Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia

Abstract

Featured Application: The primary application of this work is in environmental resource management, specifically in the detection and monitoring of vegetation patterns and changes. By employing a machine learning approach, specifically the Support Vector Machines (SVM) algorithm, the study demonstrates that including vegetation indices alongside multispectral bands significantly improves the accuracy of vegetation detection, achieving an overall classification accuracy of up to 99.01%. The study’s findings underscore the potential of machine learning and remote sensing in vegetation detection and monitoring and highlight the importance of incorporating vegetation indices to enhance classification accuracy. The matter above has significant implications for decision-making processes in environmental resource management, particularly in regions with diverse forest ecosystems. The potential applications of this work extend beyond the specific geographical context of the study. The methodology and findings could be applied to other regions and ecosystems, providing valuable insights for the preservation and conservation of forest ecosystems globally. Future research could further explore the applicability of these findings in different geographical regions and investigate other vegetation indices to improve the accuracy of forest detection and monitoring processes. Vegetation plays an active role in ecosystem dynamics, and monitoring its patterns and changes is vital for effective environmental resource management. This study explores the possibility of machine learning techniques and remote sensing data to improve the accuracy of forest detection. The research focuses on the southeastern part of the Republic of Serbia as a case study area, using Sentinel-2 multispectral bands. The study employs publicly accessible satellite data and incorporates different vegetation indices to improve classification accuracy. The main objective is to examine the practicability of expanding the input parameters for forest detection using a machine learning approach. The classification process is performed by employing support vector machines (SVM) algorithm and utilising the SVM module in the scikit-learn package. The results demonstrate that including vegetation indices alongside the multispectral bands significantly improves the accuracy of vegetation detection. A comprehensive assessment reveals an overall classification accuracy of up to 99.01% when the selected vegetation indices (MCARI, RENDVI, NDI45, GNDVI, NDII) are combined with the Sentinel-2 bands. This research highlights the potential of machine learning and remote sensing in forest detection and monitoring. The findings underscore the importance of incorporating vegetation indices to enhance classification accuracy using the Python programming language. The study’s outcomes provide valuable insights for environmental resource management and decision-making processes, particularly in regions with diverse forest ecosystems.