地基激光雷达灌丛化草原小叶锦鸡儿生物量估算

小叶锦鸡儿是内蒙古灌丛化草原中最具代表性的景观植物，准确估算小叶锦鸡儿灌丛的地上生物量对研究灌丛化草原生态系统、监测草原灌丛化程度具有重要意义。地基激光雷达TLS(Terrestrial Laser Scanning)可通过获取高密度点云数据准确估算灌木体积，被广泛应用于反演灌木生物量，但在灌丛化草原中尚未得到有效应用。本研究首先在中国科学院灌丛化草地植被恢复试验区获取了5个样方(10 m×10 m)共42株灌丛的TLS点云数据及实测生物量信息；然后分别使用整体凸包法、切片凸包法、切片分割法、体积表面差分法、体素法5种方法计算灌丛体积并与实测生物量进行回归分析；最后，通过留一交叉验证对5种方法建立的生物量估算模型精度进行对比分析。结果表明：TLS可在不破坏植被的情况下实现快速、准确地小叶锦鸡儿灌丛生物量反演，是传统野外调查方法的可靠替代技术。研究中采用的5种方法均能较好地估算灌丛生物量，其中：（1）相比于整体凸包法（R ²=0.87, p<0.001, RMSE=30.50 g），切片凸包法（R ²=0.89, p<0.001, RMSE=28.01 g）与切片分割法（R ²=0.88, p<0.001, RMSE=29.03 g）可有效减弱离群点造成的体积高估，生物量估算精度有所提升；（2）格网大小为3 cm、高度统计变量选取标准差时，体积表面差分法计算的体积与实测生物量拟合度最好(R ²=0.89, p<0.001, RMSE=28.89 g)，表明高度标准差是估算小叶锦鸡儿灌丛生物量的强预测因子；（3）体素法解释了生物量估计值90%的变化(R ²=0.90, p<0.001, RMSE=26.28 g)，是适合小叶锦鸡儿灌丛生物量反演的最优模型。     

定量结构模型的地面激光雷达单木分割应用

针对传统每木检尺的小班调查因子测量方法耗时长、效率低、人为操作误差大等问题,该文研究了一种定量结构模型(QSM)实现单木的三维重建,并基于地面激光雷达(TLS)所获取的点云数据提取研究区单木的胸径、树高参数.该算法重构了单木的三维模型,且TLS数据所求得的胸径、树高与实测数据具有较好的一致性,提取结果精度较高.本研究采...     

三维激光扫描数据的单木树冠体积精确计算

针对现存单木树冠体积计算方法不能剔除树冠外部较大空隙以及树冠边界提取粗糙的问题,该文在对生长算法改进的基础上,提出了基于过滤三角网的树冠边界精确提取算法,确定了树冠最优分层间距与过滤阈值,实现了树冠体积的准确计算。该方法在对扫描的单木树冠点云数据进行拼接和过滤后,进行等间隔分层处理获取其切片点云,然后采用过滤三角网算法生成符合树冠实际情况的边界,再通过计算的切片面积获取各层点云间的体积,最终累加各层点云体积得到树冠体积的精确值。对校内树冠三维激光扫描实测数据进行计算与分析,结果表明过滤三角网算法提取的树冠边界能顾及树冠外部存在的空隙现象,进而得到准确的树冠体积值;此外,过滤三角网算法对树冠点云数据的密度要求远低于体元法,具有较高的算法稳定性。     

基于地面激光扫描数据的单木特征因子提取与分析

本文利用三维激光扫描仪对树木进行扫描获取树点云数据,经过格式转换、分离、提取后,对树木各测量因子包括胸径、树高、树冠、材积量进行测定与测量方法与意义的分析。通过实验分析,可以得出:树冠测定因子通过测定树冠的叶面积指数来更精确地反映树冠的生理学意义;通过不规则三角网构建的多面体计算的树干体积较以平均断面积、中央断面积求树干材积更为准确与便捷。     

