# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 5 Discussion
### Classification and Accuracy Assessment  
The GEOBIA paradigm coupled with the use of machine learning classification algorithms is currently considered an excellent \"first-choice approach\" for the classification of forest tree species and the general derivation of forest information [28; 31; 41; 55; 63]. As reported by [PERSON] et al. [55] and [PERSON] et al. [41] and confirmed in this study, the default values of the OTB parameters for training and classification processes provide optimal results, and they were maintained in the final test. Moreover, both classifiers were very fast, requiring just around 10 seconds for their execution. Generally, the quality of the classification can be considered good according to the quality errors, which are intrinsic to the image. This is confirmed by the kappa coefficient, whose values ranged from 0.928 to 0.973 for RF and from 0.847 to 0.935 for SVM. For both spring and summer imagery, the comparison between the SVM and RF algorithms does not reveal large differences in the classification maps, and any differences detected are in very small polygons. However, a comparison of the kappa results with the overall accuracy reveals that the RF algorithm turns out to be more accurate than SVM for both flights, and this is also reflected by the confusion matrix. The most repeated error, particularly in the images classified with SVM, is confusion between the shrub class and the shadows class, limited to the edges of the shadows. On the contrary, shrubs are clearly distinguished from tree crowns in the whole scene. The classes, if taken independently, can be analyzed through the user's and the producer's accuracy, which indicate commission and omission errors, respectively. For both flights and both algorithms, the cork oak class obtains better classification accuracy, with values that are always higher than 95%. On the other hand, the lowest accuracy is obtained for the shrubs class with the SVM algorithm and the summer flight. It can be noted from observing the confusion matrix that shrubs are more often confused with shadows or herbaceous vegetation than they are with trees. A preliminary visual analysis is sufficient to notice a common error in all of the maps: some polygons representing the edges of the shadows (at which there is a chromatic transition from the shadow to the adjacent cover) are confused with shrubs, but the confusion of shrubs with grass instead can be justified. It is hard to formulate a reliable assessment of the results for the bare soil class because of the low number of samples, even if the result is representative of the real distribution and width of the class in the whole region (<1.7% of the total surface). The bare soil class is mainly represented by the dirt road present at the site. Trees and shrubs have similar shapes and spectral features from a nadiral view, and the largest distinction is the height, as well as the epigeal structural form. Thus, the DSM, which has a GSD of 10 cm and underlines the differences in height, plays an important role in discriminating between the two vegetation layers. Moreover, the NDVI is important for the distinction between herbaceous vegetation and shrubs, as well as between the herbaceous layer and bare soil; the NDVI also has a key role in the identification of shrubs. The class separability and thus discrimination provided by these two bands brings additional information, relatively, to the original mosaic bands (R-G-NIR), which is relevant for both processes of segmentation and classification. Of the two flight seasons, the spring imagery yielded the best classification in terms of overall accuracy and kappa coefficient according to both classification algorithms. This is mainly because of the features of shrubs, particularly _Cistus_, which has a tendency to dry out during the summer and can potentially be mistaken for shadows and dry grass. This factor increases the chromatic and the NDVI contrasts among the plants.