Influence of voxel size on forest canopy height estimates using full-waveform airborne LiDAR data

Background: Forest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR(Light Detection and Ranging), small-footprint full-waveform airborne LiDAR(FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.Methods: A range of voxel sizes(from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxelbased LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest(RF) regression method.Results and conclusions: The results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies(R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m(R2= 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement(33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.

Effect of layer thickness and voxel size inversion on leaf area density based on the voxel-based canopy profiling method

Voxel-based canopy profiling is commonly used to determine small-scale leaf area.Layer thickness and voxel size impact accuracy when using this method.Here,we determined the optimal combination of layer thickness and voxel size to estimate leaf area density accurately.Terrestrial LiDAR Stonex X300 was used to generate point cloud data for Masson pines(Pinus massoniana).The canopy layer was stratified into 0.10-1.00-m-thick layers,while voxel size was 0.01-0.10 m.The leaf area density of individual trees was estimated using leaf area indices for the upper,middle,and lower canopy and the overall canopy.The true leaf area index,obtained by layered harvesting,was used to verify the inversion results.Leaf area density was inverted by nine combinations of layer thickness and voxel size.The average relative accuracy and mean estimated accuracy of these combined inversion results exceeded 80%.When layer thickness was 1.00 m and voxel size 0.05 m,inversion was closest to the true value.The average relative accuracy was 92.58%,mean estimated accuracy 98.00%,and root mean square error 0.17.The combination of leaf area density and index was accurately retrieved.In conclusion,nondestructive voxel-based canopy profiling proved suitable for inverting the leaf area density of Masson pine in Hetian Town,Fujian Province.

Automatic calibration method of voxel size for cone-beam 3D-CT scanning system

For a cone-beam three-dimensional computed tomography （3D-CT） scanning system, voxel size is an important indicator to guarantee the accuracy of data analysis and feature measurement based on 3D-CT images. Meanwhile, the voxel size changes with the movement of the rotary stage along X-ray direction. In order to realize the automatic calibration of the voxel size, a new and easily-implemented method is proposed. According to this method, several projections of a spherical phantom are captured at different imaging positions and the corresponding voxel size values are calculated by non-linear least-square fitting. Through these interpolation values, a linear equation is obtained that reflects the relationship between the voxel size and the rotary stage translation distance from its nominal zero position. Finally, the linear equation is imported into the calibration module of the 3D-CT scanning system. When the rotary stage is moving along X-ray direction, the accurate value of the voxel size is dynamically exported. The experimental results prove that this method meets the requirements of the actual CT scanning system, and has virtues of easy implementation and high accuracy.

CT图像的体元大小对EGSnrc蒙特卡罗剂量计算的影响

目前的放射治疗计划系统采用经验或半经验常规剂量算法,计算得到的剂量分布不够精确,而蒙特卡罗剂量算法是一种精确的剂量计算方法,但其计算时间冗长问题最终影响临床的应用。本文基于一例病人头部电子计算机体层成像(Computer Tomography,CT)数据,应用EGSnrc(Electron Gamma Shower NRC,EGSnrc)蒙特卡罗程序,研究不同体元大小对剂量分布计算精度和计算时间等的影响。

Decoding brain responses to pixelized images in the primary visual cortex: implications for visual cortical prostheses

Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only ＂see＂ pixelized images, and how to obtain the specific brain responses to different pixelized images in the primary visual cortex（the implant area） is still unknown. We conducted a functional magnetic resonance imaging experiment on normal human participants to investigate the brain activation patterns in response to 18 different pixelized images. There were 100 voxels in the brain activation pattern that were selected from the primary visual cortex, and voxel size was 4 mm × 4 mm × 4 mm. Multi-voxel pattern analysis was used to test if these 18 different brain activation patterns were specific. We chose a Linear Support Vector Machine（LSVM） as the classifier in this study. The results showed that the classification accuracies of different brain activation patterns were significantly above chance level, which suggests that the classifier can successfully distinguish the brain activation patterns. Our results suggest that the specific brain activation patterns to different pixelized images can be obtained in the primary visual cortex using a 4 mm × 4 mm × 4 mm voxel size and a 100-voxel pattern.

X线蒙特卡洛算法与有限元笔形束算法在旋转容积调强放疗计划中的剂量学比较

目的:比较Monaco治疗计划系统X线蒙特卡洛算法(X-ray voxel Monte Carlo,XVMC)和有限元笔形束算法(finite size pencil beam,FSPB)在食管癌和直肠癌旋转容积调强放疗计划中的剂量学差异。方法:选取7例食管癌和7例直肠癌旋转容积调强放疗计划,分别使用XVMC算法和FSPB算法进行剂量计算,比较靶区和危及器官的剂量分布差异。结果:XVMC算法的剂量计算值均高于FSPB算法,在食管癌的靶区和脊髓、直肠癌的靶区和危及器官,各项指标差值都小于3%,肺的各项指标差值最大达到10%。结论:在临床旋转容积调强治疗中,尤其是计算区域含有低密度组织较多的病例,优先推荐更为精确的XVMC算法进行剂量计算。

点云密度和体素大小对单木LAI反演的影响

为了提高立体像素法对单木叶面积指数(leaf area index,LAI)的反演精度,探讨了点云密度和体素大小对单木LAI反演结果的影响。获取滇朴和雪松2种具有典型代表性的单木地面激光雷达点云数据和实测LAI数据,分别对单木点云进行0.02~0.1和0.2~1倍不同程度抽稀,以点云平均最邻近距离表征点云密度,探讨了在不同点云密度下估测LAI随体素大小变化的关系。结果表明:(1)点云密度和体素大小对单木LAI的反演精度影响较大。相同体素大小下,反演的LAI值随点云平均最邻近距离的减小而增大,即点云密度越大,估测LAI越大;相同点云平均最邻近距离即同一点云密度下,反演的LAI值随体素的增大而增大。(2)以反演精度最高的体素大小为最优体素,不同点云密度下最优体素值不同,应根据点云密度选取体素大小以提高精度。

强度体元基元下的机载LiDAR 3D滤波

针对现有基于二值体元基元的机载LiDAR三维(3 Dimensional,3D)滤波算法仅利用了数据的高程特征、无法区分相连的地面和非地面目标的问题,提出了一种基于强度体元基元的机载LiDAR 3D滤波算法。首先,基于计算几何理论,将机载LiDAR数据规则化为强度(体元内激光点的量化平均反射强度值)体元结构。然后,基于3D连通区域构建理论,选取局部高程最低的非0值体元为地面种子进而搜寻并标记与地面种子,空间连通、反射强度及坡度值均接近的连通区域内体元为地面体元。算法综合利用LiDAR数据的高程、反射强度及坡度特征,支持相连但强度不同的地面和非地面目标的区分,为相连的地面和非地面目标的精确区分提供更有效的信息。算法有助于提高滤波精度,并扩展基于体元基元的3D滤波算法适用于更复杂的场景。实验基于ISPRS提供的专门用于滤波算法测试的LiDAR点云数据测试了"空间邻域尺度"参数的敏感性及提出算法的精度。定量评价的结果表明:51邻域为最佳邻域尺度;提出算法的平均Kappa系数在相对平坦、陡坡及不连续地形分别为0.9380、0.7749和0.6866;从总误差测度来看,提出算法对比经典的Axelsson算法改进了15个样本中的7个样本精度,且其对比其他二值体元基元下的滤波算法平均总误差最低。