## 5 Conclusions  
The utilisation of machine learning for vegetation identification through satellite imagery is a notable instance of its application. Typically, multispectral images captured by satellite sensors are employed as input parameters. This study is initiated with a hypothesis: suitable vegetation indices, used with multispectral bands, could serve as effective input parameters for machine learning classification.  
The study was performed with three sets of input parameters--the initial set comprised ten bands from the Sentinel-2 mission, the second set included 23 distinct vegetation indices, and the final set was established after analysing the 23 indices to select the most effective vegetation indices for inclusion in the input parameters. Following the analysis, five vegetation indices were chosen. The first set of input parameters achieved an accuracy of 98.18%, a creditable outcome. The accuracy increased to 98.56% with the second set of input parameters and further to 99.01% with the third set. The increased accuracy achieved by including the vegetation index in the input parameters confirmed that this approach could improve the quality of machine learning classification results.  
Conducted in the southeastern region of Serbia, this study provided valuable insights into applying machine learning for forest detection within a specific geographical context. The study area's diverse vegetation and terrain characteristics posed a significant challenge for accurate forest classification. However, the accuracy of the classification process could be improved by incorporating suitable vegetation indices.  
MCARI, RENDVI, NDI45, GNDVI, and NDII were particularly effective in enhancing classification accuracy among the chosen vegetation indices. These indices captured critical spectral responses of vegetation, such as chlorophyll absorption and near-infrared reflectance, which are vital for distinguishing between forest and non-forest areas.  
This research underscored the importance of incorporating vegetation indices into machine learning classification for forest detection. The results showed that the accuracy of forest classification significantly improved when these indices were combined with multispectral bands. These findings had significant implications for environmental resource management, emphasising the potential of integrating advanced technologies like machine learning and remote sensing to enhance forest ecosystem preservation and conservation.  
Future research should explore the applicability of these findings in other geographical regions and further investigate other vegetation indices to improve the accuracy of forest detection and monitoring processes.  
Conceptualisation, Z.S.; methodology, I.P.; resources, S.B.; software, B.V.; supervision, D.D.; validation, J.M.J.; visualisation, R.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This paper is written as part of Project 1.23/2023 of the Ministry of Defense and the Serbian Army. \(\copyright\) OpenStreetMap contributors' data are available under the Open Database Licence.  
The authors declare no conflict of interest.