# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 1 Introduction  
Cork oak woodlands characterize forested Mediterranean landscapes, and their ecological and economic values are largely recognized, although not adequately valorized [1]. This ecosystem covers about 2.2 million hectares across the globe, but the most extensive groves are located on the Atlantic coast of the Iberian Peninsula, to the extent that Portugal and Spain produce 75% of the world's cork [2]. Cork is the sixth-most important non-wood forest product globally, and it is used to produce wine stoppers and a wide variety of other products, including flooring components, insulation and industrial materials, and traditional crafts. The most frequent landscape in the Mediterranean region is characterized by open woodland systems in which scattered mature trees coexist with an understory composed of grassland for livestock or cereal crops and shrubs. The ecosystem has high conservation value and provides a broad range of goods and services besides cork [2]. It is important to increase the knowledge and skills that enable the use of the most innovative technologies to monitor these forest ecosystems in the support of forest management. The use of unmanned aerial vehicles (UAVs) or UA systems (UASs) in forest and environmental monitoring [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15] is currently in an expansion phase, encouraged by the constant development of new models and sensors [3; 16; 17]. Detailed reviews focusing on the use of UAVs in agro-forestry monitoring, inventorying, and mapping are provided by [PERSON] and [PERSON] [16], [PERSON] et al. [3], and [PERSON] et al. [18]. Additionally, the short time required for launching a UAV mission makes it possible to perform high-intensity surveys in a timely manner [16; 19; 20], thus granting forest practices with more precision and efficiency while opening up new perspectives in forest parameter evaluation. Therefore, UAVs are particularly suitable for multi-temporal analysis of small- and medium-sized land parcels and complex ecosystems, for which frequent surveys are required [17]. There is a large diversity of UAV applications, mainly as a result of the variety of available sensors [21]. The most common sensors assembled on UAVs are passive sensors, which include classic RGB (red, green, blue) cameras, NIR (near-infrared), SWIR (short-wave infrared), MIR (mid-infrared), TIR (thermal infrared), and their combinations in multispectral (MS) and hyperspectral (HS) cameras and active sensors such as RADAR (radio detection and ranging) and LiDAR (laser imaging detection and ranging) [3; 16]. An increasingly common agro-forestry application that uses multispectral images acquired from sensors assembled on UAVs is land cover classification [18; 22]. Until a decade ago, most of the classification methods for agro-forestry environments were based on the statistical analysis of each separate pixel (i.e., pixel-based method), and performed well when applied to satellite imagery covering large areas [23; 24; 25]. The recent emergence of very high resolution (VHR) images, which are becoming increasingly available and cheaper to acquire, has introduced a new set of possibilities for classifying land cover types at finer levels of spatial detail. Indeed, VHR images frequently show high intra-class spectral variability [19; 20; 26]. On the other hand, the higher spatial resolution of these images enhances the ability to focus on an image's structure and background information, which describe the association between the values of adjacent pixels. With this approach, spectral information and image classification are largely improved [27]. Recently, object-based image analysis (OBIA) has emerged as a new paradigm for managing spectral variability, and it has replaced the pixel-based approach [27; 28].