Tested threshold height | ‘Touch’ | ‘Centroid’
um R value R value
0.6 0.770 0.687
0.65 0.779
Table 2. ALS data characteristics
Whatever the variant was, in depended variables, best
explanation of volume were given by: SUMH, VC and CAR.
For ‘centroid’ variant and relative tree height equal 0.75 these
variables had following values and correlation coefficients.
Parameters Value P
"Thefteeterm 885 | 025 ..
.SUMH sp aer) —— — 0.72278 —2.71E-03.
VC paca) TT 0.270 1.07E-05__
CAR (m’xsp_area’) -2.66 1.07E-03
3.3 Volume improvements after dead trees removal
Rejection of dead trees from the analysis, resulted with greater
strength of correlation (Fig. 4). In variant of ‘centroid’, where
applied cut was 0.75 of maximum tree height, the correlation
coefficient was R = 0.849 (0.748 without dead wood exclusion).
The parameters of the equation and their importance were as
follows:
Parameters Value p
“Ile ice (er of 0310 acest 088 .
.SUMH (mxsp area) ^ 0.825 6.90E-03 |
VC (mxsp aren). 1 0605 ns 1.20E-05 |
_CAR (m’xsp area) - 2.65 1.88E-02 |
SUMH*VC -0.00042 3.21E-02
Area of a sample plot: sp_area=500m’ TT
900
| 2
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Volume -field beased measurements [m /ha]
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Volume - LIDAR based measurements [m’/ha]
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Figure 4. Determination value between live trees volume
calculated based on field measurements and calculated from
LIDAR based characteristics (SUMH, VC, CAR, SUMHxVC)
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
4. DISCUSSION AND CONCLUSIONS
Segmentation results were comparable with that received in
other studies (Vauhkonen et al., 2010). Analyzed area is very
rough regarding to complicated and varied Digital Terrain
Model and large number of dead trees.
Based on regression analysis it was proved that variant ‘touch’
is better for volume calculations than ‘centroid’. This
unexpected finding can be cause by larger than 500 m° real plot
size. In addition using GPS measurements caused additional
error in sample plot locations. This influenced results,
especially in stands with large differences in spatial forest
structure (for example: with variation in trees density and
height).
Used models are well fitted. Received correlation is comparable
with other study for Norway spruce (Maltamo et al., 2004;
Hollaus et al, 2007). Received results are very promising
especially in context of difficult forest species and spatial
composition as well as rough terrain in the study area.
Results proved need for single tree delineation and its
parameters extraction. It is due to fact that the best correlation
between LIDAR based volume and field measurements was
based on: sum of crown volume, sum of tree heights and the
sum of crown projection area respectively. Regarding to this last
parameter, single tree detection is of little importance.
Correlation between volume based field measurements and
based on LIDAR characteristics was much higher when dead
trees were excluded from analysis. It is especially important for
protected areas where dead trees are quite frequently part of the
environment, and their spatial location is very inhomogeneous.
Future works will be concentrated on additional sample plots
location corrections and finding suitable method for dead trees
exclusion. Incorrect spatial ^ correspondence between
orthophotos and LIDAR based CHM, causes problems in
automatic methods for dead trees detection. Additionally lack of
accurate Georeferencing between datasets, decrease the value of
correlation between compared variables and increase the error
of performed analysis.
5. REFERENCES
Baltsavias, E.P., 19992. Airborne laser scanning : basic relations
and formulas. ISPRS Journal of Photogrammetry and
Remote Sensing 54, 199-214.
Baltsavias, E.P., 1999b. A comparison between
photogrammetry and laser scanning. ISPRS Journal of
Photogrammetry and Remote Sensing 54, 83-94.
Bruchwald, A., Rymer-Dudzinska, T., Dudek, A., Michalak, K.,
Wröblewski, L., Zasada, M., 2000. Empirical formulae for
defining height and dbh shape figure of thick wood.
Sylwan. 144 (10), 5-13 (in Polish with English summary).
Hollaus, M., Wagner, W., Maier, B., Schadauer, K., 2007.
Airborne Laser Scanning of Forest Stem Volume in a
Mountainous Environment. Sensors 7, 1559-1577.
Kaartinen, H., Hyyppä, J., 2008. Tree Extraction. SDI Raport.
Kwak, D.-A., Lee, W.-K., Cho, H.-K., Lee, S.-H., Son, Y.,
Kafatos, M., Kim, S.-R., 2010. Estimating stem volume
and biomass of Pinus koraiensis using LiDAR data.
Journal of Plant Research 123, 421-432.
Lefsky, M.A., Harding, D., Cohen, W.B., Parker, G., Shugart,
H.H., 1999. Surface Lidar Remote Sensing of Basal Area
and Biomass in Deciduous Forests of Eastern Maryland ,
USA. Remote Sensing of Environment 67, 83-98.
Maltamo, M., 2004. Estimation of timber volume and stem
density based on scanning laser altimetry and expected
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