# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 6 Conclusions  
Technological development for accurately monitoring vegetation cover plays an important role in studying ecosystem functions, especially in long-term studies. In the present work, we investigated the reliability of coupling the use of multispectral VHR UAV imagery with GEOBIA classification implemented in OTB free and open-source software. Supervised classification was implemented by testing two object-based classification algorithms, random forest (RF) and support vector machine (SVM).  
The overall accuracy of the classification was optimal, with overall accuracy values that never fell below 89% and kappa coefficient values that were at least 0.847. Both classifiers obtained good levels of accuracy, although the RF algorithm provided better results than SVM for both images. This process does not seem to have been examined in previous studies; as far as we are aware, this study is the first that uses multispectral R-G-NIR images with DSM and NDVI for the classification of forest vegetation from UAV images using open-source software (OTB).  
An open question worth investigating relates to the application of our findings to other forest ecosystems. The OTB suite is free and open-source software that is regularly updated and has proven to provide free processing tools with a steep learning curve coupled with robust results in forest ecosystems that have a complex vertical and horizontal structure. Moreover, as our findings show, OTB can effectively process UAV imagery when implemented on low-power hardware. Therefore, the proposed workflow may be highly valuable in an operational context.  
However, some open questions and future research directions remain worthy of investigation. On the one hand, further investigation into the use of smoothing to try to optimize potential usefulness should be carried out in different operational conditions. On the other hand, for the issue with tree shadows, our experience in this study reveals that flight schedules can significantly affect the quality of the obtained imagery, especially in open forest ecosystems such as cork oak woodlands. The summer imagery is affected by about 30% more shadowed area than the spring one.  
Our findings confirm the reliability of UAV remote sensing applied to forest monitoring and management. Moreover, although a massive amount of UAV hyperspectral and LiDAR data can be obtained with ultra-high spatial and radiometric resolutions, these sensors are still heavy, too expensive, and in most cases require two flights to obtain radiometric and topographic information. Our sensor, which is based on standard R-G-NIR imagery, has the significant advantage of being a very affordable solution in both scientific and operational contexts. Additionally, mounting this sensor on a fixed-wing UAV enables the surveying of surfaces spread over hundreds of hectares in a short time and has thus proven to be a very cost-effective and reliable solution for forest monitoring.  
Conceptualization, J.M.N.S., S.C., and G.M.; methodology, G.D.L., J.M.N.S., and G.M.; formal analysis, G.D.L., J.M.N.S., and G.M.; investigation, G.D.L.; data curation, G.D.L., J.M.N.S., J.A. and G.M.; writing--original draft preparation, G.D.L., J.M.N.S., S.C. and G.M.; writing--review and editing, all the authors.  
G.D.L. was funded by the European Union Erasmus+ program (Erasmus+ traineeship 2017-2018 Mobility Scholarship). We would like to thank the support from the MEDSPEC project (Monitoring Gross Primary Productivity in Mediterranean oak woodlands through remote sensing and biophysical modelling), funded by Fundacao para a Ciencia e Tecnologia (FCT), Ministerio da Ciencia, Tecnologia e Ensino Superior, Portugal (PTDC/AAG-MAA/3699/2014). CEF is a research unit funded by FCT (UID/AGR/00239/2013).  
Authors acknowledge the Herdade da Machoqueira do Grou for permission to undertake this research. The authors would like to express their great appreciation to the four anonymous reviewers for their very helpful comments provided during the revision of the paper.