maples (Acer velutinum), Persian Ironwood Tree (Parrotia 
persica) and few other species, which is typical for the region. 
Data set consist of 76 ortho-rectified CIR images of UltraCamD 
which has been in 19.8.2006. Focal length, mapping scale, 
ground pixel size and radiometric resolution were 101.4 mm, 
1:15000, 14 cm and 8 bit, respectively. 
Digital surface models (DSMs) were generated automatically 
using an image matching approach from CIR aerial images with 
a spatial resolution of 1 to 10 m size by 1m span. All DSMs 
    
  
   
   
  
     
  
   
  
   
    
   
   
     
    
       
were checked manually for probable errors. 
  
Tonekabon 
  
Figure 1. The location of study area 
2.2 Ground data and statistical methods 
120 ground circular sample plot were located at study area and 
diameter, species and height of three random trees were 
recorded and standing volume in each plot were calculated. 
Also, corresponding to each ground sample plot, standard 
deviation of DSM pixels was calculated. 80 percent of samples 
were used for constructing regression equations and 20 percent 
for validating the equations. Non-linear regression analysis was 
used for modelling. Bias, relative bias, RMSE and relative 
RMSE of estimating standing volume were calculated using 
following equations: 
SG, zy.) 
Bias — 4L — — (1) 
n 
Bus =100x 22 
  
@) 
y 
  
    
   
   
  
   
   
   
  
   
   
   
  
  
    
   
    
   
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 
    
Y 6, -y,y 
  
RMSE z4|: (3) 
n 
RMSE, «19042 MSE (4 
M 
where: 
9, and y,: estimated and true value 
for i, observation 
n: number of observations 
y : mean of true observations 
3. RESULTS 
Results showed that at 5 to 7 m pixel size, the correlation 
coefficient of standard deviation of pixels and standing volume 
of sample plots are higher than other pixel sizes (figure 2). So. 5 
m resolution DSM (which was the most correlated pixel size) 
was used for further analysis. 
  
0.60 
0.50 - 
0.40 4 
0.30 4 | 
0.20 + | 
0.10 + | 
0.00 | 
  
correlation coefficient 
  
  
  
T | 
| 
0 1. 245344. 5. 6. 7, 8. 9. 19. 1] 
| Pixel size (m) 
/ 
  
Figure 2. Correlation of standard deviation of different pixel 
size of DSM and forest standing volume at sample plots 
Table 1. Results of regression analysis, y: stand volume in 
sample plot and x: standard deviation of DSM pixels of sample 
  
  
plot 
Model Equation r Sig. 
Power Model y-17.334x ^? 0.58 TE 
  
Different regression models were fitted and the most suitable 
one based on correlation coefficient and standard error of model 
was power regression model (table 1 and figure 3). 
  
   
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