# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 1 Introduction
OBIA works with groups of homogeneous and contiguous pixels [19] (i.e., geographical objects, also known as segments) as base units to perform a classification, so it differs from the classic pixel-oriented methods that classify each pixel separately; thus, the segmentation approach reduces the intra-class spectral variability caused by crown textures, gaps, and shadows [19; 27; 28]. OBIA includes two main steps: (i) identification, grouping, and extraction of significant homogeneous objects from the input imagery (i.e., segmentation); (ii) labeling and assigning each segment to the target cover class (classification) [19; 20; 28]. More recently, considering that OBIA techniques are applied in many research fields, when referring to a geospatial context, the use of GEOBIA (geographic OBIA) [27; 29; 30] has become the preferred method. Moreover, GEOBIA should be considered as a sub-discipline of GIScience, which is devoted to obtaining geographic information from RS imagery analysis [27; 29; 30]. GEOBIA hasproven to be successful and often superior to the traditional pixel-based method for the analysis of very high resolution UAV data, which exhibits a large amount of shadow, low spectral information, and a low signal-to-noise ratio [6; 19; 20; 26]. The availability of hundreds of spectral, spatial, and contextual features for each image object can make the determination of optimal features a time-consuming and subjective process [20]. Therefore, high radiometric and geometric resolution data require the simultaneous use of more advanced and improved computer software and hardware solutions [26]. Several works dealing with the classification of forest ecosystems or forest tree species by coupling GEOBIA and UAV imagery can be found in the literature [4; 15; 16; 22; 31]. [PERSON] and [PERSON] [31] reported the difficulty in performing an accurate tree crown reconstruction, characterizing fine-scale heterogeneity or texture, and achieving operational species-level classification accuracy with low or limited radiometric control. Deciduous tree species are typically more difficult to segment into coherent tree crowns than evergrerens [31] and tend to be classified with lower accuracies [32]. In terms of using GEOBIA algorithms, most studies are implemented by means of proprietary software (e.g., [4; 6; 11; 15; 19; 31]). Considering their cost, especially in an operational context, it can be argued that only a relatively restricted group of operators can use them. Nowadays, several free and open-source software packages for geospatial (FOSS4G) analysis with GEOBIA algorithms are available, thus making their use accessible to a larger number of end users [20]. Among others, a very promising FOSS4G package equipped with GEOBIA algorithms is Orfeo ToolBox (OTB), developed by the French Centre National d'Etudes Spatiales (CNES). OTB can be operated either autonomously or through a second open-source software (i.e., QGIS), used as a graphical interface that enables a graphical analysis of data processing in real time [33; 34]. This work was conducted in the framework of a wider project aiming to derive a methodology for monitoring the gross primary productivity (GPP) of Mediterranean cox oak (_Quercus suber_ L) woodland by integrating spectral data and biophysical modeling. The spatial partition of the ecosystem into different coexisting vegetation layers (trees, shrubs, and grass), which is the task encompassed by this study, is essential to the fulfillment of the objective of the overarching project. The main objective of this work was to develop a supervised classification procedure of the three vegetation layers in a Mediterranean cork oak woodland by applying a GEOBIA workflow to VHR UAV imagery. In order to optimize the procedure, two different algorithms were compared: support vector machine (SVM) and random forest (RF).