Corrigendum to “Airborne lidar provides reliable estimates of canopy base height and canopy bulk density in southwestern ponderosa pine forests” [For. Ecol. Manag. 481 (2021) 118695] (Forest Ecology and Management (2021) 481, (S037811272031464X), (10.1016/j.foreco.2020.118695))

1. The authors regret the R² and RMSE presented in the abstract for CBD should be corrected from “0.76 and 0.021 kg⁻³” to “0.41 and 0.023 kg m⁻³” respectively. 2. The authors regret the equation 1 should be corrected as [Formula Presented] in “2.4. Lidar Data Analysis”, with a corresponding standard error of the residuals (SE_resid) that should be corrected from 2.601 to 5.652 and R² corrected from 0.8896 equal to 0.8668 and a Fig. 5 corrected as shown below. 3. The authors regret describing initial comparisons between lidar- and field-derived CBD revealed as “positive” which should be corrected to “negative”, model fit statistics obtained as “R² of 0.68, RMSE of 0.34 kg m⁻³, and relatively high bias of 0.027″ should be corrected as “R² of 0.41, RMSE of 0.042 kg m⁻³, and relatively high bias of 0.0312″ in “3.2. Canopy Bulk Density”. 4. The author regret the “obtained an R² of 0.68, RMSE of 0.017 kg m⁻³, and bias of 0.000002 for this comparison, indicating a strong correlation between lidar- and field-derived estimates of CBD.” should be corrected to “obtained an R² of 0.41, RMSE of 0.023 kg m⁻³, and bias of < 0.0001 for this comparison, indicating a moderately strong correlation between lidar- and field-derived estimates of CBD.” in “3.2. Canopy Bulk Density” with corresponding Fig. 8 corrected as shown below. 5. The authors regret the “In the managed plots, we obtained an R² of 0.48, RMSE of 0.015 kg m⁻³, and bias of 0.008. In the unmanaged plots, we obtained an R² of 0.49, RMSE of 0.018 kg m⁻³, and bias of − 0.007.” in “3.2. Canopy Bulk Density” should be corrected as “In the managed plots, we obtained an R² of 0.39, RMSE of 0.020 kg m⁻³, and bias of 0.014. In the unmanaged plots, we obtained an R² of 0.18, RMSE of 0.025 kg m⁻³, and bias of − 0.012.” with corresponding Fig. 9 corrected as shown below. 6. The authors regret the “We obtained an R² value of 0.76, RMSE of 0.021, and bias of –0.013, suggesting a strong correlation with a small negative bias in higher density plots (Fig. 11).” in “3.4. Independent Validation” should be corrected as “We obtained an R² value of 0.61, RMSE of 0.022, and bias of −0.006, suggesting a strong correlation with a small negative bias in higher density plots (Fig. 11).” with corresponding Fig. 11 corrected as. 7. The authors regret the “R² value of 0.76″ and “RMSE value of 0.021 kg m⁻³” should be corrected to “R² value of 0.61″ and “RMSE value of 0.022 kg m⁻³” in as presented for our study in “3.3. Comparisons to Other Studies” both in the text and in Table 2. 8. The authors regret the R² value of “0.76″, RMSE value of “0.021″ kg m⁻³, and bias of “0.013″ ” throughout “4. Discussion” should be corrected to “0.61″, “0.022″ and “0.006″, respectively. 9. The authors regret the comparison of RMSE for CBD values produced from area-based models developed by Mitchell et al. (2012) described as “higher” should be corrected to “lower”. 10. Lastly, the authors regret the discussion of how lidar consistently “underestimated” CBD values should be corrected to “overestimated” in “4. Discussion” and the corresponding text “Several explanations may exist for this observed bias. It is most likely that the underestimated CBD values were the result of the lower total tree counts obtained from the lidar tree-segmentation process. Our lidar-derived tree lists had, on average, one third of the trees as reported in their associated field-derived tree lists. This under-segmentation was expected, as it is well-documented in the literature that lidar tree-segmentation algorithms are often unable to detect subordinate trees that grow under the canopy of larger trees (Maltamo et al., 2009, Lamb et al., 2017, North et al., 2017, Jeronimo et al., 2018). For example, Klauberg et al. (2019) found that lidar under-segmented trees by about one third, with an increased discrepancy in unmanaged forest stands. With only one third of the trees, less canopy mass is estimated by FVS-FFE, and thus lower CBD values are reported. This issue of under-segmentation is the most likely cause of the high negative bias initially observed in our lidar-derived CBD estimates.” be corrected and amended as. “Our lidar derived tree lists had approximately 1/3rd of the total tree counts as observed in the field data. Under-segmentation of trees from lidar data is well-documented in the literature since lidar is often unable to detect subordinate trees that grow under the canopy of larger trees (Maltamo et al., 2009; Lamb et al., 2017; North et al., 2017; Jeronimo et al., 2018). For example, Klauberg et al. (2019) found that lidar under-segmented trees by about one third, with an increased discrepancy in unmanaged forest stands. We suspect that the under-segmentation of trees in our lidar plots may have indeed resulted in higher CBD values estimated from FVS. FVS-FFE estimates CBD as the 4.5 m running mean of tree-level crown fuel weights within 0.3 m deep canopy layers within each plot. To produce these estimates, FVS uses tree diameters and plot-level tree densities to first compute a crown radius for each tree which is then used, along with tree height and canopy base height, to estimate crown fuel weight for individual trees (Scott and Reinhardt 2001; Reinhardt and Crookston 2003). In lidar plots with low total tree densities but relatively high DBHs, FVS would tend to estimate wider crown radii, which would then result in higher estimates of tree-level crown fuel weights, and thus higher estimates of CBD. FVS-FFE provides just one option for estimating plot-level CBD, and we recommend that future research evaluates how different approaches for estimating CBD may alter bias and prediction accuracy from lidar data.” The authors would like to apologise for any inconvenience caused.