2.2 Ground truth map 
To assess the capability of IRS-P6-LISS IV images for forest 
density mapping, an accurate ground truth was prepared 
through fieldwork, since there is no update aerial photo or other 
very high resolution images. An inventory grid of 500mx500m 
and 34 plots (each plot 1 ha) with random systematic 
distribution were designed on a topographic map. Coordinates 
center of the samples were entered to a handheld GPS. After 
revealing the plots in forest, their densities were estimated 
qualitatively in density classes of 0-594, 5-1094, 10-15%, 15- 
20% and > 20%. There was no forest area with density more 
than 25% in the study region. Due to coppice forest and low 
BHD of trees, number of trees in hectare could not be an 
appropriate parameter to estimate the density. Therefore, 
percent of tree canopy cover was estimated in each sample plot. 
The resulted density vector map was converted to raster format 
(Figure 3). Finally a ground truth map with 3 classes was 
produced to be compared with map resulted of satellite image 
analysis. 
3000 Matars 
Figure 3. Sample ground truth overlied on the satellite image of 
the study area 
3. DATA 
3.1 Satellite data and geometric correction 
A subset of a map oriented IRS-P6-LISS IV with 3 bands (B3, 
B3 and B4) and 10m resolution dated 3 1-August 2007 has been 
used. The image underwent level 1G processing (geometrically 
and radiometrically corrected) and had no cloud cover. The 
subset was rectified to another precise orthorectified IRS-P6 
from the same region and year with GCP method (RMSe<5m). 
The nearest neighbour resampling method was performed to 
produce image with the same resolution of ground truth (5m). 
The image was geocoded to the UTM coordinate system. 
3.2 Image processing 
In order to extract more accurate information from satellite 
data, various suitable enhancements such as principal 
component analysis [Eastman, 2006] and band rationing were 
performed. Since the canopy cover is very low, distance-based 
vegetation indices were also calculated to reduce influence of 
International Archives of the Photogrammetry, Remote Sensin 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
   
g and Spatial Information Sciences, Volume XXXIX-B8, 2012 
soil background using soil line parameters [Alavipanah, 2003]. 
These parameters were estimated based on regression analysis 
of Red and NIR reflection of different soil types existed in the 
study area. 
3.3 Image classification 
Supervised classification methods were used for image analysis. 
The training set into 5 density classes was defined. 
The best spectral band-sets were selected using bhattacharrya 
distance and transformed divergence criteria based on training 
areas. Classification utilizing original and synthetic bands with 
maximum likelihood (ML), minimum distance to mean (MD) 
and fuzzy classifier was performed. Since the primary results 
indicated spectral interference between some density classes, 
these classes were merged together and the classifications were 
repeated. In order to eliminate single pixels deviating from the 
neighbourhood, a majority filter (7x7 pixels = 35mx35m) was 
done on the resulted maps. Accuracy assessment of 
classification outputs was accomplished through the use of error 
matrices detailing producer, user and overall accuracy and an 
overall kappa statistic [Congalton & Green, 1999]. 
4. RESULTS 
-Desired coincidence between crest and valley layers of digital 
topographic maps and the rectified satellite image indicated 
high precisian of the image rectification. 
-The ground truth map included of five density classes (0-594, 
5-10%, 10-15%, 15-20% and > 20%) was prepared for about 
7% of the study area (Figure 3). 
-Classification outputs with 5 density classes showed 
undesirable overall accuracy and kappa coefficient of 50% and 
0.31 respectively. Merging some classes with spectral similarity 
improved the result. The best result of forest density 
classification was acquired by fuzzy classifier with 3 classes (0- 
5%, 5-20%, >20%). The overall accuracy and kappa coefficient 
were 70% and 0.44, respectively (Table 1). Figure 4 presents 
the result of recent classification. 
4051000 | 
34051000 
4043000 4043000 
404000 
  
274000 272000 275000 274000 DE 
9 1000 $ 2006 3000 Metsrs 
Figure 4. Map of canopy density with three classes resulted 
from fuzzy classifier (UTM Zone 41, WGS84) 
  
     
    
  
  
     
    
    
    
    
    
    
  
       
   
  
  
    
   
  
  
   
   
  
  
  
  
     
     
   
  
  
  
  
  
  
   
   
      
   
  
Int: 
T: 
Base 
class 
accu 
class 
Cons 
to ] 
class 
cont 
[Ski 
meth 
dens 
area. 
Ove 
appr 
Higl 
aeris 
class 
In th 
perl 
Alay 
eart