基于三维点云模型获取单木树高和胸径的研究

使用无人机和智能手机分别从空中和地面拍摄的样地林分影像构建三维点云模型，并从三维点云模型中获取样地内单木树高和胸径参数。本研究以池杉人工林为研究对象，利用PhotoScan Agisoft软件对无人机倾斜摄影和智能手机近景摄影的样地影像进行三维重建，通过对齐照片、控制点刺点、对齐优化、建立密集点云等步骤，构建出与样地实景相符的三维点云模型；通过LiDAR 360软件从样地三维点云模型中获取单木的树高和胸径参数，将其与实地测量获取的单木树高和胸径参数进行对比分析。利用无人机和智能手机影像构建的三维模型可以满足《数字航空摄影测量测图规范》的精度要求。通过实测数据和点云数据获取的树高和胸径的平均差值分别为-0.9 m和-0.8 cm,平均相对误差分别为5.4%和7.1%。以实测数据作为自变量x,以点云数据作为因变量y,树高和胸径回归模型的R²分别为0.809 5和0.918 4。将倾斜摄影和近景摄影的点云模型统一在同一空间参考基准下可构建出与样地实景相匹配的三维点云模型，从样地三维点云模型中获取的单木树高和胸径与实地测量结果具有较好的线性相关性，本研究所使用的方法可以代...     

基于地面三维激光扫描技术的林木胸径提取算法分析

胸径是树木最重要的测树因子之一,其精度直接影响材积的测定。传统的树木胸径测量效率低,范围较小;采用遥感反演间接测量胸径,精度较低,且不能直接获取单木的点云数据。本文利用三维激光扫描技术提取立木的3D点云数据,提出一种自动、高效提取单木胸径的算法。利用三维激光扫描仪对样地8棵杨树进行扫描,得到三维点云数据;同时,开展数据分割、精简、降噪处理,得到简化后的点云数据,最后对提取的胸径点云数据进行分层设置,将截取层厚度设置为0,0~1,1~2,2~3 cm 4个等级,利用快速凸包算法将点云数据闭合成一个多边形,运用Arc Engine控件调用Arc GIS中测算多边形长度的方法计算闭合平面周长,换算出立木胸径值,并结合同步实测数据与传统算法、拟合圆算法进行对比试验。结果表明:采用传统算法、拟合圆算法和快速凸包算法的模型决定系数R2分别为0.857、0.941和0.957,说明运用快速凸包算法提取立木胸径是一种高效且比较可行的方法。     

基于单站三维激光扫描数据的林木胸径提取方法

2018年4月以天津市滨海新区海滨大道临港工业区段西侧约1km绿化段为试验区域,通过地面三维激光扫描仪获取单站点云数据,基于最小二乘圆拟合算法,利用LISP语言编制程序对林木胸径值进行自动提取,再通过现场抽测39株‘107杨’Populus×euramericana‘74/76’和65株刺槐Robiniapseudoacacia树对计算结果进行精度统计。结果表明,‘107杨’计算中误差为0.8cm,刺槐计算中误差为0.7cm,整体计算中误差为0.7cm,整体计算中误差<1cm。表明采用最小二乘圆拟合算法对单站点云数据进行胸径计算,效率更高、精度可靠,可应用于实际工程项目中。     

基于无人机可见光遥感的单木树高提取方法研究

利用目前流行的高分辨率可见光无人机遥感影像生成树木冠层高度模型,采用分水岭分割算法提取单木树高的研究具有重要理论和实践意义。以位于云南省富民县的天然云南松纯林为研究对象,通过大疆Phantom 4 Pro无人机获取低空可见光遥感影像,利用Pix4D Mapper对无人机影像进行预处理及三维重建,生成三维点云,利用LiDAR360处理三维点云,构建DSM,DEM并生成CHM;采用分水岭分割算法对不同郁闭度条件下获得的CHM进行单木分割及树高提取,对提取结果进行精度评价。结果表明:分水岭分割算法能够准确分割CHM,利用无人机可见光遥感影像进行单木树高提取是可行的;将基于无人机可见光影像提取的树高值与野外实地调查得到的树高值进行对比,R²为0.893,RMSE为1.23m,估测精度为87.58%;同时,林分郁闭度会对单木树高估测产生影响,根据不同郁闭度条件下提取的3组样木树高与实地测量树高的决定系数(R²)分别是0.857,0.939和0.921,RMSE分别为1.450,1.097,0.896m,在低郁闭度林分内树高估测的精度显著高于高郁闭度林分。     

日本落叶松林分密度与胸径、树高生长关系的探讨

日本落叶松林分密度与胸径、树高生长关系的探讨苑辉（辽宁省林科院１１００３２）目前，日本落叶松（ＬａｒｉｘｋａｅｍＰｆｅｒｉ）人工林在辽宁省已大面积郁闭成林，中、幼林占的比例较大，调节和控制林分的密度，可以在林木各生长阶段改变干材的质量和村积生长量，并...     

