Optimizing local maxima parameters for UAV-based detection of Pinus merkusii in tropical forest plantations

Accurate and efficient detection of individual trees is crucial for sustainable forest management, particularly in large-scale production forests such as Indonesia’s pine (Pinus merkusii) plantations. Conventional manual counting of trees from aerial photographs is labour-intensive and prone to human error. This study aims to enhance the precision of pine tree detection using the local maxima (LM) method applied to UAV-based aerial imagery collected in Sagaranteun, Sukabumi, West Java. The LM algorithm identifies canopy peaks based on the canopy height model (CHM), with detection performance evaluated through two parameters: canopy height and window size. Four sample plots were analyzed to quantify detection accuracy using commission and omission error metrics. Results indicate an average commission error of 0.07, an omission error of 0.14, and an overall accuracy of 0.81, suggesting reliable detection performance. However, higher omission errors indicate potential under-detection in densely forested areas, emphasizing the need for further parameter optimization. The study demonstrates that the LM approach is a robust and adaptable method for automatic pine tree detection and can significantly accelerate forest inventory processes for Perum Perhutani and similar forestry institutions.