# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 5 Conclusions  
This study aimed at the early detection of tree losses in the forests of Cote d'Ivoire using aerial photographs taken by drones. The methodological approach was based on the use of aerial images acquired by drones (spatial resolution 6 to 12 cm) to assess tree losses in the classified forest of Bossematie. The choice of this classified forest is justified by the low annual deforestation rate recorded between 2016 and 2019, which is estimated at 0.35%. This approach allowed us to evaluate tree losses in this relatively well-preserved classified forest subjected to strong anthropic pressures with satisfactory accuracy. Indeed, it allowed for the detection of a minimum of 107 tree losses corresponding to a clearing area of 2 ha on all sites studied in the classified forest of Bossematie during the period from November 2018 to April 2019 with a satisfactory overall accuracy of 97%. Therefore, this study shows the interest in using drones in the management and monitoring of classified forests and protected areas in Cote d'Ivoire. The drone is therefore a precise tool whose capabilities are constantly improving. In addition to its proven value in terms of early detection of the first standing tree burns, regular drone overflights are also proving to be a strong deterrent to cocoa farmers infiltrating protected forests, who realize that the forest is under close surveillance.  
The results of this work must be integrated into the development and management plans of classified forests and protected areas. Indeed, the use of this spatial tool (the drone) could contribute to the good management of these protected areas through better monitoring of the land (fine and regular mapping, detection of forest disturbances, and early warning of clearings).  
Conceptualization, T.A.O., K.F.K., C.G. and R.V.; methodology, T.A.O., V.-C.J.S., I.C.Z.-B., K.F.K., C.G. and R.V.; software, T.A.O. and C.G.; validation, T.A.O., C.G. and R.V.; formal analysis, T.A.O. and C.G.; investigation, T.A.O., C.G. and R.V.; resources, T.A.O., V.-C.J.S., I.C.Z.-B., K.F.K., C.G. and R.V.; data curation, T.A.O. and C.G.; writing--original draft preparation, T.A.O., V.-C.J.S., I.C.Z.-B., C.G. and R.V.; writing--review and editing, T.A.O., V.-C.J.S., I.C.Z.-B., K.F.K., C.G.; visualization, T.A.O., V.-C.J.S., I.C.Z.-B., K.F.K., C.G. and R.V.; supervision, I.C.Z.-B., K.F.K., C.G. and R.V.; project administration, T.A.O., C.G. and R.V.; funding acquisition, T.A.O., V.-C.J.S. and C.G. All authors have read and agreed to the published version of the manuscript.  
This research was conducted as part of the Me REDD+ project funded by the Agence Francaise de Developpement (AFD).  
The authors would like to thank the Agence Francaise de Developpement (AFD) for funding the Me REDD+ Project, through which this work was conducted. Then the INPROBOIS  
Figure 17: Illustration of the potential of drone and Sentinel sensors for monitoring forest disturbances. (**A**) Orthomosaic (Site 1 November 2018). (**B**) DSM (Site 1 November 2018). (**C**) Sentinel-2 (Site 1 March 2019, natural colored composition). (**D**) Sentinel-1 C-band (Site 1 April 2019, colored composition: vv/vh–vh).  
(Industrie et Promotion du Bois) and the SODEFOR (Societe de Developpement des Forets) for allowing us to have access to the Bossematie classified forest.  
The authors declare no conflict of interest.