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Forests 2015, 6(6), 1839-1857;

Sparse Density, Leaf-Off Airborne Laser Scanning Data in Aboveground Biomass Component Prediction

Department of Forest Sciences, University of Helsinki, Helsinki FI-00014, Finland
Centre of Excellence in Laser Scanning Research, Finnish Geodetic Institute, Masala FI-02431, Finland
School of Forest Sciences, University of Eastern Finland, Joensuu FI-80101, Finland
Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, Masala FI-02431, Finland
Department of Real Estate, Planning and Geoinformatics, Aalto University, Aalto FI-00076, Finland
Civil engineering and building services, Helsinki Metropolia, University of Applied Sciences, Helsinki FI-00079, Finland
Department of Geography and Geology, University of Turku, Turku FI-20014, Finland
Author to whom correspondence should be addressed.
Academic Editor: Eric Jokela
Received: 14 April 2015 / Revised: 5 May 2015 / Accepted: 25 May 2015 / Published: 28 May 2015
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The demand for cost-efficient forest aboveground biomass (AGB) prediction methods is growing worldwide. The National Land Survey of Finland (NLS) began collecting airborne laser scanning (ALS) data throughout Finland in 2008 to provide a new high-detailed terrain elevation model. Similar data sets are being collected in an increasing number of countries worldwide. These data sets offer great potential in forest mapping related applications. The objectives of our study were (i) to evaluate the AGB component prediction accuracy at a resolution of 300 m2 using sparse density, leaf-off ALS data (collected by NLS) derived metrics as predictor variables; (ii) to compare prediction accuracies with existing large-scale forest mapping techniques (Multi-source National Forest Inventory, MS-NFI) based on Landsat TM satellite imagery; and (iii) to evaluate the accuracy and effect of canopy height model (CHM) derived metrics on AGB component prediction when ALS data were acquired with multiple sensors and varying scanning parameters. Results showed that ALS point metrics can be used to predict component AGBs with an accuracy of 29.7%–48.3%. AGB prediction accuracy was slightly improved using CHM-derived metrics but CHM metrics had a more clear effect on the estimated bias. Compared to the MS-NFI, the prediction accuracy was considerably higher, which was caused by differences in the remote sensing data utilized. View Full-Text
Keywords: remote sensing; forest inventory; LiDAR remote sensing; forest inventory; LiDAR

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Kankare, V.; Vauhkonen, J.; Holopainen, M.; Vastaranta, M.; Hyyppä, J.; Hyyppä, H.; Alho, P. Sparse Density, Leaf-Off Airborne Laser Scanning Data in Aboveground Biomass Component Prediction. Forests 2015, 6, 1839-1857.

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