SCAN ANGLE IMPACT ON LIDAR-DERIVED METRICS USED IN ABA MODELS FOR PREDICTION OF FOREST STAND CHARACTERISTICS: A GRID BASED ANALYSIS

The creation of advanced mapping models for forest attributes, utilizing remote sensing data and incorporating machine or deep learning methods, has become a key area of interest in the domain of forest observation and monitoring. This paper introduces a method where we blend and collectively interpret five models dedicated to estimating forest canopy height. We achieve this through Bayesian model averaging, offering a comprehensive analysis of these remote-sensing-based products.