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4.2 Sub-pixel Classification
Figure 7.a shows the typical result of the sub-pixel classification. Eight different MOI, which is the Materials Of Interest, (MOI here is newly logged points) fractions that can be seen ranging from 0.2 to 1. As discussed in methods earlier, the mean MOI fraction for signature was selected 0.21 that means the disturbance due to logging (opening etc) was considered 21% of the area of a pixel. Therefore, the lower class of MOI fraction could be natural opening and simple other disturbances but not logged points. That’s why, only the area classified as 0.8 or higher MOI fraction was considered as newly logged points. Figure 7.b shows the result of newly logged area after considering that criterion.


Figure 7. The result of sub-pixel classification with different fraction of MOI (a) and NLP after considering area with 0.8 or more as NLP (b).

Figure 8. Part of RKL6 showing NLP
Figure 8 shows the result of sub-pixel classifier of the same area as shown in Figure 4 overlaying road on it. It is not an easy comparison with previous figure as it has only two classes i.e. the road/highly degraded and un-logged have been merged as others. But, careful observation can conclude that relatively less area was classified as NLP than in case of ML classifiers.

Although it is not directly comparable, the overall accuracy of this procedure was 86% a bit higher than ML with fused image. The average user’s and producer’s accuracy of this procedure was found 87% and 84% respectively. But, the kappa and class mapping accuracy of NLP was found 0.71 and 0.69 a bit lower that the ML method. The significance of the differences could not be tested, as they don’t have similar classes.

4.3 Performance of Fused Image and Maximum Likelihood Classification
As anticipated, the ML classification result from fused image was found better than classification of un-fused ETM bands. The significantly better result in terms of overall accuracy kappa and class mapping accuracy of NLP shows the usefulness of fusion of ETM+ panchromatic bands with multispectral bands. It is obvious that the main reason of better accuracy is the high spatial resolution of panchromatic image. In this particular case of selective logging, a felling of a single tree can create the canopy disturbance of whole pixel area (225 m2) of ETM+ pan image. Certainly, it is not the case in multispectral bands of same image where it is expected as mixed pixel in most of the cases. Due to the mixed effect, the reflectance in multispectral bands will not be much different than the surrounding un-logged forest. Therefore, the merging of the panchromatic band will create more variation between those two classes and ultimately can give the good classification result.

Many authors have successfully used different data type and various fusion techniques for different purpose such as image sharpening, feature enhancement, improve classification result etc (Pohl & Van Genderen, 1998). Hussin and Shaker(1996) used radar image to fuse with TM image and get better visual interpretation as well as classification result. Munechika et al. (1993) have also reported the improvement in classification accuracy after merging TM multispectral and SPOT PAN image. Those finding supports the better classification accuracy of fused image though the data type and merging techniques were different in either case. But few researchers also reported some disagreement about the superiority of fused image over original dataset for the classification purpose. Shaban & Dikshit (2002) have reported significantly lower classification accuracy from merged data of SPOT XS and PAN as compared to original data in an urban area. They have pointed the high heterogeneity of urban area as a reason of poor result of merged data as an agreement with van der Meer (1997). Talking mainly about visual interpretation, however van der Meer (1997) has suggested that image fusion will be more successful with spatially homogeneous data means where there is no spatial dependence.

It can be noted from the view of different authors ( Zhang, 1999; Zhou et al., 1998) that the result of fused data depend on various factors such as data type, merging techniques and purpose of using. As the ETM panchromatic data is relatively new, very few work have been reported about merging of these data. Therefore, to find the reference of similar data type and merging method for similar purpose is not easier. However, Hung et al. (2002) used fused ETM+ PAN and multispectral data and found better classification result in some geological study. One obvious benefit of merging ETM+ PAN and multispectral data is temporal consistency as both images are acquired at simultaneously, which is hardly the case of multisensor data fusion.

The logging points can be considered as objects with no spatial dependence though there might be little affect of road. So we can consider the findings of this study is in agreement with the observation of van der Meer (1997) in that sense.

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