Manual versus Digital Classification of UAV Images in Oak Phenological Studies

Abstract

This research concerns the phenological phenomenon of the autumn discolorations of sessile oak leaves as the trees prepare for winter dormancy. Sessile oak trees were categorized into five classes according to the general colors of their crowns: from green to brown. Low-altitude UAV-acquired images from the visible B, G, and R bands were used, compared, and evaluated against the results of several classification methods: those that were carried out in the field, visually based on orthomosaic observations, and four variants of digital classification. The analysis showed that those methods that were based on observer assessments were highly subjective. At the same time, there was also the problem of the reference data to which the results of the individual methods could be referred. It was expected that the analyzed phenomenon of tree-crown discoloration would be better visible in aerial photographs than in field observations"," However, visual color classifications using orthomosaics can be too subjective (as has been shown). It is recommended to use supervised digital classification with a careful selection of reference (training) objects. To switch from pixel-classification results to individual tree classifications, a novel approach was adopted in which the class value that was most abundant within the images of each canopy (determined by the supervised classification method selected) could be used. Among the supervised digital-classification methods that were applied, the results that were closest to the classification performed in the field were obtained by using the ML and Fisher algorithms (followed by kNN).