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

Shendryk, Iurii; Hellström, Margareta LU ; Klemedtsson, Leif and Kljun, Natascha (2014) In Forests 5(5). p.992-1010
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
aboveground biomass, canopy height model, low-density airborne LiDAR, remote sensing, SPOT-5, tree height, TreeVaW, vegetation classification
in
Forests
volume
5
issue
5
pages
992 - 1010
publisher
MDPI AG
external identifiers
  • wos:000337252800008
  • scopus:84902482061
ISSN
1999-4907
DOI
10.3390/f5050992
project
MERGE
BECC
language
English
LU publication?
yes
id
b0565878-1c6d-4ff2-88f9-104a2a551297 (old id 4609763)
date added to LUP
2014-08-22 15:19:30
date last changed
2017-11-12 03:49:22
@article{b0565878-1c6d-4ff2-88f9-104a2a551297,
  abstract     = {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.},
  author       = {Shendryk, Iurii and Hellström, Margareta and Klemedtsson, Leif and Kljun, Natascha},
  issn         = {1999-4907},
  keyword      = {aboveground biomass,canopy height model,low-density airborne LiDAR,remote sensing,SPOT-5,tree height,TreeVaW,vegetation classification},
  language     = {eng},
  number       = {5},
  pages        = {992--1010},
  publisher    = {MDPI AG},
  series       = {Forests},
  title        = {Low-Density LiDAR and Optical Imagery for Biomass Estimation over Boreal Forest in Sweden},
  url          = {http://dx.doi.org/10.3390/f5050992},
  volume       = {5},
  year         = {2014},
}