Cloth simulation-based construction of pitfree canopy height models from airborne LiDAR data

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.

A General and Effective Method for Wall and Protrusion Separation from Facade Point Clouds

As a critical prerequisite for semantic facade reconstruction,accurately separating wall and protrusion points from facade point clouds is required.The performance of traditional separation methods is severely limited by facade conditions,including wall shapes(e.g.,nonplanar walls),wall compositions(e.g.,walls composed of multiple noncoplanar point clusters),and protrusion structures(e.g.,protrusions without regularity,repetitive,or self-symmetric features).This study proposes a more widely applicable wall and protrusion separation method.The major principle underlying the proposed method is to transform the wall and protrusion separation problem as a ground filtering problem and to separate walls and protrusions using ground filtering methods,since the 2 problems can be solved using the same prior knowledge,that is,protrusions(nonground objects)protrude from walls(ground).After transformation problem,cloth simulation filter was used as an example to separate walls and protrusions in 8 facade point clouds with various characteristics.The proposed method was robust to the facade conditions,with a mean intersection over union of 90.7%,and had substantially higher accuracy compared with the traditional separation methods,including region growing-,random sample consensus-,multipass random sample consensus-based,and hybrid methods,with mean intersection over union values of 69.53%,49.52%,63.93%,and 47.07%,respectively.Besides,the proposed method was general,since existing ground filtering methods(including the maximum slope,progressive morphology,and progressive triangular irregular network densification filters)can also perform well.

Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based method

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.

Estimation of Larch Growth at the Stem,Crown,and Branch Levels Using Ground-Based LiDAR Point Cloud

Tree growth is an important indicator of forest health and can reflect changes in forest structure.Traditional tree growth estimates use easy-to-measure parameters,including tree height,diameter at breast height,and crown diameter,obtained via forest in situ measurements,which are labor intensive and time consuming.Some new technologies measure the diameter of trees at different positions to monitor the growth trend of trees,but it is difficult to take into account the growth changes at different tree levels.The combination of terrestrial laser scanning and quantitative structure modeling can accurately estimate tree structural parameters nondestructively and has the potential to estimate tree growth from different tree levels.In this context,this paper estimates tree growth from stem-,crown-,and branch-level attributes observed by terrestrial laser scanning.Specifically,tree height,diameter at breast height,stem volume,crown diameter,crown volume,and first-order branch volume were used to estimate the growth of 55-year-old larch trees in Saihanba of China,at the stem,crown,and branch levels.The experimental results showed that tree growth is mainly reflected in the growth of the crown,i.e.,the growth of branches.Compared to onedimensional parameter growth(tree height,diameter at breast height,or crown diameter),three-dimensional parameter growth(crown,stem,and first-order branch volumes)was more obvious,in which the absolute growth of the first-order branch volume is close to the stem volume.Thus,it is necessary to estimate tree growth at different levels for accurate forest inventory.