XIX-B8, 2012 
HODS 
Mountains National 
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ce (Picea abies L.). 
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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
d) Segments which shape is not similar to tree were connected 
to neighboring segments, according to the condition: 
diameter ratio of the ellipse described on crowns extends 
beyond the range 0.3-3); 
e) Layer heights are determined (automatically or manually); 
f) Primary segments are allocated to the height layers; 
g) After allocation the segments are combined; 
h) While there are more than one height layer, those from one 
height layer are grouped as separate spatial objects (groups 
of primary segments — stands/parts of the stands with 
similar height/crown size); 
i) Height layers are re-filtered using Gaussian filter, of varying 
sizes - the higher layers of larger and lower smaller; 
j) In this case three groups were established - the height limit 
was 28 m and 16.6 m, filter size of 7, 5 and 3 pixels, 
respectively, for layer heights; 
k) After this segmentation average area of each tree segment 
were determined. If the crown had a surface area greater 
than the mean + standard deviation, it was again filtered 
Gaussian filter of 2 pixels less at first and segmented one 
again. 
1) Segments which shape is not similar to tree were connected 
to neighboring segments, according to the condition: 
diameter ratio of the ellipse described on crowns extends 
beyond the range 0.33-3.5); 
m) For every final segment, based on maximum height from 
CHM was determined and pixels below 0.7xHinax were 
removed 
For final segments such parameters as maximum height (Huy), 
minimum height (H,;,) and crown radius (Cua) were 
calculated. 
Based on these characteristics, the mapped forest fragments, 
which corresponded to the size and location of the ground 
sample plots, the following characteristics were calculated: the 
number of trees (NT), total tree height (SUMH), average tree 
height (HA), the sum of crown projection area (CAR), the sum 
of crown volume (VC). The crown volume was taken as a cone 
with base equal to the projection of the crown and height such 
as the length of the crown. All the features were related to trees 
with a minimum 7 m height. Dead trees were removed based on 
orthophoto interpolation. 
2.6 Variants comparison 
In the study the extent of the crown was associated with the 
height of the tree (Fig. 1). 
CoixH,. (1) 
where: 
C 7 crown extend CHM value 
H,, = maximum CHM value inside final single tree 
region 
i = tested threshold height 
In the presented study the test based on 34 sample plots was 
carried out. Two variants of the assigning separate crowns in the 
sample were used: (1) according to the centroid position 
(‘centroid’), (2) according to the location of any fragment of the 
crown inside the sample plot boundary (‘touch’) (Fig. 3). In 
each of the variants five series of measurements with different 
relative height of the range 0.65-0.8 (with a gap of 0.05) were 
carried out. 
For each selected height threshold and sampling rule calculated 
correlation between the volume of live trees (field 
measurements) and the features measured based on LIDAR 
data. Set of features was determined on the basis stepwise 
regression with the backward elimination model. The best 
solution was chosen based on the highest value of correlation 
(R). 
Relationship between the stock volume of trees measured on the 
ground and LIDAR parameters based on CHM was determined 
in first set. Due to impact of dead trees, in second analysis, dead 
trees were excluded from analysis. All calculations were 
conducted using STATISTICA 8 (StatSoft, Inc.) 
  
  
0 5 10 20 
Meters 
  
  
  
Figure 3. Results of two sample rules based on centroid 
(polygons (trees segments) with solid line) and ‘touch’ 
(polygons (trees segments) with dashed line and with solid line) 
based on sample plot area (gray circle with 12.62 m radius) 
3. RESULTS 
3.1 Segmentation accuracy 
Accuracy evaluation was carried out in 9 different stands, on 5 
sample plots. Photogrammetric measurements were performed 
in the DEPHOS photogrammetric station, and were used as a 
reference data. The spatial resolution of the images was 0.15 m. 
The accuracy of the segmentation obtained for spruce was 
82.3%. The species specific segmentation process correctly 
detected 1156 of 1404 trees located in the stands from 27 to 110 
years old. The 186 trees (13.3 %) were not detected and the 62 
tree peaks (4.4 %) identified during stereo photogrammetric 
observations were removed due to the occurrence of two 
vertices of a tree in a single segment. In presented test 
segmentation properties were not species specific, but general 
(described in 2.5 section). This can cause a little worse 
detection rate, and accuracy about 75 % (visual comparison 
both segmentation results). 
3.2 Volume analysis 
Strength of the correlation between the volume of trees (defined 
on the ground) and the characteristics of the LIDAR data set 
was greater in the variant "touch". Optimal cut was in this case 
the relative tree height of 0.7-0.75 (Tab. 2). In ‘centroid’ 
variant, strength of association was slightly smaller, and it was 
the best cut for the relative height of 0.75.