Authors: MURAT KURUCA, DİLEK KÜÇÜK MATCI, UĞUR AVDAN
Abstract: Using remotely sensed data to identify burnt forest areas produces fast, economical, and highly accurate results. Accordingly, in this study we investigate the capabilities of Göktürk-2, Turkey's national satellite, for mapping burnt forest areas. We compare our results with those obtained from Landsat-8 and Worldview-2 satellite images, which are frequently used for mapping burnt areas. The capabilities of the satellites are compared, in terms of detecting burnt forest areas, using support vector machine (SVM) and rotation forest (RF) classification, which are advanced methods. According to the results of the accuracy analysis, SVM classification gives similar kappa statistics and overall accuracy for Göktürk-2 and Landsat-8 images, while the performance of Worldview-2 shows greater general accuracy. Although there is no significant difference between the two classification methods forLandsat-8 images, SVM gives better results than RF for both Göktürk-2 and Worldview-2. The results of our study show that Göktürk-2 images are an effective source for mapping burnt forest areas.
Keywords: Göktürk-2, mapping burnt forest area, object-based image analysis, rotation forest, support vector machine
Full Text: PDF