Object-oriented classifier for detection tropical deforestation using Landsat ETM+ in Berau, East Kalimant, Indonesia

Cui Yijun and Yousif Ali Hussin
Department of Natural Resources,
The International Institute for Geoinformation Science and Earth Observation (ITC),
Hengelosstraat 99, 7500 AA,
Enschede, Netherlands,
Fax: (31) 53-4874-388,
Email: Hussin@itc.nl, Cui@itc.nl


Abstract
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. The use of satellite remotely sensed data and image analysis (i.e. classification) has proven its effectiveness in detecting tropical deforestation. In this research, two sets of Landsat-7 ETM+ data acquired on 26 August 2000 and 16 August 2002 were used in this research. The Object-oriented image analysis, which is implemented through software eCognition, was used for image classification. The GIS software ILWIS was used for integrated analysis of classification results and other geographic data. Two projects were created in eCognition. First one was applied on fused data set that covers a relative small and cloud free area on both images. Eight classes were classified, namely dense forest, moderate dense forest, sparse forest, heavily logged area, less heavily logged area, old conversion area, new conversion area and cloud and shadow. The overall accuracy for this classification is 81.3% and KIA (Kappa Index of Agreement) is 78.1%. The second project was applied on original data set and two thematic layers. Seven classes were classified, namely illegal logging, legal logging, slightly logged area, non-forest, unlogged area, moderate dense forest and sparse forest. The overall accuracy is 76%, and the KIA is 70.4%. These classification results and the classification process revealed the great potential of using Object-oriented image analysis to extract information from satellite image to detect tropical deforestation.