Comparative Analysis of Machine Learning Models for Tree Species Classification from UAV LiDAR Data

Abstract

Forest ecosystems play a pivotal role in maintaining global biodiversity and climate balance. The precise identification of tree species via remote sensing technologies is vital for effective ecological surveillance and forest stewardship. This research conducts a comparative analysis of various machine learning algorithms for the binary classification of tree species utilizing LiDAR data captured by Unmanned Aerial Vehicles (UAVs). We analyzed a dataset featuring 192 trees from a diverse forest, employing models such as Logistic Regression, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), Gradient Boosting, and Decision Trees. These models were assessed on their accuracy, precision, recall, and F1-scores to ascertain their efficacy. Our findings reveal that Logistic Regression and SVM were superior, achieving precision and recall scores up to 0.96, indicating their robust predictive capability. In contrast, KNN underperformed, suggesting the need for parameter refinement. Although ensemble methods demonstrated resilience, they were more prone to overfitting in comparison to the more straightforward Logistic Regression and SVM models. Preliminary data preprocessing and feature engineering techniques are discussed, enhancing the models' performance. This work enriches the domain of remote sensing and ecological monitoring by offering an in-depth evaluation of machine learning models for tree species classification, underscoring their advantages and constraints. It underscores the transformative potential of machine learning in refining ecological analysis precision, thereby aiding in the pursuit of sustainable forest management. Future research directions could include model refinement through advanced feature selection or the exploration of novel machine learning algorithms for improved classification accuracy.