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misclassification. The LC class “Settlements” was extracted 
with relatively more manual editing and hence the omission and 
commission errors for this class are relatively low. 
Settle- | Other Row Omission 
ments | Land Error (in % 
(Pixels) 
115 18.3 
32 12.5 
21.6 
24.0 
12.5 
30.8 
Cropland Wetlands 
Land 
Overall 
80 
Sample Calculation: 
Omission Error of Forest Land = 21/115 = 18.3% 
Commission Error of Forest Land = 4/98 = 4.1% 
Overall Accuracy = 186 / 230 = 80.9% 
Table 1. Accuracy Assessment of LC Result from ALOS 
AVNIR-2 
LC pattern among the analyzed 3 epochs is similar, Figure 5. 
This is also supported from the result of pixel tracking between 
1995 and 2009 LC result so presented in Table 2 which 
indicates vey less change in forest change for the tracked pixels. 
Landsat 5 TM (1990) Image and Classified LC 
Figure 5. Satellite Image of 3 Epochs (bands 4, 3,2 as R, G, B) 
and Classified LC 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
   
  
   
  
  
  
  
  
  
  
  
Med Land Cover 2009 | Forest | | Settte. | Orher 
bacon Land : Cropland | Grassiand | Wetlands ments | Land 
5 hd Bixci Court 
Laud Cover 1985 : al = : 38 17 33 i 3 | i 
Forest Land 158 & 3 it b i ; f 
Cropland 25 m 7 1 o | 0 
Grassiand 18 è A 0 o i i 
Wettands as à e A: ü n 
Settlements | 3 0: f o f | x 9 
Other Land 3 6: 1 2 oj ef à 
  
  
  
  
  
  
  
  
  
Total Number of Pixels Analyzed = 242 
Table 2. Result of Analyzed Pixels for LC Change from 1995 to 
2009 
Comparatively, the change between cropland and grassland is 
more and mis-classification may be a reason for this. Moreover, 
it shall be noted that, spatial resolution for used satellite data are 
different. The spatial resolution is 30m for Landsat TM and 
ETM+, and 10m for ALOS AVNIR-2. Also, the “Unsupervised 
classification” was carried out with 6 bands for Landsat and 
ETM+ data and 4 bands for ALOS AVNIR-2 data. 
Moreover, in the analyzed area, there is location specific 
change of LC and can be traced out only upon detail analysis, 
such as at location presented in Figure 6. 
Landsat TM (1995) Landsat ETM+ (2002) : ALOS AVNIR-2 (2009) 
Image and Classified IC Image and Classified LC fmage and Classified LC ; 
Figure 6. An Example of Land Cover Change (1995 to 2009) 
5. CONCLUSIONS 
The approach devised for cloud/shadow and haze removal and 
LC change detection has good results. The accuracy of LC 
classification is also promising one. The program used for 
processing also considers classification of large number of 
imageries in the shortest possible time with required number of 
LC classes, specifically for REDD+ application with [PCC 
standard. This involves less human intervention and can 
classify imagery for extracting LC in wide variety of landscape 
and different dated data; thus can be considered as semi- 
automatic. 
Thus, this semi-automated methodology is expected to be suited 
for diversified conditions and allow processing of large number 
of imageries in comparatively less time, which will be very 
useful in analysis of successive change/monitoring of tropical 
rain forest in Africa.