ISPRS Commission III, Vol.34, Part 3A ,Photogrammetric Computer Vision“, Graz, 2002
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Easting (m)
Figure 6. Planimetric view of MAP estimate of crown
configuration (black circles), photogrammetric
crown measurements (short dashes) and 0.081 ha
circular inventory plot boundary (long dashes).
S. DISCUSSION
Results indicate that the algorithm is generally successful in
identifying structures associated with individual tree crowns
within this forest area. The MAP estimate of the crown
configuration generated by the algorithm closely matches the
spatial patterns evident in the LIDAR data (Figure 5). The
algorithm appears to be very sensitive to the data, and in some
areas added spurious small crowns to increase the likelihood of
the data.
In general, the MAP estimate of crown locations corresponds to
the photogrammetric crown measurements (see Figure 6). It
should be noted that accurate recognition and delineation of
overlapping tree crowns is difficult even in high-resolution
aerial imagery. In this case, there is a systematic discrepancy of
1-4 meters in the north-south direction between algorithm-based
crown locations and photo-based crown locations. This offset is
probably due to the effect of crown layover and/or
misregistration of the aerial photography.
Field data was available for a 0.081 ha circular inventory plot
located within the study area (see Figure 6). Interestingly, the
number of codominant (overstory) trees found within the plot in
the field (14) matches the number found by the algorithm and
measured in the photographs.
6. CONCLUSIONS
Bayesian object recognition provides a promising framework
for the analysis of complex forest scenes using high-density,
three-dimensional LIDAR data. It is clear that modelling
assumptions will have a strong influence on the results; for
example, it is apparent that crowns with an asymmetrical,
irregular shape will be difficult to detect given the constraints of
the generalized ellipsoidal crown model used here. The use of
more complex crown models may improve recognition of
irregularly shaped crowns.
Future research will focus on comparing algorithm results to
field-based measurements and assessing the influence of
automated measurement error on stand-level parameter
estimates. In addition, Bayesian object recognition offers a
flexible modelling approach that allows for fusing the
information content from multiple sources of data. Such
multiple data sources are becoming more available as vendors
offer simultaneous acquisition of georeferenced imagery and
LIDAR data. As the data enter the model only through the
likelihood function in Bayesian object recognition, other types
of remotely sensed data (including aerial photography and high
resolution satellite imagery) can be easily incorporated into the
model through adjustment of the likelihood function.
7. REFERENCES
Andersen, H., S. Reutebuch, and G. Schreuder, 2001.
Automated individual tree measurement through morphological
analysis of a LIDAR-based canopy surface model. In:
Proceedings of the First International Precision Forestry
Symposium, Seattle, WA, USA.
Baddeley, A., and M. van Lieshout, 1993. Stochastic geometry
models in high-level vision. In: Mardia, K.V. and G.K. Kanjii,
eds. Advances in Applied Statistics 1. Carfax, Abingdon,
Oxfordshire, pp. 231-256.
Besag, J., 1993. Towards Bayesian image analysis. In: Mardia,
K.V. and GK. Kanjii, eds. Advances in Applied Statistics 1.
Carfax, Abingdon, Oxfordshire, pp. 107-119.
Dralle, K., and M. Rudemo, 1997. Automatic estimation of
individual tree positions from aerial photos. Canadian Journal
of Forest Research, 27, pp. 1728-1736.
Goudriaan, J., 1988. The bare bones of leaf-angle distribution in
radiation models for canopy photosynthesis and energy
exchange. Agricultural and Forest Meteorology 43, pp. 155-
169.
Green, P., 1995. Reversible jump Markov chain Monte Carlo
computation and Bayesian model determination. Biometrika,
82(4), pp. 711-732.
Gulden, R., 2000. Remote sensing at the dawn of a new
millennium: A Washington DC perspective. In: Proceedings of
the Eighth Biennial Remote Sensing Applications Conference,
Albuquerque, NM, USA.
Kirkpatrick, S., C.D. Gelatt, Jr., and M.P. Vecchi, 1983.
Optimization by simulated annealing. Science, 220(4598), pp.
671-680.
Larsen, M., 1998. Finding an optimal match window for spruce
top detection based upon an optical tree model. In: Proceedings
of the International Forum on Automated Interpretation of
High Spatial Resolution Digital Imagery for Forestry, Victoria,
BC, Canada.
Lund, J. and M. Rudemo, 2000. Models for point processes
observed with noise. Biometrika, 87(2), pp. 235-249.
Magnussen, S., P. Eggermont, and V.N. LaRiccia, 1999.
Recovering tree heights from airborne laser scanner data. Forest
Science, 45(3), pp. 407-422.
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