TREE CROWN DELINEATION ON UAV IMAGERY USING COMBINATION OF MACHINE LEARNING ALGORITHMS WITH MAJORITY VOTING

Abstract. Crown area is one of the key parameters in determining tree growth and an important basis for estimation of biophysical characteristics at single-tree levels in natural and man-made forests. Therefore, the present study was aimed to improve the estimation of crown area on unmanned aerial vehicle (UAV) data using a novel method in a Pinus eldarica man-made forest. The UAV-based RGB images with spatial resolution of 2 cm were acquired from the study area and then resampled to four pixel sizes of 10, 30, 50 and 70 cm. The resampled images were classified by three methods, i.e., Support vector machine (SVM), Random forest (RF), and Artificial neural network (ANN), which are all ensemble (bagging) classification methods. In the next step, the maps of three classification methods for each pixel size were combined by majority voting algorithm at pixel level. The results showed the robustness of ANN in all pixel sizes compared to RF and SVM. Additionally, the combination of the machine learning method by majority voting algorithms had significantly improved the accuracy of P. eldarica crown delineation and its area estimation on the UAV orthoimages with the investigated pixel sizes.