Using laser altimetry-based segmentation to refine automated tree identification in managed forests of the Black Hills, South Dakota

The success of a local maximum (LM) tree detection algorithm for detecting individual trees from lidar data depends on stand conditions that are often highly variable. A laser height variance and percent canopy cover (PCC) classification is used to segment the landscape by stand condition prior to stem detection. We test the performance of the LM algorithm using canopy height model (CHM) smoothing decisions and crown width estimation for each stand condition ranging from open savannah to multi-strata stands. Results show that CHM smoothing improves stem predictions for low density stands and no CHM smoothing better detects stems in dense even-aged stands, specifically dominant and co-dominant trees (R² = 0.61, RMSE = 20.91 stems with smoothing; R² = 0.85, RMSE = 46.02 stems with no-smoothing; combined smoothed CHM for low density and unsmoothed CHM for high density stands R² = 0.88, RMSE = 28.59 stems). At a threshold of approximately 2,200 stems ha ^-1, stem detection accuracy is no longer obtainable in any stand condition.