A robust approach to generate canopy cover maps using UltraCam-D derived orthoimagery classified by support vector machines in Zagros woodlands, West Iran

摘要

An approach was developed to construct a percent canopy cover (PCC) map of Zagros semi-arid woodlands, West Iran, using UltraCam-D airborne imagery. We detected crowns of Persian oak coppice trees on the imagery by use of the support vector machine (SVM) classifier optimized via Taguchi method. Then, PCC was calculated in raster grids with various block sizes and their accuracy metrics revealed the appropriate sizes. Results showed the optimized SVM success in separating Persian oak crowns as revealed in receiver operating characteristic (ROC) curve analysis (area under curve: AUC ?￠???? 0.82). After filtering the raster maps and reassessing their accuracies, validation outputs of the final PCC map with 3000 m 2 resolution yielded an overall accuracy of 90% (KHAT=0.71) and was introduced as the optimal map in this study.