Tree Canopy Segmentation and Characterization Using LiDAR for Machine Learning Models

This work proposes an alternative solution to address the problem of tree canopy classification, given a digital elevation model (DEM) of the study area obtained using a Light Detection and Ranging (LiDAR) device. This proposal presents a comprehensive methodology that enables the feasible application of machine learning (ML) models for tree canopy classification. Our approach allows to obtain a composite DEM of the tree canopy, which will be characterized using a tree cover selection and extraction strategy based on Euclidean dis- tance and feature point clustering. Subsequently, statistical and geometric measures of the point clouds are calculated for each segmented canopy of the DEM in order to prepare a dataset, which includes some tree canopy features represented by column vectors of 77 predictors. Finally, the generated dataset was trained and validated using the MatLab’s classification learner, obtaining considerable performance results in canopy classification, highlighting the improvement in classification performance of models based on Naive Bayes, Neural Networks, and Support Vector Machines. Furthermore, beyond its tech- nical scope, this research has a direct social and environmental impact by promoting urban resilience, biodiversity conserva- tion, and strengthening sustainable development policies. The proposed methodology contributes to global agendas such as the United Nations Sustainable Development Goals (SDGs) and the Mexican National Strategic Programs (PRONACES), positioning remote sensing and artificial intelligence as tools for climate action and social well-being.