This study presents a methodology for evaluating the impacts of windstorm damage on timberlands. The approach involved image classification techniques (Maximum Likelihood, Decision Tree, and Random Forest) applied to high-resolution LiDAR and RGB data and their derivatives. The findings offer important insights into the efficient management of windstorm damage.
Learning Objectives:
Upon completion, participants will be able to understand the effectiveness of UAV-LiDAR in detecting and mapping storm-damaged forest stands in windstorm-affected areas of the southeastern US.
Upon completion, participants will be able to utilize a methodological framework that applies image classification techniques (Maximum Likelihood, Decision Tree, Random Forest) to UAV-LiDAR data for accurately managing windstorm-affected timberlands.
