Abstract | Full Paper | PDF | Printer friendly format

Page 2 of 5
| Previous| Next |

2. Study area
Labanan forest concession area, which was selected as the study area for this research, is located between latitude 2°10’ N and 1°45’ N, and longitude 116°55’E and 117°20’ E (Figure 1). It is in Berau regency, one of four regencies in East Kalimantan province, Indonesia. Labanan forest concession area covers 8100 ha production forest. Inhutani I, a state owned forest concession company, has managed this area for more than 30 years and selective logging has been applying since 1970s (Fauzi, 2001). At present, Inhutani I is carrying out the Berau Forest Management Project (BFMP), which is jointly financed by the Government of Indonesia (GOI) and the European Commission (EC). Inhutani I has managed the Labanan concession as an international showcase of forest management, at the request of the ministry. They have already achieved ISO 14001 certification for the concession management and are currently seeking Forest Stewardship Council (FSC) and LEI certification (BFMP, 2002). Remotely sensed and other ancillary data such as Landsat ETM+ images and various maps and records of temporary or permanent forest sampling plots are available and accessible. All these facts made Labanan concession area an ideal place for carrying out this research.


Figure.1 Location of the study area, map source: (BFMP, 2000)
The study area has a typical tropical climate; the annual rainfall is about 2000 mm and every month receives more than 100 mm rainfall in most years. A high botanical diversity characterizes the Berau area. Lowland mixed dipterocarp forest dominates the natural vegetation of East Kalimantan. The logging system adopted here is called RKL or five-year working plan. The whole production area was arbitrarily divided into square blocks of 100 ha each from which they selectively log 1000 ha per year. Since 1995, natural boundary of watershed has been used as logging boundary (Hussin, 2002).

Two Landsat-7 ETM+ satellite image acquired on August 26, 2000 and 16 August 2002, which were used for this research. The 7 bands were resampled to the panchromatic image acquired at the same time. Then, the fusion technique of RGB and IHS transformation was used to integrate this multi spectral data set with panchromatic image (ITC, 2001). This resulted in a sharpening of image due to the higher spatial resolution of the panchromatic image, and the resolution of the resulting image became 15 meters (Lillesand & Kiefer, 2000). According to Franklin (2001), the striking enhancements for visual interpretation can be achieved using above-mentioned method. In fact, the result of our fusion operation confirmed this conclusion. Much more detailed information could be drawn visually from this fused image than that from separate original data. In the same time the image of the original 30 meters spatial resolution were used and compared with the enhanced 15 meters resolution.

3. Methodology
Two classification projects were implemented in this research. The first classification project contains two subsets of fused images of 2000 and 2002. Altogether 3 levels were constructed (Figure 2). Segmentation is the first operation in any eCognition project, which is a process to subdivide the image into separated homogenous areas according to the given segmentation parameters. Different segmentation parameters result in different objects size. The parameters used in this research were basically obtained through tries according to different segmentation purposes. The first segmentation of this project, which considers band 4, 5 and 7 of two images, was made for level 2. This segmentation is aimed to get objects that represent the conversion areas and selectively logged areas properly. The second classification project (Figure 3) contains not only two subsets of original images of 2000 and 2002, but also two thematic layers (boundary and RKL map). For image data, the panchromatic band was included and band 6 was excluded. eCognition distinguished two basic types of data: image layers and thematic layers. The image layers contain continuous information, while the information of thematic layers is discrete. The two types of layers have to be treated differently in both segmentation and classification (Definiens Imaging GmbH, 2001). Altogether 4 levels were constructed in this project.


Figure 2. Process involved in fused data classification and accuracy assessment

Figure 3. Steps involved in original data classification and accuracy assessment