结合多光谱影像降维与深度学习的城市单木树冠检测

多光谱数据的降维处理对基于深度学习的单木树冠检测研究有重要意义,如何使用合适的降维方法以提高单木检测的精度却少有研究讨论。本文使用无人机搭载多光谱相机进行航拍作业,采集研究区内银杏树种多光谱影像。将原始多光谱影像通过特征波段选择、特征提取、波段组合的方法生成5种不同的数据集用于训练3种经典的深度学习网络FPN-Faster-R-CNN,YOLOv3,Faster R-CNN。其中由波段组合方法得到的近红外、红色、绿色波段组合在不同类型的目标检测网络中都有最好的检测结果,其中FPN-Faster-R-CNN网络对银杏树冠的检测精度最高为88.4%,由OIF指标得到的蓝色、红色、近红外波段组合信息量最高,但在所有网络中的平均检测精度最低,仅为79.3%。实验结果表明：在不同波段降维方法中,若降维后的影像中目标物体的色彩与背景差异较明显,且轮廓清晰,则深度学习网络对树冠的检测可获得较好的结果。而影像自身的信息量则对深度学习网络的树冠检测能力的提升作用有限。本研究中针对多光谱影像的降维方法分析,为基于深度学习的单木树冠检测研究提供了重要的实验参考。     

利用地面激光扫描数据提取单木结构参数

针对现有提取单木结构参数的方法精度不高的现状,文章研究利用地面三维激光扫描数据提取单木的高度、胸径和冠幅,特别是将RANSAC算法用于圆拟合,提高了胸径的反演精度。最后利用野外实测数据进行验证,并比较分析了Hough变换和RANSAC算法在提取胸径中的差异。结果表明,本文方法提取的单木参数精度较高;并且无论从相关系数、平均残差还是均方根误差等方面,RANSAC算法提取的胸径精度均高于Hough变换方法。     

利用遥感进行退耕还林成活率及长势监测方法的研究

本文以张家口退耕还林工程的新造经济林为例,提出了一种利用高分辨率遥感技术监测新造林成活率及长势的方法。主要采用面向对象的信息提取技术提取退耕地新造林的树冠信息。开发了基于树冠分布图的树冠因子提取程序,计算树冠因子,统计造林成活率,从而掌握新造林地的现状。最后,根据实际测量的数据进行误差检验,由遥感数据自动提取的树冠冠幅平均误差为:东西冠幅为0.337m,南北冠幅为0.433m;计算新造林成活率的精度达到了89.837%。为退耕还林工程科学,高效的管理及决策支持提供了依据。     

移动多视图立体摄影的单木结构参数提取北大核心CSCD

针对目前基于地面激光扫描和传统方法测量单木结构参数的成本高和效率低的问题,提出了一种基于移动多视图立体摄影的方法测量树高和胸径。该文利用移动多视图立体摄影技术将由低成本的手持相机获取单木的二维像片生成三维点云数据,并通过对生成的单木点云建模来估算树高和胸径。实验显示采用三维模型估算树高和胸径的结果与实测数据存在明显的线性相关关系。胸径和树高的平均均方根误差分别是7.1%(R2=0.964)、7.9%(R2=0.903),两者的总体估算精度都达到90%以上。结果表明,移动多视图立体摄影技术对于降低树木点云获取成本,丰富林木资源调查的手段具有一定意义。     

移动多视图立体摄影的单木结构参数提取

针对目前基于地面激光扫描和传统方法测量单木结构参数的成本高和效率低的问题,提出了一种基于移动多视图立体摄影的方法测量树高和胸径。该文利用移动多视图立体摄影技术将由低成本的手持相机获取单木的二维像片生成三维点云数据,并通过对生成的单木点云建模来估算树高和胸径。实验显示采用三维模型估算树高和胸径的结果与实测数据存在明显的线性相关关系。胸径和树高的平均均方根误差分别是7.1%(R2=0.964)、7.9%(R2=0.903),两者的总体估算精度都达到90%以上。结果表明,移动多视图立体摄影技术对于降低树木点云获取成本,丰富林木资源调查的手段具有一定意义。     

基于无人机可见光影像的单木胸径估算方法

胸径(Diameter at Breast Height,DBH)是指树木主干离地表面胸高处的直径,根据无人机可见光影像估算单木DBH对林业资产管理与评估具有重要意义。以云南师范大学呈贡校区内的银杏为研究对象,首先,获取其无人机可见光影像,基于摄影测量原理生成数字正射影像图;然后,在此基础上提取银杏单木的冠幅(Crown Width,CW);最后,建立CW与DBH的4个回归模型,通过该模型估算得到DBH值。将实际测量的DBH值与估算值进行精度验证,最终一元二次函数模型R²为0.75,均方根误差为0.012 9 m,平均误差率为3.22%,均小于其他3个模型,具有较高的精度。实验结果表明基于无人机可见光影像可以较为准确地估算单木DBH。     

