Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR

Background: Accurate estimation of aboveground forest biomass (AGB) and its dynamics is of paramount importance in understanding the role of forest in the carbon cycle and the effective implementation of climate change mitigation policies. LiDAR is currently the most accurate technology for AGB estimation. LiDAR metrics can be derived from the 3D point cloud (echo-based) or from the canopy height model (CHM). Different sensors and survey configurations can affect the metrics derived from the LiDAR data. We evaluate the ability of the metrics derived from the echo-based and CHM data models to estimate AGB in three different biomes, as well as the impact of point density on the metrics derived from them. Results: Our results show that differences among metrics derived at different point densities were significantly different from zero, with a larger impact on CHM-based than echo-based metrics, particularly when the point density was reduced to 1 point m(-2). Both data models-echo-based and CHM-performed similarly well in estimating AGB at the three study sites. For the temperate forest in the Sierra Nevada Mountains, California, USA, R-2 ranged from 0.79 to 0.8 and RMSE (reIRMSE) from 69.69 (35.59%) to 70.71 (36.12%) Mg ha(-1) for the echo-based model and from 0.76 to 0.78 and 73.84 (37.72%) to 128.20 (65.49%) Mg ha(-1) for the CHM-based model. For the moist tropical forest on Barro Colorado Island, Panama, the models gave R-2 ranging between 0.70 and 0.71 and RMSE between 30.08 (12.36%) and 30.32 (12.46) Mg ha(-1)[between 0.69-0.70 and 30.42 (12.50%) and 61.30 (25.19%) Mg ha(-1)] for the echo-based [CHM-based] models. Finally, for the Atlantic forest in the Sierra do Mar, Brazil, R-2 was between 0.58-0.69 and RMSE between 37.73 (8.67%) and 39.77 (9.14%) Mg ha(-1) for the echo-based model, whereas for the CHM R-2 was between 0.37-0.45 and RMSE between 45.43 (10.44%) and 67.23 (15.45%) Mg ha(-1). Conclusions: Metrics derived from the CHM show a higher dependence on point density than metrics derived from the echo-based data model. Despite the median of the differences between metrics derived at different point densities differing significantly from zero, the mean change was close to zero and smaller than the standard deviation except for very low point densities (1 point m(-2)). The application of calibrated models to estimate AGB on metrics derived from thinned datasets resulted in less than 5% error when metrics were derived from the echo-based model. For CHM-based metrics, the same level of error was obtained for point densities higher than 5 points m(-2). The fact that reducing point density does not introduce significant errors in AGB estimates is important for biomass monitoring and for an effective implementation of climate change mitigation policies such as REDD + due to its implications for the costs of data acquisition. Both data models showed similar capability to estimate AGB when point density was greater than or equal to 5 point m(-2).