Next Article in Journal
Elevated Atmospheric CO2 and Warming Stimulates Growth and Nitrogen Fixation in a Common Forest Floor Cyanobacterium under Axenic Conditions
Previous Article in Journal
Erratum: Spatial Upscaling of Soil Respiration under a Complex Canopy Structure in an Old-Growth Deciduous Forest, Central Japan; Forests 2017, 8, 36
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Forests 2017, 8(3), 72;

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

LUT School of Engineering Science, Lappeenranta University of Technology, FI-53851 Lappeenranta, Finland
Arbonaut Ltd., Kaislakatu 2, FI-80130 Joensuu, Finland
Department of Cartography and Geoinformatics, Perm State University, RU-614990 Perm, Russia
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
Full-Text   |   PDF [1132 KB, uploaded 7 March 2017]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top