Estimating residual biomass of olive tree crops using terrestrial laser scanning

Agricultural residues have gained increasing interest as a source of renewable energy. The development of methods and techniques that allow to inventory residual biomass needs to be explored further. In this study, the residual biomass of olive trees was estimated based on parameters derived from using a Terrestrial Laser Scanning System (TLS). To this end, 32 olive trees in 2 orchards in the municipality of Viver, Central Eastern Spain, were selected and measured using a TLS system. The residual biomass of these trees was pruned and weighed. Several algorithms were applied to the TLS data to compute the main parameters of the trees: total height, crown height, crown diameter and crown volume. Regarding the last parameter, 4 methods were tested: the global convex hull volume, the convex hull by slice volume, the section volume, and the volume measured by voxels. In addition, several statistics were computed from the crown points for each tree. Regression models were calculated to predict residual biomass using 3 sets of potential explicative variables: firstly, the height statistics retrieved from 3D cloud data for each crown tree, secondly, the parameters of the trees derived from TLS data and finally, the combination of both sets of variables. Strong relationships between residual biomass and TLS parameters (crown volume parameters) were found (R² = 0.86, RMSE = 2.78 kg). The pruning biomass prediction fraction was improved by 6%, in terms of R², when the variance of the crown-point elevations was selected (R² = 0.92, RMSE = 2.01 kg). The study offers some important insights into the quantification of residual biomass, which is essential information for the production of biofuel.     

Automatic Tree Identification and Diameter Estimation Using Single Scan Terrestrial Laser Scanner Data in Central Indian Forests

Forest inventory parameters, primarily tree diameter and height, are required for several management and planning activities. Currently, Terrestrial Laser Scanning (TLS) is a promising technology in automated measurements of tree parameters using dense 3D point clouds. In comparison with conventional manual field inventory methods, TLS systems would supplement field data with detailed and relatively higher degree of accurate measurements and increased measurement frequency. Although, multiple scans from TLS captures more area, they are resource and time consuming to ensure proper co-registration between the scans. On the other hand, Single scans provide a fast and recording of the data but are often affected by occlusions between the trees. The current study evaluates potential of single scan TLS data to (1) develop an automatic method for tree stem identification and diameter estimation (diameter at breast height—DBH) using random sample consensus (RANSAC) based circle fitting algorithm, (2) validate using field based measurements to derive accuracy estimates and (3) assess the influence of distance to scanner on detection and measurement accuracies. Tree detection and diameter measurements were validated for 5 circular plots of 20 m radius using single scans in dry deciduous forests of Betul, Madhya Pradesh. An overall tree detection accuracy of 85 and 70% was observed in the scanner range of 15 and 20 m respectively. The tree detection accuracies decreased with increased distance to the scanner due to the decrease in visible area. Also, estimated stem diameter using TLS was found to be in agreement with the field measured diameter (R² = 0.97). The RMSE of estimated DBH was found to be 3.5 cm (relative RMSE ~20%) over 202 trees detected over 5 plots. Results suggest that single scan approach suffices the cause of accuracy, reducing uncertainty and adds to increased sampling frequency in forest inventory and also implies that TLS has a seemingly high potential in forest management.     

Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns

To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated the relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements. However, few studies have compared the accuracy of different remote sensing technologies for the estimation of tree height. In this study, we evaluated the accuracy, flexibility, aerial coverage and limitations of five techniques to measure the height of two types of horticultural tree crops, mango and avocado trees. Canopy height estimates from Terrestrial Laser Scanning (TLS) were used as a reference dataset against height estimates from Airborne Laser Scanning (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB and multi-spectral imagery, and field measurements. Overall, imagery obtained from the UAV platform were found to provide tree height measurement comparable to that from the TLS (R² = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R² = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1–10 km² due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R² = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R² = 0.63, RMSE = 0.43 m and rRMSE = 5.04 % for avocado trees), providing both optimal point density and flight altitude, and therefore offers an effective platform for large areas (10 km²–100 km²). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R² = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R² = 0.45, RMSE = 0.74 m and rRMSE = 8.51 % for avocado trees) when compared to other remote sensing platforms, but may still present a viable option due to cost and commercial availability when large area coverage is required. This research provides industries and growers with valuable information on how to select the most appropriate approach and the optimal parameters for each remote sensing platform to assess canopy height for mango and avocado trees.     

