Accurate and rapid estimation of canopy cover(CC)is crucial for many ecological and environmental models and for forest management.Unmanned aerial vehicle-light detecting and ranging(UAV-LiDAR)systems represent a prom...Accurate and rapid estimation of canopy cover(CC)is crucial for many ecological and environmental models and for forest management.Unmanned aerial vehicle-light detecting and ranging(UAV-LiDAR)systems represent a promising tool for CC estimation due to their high mobility,low cost,and high point density.However,the CC values from UAV-LiDAR point clouds may be underestimated due to the presence of large quantities of within-crown gaps.To alleviate the negative effects of within-crown gaps,we proposed a pit-free CHM-based method for estimating CC,in which a cloth simulation method was used to fill the within-crown gaps.To evaluate the effect of CC values and withincrown gap proportions on the proposed method,the performance of the proposed method was tested on 18 samples with different CC values(40−70%)and 6 samples with different within-crown gap proportions(10−60%).The results showed that the CC accuracy of the proposed method was higher than that of the method without filling within-crown gaps(R^(2)=0.99 vs 0.98;RMSE=1.49%vs 2.2%).The proposed method was insensitive to within-crown gap proportions,although the CC accuracy decreased slightly with the increase in withincrown gap proportions.展开更多
Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inve...Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory parameters.Methods:We develop an algorithm based on cloth simulation for constructing a pit-free CHM.Results:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details.Our pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free CHMs.Moreover,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.展开更多
基金supported by the National Natural Science Foundation of China(grant numbers 41971380 and 41671414)Guangxi Natural Science Fund for Innovation Research Team(grant number 2019JJF50001)the Open Fund of State Key Laboratory of Remote Sensing Science(grant number OFSLRSS201920).
文摘Accurate and rapid estimation of canopy cover(CC)is crucial for many ecological and environmental models and for forest management.Unmanned aerial vehicle-light detecting and ranging(UAV-LiDAR)systems represent a promising tool for CC estimation due to their high mobility,low cost,and high point density.However,the CC values from UAV-LiDAR point clouds may be underestimated due to the presence of large quantities of within-crown gaps.To alleviate the negative effects of within-crown gaps,we proposed a pit-free CHM-based method for estimating CC,in which a cloth simulation method was used to fill the within-crown gaps.To evaluate the effect of CC values and withincrown gap proportions on the proposed method,the performance of the proposed method was tested on 18 samples with different CC values(40−70%)and 6 samples with different within-crown gap proportions(10−60%).The results showed that the CC accuracy of the proposed method was higher than that of the method without filling within-crown gaps(R^(2)=0.99 vs 0.98;RMSE=1.49%vs 2.2%).The proposed method was insensitive to within-crown gap proportions,although the CC accuracy decreased slightly with the increase in withincrown gap proportions.
基金the National Natural Science Foundation of China(Grant Nos.41671414,41971380 and 41171265)the National Key Research and Development Program of China(No.2016YFB0501404).
文摘Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory parameters.Methods:We develop an algorithm based on cloth simulation for constructing a pit-free CHM.Results:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details.Our pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free CHMs.Moreover,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.
