The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
1206 
Combing GVI and curvature feature with certain conditions 
applied into sub-areas after JSEG segmentation, we can obtain 
forest areas (CHM-GVI-JSEG). The comparison between 
Gabor wavelet texture features between CHM-GVI-JSEG forest 
area and sub-areas obtained from JSEG lead to successful forest 
boundary delineation. Fig. 8 shows a schematic work flow of the 
overall forest delineation process. 
3. EXPERIMENTAL RESULT AND CONCLUSION 
JSEG has one parameter which controls the region merge 
process during the segmentation. In our experiments, we set it 
as 0 so that we can obtain over-segmented sub-areas which 
have more homogeneous texture pattern. Examples of the forest 
delineation result are shown as Fig.9a (high-lighted with white 
lines) while Fig. 9b shows the manual forest boundary 
delineation result according to NFI forest definition, (high 
lighted with red lines) 
Fig. 9a Final forest Fig. 9 Manual delineation 
Boundary derived from JSEG 
Experimental results indicate that forest area can be 
distinguished by using remote sensing data, e.g. aerial images 
and LIDAR data. The color features extracted from aerial 
images remove successfully non green areas while the curvature 
feature extracted from LIDAR remove building areas. The 
comparison of texture features extracted from Gabor wavelet 
between CHM-GVI-JSEG forest area and sub-areas after JSEG 
segmentation gives the chance for detecting sub-areas with low 
quality CHM and lead to forest boundary delineation in more 
semantic way. The approach presented in this paper offers an 
automatic process for forest/non forest detection in Swiss NFI. 
It’s a challenge to describe NFI forest /non-forest definition 
with automatic computer-based method. For example, some 
temporary unstocked areas are still delineated manually as 
forest because their forest land-use functions. It will be difficult 
to interpret such kind of subjective definition with low level 
features extracted from remote sensing data. However, current 
study provides an encouraging basic for further development 
and testing. 
REFERENCE: 
Brassel P. (Ed.),(2001). Swiss national forest inventory: 
methods and models of the second assessment. Birmensdorf: 
Swiss Federal Research Institute WSL 
Brassel P. and Brandli (Eds.),(1999). Schweizerisches 
Landesforstinventar Ergebnisse der Zweitaufnahme 1993-1995. 
Bern: Haupt 
Deng Y. and B. S. Manjunath,2001. Unsupervised segmentation 
of color-texture regions in images and video. IEEE 
Transactions on Pattern Analysis and Machine Intelligence 
23(8): pp. 800-810. 
Desclee B., P. Bogaert and P. Defoumy,2006. Forest change 
detection by statistical object-based method. Remote Sensing of 
Environment, 702(1-2): pp. 1-11. 
Donoghue D. N. M., P. J. Watt, N. J. Cox and J. Wilson,2007. 
Remote sensing of species mixtures in conifer plantations using 
LiDAR height and intensity data. Remote Sensing of 
Environment, 770(4): pp. 509-522. 
Haara A. and M. Haarala,2002. Tree species classification using 
semi-automatic delineation of trees on aerial images. 
Scandinavian Journal of Forest Research, 77(6): pp. 556-565. 
Husch B., C. I. Miller and T. W. Beers, 1982. Forest 
mensuration 3rd ed. New York: John Wiley & Sons, 
J. Hyyppa H. H., et al,2000. Accuracy comparison of various 
remote sensing data source in the retrieval of forest stand 
attributes. Forest Ecology and Management, 128: pp. 109-120. 
Lu D. S., P. Mausel, E. Brondizio and E. Moran,2004. 
Relationships between forest stand parameters and Landsat TM 
spectral responses in the Brazilian Amazon Basin. Forest 
Ecology and Management, 795(1-3): pp. 149-167. 
M. A. Lefsky W. B. C., and T. A. Spies,2001. An evaluation of 
alternate remote sensing products for forest inventory, 
monitoring, and mapping in Douglas-fir forests of western 
Oregon Canadian Journal of Forest research, 31: pp. 78-87. 
Maltamo M., K. Eerikainen, J. Pitkanen, J. Hyyppa and M. 
Vehmas,2004. Estimation of timber volume and stem density 
based on scanning laser altimetry and expected tree size 
distribution functions. Remote Sensing of Environment, 90(3): 
pp. 319-330. 
McRoberts R. E., E. O. Tomppo, A. O. Finley and J. Heikkinen, 
2007. Estimating areal means and variances of forest attributes 
using the k-Nearest Neighbors technique and satellite imagery. 
Remote Sensing of Environment, 777(4): pp. 466-480. 
Morsdorf F., Meier, E., Kotz, B., Itten, K.I., Dobbertin, M. and 
Allgower, B.,2004. LIDAR-based geometric reconstruction of