机载激光雷达及高光谱的森林乔木物种多样性遥感监测

利用机载LiDAR和高光谱数据并结合37个地面调查样本数据,基于结构差异与光谱变异理论,通过相关分析法分别筛选了3个最优林冠结构参数和6个最优光谱指数,在单木尺度上利用自适应C均值模糊聚类算法,在神农架国家自然保护区开展森林乔木物种多样性监测,实现了森林乔木物种多样性的区域成图。研究结果表明,(1)基于结合形态学冠层控制的分水岭算法可以获得较高精度的单木分割结果(R~2=0.88,RMSE=13.17,P0.001);(2)基于LiDAR数据提取的9个结构参数中,95%百分位高度、冠层盖度和植被穿透率为最优结构参数,与Shannon-Wiener指数的相关性达到R~2=0.39—0.42(P0.01);(3)基于机载高光谱数据筛选的16个常用的植被指数中,CRI、OSAVI、Narrow band NDVI、SR、Vogelmann index1、PRI与Shannon-Wiener指数的相关性最高(R~2=0.37—0.45,P0.01);(4)在研究区,利用以30 m×30 m为窗口的自适应模糊C均值聚类算法可预测的最大森林乔木物种数为20,物种丰富度的预测精度为R~2=0.69,RMSE=3.11,Shannon-Wiener指数的预测精度为R~2=0.70,RMSE=0.32。该研究在亚热带森林开展乔木物种多样性监测,是在区域尺度上进行物种多样性成图的重要实践,可有效补充森林生物多样性本底数据的调查手段,有助于实现生物多样性的长期动态监测及科学分析森林物种多样性的现状和变化趋势。     

Tree species biomass and carbon stock measurement using ground based-LiDAR

This study scrutinises the use of terrestrial laser scanning (TLS) to measure diameter at breast height (DBH) and tree height at individual tree species level. LiDAR point cloud scans are collected from uniformly defined control points. The result of processed TLS data demonstrates the precise measurements of tree height and DBH by comparing it with field data (DBH, tree height, tree species and location). The average tree height and DBH obtained through TLS measurements were 9.44?m and 43.30?cm, respectively. A linear equation between TLS derived parameters and field measured values were established, which gave the coefficient of determination (r2) of 0.79 and 0.96 for tree height and DBH, respectively. Further, these parameters were used to calculate above ground biomass (AGB) for individual tree species by considering a non-destructive approach. The total AGB and carbon stock from 80 different trees are computed to be 49.601 and 22.320?tonnes, respectively.     

Predicting individual tree attributes from airborne laser point clouds based on the random forests technique

This paper depicts an approach for predicting individual tree attributes, i.e., tree height, diameter at breast height (DBH) and stem volume, based on both physical and statistical features derived from airborne laser-scanning data utilizing a new detection method for finding individual trees together with random forests as an estimation method. The random forests (also called regression forests) technique is a nonparametric regression method consisting of a set of individual regression trees. Tests of the method were performed, using 1476 trees in a boreal forest area in southern Finland and laser data with a density of 2.6 points per m². Correlation coefficients (R) between the observed and predicted values of 0.93, 0.79 and 0.87 for individual tree height, DBH and stem volume, respectively, were achieved, based on 26 laser-derived features. The corresponding relative root-mean-squared errors (RMSEs) were 10.03%, 21.35% and 45.77% (38% in best cases), which are similar to those obtained with the linear regression method, with maximum laser heights, laser-estimated DBH or crown diameters as predictors. With random forests, however, the forest models currently used for deriving the tree attributes are not needed. Based on the results, we conclude that the method is capable of providing a stable and consistent solution for determining individual tree attributes using small-footprint laser data.     

Terrestrial laser scanning to estimate plot-level forest canopy fuel properties

This paper evaluates the potential of a terrestrial laser scanner (TLS) to characterize forest canopy fuel characteristics at plot level. Several canopy properties, namely canopy height, canopy cover, canopy base height and fuel strata gap were estimated. Different approaches were tested to avoid the effect of canopy shadowing on canopy height estimation caused by deployment of the TLS below the canopy. Estimation of canopy height using a grid approach provided a coefficient of determination of R² = 0.81 and an RMSE of 2.47 m. A similar RMSE was obtained using the 99th percentile of the height distribution of the highest points, representing the 1% of the data, although the coefficient of determination was lower (R² = 0.70). Canopy cover (CC) was estimated as a function of the occupied cells of a grid superimposed upon the TLS point clouds. It was found that CC estimates were dependent on the cell size selected, with 3 cm being the optimum resolution for this study. The effect of the zenith view angle on CC estimates was also analyzed. A simple method was developed to estimate canopy base height from the vegetation vertical profiles derived from an occupied/non-occupied voxels approach. Canopy base height was estimated with an RMSE of 3.09 m and an R² = 0.86. Terrestrial laser scanning also provides a unique opportunity to estimate the fuel strata gap (FSG), which has not been previously derived from remotely sensed data. The FSG was also derived from the vegetation vertical profile with an RMSE of 1.53 m and an R² = 0.87.     

