Abstract | Full Paper | PDF | Printer friendly format

Page 1 of 5
| Next |

1. Introduction
Forests are very important natural resources, and tropical rain forests are even more important due to the abundance of biodiversity. But the rate of deforestation in the tropics is alarmingly high because of the rapid population growth and economic development.

Tropical countries, like Indonesia, cannot afford to stop logging natural forests because they need the timber to generate revenue and to open more land for agriculture to produce food in order to support their people and economy. On the other hand, timber-consuming countries, including developed and developing ones, are not rich enough or generous enough to help tropical countries to stop logging tropical forests. As a matter of fact, they are quite enjoying the quality products from these precious tropical forests.

Sustainable forest management (SFM) is a concept or practice that satisfies the needs of both producer and consumer countries. Further more it can be an answer to the increasing environmental concerns of professionals, the public, and the media worldwide. SFM can be considered as a compromise between ban of logging and uncontrolled logging.

Indonesia’s forests are very important, not only to Indonesia but also to the whole world, due to its capability of supporting biodiversity and ameliorating climate. However, the sustainability of forest management in Indonesia, in general, is far from desired (Brown, 1999). The certification of SFM is considered an important method to push the forest management towards sustainable manner. In order to efficiently carry out SFM certification and monitor the already certified forest management unit’s performance, objective, unambiguous and timely information about the target forest areas is needed. For the frequent information acquirement on large and, usually, remote forest areas, only depending on field survey is not feasible both in terms of money and time. Therefore, the remote sensing data and techniques must be considered. In fact it is the only way to obtain timely information for large and remote tropical rain forest. Theoretically, there is no doubt that remote sensing data can be a useful tool in supporting the acquirement of this information.

Many studies have been carried out on the use of remote sensing products to detect tropical deforestation using traditional supervised Maximum Likelihood Classification. But extra classification capability such as object detection classification has not been used in classifying tropical deforestation.

Object-oriented image analysis is different from conventional pixel base image analysis e.g. Maximum Likelihood Classification, which analyses the image based on image objects rather than pixels. As an example to Object-oriented image classifier, eCognition is one of the software to implement this concept. It is a powerful and versatile technology for multiscale analysis of earth observation data, particularly suited for the analysis of very high resolution optical and radar data. It can handle even complex problems, which require the consideration of local context information. Object-oriented image such as eCognition is based on the concept that important semantic information necessary to interpret an image is not represented in single pixels, but rather in meaningful image objects and their mutual relations. Therefore, the image classification is based on attributes of image objects rather than on the attributes of individual pixels. Therefore, Object-oriented classifier can deliver results noticeably better than conventional methods. It leads to higher classification accuracy and to better semantic differentiation.

Object-oriented classifier is based upon contiguous, homogeneous image regions that are generated by initial image segmentation. Connecting all the regions, the image content is represented as a network of image objects. These image objects act as the building blocks for the subsequent image analysis. In comparison to pixels, image objects carry much more useful information. Thus, they can be characterized by far more properties than pure spectral or spectral-derivative information, such as their form, texture, neighbourhood or context. Classifying an image using Object-oriented approach means classifying the image objects either based on sample objects (training areas) or according to class descriptions organized in an appropriate knowledge base. The knowledge base itself is created by means of inheritance mechanisms.

The objective of this research is to assess the effectiveness of the use of Object-oriented Classifier in detecting tropical deforestation in Berau, East Kalimantan, Indonesia, using Landsat ETM+ images.