New high-resolution field surveying methods for validation of crown attributes from 3D scanning laser data

High density airborne LiDAR data has been used to provide detailed information on tree canopy structure. Recent research has reported that it is difficult to determine where the LiDAR data errors appear on the tree crown, as the returns generally underestimate tree height. This study introduces a novel way to assess vertical and horizontal patterns of airborne LiDAR data error using an error mapping technique. We use two field sampling techniques to obtain accurate measurements of tree crowns, including a surveying total station (Nikon Inc., USA) and the ground-based LIDAR system (Leica HDS 3,000, Leica Geosystems, USA). These field observations are compared with airborne LiDAR data to clarify the capability of airborne LIDAR sensors. We use an isolated pine tree in the Washington State Park Arboretum. To quantify the error between field observation and airborne LiDAR data, we first generate a numerical 'wrapped' surface around a cloud of discrete LIDAR points. Then the errors of two different field assessment approaches are mapped and are quantified relative to the wrapped surface of airborne LiDAR systems. Field validation using the total station had a mean error of 6.8 cm. The mean error from a ground based LiDAR was 22.9 cm. Both field tools produce an overestimation of crown volume relative to the crown volume produced from airborne LiDAR data. This error mapping technique is also used to detect tree growth using LiDAR data from different years, which is useful information for managers and tree physiologists.