基于背包式激光雷达的林木胸径和树高提取

背包式激光雷达扫描系统易操作、查看简洁方便、效率高,但目前还没有广泛应用到林业调查中。本研究通过利用背包式激光雷达扫描数据提取样地单木胸径、树高,计算相应蓄积量,为森林调查工作提供参考。采用LiBackpack 50背包式激光雷达对广西派阳山林场的7个样地树木胸径、树高进行数据采集,通过数据处理软件LiDAR 360提取胸径和树高,测算蓄积量。结果显示,密度较小,杂灌较少,通视条件较好、干形规则的样地,胸径、树高实测值与提取值间的相关性较高,蓄积量差异较小,可采用激光雷达进行森林资源辅助调查。     

基于地面三维激光扫描技术的单木胸径树高提取精度研究

利用地面三维激光扫描仪提取单木参数,已经成为林业测量领域的研究热点,尤其在编制林业数表方面极具应用前景。基于地面三维激光扫描仪采集19株柏木和17株马尾松样本数据,提取单木胸径和树高两个主要测树因子,并与实际数据进行对比分析,结果表明:胸径因子采用凸包算法提取精度更高,相对误差为1.18%;树高测量精度较高,平均相对误差为-1.66%;基于地面三维激光扫描技术提取单木胸径和树高,完全满足调查精度要求,可以广泛应用于森林资源调查,以及单木及林分不同尺度数表编制等方面。     

药乡林场赤松树高与胸径相关关系的研究

用3种拟合方程研究了药乡林场赤松胸径与树高的相关关系，其中以H=-2.829423 1.051789D-0.01688D^2的相关系数最高，计算出的林分平均高相对误差率最低。     

麻栎树高与胸径相关关系的研究

采用带状样地法在山东药乡林场的麻栎林中共布设样地10块,测得样木共计406株的树高、胸径,利用回归分析方法研究了树高与胸径之间的相关关系,选出了适用于不同范围的回归方程,回归方程达到极显著水平（P〈0．01）,其估计精度较高均在93％以上,所建方程可用于计算麻栎树高及其林分平均高。     

柞树树高与胸径相关关系的研究

使用Logistic方程、直线方程、对数方程、多项式方程、乘幂式方程和指数式方程6种理论生长方程拟合不同立地条件下柞树树高生长过程,从中选择最优的树高生长方程。研究结果显示:通过比较拟合统计量,最终确定Logistic方程为柞树树高生长的最优模型。将检验数据的胸径值代入最优树高生长模型中,求出树高预测值,并对树高实测值与预测值进行T检验,研究发现柞树实测值与预测值之间无显著差异(p=0.985 748>0.05),表明该树高模型可以实现柞树树高的有效预测。     

树高、胸径、树皮厚对厚朴产量的影响

为研究厚朴树高、胸径和树皮厚度对其产量的影响,从而为厚朴品种选优及丰产栽培提供理论指导。采用系统抽样方法抽取了10~14 a的90个厚朴单株样本,测量其单株树高、胸径、树皮厚和树皮产量,通过建立和分析厚朴产量回归方程可知,胸径、树高和树皮厚与产量均成正相关关系;胸径、树高、树皮厚对产量的直接影响从大到小依次为胸径、树高、树皮厚,其中胸径对产量的影响以直接影响为主,树高对产量的间接影响稍高于直接影响,其间接影响主要是通过胸径对产量产生影响,树皮厚主要通过胸径和树高对产量产生间接影响,直接影响较小。     

Comparison of four methods for estimating leaf area index based on terrestrial three-dimensional laser scanning

