Remote sensing provides a variety of data and useful resources for forest mapping. Today, one of the most common uses in forestry is the identification of trees and tree species using object-based analysis and classification of satellite or aerial images. This research focuses on the identification of the canopy of trees at the individual level. The purpose of this study is to investigate the potential of high resolution WorldView-2 satellite imagery obtained in 2014 for mapping trees with nonparametric classification methods in the surrounding forests of Shiraz. In the current study, assessment of forest parameters were performed with the focus on the extraction of single trees using two classification methods, Object-Based and support vector analysis, verified with complexity matrix and area under operating characteristic curve (AUC) methods with the help of Unmanned Aerial Vehicle(UAV) aerial imagery of Phantom 4 aircraft obtained in 2018 in two separate regions. After making the necessary corrections on satellite imagery, forest and non-forest classes were defined and educational samples were selected. The classification results indicate that the Object-Based classification has the highest accuracy in assessing the parameters of single trees, and after that the support vector is placed. The results of regression analysis indicated that using WV-2 images (R
2= 0.97) was suitable for estimating canopy area on the city. Based on our results, it can be concluded that considering the high accuracy and quick interpretation of satellite images, WV-2 images can be reliable alternatives for ground survey method in measuring tree canopies in cities with similar forest structure. This study confirms that it is possible to extract the parameters of single trees in the forest using WV-2 sensor data.
