Remote sensing and GIS applications for suitable afforestation area selection in Turkey

The aim of the study, the potential afforestation areas locate using remote sensing data and geographic information system. in this study, arit and esme-gure forest district areas that have different site conditions, vegetation and topographic conditions was chosen. landsat tm image was used do pixel based supervised classification and maximum likelihood classification strategy were applied. at first, the criteria that will be potential afforestation area were determined, then the training areas selected on the remote sensing images using on maps to the best classification of potential afforestation areas. accuracy assessment was evaluated of supervised classification and the result images generated vector. the study revealed that 2032 ha is total potential afforestation forest area for arit forest district (overall accuracy; 81%) and 38447 ha is total potential afforestation forest area for esme-gure forest district (overall accuracy; 89%). the study has demonstrated a method that can be used due to the fact that higher accuracy.