Multiplatform Approach to Forest Inventory from Laser Scanning and MLS Collection Optimization

Although traditional forest inventory methods are comprehensive, they continue to be labor-intensive and time-consuming. Light detection and ranging (lidar) data collected from various platforms can address these shortcomings. This paper presents a case study of multimodal laser scanning for forest inventory at the Millhopper VARIETIES II Forest experimental site, a pine plantation owned by the University of Florida. The study explores the integration of mobile laser scanning (MLS), terrestrial laser scanning (TLS), and uncrewed aerial laser scanning (ULS) for enhanced forest metric extraction. It employed varying scanner mounting angles and laser pulse repetition rates (PRR) in MLS data collection to optimize point cloud density and reduce occlusions. Additionally, it combined MLS and ULS to improve the accuracy of individual tree detection (ITD) and tree height estimation compared to single-platform approaches. A least squares technique was utilized to fit spherical targets geometrically, ensuring precise alignment and fusion of datasets across all modalities. Results indicated that the integrated MLS+TLS+ULS dataset achieved superior ITD accuracy with a precision of approximately 90%, recall of about 99%, F1-score of roughly 94%, and a height root mean square error (RMSE) of 0.36 m. Notably, the MLS+ULS dataset reached a recall of approximately 95%, a precision of around 90%, an F1-score of about 93%, and an RMSE of 0.31 m. The integrated approach provided more accurate and comprehensive forest data than individual laser scanner modality datasets. These findings emphasize the importance of precise lidar data fusion and tuning lidar sensor parameters for scalable and accurate forest inventory, enabling improved tree count estimation and enhanced forest health assessment for sustainable forest management.