ha” (249 m ha!) 
. 
by the TopEye Mk 
| a 25 cm footprint. 
le of 250 m agl, 
| per square meter. 
as classified as a 
gressive Triangular 
d (Axelsson, 1999, 
e (Soininen, 2004). 
ell size was created 
? of ground returns 
lls without ground 
ing DEM cells. 
. 28 June 2005 (at 
stem operated by 
ic and four spectral 
°s are stitched into 
to create one pan- 
pixels (Hinz et al., 
) m a.g.l. using one 
overlap. As result, 
ck is about 0.48 m. 
lle adjustment and 
fication 
is performed using 
(Anon, 2011) to 
lone by sequential 
1 least squares and 
ollowing Packalén 
; colorized by ray 
coordinates, using 
images. Each point 
images the point is 
reen colored point 
were normalized by 
lass corresponding 
"med by supervised 
es composition, i.e. 
surveyed volume 
es (40, 351 and 18 
s below 0.5 m were 
moved prior to the 
> point cloud was 
ith equal priors. 
ata, such as height 
cs, were calculated 
| using the Fusion 
leveloped by US 
[hese metrics Were 
e addressed forest 
  
variables. Point cloud metrics were extracted for both sample 
plots and wall-to-wall rasters with 18 m x 18 m cellsize (similar 
to the sample plot area). Six metrics describing canopy height 
were used, the percentiles corresponding to the 20, 40, 60, 80, 
90 and 95 quantiles of the point height distribution (po, po. .., 
pos). To describe canopy density, the two metrics "Canopy relief 
ratio" ((mean - min) / (max — min)) and "percentage of points 
above Mode" where generated. In addition, the species 
classified points were used to generate three metrics, one for 
each species proportion. However, only two of the three species 
proportion metrics are needed in the model. 
The target forest variables: tree height; basal area; pine volume; 
spruce volume; deciduous volume and total volume were 
estimated using k&-MSN, with k = 1. Stem volume and basal area 
were logarithmically transformed in order to achieve linear 
relationships in the canonical correlation transformation. 
Estimation was done using the Yalmpute library in the R 
statistical software package (R Development Core Team, 2010) 
and resulted in raster data sets for the target variables. 
3.3 Accuracy assessment 
Finally, stand-level estimation accuracy was assessed on all 
stands with six or more plots (to achieve reasonably accurate 
field surveyed stand level means), resulting in 41 stands. For 
each stand, the averages of raster cell estimates and averages of 
measured plot values were calculated. At these stands the tree 
height range is 3.5 — 27.9 m (with an average of 17.6 m), stem 
volume 5.4 — 558 m?ha'! (231 m’ha!), basal area 1.6 — 45.7 
m/ha! (24.9 m?ha') and with mean stand size of 2.8 ha. The 
results were evaluated in terms of RMSE and bias (in percent of 
the surveyed stand mean). 
3. Results and discussion 
The results (Table 1, Figure 2) show higher accuracy for the 
total volume estimates compared to species-specific stem 
volume estimates, as measured by RMSE in percentage of the 
surveyed mean. The errors in absolute terms show different 
relations, though, indicating similar accuracy for all cases (see 
also Figure 2). This relation is also present for the observed 
biases. 
Table 1. Stand-level accuracy of mean tree height (H), mean 
basal area (BA), total stem volume (Vier) and stem volume by 
tree species; pine (V), spruce (V), and deciduous (V,) 
  
  
RMSE RMSE [%] Bias Bias [94] 
H 1.32 7.45 0.23 1.3 
BA 235 11.4 0.26 1.1 
Fa 306 13.2 2:5 11 
HK 255 90.6 27 9.7 
y. 461 26.4 021 -0.12 
Ve © ]06 72.6 -1.6 -10.6 
  
This study shows that 3D data from the standard aerial image 
acquisition carried out with DMC by Lantmáteriet can be used 
fo accurately estimate tree height, stem volume and basal area 
for forest management planning, and also provide species- 
Specific estimations of stem volume. Estimation was made using 
only remote sensing data already available, to a very low cost, 
In combination with a field sample of reference plots. 
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deciduous (c), and total volume (a). 
pine (a), spruce (5), 
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