The influence of scan mode and circle fitting on tree stem detection,stem diameter and volume extraction from terrestrial laser scans

Terrestrial laser scanning (TLS) has been used to estimate a number of biophysical and structural vegetation parameters. Of these stem diameter is a primary input to traditional forest inventory. While many experimental studies have confirmed the potential for TLS to successfully extract stem diameter, the estimation accuracies differ strongly for these studies – due to differences in experimental design, data processing and test plot characteristics. In order to provide consistency and maximize estimation accuracy, a systematic study into the impact of these variables is required. To contribute to such an approach, 12 scans were acquired with a FARO photon 120 at two test plots (Beech, Douglas fir) to assess the effects of scan mode and circle fitting on the extraction of stem diameter and volume. An automated tree stem detection algorithm based on the range images of single scans was developed and applied to the data. Extraction of stem diameter was achieved by slicing the point cloud and fitting circles to the slices using three different algorithms (Lemen, Pratt and Taubin), resulting in diameter profiles for each detected tree. Diameter at breast height (DBH) was determined using both the single value for the diameter fitted at the nominal breast height and by a linear fit of the stem diameter vertical profile. The latter is intended to reduce the influence of outliers and errors in the ground level determination. TLS-extracted DBH was compared to tape-measured DBH. Results show that tree stems with an unobstructed view to the scanner can be successfully extracted automatically from range images of the TLS data with detection rates of 94% for Beech and 96% for Douglas fir. If occlusion of trees is accounted for stem detection rates decrease to 85% (Beech) and 84% (Douglas fir). As far as the DBH estimation is concerned, both DBH extraction methods yield estimates which agree with reference measurements, however, the linear fit based approach proved to be more robust for the single scan DBH extraction (RMSE range 1.39–1.74 cm compared to 1.47–2.43 cm). With regard to the different circle fit algorithms applied, the algorithm by Lemen showed the best overall performance (RMSE range 1.39–1.65 cm compared to 1.49–2.43 cm). The Lemen algorithm was also found to be more robust in case of noisy data. Compared to the single scans, the DBH extraction from the merged scan data proved to be superior with significant lower RMSE’s (0.66–1.21 cm). The influence of scan mode and circle fitting is reflected in the stem volume estimates, too. Stem volumes extracted from the single scans exhibit a large variability with deviations from the reference volumes ranging from −34% to 44%. By contrast volumes extracted from the merged scans only vary weakly (−2% to 6%) and show a marginal influence of circle fitting.     

Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data

Site productivity is essential information for sustainable forest management and site index (SI) is the most common quantitative measure of it. The SI is usually determined for individual tree species based on tree height and the age of the 100 largest trees per hectare according to stem diameter. The present study aimed to demonstrate and validate a methodology for the determination of SI using remotely sensed data, in particular fused airborne laser scanning (ALS) and airborne hyperspectral data in a forest site in Norway. The applied approach was based on individual tree crown (ITC) delineation: tree species, tree height, diameter at breast height (DBH), and age were modelled and predicted at ITC level using 10-fold cross validation. Four dominant ITCs per 400 m² plot were selected as input to predict SI at plot level for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.). We applied an experimental setup with different subsets of dominant ITCs with different combinations of attributes (predicted or field-derived) for SI predictions. The results revealed that the selection of the dominant ITCs based on the largest DBH independent of tree species, predicted the SI with similar accuracy as ITCs matched with field-derived dominant trees (RMSE: 27.6% vs 23.3%). The SI accuracies were at the same level when dominant species were determined from the remotely sensed or field data (RMSE: 27.6% vs 27.8%). However, when the predicted tree age was used the SI accuracy decreased compared to field-derived age (RMSE: 27.6% vs 7.6%). In general, SI was overpredicted for both tree species in the mature forest, while there was an underprediction in the young forest. In conclusion, the proposed approach for SI determination based on ITC delineation and a combination of ALS and hyperspectral data is an efficient and stable procedure, which has the potential to predict SI in forest areas at various spatial scales and additionally to improve existing SI maps in Norway.