Results and discussion

Image analysis results

Processed by ERDAS software

The supervised classification image (Figure 5) produced five forest classes that, when verified on the ground, gave the following results (Table 5).

Table 5. Verification of the forest classes in the ERDAS supervised image

Class	Area (ha)	Verification
1	2 463	Ridge areas with a canopy structure consisting of two layers. Trees consist mainly of medium-sized commercial trees and many seedlings. Some open areas are colonized, mainly by ferns.
2	4 955	Lowland areas with three distinct canopy layers. These regenerating forests consist of mainly less valuable non-dipterocarp trees, which are small with dbh < 60 cm, although occasionally there are some bigger trees.
3	3 678	Forest areas, which are more open (probably due to heavy logging). They have a one-layered canopy structure with very few climax species. They are mostly colonized by pioneers and other light-demanding species. Bamboos and palms are abundant in some areas.
4	539	These are areas that are more representative of the original forests in terms of their canopy structure. They have three canopy layers and many large trees. However, non-dipterocarps dominate. Occasional dipterocarps may be protected and other trees that were not removed during logging.
5	239	These areas are also representative of the original forests both in canopy structure and species composition. They may consist of fully regenerated forests or areas that were not disturbed by logging activities. The canopy has three layers and valuable commercial species of dipterocarps and non-dipterocarps abound.

Another feature that is clear from the image is the elevation of the study area (Figure 9). When overlaid onto the contour map, the classified image clearly showed the differences in elevation. This was also verified by the ground truthing.

The map depicting the year of the last logging entry was also overlaid onto the classification image (Figure 10). This procedure intends to relate the forest condition with the year the forest was logged. The compartment number, size and year since logging are shown in Table 2. Attempts were made to obtain information on the logging intensities and volume of timber removed from the areas. Visual comparison of forest conditions indicated that years since logging may be a poor parameter for stratification of the forests because of differences among logging operations. The condition of the residual stand depends to a considerable extent on the logging practices employed by different contractors. In extreme cases of poor logging the area was badly logged and the residual forest was not able to regenerate according to the prescribed cutting cycle.

Processed by the FCD model

Supervised classification reduced the 10 classes assigned by the unsupervised classification to four classes (Table 6). Forest density class 2 is most extensive covering 46 percent of the area. The second most extensive class is forest density class 1, which covers 34 percent of the area. Based on the canopy densities, i t can be assumed that these two classes have fewer big trees and more saplings and seedlings. This indicates a stand that has been logged more recently and requires a longer time to recover before harvesting can be carried out again. The supervised classification also shows that the area with more mature trees and higher canopy densities covers only about 20 percent of the total area.

Figure 9. Elevation overlaid onto classification image

Table 6. FCD results based on the supervised image

FCD classes (% crown)	Class	Colour	�Coverage area
FCD classes (% crown)	Class	Colour	(ha)	%
>70	4	Green	472	4
50-70	3	Yellow	1 889	16
30-50	2	Brown	5 431	46
<30	1	Blue	4 014	34
Total			11 806	100

�

Figure 10.� Year of logging overlaid to supervised classification image.

Field data collection

Data collection in the field took place between late December 2001 and February 2002. The planned activities were slowed down by unusually heavy rains. A reconnaissance inventory consisting of a small numbers of plots was conducted in each forest stratum. Plots were located at grid intersections close to roads.

For the point sampling of the large trees, a wedge prism of basal area factor (BAF) 4 was used. The choice of BAF usually depends on the density and size of trees within the stand. Denser stands and larger trees require a higher BAF factor. Wide-angle relascopes could also be used, but they are more expensive and more cumbersome to handle especially in moist conditions. However, they are easier to apply on steep terrain, as relascopes automatically compensate for elevation. The wedge prism was considered adequate for the purpose of the study. It is usually easier to perform the survey with the same BAF for the whole inventory, although changing the BAF for different forest strata does not affect the calculations. In fact, if the crew is not constrained in the field, it is recommended to use a lower BAF of 2 or 3 for more open forest strata with a larger proportion of small trees.

Two field crews of four persons each were deployed in the field. Each crew consisted of a field leader who also recorded information. One person equipped with a compass and a GPS led the team to the sampling lines and located plots (This person may also take the prism readings for the point sampling.). The other two persons measured trees, established plots and sometimes took prism readings. The standard equipment used included: