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Remote Sens. 2013, 5(5), 2257-2274; doi:10.3390/rs5052257

Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR

1,* , 1
1 Department of Forest Sciences, University of Helsinki, PL 27, FI-00014 Helsinki, Finland 2 Forest Resources and Climate Unit, Institute for Environment and Sustainability, Joint Research Centre, Via E. Fermi 2749, I-21027 Ispra (VA), Italy 3 Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, PL 15, FI-02431 Masala, Finland 4 School of Science and Technology, Aalto University, PL 14100, FI-00076 Aalto, Finland 5 Helsinki Metropolia University of Applied Sciences, PL 4000, FI-00079 Helsinki, Finland 6 Department of Geography and Geology, University of Turku, FI-20014 Turku, Finland 7 HAMK University of Applied Sciences, Saarelantie 1, FI-16970 Evo, Finland;
* Author to whom correspondence should be addressed.
Received: 12 March 2013 / Revised: 7 April 2013 / Accepted: 7 May 2013 / Published: 13 May 2013
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Airborne scanning LiDAR is a promising technique for efficient and accuratebiomass mapping due to its capacity for direct measurement of the three-dimensionalstructure of vegetation. A combination of individual tree detection (ITD) and an area-basedapproach (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 totest the usability and accuracy of LiDAR in biomass mapping. The nearest neighbourmethod was used in the ABA imputations and the accuracy of the biomass estimation wasevaluated in the Finland, where single tree-level biomass models are available. The relativeroot-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 ITDmeasurements 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 beachieved with the ABA. The reduction of bias in ABA estimates in AGB and VOL wasencouraging when visually corrected ITD (ITDvisual) was used in training. We conclude that itis not feasible to use ITDvisual in wall-to-wall forest biomass inventory, but it could provide acost-efficient application for acquiring training data for ABA in forest biomass mapping.
Keywords: laser scanning; forest inventory; nearest neighbour; aboveground biomass laser scanning; forest inventory; nearest neighbour; aboveground biomass
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Kankare, V.; Vastaranta, M.; Holopainen, M.; Räty, M.; Yu, X.; Hyyppä, J.; Hyyppä, H.; Alho, P.; Viitala, R. Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR. Remote Sens. 2013, 5, 2257-2274.

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