一种基于全站仪量测树木材积的方法

通过伐倒树木进行解析可以精准获取材积,但此方法除了劳力费时外,还对树木有着毁灭性伤害;而传统的几种估测立木材积的方法精度又得不到保障。随着测量仪器的快速发展以及当今对生态文明建设的高度重视,林业量测有了更为准确和严格的要求,本文提出一种基于全站仪量测树木材积的方法：通过采集树木的胸径、地径和全站仪与树干任意处左右两侧切线的夹角,根据区分求积法原理精准计算立木材积,将通过全站仪量测获取的树木材积值与伐倒该树木测得的材积值进行比较分析。数据表明：该方法量测材积精度高达93％以上,完全可以替代伐倒树木测量材积的方法。     

基于车载点云数据的树木提取与分析

本文基于高速公路高精度点云数据,首先通过点云数据的分类处理实现对树木点云数据的提取,将树木点云投影到水平面,采用DBSCAN密度聚类算法实现单根树木的提取;然后在数据密集区域存在树木树冠点云重叠的区域,本文结合树干几何特征提取树干的位置信息,计算所有点云到树干中心的欧氏距离,将所有点云归类到最近的树干进行粗分割;最后根据粗分割的树木轮廓特征确定树冠模型与树冠中心,提出了采用基于密度特征的格网竞争算法对重叠的区域进行精细分割。试验表明,本文采用的树木分割方法能够实现单棵树木精确提取。     

利用电子经纬仪改进望高法测算立木材积

目前精确测算树木材积的主要方法是将树木伐倒后分段测量材积,这种破坏性试验造成了大量的财力和物力浪费,而传统无伐倒活、立木测量材积的方法的精度又难以保证。本文提出了一种利用电子经纬仪改进望高法测算立木材积的方法,通过测量树木的胸径、地径,树干任意位置的水平夹角、天顶距,计算任意处直径,利用内插的方法找到望点所在位置,应用改进望高法求算出立木材积。并以内蒙古自治区旺业甸实验林场实测196棵落叶松数据为例,利用本文提出的改进望高法计算立木材积,同时与传统的区分求积法计算材积进行对比。结果表明该方法测量的平均相对误差在5%以内,可替代伐倒树木测量材积的方法,在森林计测中有广泛的应用前景。     

一种电子经纬仪立木材积精准测算方法

伐倒树木测量其材积是传统林业一直惯用的方法,其精度高,但该过程是破坏性实验,不但严重破坏了生态环境,而且花费大量的财力和物力,传统一些无伐倒估量材积方法的精度又太低。针对当今高度重视生态文明建设,对林业量测的有了精准的要求,本文提出一种利用电子经纬仪测量立木材积的方法：通过采集树木的胸径、地径、和电子经纬仪与树干任意处左右两侧切线的夹角,根据区分求积法原理精准计算立木材积;并对影响该方法精度的因数（观测距离、区分段个数）进行分析,研究表明,该方法量测材积精度高达92%以上,且最佳观测距离应与树高相当,2米左右为最佳区分段高度。该方法可替代伐倒树木测量材积的方法,并且它在森林计测中有广泛的应用前景。     

3维激光扫描树干提取与建模研究

由于树木生长的不规则性,造成对其各部分组件的测定与建模具有复杂性与挑战性。本文利用3维激光扫描仪获得树木的点云数据,对树干进行分离提取与3维建模研究。通过对四棵树的树干点云进行分离与提取、3维建模并计算其体积,得出由于遮挡等问题,全部提取树干点云难以实现。在提取主要树干点云的基础上,采用最大距离封装树干表面效果较好,封装表面采用网格修补可建立树干3维立体模型,实现体积计算。     

地基LiDAR林木点云估算枝干材积

材积是森林清查工作的一个重要参数，基于地基激光雷达点云的树木定量结构模型（QSM）重建方法能够实现林木材积的非破坏性获取，解决了传统森林原位调查方式耗时耗力的问题。但由于伐木材积真值的获取较难实现，使得量化结构模型方法的材积获取能力在树干及各级树枝水平上尚未开展研究，且仅应用于单木尺度地基激光雷达点云中，缺乏基于样方尺度扫描点云进行材积获取的探究。因此本文分别在单木及样方尺度完成QSM重建方法在树干及各级枝材积估算结果评估。实验结果表明，基于单木及样方尺度地基激光雷达点云均能有效地获取树干和一级枝的材积，而次级枝的材积估算存在明显的偏差：样方扫描尺度点云的树干及全树材积估算精度与单木尺度相当，估算偏差均为5%及10%左右，而一级枝材积估算偏差略大，其中单木尺度一级枝估算偏差在10%左右，样方尺度一级枝材积估算偏差在15%左右；此外，林分密度与样方尺度枝干材积估算精度呈负相关关系，在较低林分密度（425株/ha、625株/ha和925株/ha）的样方中树干材积估算误差均在5%以内，一级枝材积估算误差在15%左右，另外受树干及一级枝材积低估与各次级枝材积高估的部分中和效应影响，样方内总蓄积...     

