Abstract | Full Paper | PDF | Printer friendly format

Page 3 of 5
| Previous | Next |

3. Methodology
Research methodology was done in three stages namely: pre-fieldwork stage, fieldwork stage and post-fieldwork stage. Figure 3 shows the detailed methodology of this research.


Figure 3. Flowchart showing research procedure.
4. Results and Discussions

4.1 Maximum Likelihood Classification
The classification of Landsat-7 ETM+ 2002 image using maximum likelihood classifier showed that the method is not able to well differentiate the selectively logged points from un-logged area. However, the fused data set of panchromatic band with multispectral showed that the differentiation becomes better. Both The qualitative and quantitative assessment of the classified maps shows that the result of the fused image is much better than its un-fused counterpart.

It can be noted from Figure 5 that the transmigration area of the upper left corner of the map has been clearly classified as the highly degraded category from the fused image but it has been mixed with newly logged points in case of un-fused image. Similarly, the superiority of the result from fused image can also be seen in Figure 4. The figure shows a part of RKL 6 where the official logging is presently going on. It can be seen that most of the area in the case of un-fused image has been classified as NLP whereas the result from fused image differentiate three different classes more clearly. The latter situation can be considered more natural because some area can become more degraded due to log skidding etc and some area can remain intact, as the selective logging is not always evenly distributed.

Figure 6 shows the result of quantitative evaluation. The overall accuracy as well as kappa was found much higher in case of fused image. The overall accuracy of un-fused image was 72% and kappa 0.56 whereas it was 84% and 0.75 in the case of fused image. As the main objective was to assess the capability of detecting newly logged areas, the class mapping accuracy of NLP was also calculated. The class mapping accuracy of NLP was found 74% in case of fused image but it was only 47% without fusion. It shows that the relatively higher overall accuracy of un-fused image was due to its comparatively better performance in other class i.e. un-logged and road/ highly degraded. Moreover, the much higher kappa shows the benefit of fused image to detect selective logging as the kappa is the better tool for comparing two methods of map preparation (Skidmore, 1999). The test for kappa confirmed that difference in result of these two techniques is highly significant ( =20.5, =0.00).


Figure 4. Part of RKL6 showing the classification result of un-fused (a) and fused (b).

Figure 5. Classified maps using maximum likelihood classifier from unfused (a) and from fused image (b).

Figure 6. Comparison of accuracies between maps produced from fused image and without fusion. OA= Overall accuracy, KA= Kappa and CA NLP= Class accuracy of Newly logged points.

Page 3 of 5