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Forests 2017, 8(3), 72; doi:10.3390/f8030072

Airborne Laser Scanning Based Forest Inventory: Comparison of Experimental Results for the Perm Region, Russia and Prior Results from Finland

1
LUT School of Engineering Science, Lappeenranta University of Technology, FI-53851 Lappeenranta, Finland
2
Arbonaut Ltd., Kaislakatu 2, FI-80130 Joensuu, Finland
3
Department of Cartography and Geoinformatics, Perm State University, RU-614990 Perm, Russia
4
Faculty of Science and Forestry, University of Eastern Finland, FI-80101 Joensuu, Finland
*
Author to whom correspondence should be addressed.
Academic Editors: Joanne C. White and Timothy A. Martin
Received: 9 December 2016 / Revised: 22 February 2017 / Accepted: 1 March 2017 / Published: 7 March 2017
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Abstract

Airborne laser scanning (ALS) based stand level forest inventory has been used in Finland and other Nordic countries for several years. In the Russian Federation, ALS is not extensively used for forest inventory purposes, despite a long history of research into the use of lasers for forest measurement that dates back to the 1970s. Furthermore, there is also no generally accepted ALS-based methodology that meets the official inventory requirements of the Russian Federation. In this paper, a method developed for Finnish forest conditions is applied to ALS-based forest inventory in the Perm region of Russia. Sparse Bayesian regression is used with ALS data, SPOT satellite images and field reference data to estimate five forest parameters for three species groups (pine, spruce, deciduous): total mean volume, basal area, mean tree diameter, mean tree height, and number of stems per hectare. Parameter estimates are validated at both the plot level and stand level, and the validation results are compared to results published for three Finnish test areas. Overall, relative root mean square errors (RMSE) were higher for forest parameters in the Perm region than for the Finnish sites at both the plot and stand level. At the stand level, relative RMSE generally decreased with increasing stand size and was lower when considered overall than for individual species groups. View Full-Text
Keywords: remote sensing; LiDAR; Sparse Bayesian regression; validation remote sensing; LiDAR; Sparse Bayesian regression; validation
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Kauranne, T.; Pyankov, S.; Junttila, V.; Kedrov, A.; Tarasov, A.; Kuzmin, A.; Peuhkurinen, J.; Villikka, M.; Vartio, V.-M.; Sirparanta, S. Airborne Laser Scanning Based Forest Inventory: Comparison of Experimental Results for the Perm Region, Russia and Prior Results from Finland. Forests 2017, 8, 72.

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