Classification of Forest Type Using High Resolution Imagery of Satellite IKONOS
Kee-Hyun Chung, Woo-Kyun Lee, Jun-Hak Lee, Kwon-Hyeok Kim, and Seung-Ho Lee

Abstract : This study was carried out to evaluate high resolution satellite imagery of IKONOS for classifying the land cover, especially forest type. The IKONOS imagery of ll krn x llkm size was taken on April 24, 2000 in Bong-pyoung Myun Pyungchang-Gun, Kangwon Province. Land cover classes were water, coniferous evergreen, Larix leptolepis, broad-leaved tree, bare land, farm land, grassland, sandy soil and asphalted area. Supervised classification method with algorithm of maximum likelihood was applied for classification. The terrestrial survey was also carried out to collect the reference data in this area. The accuracy of the classification was analyzed with the items of overall accuracy, producer's accuracy, user's accuracy and k for test rn·ca through the error matrix. In the accuracy analysis of the test area, overall accuracy was 94.3%, producer's accuracy was 77.0-99.9%, user's accuracy was 71.9-100% and k was 0.93. Classes of bare land, sandy soil and farm land were less clear than other classes, whereas classification result of IKON OS in forest area showed higher perfonnance than that of other resolution(5-30m) satellite data.

 

Key Words : Satellite Imagery IKONOS. High Resolution, Classfication, Forest Type