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Low-Density LiDAR and Optical Imagery for Biomass Estimation over Boreal Forest in Sweden

Journal article
Authors I. Shendryk
M. Hellstrom
Leif Klemedtsson
N. Kljun
Published in Forests
Volume 5
Issue 5
Pages 992-1010
ISSN 1999-4907
Publication year 2014
Published at Department of Earth Sciences
Pages 992-1010
Language en
Keywords aboveground biomass; canopy height model; low-density airborne LiDAR; remote sensing; SPOT-5; tree height; TreeVaW; vegetation classification
Subject categories Earth and Related Environmental Sciences


Knowledge of the forest biomass and its change in time is crucial to understanding the carbon cycle and its interactions with climate change. LiDAR (Light Detection and Ranging) technology, in this respect, has proven to be a valuable tool, providing reliable estimates of aboveground biomass (AGB). The overall goal of this study was to develop a method for assessing AGB using a synergy of low point density LiDAR-derived point cloud data and multi-spectral imagery in conifer-dominated forest in the southwest of Sweden. Different treetop detection algorithms were applied for forest inventory parameter extraction from a LiDAR-derived canopy height model. Estimation of AGB was based on the power functions derived from tree parameters measured in the field, while vegetation classification of a multi-spectral image (SPOT-5) was performed in order to account for dependences of AGB estimates on vegetation types. Linear regression confirmed good performance of a newly developed grid-based approach for biomass estimation (R-2 = 0.80). Results showed AGB to vary from below 1 kg/m(2) in very young forests to 94 kg/m(2) in mature spruce forests, with RMSE of 4.7 kg/m(2). These AGB estimates build a basis for further studies on carbon stocks as well as for monitoring this forest ecosystem in respect of disturbance and change in time. The methodology developed in this study can be easily adopted for assessing biomass of other conifer-dominated forests on the basis of low-density LiDAR and multispectral imagery. This methodology is hence of much wider applicability than biomass derivation based on expensive and currently still scarce high-density LiDAR data.

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