The leaf area index (LAI) of 16 sample plots was estimated based on terrestrial three-dimensional laser scanning. The point-cloud data of stand canopy were first scaled and projected onto a hemisphere according to Lambert azimuthal equal-area projection or stereographic projection, and the resulting hemispherical point-cloud images were used to extract the canopy porosity coefficients. Then, single-angle inversion and Miller formula inversion methods were used, respectively, to calculate the effective leaf area indices with canopy porosity coefficients. Results showed that the effective LAIs estimated by single-angle inversion method with Lambert projection and stereographic projection were within the range of 2.14~5.36 and 1.83~4.67, respectively. The effective LAIs obtained by Miller formula inversion method with Lambert projection and stereographic projection were within the range of 1.84~4.67 and 1.68~4.34, respectively. As a comparison, the LAI measured with a fish-eye camera ranged from 1.55 to 3.87. The LAI values estimated with four different calculation methods were linearly correlated with those measured by a fish-eye camera. The highest coefficient of determination (R²) 90.28% was obtained by the Miller formula inversion method combined with stereographic projection, and Duncan’s new multiple range test also further showed that this method had a relatively higher precision compared to other three methods.     

巨尾桉人工林地径与胸径、树高相关模型的研究

根据巨尾桉人工林样木的地径D0、胸径D及树高H观测数据,采用多模型选优法和逐步回归法求解方程,经分析对比后分别建立了地径与胸径、地径与树高相关的2个数学模型:lnD=3.7275-18.6673/D0;H=32.0925-244.050 3/D0。应用这2个模型,配合一元材积表或二元材积表就可测定被伐木的材积。     

利用三维激光扫描系统测量立木材积的方法

介绍三维激光扫描系统的组成及工作原理,并采用该方法对立木进行扫描,测量立木材积。与伐倒木实测数据进行对比,可以看出扫描数据完全能满足林业测量的精度要求,其扫描获得的立木材积数据完全可以替代传统方法测算的立木材积数据,使用立木模型建立材积表不再需要实地大量伐木,节省大量人力、物力及财力。应用三维激光扫描技术进行立木材积测定能较好的避免传统测定方法的不足,在未来的林业数字化测量中有广阔的应用前景。     

Practical large-scale forest stand inventory using a small-footprint airborne scanning laser

Mean tree height, dominant height, mean diameter, stem number, basal area and timber volume of 116 georeferenced field sample plots were estimated from various canopy height and canopy density metrics derived by means of a small-footprint laser scanner over young and mature forest stands using regression analysis. The sample plots were distributed systematically throughout a 6500 ha study area, and the size of each plot was 232.9 m². Regressions for coniferous forest explained 60–97% of the variability in ground reference values of the six studied characteristics. A proposed practical two-phase procedure for prediction of corresponding characteristics of entire forest stands was tested. Fifty-seven test plots within the study area with a size of approximately 3740 m² each were divided into 232.9 m² regular grid cells. The six examined characteristics were predicted for each grid cell from the corresponding laser data using the estimated regression equations. Average values for each test plot were computed and compared with ground-based estimates measured over the entire plot. The bias and standard deviations of the differences between predicted and ground reference values (in parentheses) of mean height, dominant height, mean diameter, stem number, basal area and volume were ?0.58 to ?0.85 m (0.64–1.01 m), ?0.60 to ?0.99 m (0.67–0.84 m), 0.15–0.74 cm (1.33–2.42 cm), 34–108 ha^?1 (97–466 ha^?1), 0.43–2.51 m² ha^?1 (1.83–3.94 m² ha^?1) and 5.9–16.1 m³ ha^?1 (15.1–35.1 m³ ha^?1), respectively.     

Calibration of laser-derived tree height estimates by means of photogrammetric techniques

Techniques based on laser point clouds and digital terrestrial images were demonstrated for the calibration of tree-height estimation. Individual tree heights can be roughly estimated from laser scanning data by using the approximated ground level and the highest hit of the treetop. However, laser-derived measurements often underestimate tree heights. This underestimation can arise from various error sources. Digital terrestrial images can be used to verify and understand the behaviour of laser point clouds. When laser data are backprojected in a close-range image, it is possible to show where each laser beam has reflected. This, however, requires a proper orientation of the images. In this study an interactive orientation method was used to derive image orientations, using one laser strip at a time as the reference data. Consequently, the backprojection of laser point clouds confirmed the height underestimations found by comparing the tacheometer reference measurements with the laser-derived tree heights. In addition, by using the described procedure the cause of underestimating tree heights could be explained.     

