Effect of UAV-LiDAR Point Density on Estimation Accuracy of Forest Inventory Attributes
[Objective]Point density is a key factor affecting the cost and efficiency of data acquisition and pre-processing of unmanned aerial vehicle(UAV)-based light detection and ranging(LiDAR),and it is help-ful to explore the effects of point density on the estimation accuracy of UAV-LiDAR-based forest inventory attributes to optimize the technical schemes for UAV-LiDAR forest applications.[Methods]This study fo-cused on the planted Masson pine and Eucalyptus forests.The original UAV-LiDAR point cloud with a density of 247 points∙m-2 was reduced by 40%,20%,8%,4%,and 2%according to the percentage of the total point reduction algorithm to obtain six plot-level UAV-LiDAR datasets,including five sets of reduced point densities.Each dataset was pre-processed separately,including point cloud classification,ground point filtering,digital elevation model(DEM)generation,point cloud height normalization,and UAV-LiDAR-derived metric extractions.The same multiplicative power formula was used for estimating the same forest parameters(stand volume,basal area,mean height,and average diameter at breast height)for the same forest type,and each dataset was used to calibrate the model.Then,the differences in the goodness-of-fit statistics of the models were compared and analyzed based oncoefficient of determination(R2),relative root square error(rRMSE),mean prediction error(MPE),and the differences in the mean of the estimates and the UAV-LiDAR-derived metrics between the reduced point density datasets and the original point density dataset were statistically analyzed.[Results]The results indicated that the model accuracy re-mained essentially the same when the original point density was reduced to 100,50,...,5 points∙m-2,and there were no statistically significant differences(p≥0.05)in the estimates of forest inventory attributes between the reduced point density datasets and the original point density dataset.There were basically no statistically significant differences(p≥0.05)in UAV-LiDAR-derived metrics between the reduced point density datasets and the original point density dataset.[Conclusion]In the application of UAV lidar forest resource inventory and monitoring,the point cloud density can be as low as 5 points∙m-2.However,the results of this experiment still need to be verified by acquiring point cloud data at different densities at dif-ferent flight altitudes.
stand volumebasal areamean heightmean diameter at breast heightUAV-LiDAR-derived metricmultiplicative power model
NETL
NSTL
万方数据
