XIX-B8, 2012 
avipanah, 2003]. 
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forest density 
th 3 classes (0- 
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ses resulted 
84) 
  
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 
Table 1. Error matrix of the best classification with three 
density classes 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
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5. CONCLUSION 
Based on this investigation, fuzzy and maximum likelihood 
classifiers indicated almost the same and the highest overall 
accuracy and kappa coefficient. According to the best band sets, 
the distance-based vegetation indices could improve the 
classification results slightly. 
Considering the low kappa coefficient (0.44), even if reaching 
to pretty good overall accuracy (7096), the result of 
classification was not desirable because forest canopy is a 
continuous variable and the decision boundaries do overlap 
[Skidmore ef al., 1988]. Remotely sensed data and classification 
methods applied in this research could not well classify forest 
density as a continuous variable in this low density forested 
area. Similar research confirms this too [Joshi et al., 2006]. 
Overall, it could be concluded that this approach is not 
appropriate for operational mapping of vast Pistachio forests. 
Higher spectral and spatial satellite data or multispectral digital 
aerial photos such as UltraCam images and object-based 
classification should be investigated as an alternative approach. 
In this case, the tree crowns could be detected and sum of areas 
per ha can be used for density estimation. 
6. REFERENCES 
Alavipanah, S. K., 2003, Remote sensing application in the 
earth science. University of Tehran Publication, 478 pp. 
Congalton, K.G. and Green, K., 1999. Assessing the accuracy 
of remotely sensed data: Principles and practices. New York, 
NY: Lewis Publishers, 137 pp. 
Dorren, L.K., Maier, A.B. and Seijmonsbergen, A.C., 2003. 
Improved Landsat-based forest mapping in steep mountainous 
terrain using object-based classification. Forest Ecology and 
Management, 183, 31-46 (2003). 
Eastman, J. R., 2006. IDRISI Andes Guide to GIS and Image 
Processing. CLARK University, Version 15.00, 327 pp. 
Joshi, C., Leeuw, J. D., Skidmore, A. K., Duren, I. C. V. and 
Oosten, H. V., 2006. Remotely sensed estimation of forest 
canopy density: a comparison of the performance of four 
methods. International Journal of Applied Earth Observation 
and Geoinformation, 8, 84-95. 
Revised Plan of Pistachio Forest of Khajehkalat Studies, 2009. 
Natural Resources General Office of Razavi Khorasan 
Province, 139 pp. 
Skidmore, A. K., Forbes, G.W. and Carpender, D.J., 1988. Non- 
parametric test of overlap in multispectral classification. 
International Journal of Remote sensing, 9, 777-785.