A1 Refereed original research article in a scientific journal
Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR
Authors: Kankare V, Vastaranta M, Holopainen M, Raty M, Yu XW, Hyyppa J, Hyyppa H, Alho P, Viitala R
Publisher: MDPI AG
Publication year: 2013
Journal: Remote Sensing
Journal name in source: REMOTE SENSING
Journal acronym: REMOTE SENS-BASEL
Number in series: 5
Volume: 5
Issue: 5
First page : 2257
Last page: 2274
Number of pages: 18
ISSN: 2072-4292
DOI: https://doi.org/10.3390/rs5052257
Web address : http://www.mdpi.com/2072-4292/5/5/2257
Abstract
Airborne scanning LiDAR is a promising technique for efficient and accurate biomass mapping due to its capacity for direct measurement of the three-dimensional structure of vegetation. A combination of individual tree detection (ITD) and an area-based approach (ABA) introduced in Vastaranta et al. [1] to map forest aboveground biomass (AGB) and stem volume (VOL) was investigated. The main objective of this study was to test the usability and accuracy of LiDAR in biomass mapping. The nearest neighbour method was used in the ABA imputations and the accuracy of the biomass estimation was evaluated in the Finland, where single tree-level biomass models are available. The relative root-mean-squared errors (RMSEs) in plot-level AGB and VOL imputation were 24.9% and 26.4% when field measurements were used in training the ABA. When ITD measurements were used in training, the respective accuracies ranged between 28.5%-34.9% and 29.2%-34.0%. Overall, the results show that accurate plot-level AGB estimates can be achieved with the ABA. The reduction of bias in ABA estimates in AGB and VOL was encouraging when visually corrected ITD (ITDvisual) was used in training. We conclude that it is not feasible to use ITDvisual in wall-to-wall forest biomass inventory, but it could provide a cost-efficient application for acquiring training data for ABA in forest biomass mapping.
Airborne scanning LiDAR is a promising technique for efficient and accurate biomass mapping due to its capacity for direct measurement of the three-dimensional structure of vegetation. A combination of individual tree detection (ITD) and an area-based approach (ABA) introduced in Vastaranta et al. [1] to map forest aboveground biomass (AGB) and stem volume (VOL) was investigated. The main objective of this study was to test the usability and accuracy of LiDAR in biomass mapping. The nearest neighbour method was used in the ABA imputations and the accuracy of the biomass estimation was evaluated in the Finland, where single tree-level biomass models are available. The relative root-mean-squared errors (RMSEs) in plot-level AGB and VOL imputation were 24.9% and 26.4% when field measurements were used in training the ABA. When ITD measurements were used in training, the respective accuracies ranged between 28.5%-34.9% and 29.2%-34.0%. Overall, the results show that accurate plot-level AGB estimates can be achieved with the ABA. The reduction of bias in ABA estimates in AGB and VOL was encouraging when visually corrected ITD (ITDvisual) was used in training. We conclude that it is not feasible to use ITDvisual in wall-to-wall forest biomass inventory, but it could provide a cost-efficient application for acquiring training data for ABA in forest biomass mapping.
UTU Research Portal