Machine learning approaches to forest species classification using spectral analysis.

High-resolution remote sensing technologies have increasingly become a versatile and cost-effective method for monitoring diverse ecosystems and enhancing vegetative species classification. This study utilizes two remote sensing approaches to classify the dominant, canopy-contributing tree species in a Bottomland Hardwood Forest (BHF) in Northeast Louisiana, evaluating the effectiveness of consumer-grade Unmanned Aerial Systems (UAS) and advanced machine learning techniques. High-resolution RGB aerial imagery was collected via UAS and processed using photogrammetric techniques to generate orthomosaics and texture features. Two classification methods were used: a U-Net Convolutional Neural Network (CNN) and a Random Forest classifier through an Object-based Image Analysis (OBIA) segmentation approach. The CNN approach demonstrated higher accuracy over OBIA in the classification of nine dominant tree species, particularly for less dominant species, with Quercus achieving the highest precision at 83.3%. This study highlights the potential use of UAS imagery and machine learning in forest management for species inventory and ecological monitoring. Results of this study indicate that while RGB imagery can effectively classify tree species, further integration with multi-temporal datasets and more powerful sensor combinations may enhance classification accuracy and enable practical applicability in environmental monitoring. The findings support the growing role of UAS remote sensing in ecological research and forest conservation efforts.