TY - JOUR
T1 - Airborne laser scanning based forest inventory
T2 - Comparison of experimental results for the Perm region, Russia and prior results from Finland
AU - Kauranne, Tuomo
AU - Pyankov, Sergey
AU - Junttila, Virpi
AU - Kedrov, Alexander
AU - Tarasov, Andrey
AU - Kuzmin, Anton
AU - Peuhkurinen, Jussi
AU - Villikka, Maria
AU - Vartio, Ville Matti
AU - Sirparanta, Sanna
N1 - Publisher Copyright:
© 2017 by the authors.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - LiDAR
KW - Remote sensing
KW - Sparse Bayesian regression
KW - Validation
UR - http://www.scopus.com/inward/record.url?scp=85015083304&partnerID=8YFLogxK
U2 - 10.3390/f8030072
DO - 10.3390/f8030072
M3 - Article
AN - SCOPUS:85015083304
SN - 1999-4907
VL - 8
JO - Forests
JF - Forests
IS - 3
M1 - 72
ER -