Voxel-Based Spatial Filtering Method for Canopy Height Retrieval from Airborne Single-Photon Lidar

Airborne single-photon lidar(SPL) is a new technology that holds considerable potential for forest structure and carbon monitoring at large spatial scales because it acquires 3D measurements of vegetation faster and more efficiently than conventional lidarinstruments. However, SPL instruments use green wavelength (532 nm) lasers, which are sensitive to background solar noise, and therefore SPL point clouds require more elaborate noise filtering than other lidarinstruments to determine canopy heights, particularly in daytime acquisitions. Histogram-based aggregation is a commonly used approach for removing noise from photon counting lidardata, but it reduces the resolution of the dataset. Here we present an alternate voxel-based spatial filteringmethod that filters noise points efficiently while largely preserving the spatial integrity of SPL data. We develop and test our algorithms on an experimental SPL dataset acquired over Garrett County in Maryland, USA. We then compare canopy attributes retrieved using our new algorithm with those obtained from the conventional histogram binning approach. Our results show that canopy heights derived using the new algorithm have a strong agreement with field-measured heights (r2 = 0.69, bias = 0.42 m, RMSE = 4.85 m) and discrete return lidarheights (r2 = 0.94, bias = 1.07 m, RMSE = 2.42 m). Results are consistently better than height accuracies from the histogram method (field data: r2 = 0.59, bias = 0.00 m, RMSE = 6.25 m; DRL: r2 = 0.78, bias = −0.06 m and RMSE = 4.88 m). Furthermore, we find that the spatial-filteringmethod retains fine-scale canopy structure detail and has lower errors over steep slopes. We therefore believe that automated spatial filteringalgorithms such as the one presented here can support large-scale, canopy structure mapping from airborne SPL data.