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4. Results and Discussions

4.1 Classification result of fused data set
The classification result of fused data (15 meters spatial resolution) is shown in Figure 4. The accuracy assessment result is shown in Figure 5.


Figure 4. The classification result of fused data set

Figure 5. Accuracy assessment result of fused data set
Because the final classification result was derived from its sub level i.e. level 1, the accuracy assessment of level 1 is also important. In fact this assessment was done before its up level’s classification was carried out. Figure 6 shows the accuracy assessment result of level 1.


Figure 6. Accuracy assessment result for level 1
It is clear that the accuracy of level 1 is lower than the final result. This is reasonable, because the segmentation scale parameter for level 1 is very small (3). This resulted in a large amount of small homogenous objects, and they server as primitive information carriers. In fact, among these classes in level 1, only some classes have contribution to the final classification result. So the overall accuracy is not as important as that of some specific classes. In this case the most important class is ‘logging spot’, and followed by ‘tree’, ‘bush’, ‘grass’ ‘water’ and ‘other’. The accuracy assessment of ‘water’ was included in the class of ‘other’, so the ‘water’ was not in error matrix.

From Figure 5, it can be noted that the final classification result has an overall accuracy 81.3% and a KIA (Kappa index of agreement or kappa coefficient) 78.1%. Congalton (1996) stated, “Kappa values are also characterized into 3 groupings: a value greater than 0.80 (80%) represents strong agreement, a value between 0.40 and 0.80 (40 to 80%) represents moderate agreement, and a value below 0.40 (40%) represents poor agreement (Brandon & Bottomley, 1998). According to this standard, the accuracy achieved in this classification project represents moderate agreement.

By crossing the map of classification result with slope map and followed by producing attribute map. The logged area_ slope map was produced From this map it can be found that there were very few logged areas, including heavy and slightly logged, on the steep areas. It means that the regulation of “selective logging excludes slope greater than 40% area” was observed very well.

The classification result was also crossed with the elevation map that shows the areas with an elevation higher than 400 meters above see level. It was found that only 0.09% of logged areas was located in high elevation areas. So it can be concluded that the regulation of “selective logging excludes the area with an elevation higher than 400 m” was observed very well also. The reason for this finding is similar as that of steep slope areas.

From the classification map we can see the class ‘old conversion area’ which means the forest area was converted to other land cover before the 2000 image was acquired. The class of ‘new conversion area’ means the area that was forest in 2000 image, but was not forest area in 2002 image. The area of each class can be easily obtained from the map’s statistics table, and can be seen in a very clear way.