); https://doi.org/10.1117/12.801669

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available