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Airborne Laser Scanners (ALS) and Terrestrial Laser Scanners (TLS) are two lidar systems frequently used for remote sensing forested ecosystems. The aim of this study was to compare crown metrics derived from TLS, ALS, and a combination of both for describing the crown structure and fuel attributes of longleaf pine (Pinus palustris Mill.) dominated forest located at Eglin Air Force Base (AFB), Florida, USA. The study landscape was characterized by an ALS and TLS data collection along with field measurements within three large (1963 m2 each) plots in total, each one representing a distinct stand condition at Eglin AFB. Tree-level measurements included bole diameter at breast height (DBH), total height (HT), crown base height (CBH), and crown width (CW). In addition, the crown structure and fuel metrics foliage biomass (FB), stem branches biomass (SB), crown biomass (CB), and crown bulk density (CBD) were calculated using allometric equations. Canopy Height Models (CHM) were created from ALS and TLS point clouds separately and by combining them (ALS + TLS). Individual trees were extracted, and crown-level metrics were computed from the three lidar-derived datasets and used to train random forest (RF) models. The results of the individual tree detection showed successful estimation of tree count from all lidar-derived datasets, with marginal errors ranging from −4 to 3%. For all three lidar-derived datasets, the RF models accurately predicted all tree-level attributes. Overall, we found strong positive correlations between model predictions and observed values (R2 between 0.80 and 0.98), low to moderate errors (RMSE% between 4.56 and 50.99%), and low biases (between 0.03% and −2.86%). The highest R2 using ALS data was achieved predicting CBH (R2 = 0.98), while for TLS and ALS + TLS, the highest R2 was observed predicting HT, CW, and CBD (R2 = 0.94) and HT (R2 = 0.98), respectively. Relative RMSE was lowest for HT using three lidar datasets (ALS = 4.83%, TLS = 7.22%, and ALS + TLS = 4.56%). All models and datasets had similar accuracies in terms of bias (<2.0%), except for CB in ALS (−2.53%) and ALS + TLS (−2.86%), and SB in ALS + TLS data (−2.22%). These results demonstrate the usefulness of all three lidar-related methodologies and lidar modeling overall, along with lidar applicability in the estimation of crown structure and fuel attributes of longleaf pine forest ecosystems. Given that TLS measurements are less practical and more expensive, our comparison suggests that ALS measurements are still reasonable for many applications, and its usefulness is justified. This novel tree-level analysis and its respective results contribute to lidar-based planning of forest structure and fuel management. [ABSTRACT FROM AUTHOR]

• This model is a novel yet simple methodology to quantify the spatially explicit litter loads in frequently burnt longleaf pine forests. • Tree crown objects and estimates of foliage biomass derived from ALS data can be used to characterize patterns of tree leaf litter production and the discontinuity of the litter layer. • Aboveground biomass and YSF are the two major drivers of tree leaf litter accumulation in fire-maintained longleaf pine forests of the southeastern US. The continuity and depth of litter fuelbeds are major drivers of fire spread and fuel consumption. However, no established approach is available for the spatially explicit prediction of litter loads over large areas. Local fuel heterogeneity introduces large uncertainties on estimates derived from field-based models based on the extrapolation of sparse data samples. In fire-maintained pine forests of the southeastern US, litter accumulation and its distribution over the forest floor are mainly driven by vegetation productivity and by the number of years since the last fire (YSF). Some ecological models that simulate fire effects allow for a time-dependent estimation of litter by accounting for the opposing rates of litter deposition and decomposition as a function of YSF at the landscape level, but they do not account for spatial heterogeneity. We developed a conceptually simple approach for wall-to-wall estimation of tree leaf litter loads at high spatial resolution (5 m). The approach involved, first, estimating spatial patterns of tree annual litterfall. We mapped individual tree crowns through segmentation of airborne laser scanning (ALS) data, and we estimated crown foliage biomass using tree inventory data and ALS derived tree crown attributes. Tree annual litterfall was calculated as a fraction of the crown foliage biomass based on leaf turnover rates. We then quantified tree leaf litter accumulation through a spatially explicit implementation of the established Olson (1963) accumulation and negative decay model. We tested and validated our model in several management and research units at Eglin Air Force Base (Florida), Pebble Hill Plantation (Georgia), and Osceola National Forest (Florida), where managers maintain predominantly longleaf pine forests using frequent fire. Pixel-level RMSD and BIAS between tree leaf litter biomass estimated by the proposed model and reference field measurements were 0.21 and 0.01 kg m −2, and area-level RMSD and BIAS were 0.09 and 0.01 kg m −2. We concluded that linking patterns of litterfall and tree leaf litter accumulation to tree crown objects provides a means to characterizing the discontinuity of the litter layer, accounting for spatial heterogeneity largely traceable to tree crown foliage inputs. [ABSTRACT FROM AUTHOR]