Application of Soft Classification Techniques for Forest Cover Mapping

Forest cover mapping is necessary to monitor forest cover changes in order to support sustainable forest management. One of the most important factors that cause deforestation comes from Illegal logging. Illegal loggers were cutting trees selectively, based on tree-diameter and tree-species. Remote sensing is a promising tool, which can be used for detecting this type of logging and deforestation the tropical forest. This study applied two different soft classification techniques, i.e. fuzzy c-means classification and neural network method to classify forest cover as well as to detect illegal logging in a form of single tree felling. The classification results were accurately
compared to the result of conventional maximum likelihood classification using confusion matrix. This study found that neural network method resulted in a more accurate detection of single tree felling, followed by maximum likelihood technique. Fuzzy c-means technique gave less satisfactorily result due to a strong overlapping between single tree felling and high density forest training classes.