Developing an airborne laser scanning dominant height model from a countrywide scanning survey and national forest inventory data

Abstract

An airborne laser scanning (ALS) dominant height model was developed based on data from a national scanning survey with the aim of developing a digital terrain model (DTM) for Denmark. Data obtained in the ongoing Danish national forest inventory (NFI) were used as reference data. The data comprised a total of 2072 measurements of dominant height on NFI sample plots inventoried in 2006–2007 and their corresponding ALS data. The dominant height model included four variables derived from the ALS point cloud distribution. The variables were related to canopy height, canopy density and species composition on individual plots. The RMSE of the final model was 2.25 m and the model explained 93.9% of the variation (R ²). The model was successful in predicting dominant height across a wide range of forest tree species, stand heights, stand densities, canopy cover and growing conditions. The study demonstrated how low-density ALS data obtained in a survey not specifically aimed at forest applications may be used for obtaining biophysical forest properties such as dominant height, thereby reducing the overall forest inventory costs.     

Estimating single-tree branch biomass of Norway spruce with terrestrial laser scanning using voxel-based and crown dimension features

Abstract

Many remote sensing-based methods estimating forest biomass rely on allometric biomass models for field reference data. Terrestrial laser scanning (TLS) has emerged as a tool for detailed data collection in forestry applications, and the methods have been proposed to derive, e.g. tree position, diameter-at-breast-height, and stem volume from TLS data. In this study, TLS-derived features were related to destructively sampled branch biomass of Norway spruce at the single-tree level, and the results were compared to conventional allometric models with field measured diameter and height. TLS features were derived following two approaches: one voxel-based approach with a detailed analysis of the interaction between individual voxels and each laser beam. The features were derived using voxels of size 0.1, 0.2, and 0.4 m, and the effect of the voxel size was assessed. The voxel-derived features were compared to features derived from crown dimension measurements in the unified TLS point cloud data. TLS-derived variables were used in regression models, and prediction accuracies were assessed through a Monte Carlo cross-validation procedure. The model based on 0.4 m voxel data yielded the best prediction accuracy, with a root mean square error (RMSE) of 32%. The accuracy was found to decrease with an increase in voxel size, i.e. the model based on the 0.1 m voxel yielded the lowest accuracy. The model based on crown measurements had an RMSE of 34%. The accuracies of the predictions from the TLS-based models were found to be higher than from conventional allometric models, but the improvement was relatively small.     

Estimating percentile-based diameter distributions in uneven-sized Norway spruce stands using airborne laser scanner data

Abstract

A model for prediction of stand basal area and diameters at 10 percentiles of a basal area distribution was estimated from small-footprint laser scanner data from primeval conifer forest using partial least squares regression. The regression explained 44–80% and 67% of the variability of the 10 percentiles and stand basal area, respectively. The predicted percentiles, scaled by the predicted stand basal area, were used to compute diameter distributions. A cross-validation showed that the mean differences between the predicted and observed number of stems by diameter class were non-significant (p>0.05) for 22 of 29 diameter classes. Moreover, plot volume was calculated from the predicted diameter distribution and cross-validation revealed a non-significant deviation between predicted and observed volume of ?3.3% (of observed volume). An independent validation showed non-significant mean differences for 20 of 21 diameter classes for data corresponding to the model calibration data. Plot volumes calculated from the predicted diameter distributions deviated from observed volume by ?4.4%. The model reproduced diameter distributions corresponding to the model calibration data (uneven-sized forest) well. However, the model is not flexible enough to reproduce normal and uniform diameter distributions. Volume estimates derived from predicted diameter distributions were generally well determined, irrespective of the observed distribution.     

Prediction of tree height,basal area and stem volume in forest stands using airborne laser scanning

The aim of this study was to develop prediction models using laser scanning for estimation of forest variables at plot level, validate the estimations at stand level (area 0.64 ha) and test the effect of different laser measurement densities on the estimation errors. The predictions were validated using 29 forest stands (80×80 m²), each containing 16 field plots with a 10 m radius. For the best tested case, mean tree height, basal area and stem volume were predicted with a root mean square error of 0.59 m (3% of average value), 2.7 m² ha^?1 (10% of average value) and 31 m³ ha^?1 (11% of average value), respectively, at stand level. There were small differences in terms of prediction errors for different measuring densities. The results indicate that mean tree height, basal area and stem volume can be estimated in small stands with low laser measurement densities producing accuracies similar to traditional field inventories.