三维激光扫描系统在精准林业测量中的应用

提出利用Cyra三维激光扫描仪,采用控制靶标与贴片相组合的方法对林业树种进行野外数据采集。针对Cyclone软件的不足,基于三次样条插值函数的构造理论,利用面向对象的高级编程语言实现了对树冠体积和表面积、树干材积的计算,实现了对冠下高、冠下径、冠长、胸径、任意处直径等参数的量测,克服了传统林业测量的局限性,对林业测量技术的发展具有极其重要的意义。     

车载LiDAR点云相连行道树精细分割

针对车载激光雷达获取的行道树树冠点云相连问题,该文提出了基于树木生长模型的距离加权分割方法。首先通过等距原则将相连树粗分割为单株树木,采用求差和拟合算法获得树木高度、胸径等属性信息;再根据树木的理论和实际生长模型,计算得到树冠距离加权值;最后在此基础上对树冠进行迭代处理,实现邻接行道树点云的精确分割。经过不同树种试验验证,该算法能够实现对多棵相连树点云的进一步精确分割。     

利用无人机多光谱影像提取树冠信息

树冠作为树木主要组成部分之一,是树木长势监测、树种识别等内容的重要参数,对森林资源调查和生态研究具有重要意义。与传统的实地调查相比,运用无人机遥感技术提取树冠信息具有高效、便捷等优势。本文基于无人机多光谱影像提取树冠信息,在树冠点探测上结合局部最大值法与Mean Shift优化策略,较原始局部最大法探测精度提升约10%。此外,提出了一种新的树冠边界提取算法,运用动态规划思想进行全局最优边界提取。与以往分水岭分割算法相比,本文算法在较密集林区和稀疏林区均有更好的提取效果,在试验样区稀疏林区F测度提升12%,较密集区F测度提升28%。     

树冠形状对孔隙率及叶面积指数估算的影响分析

叶片在树冠尺度的聚集是森林场景中的重要聚集形式,模型中常假设树冠为规则的几何形体(椭球、圆锥、圆锥+圆柱等)。对树冠形状归属进行判断时界限并不明显,从而具有很强的主观性。本文首先扩展了Nilson的森林孔隙率模型,使其适用于椭球、圆锥、圆锥+圆柱等3种常见形状的树冠,并基于该模型分析了孔隙率、聚集指数对树冠形状的敏感性。同时,本文还分析了树冠形状对叶面积指数(LAI)地面间接测量精度的影响。基于不同形状树冠的模拟数据分析发现,树冠的体积、投影面积是树冠形状产生作用的主要因子,在冠层底部椭球形树冠和圆锥+圆柱形树冠的平均孔隙率、聚集指数都非常接近,而圆锥形树冠与两者存在较大差异。树冠形状的错误设置在极端情况下可导致估算的真实LAI误差超过25%。     

无人机航测技术在森林蓄积量估测中的应用

无人机（UAV）航测技术是近年来发展起来的快速获取高分辨率影像的测绘新技术。森林蓄积量估算需要快速高效地获取森林遥感影像。虽然利用卫星和机载雷达同样可获取高分辨率遥感影像,但无人机航测技术与其相比具有飞行成本低、外业周期短、机动灵活等优点。本文利用无人机航测系统获取了案例地区DSM和DEM,采用最大邻域法提取了树高,采用分水岭算法分割了树冠信息,并以树高和冠幅作为解释变量的立木材积二元模型估算了森林蓄积量。结果表明,树高提取精度为83.73%,冠幅提取精度为86.98%,林分蓄积量估算精度为81.80%。     

Can regional aerial images from orthophoto surveys produce high quality photogrammetric Canopy Height Model? A single tree approach in Western Europe

Forest monitoring tools are needed to promote effective and data driven forest management and forest policies. Remote sensing techniques can increase the speed and the cost-efficiency of the forest monitoring as well as large scale mapping of forest attribute (wall-to-wall approach). Digital Aerial Photogrammetry (DAP) is a common cost-effective alternative to airborne laser scanning (ALS) which can be based on aerial photos routinely acquired for general base maps. DAP based on such pre-existing dataset can be a cost effective source of large scale 3D data. In the context of forest characterization, when a quality Digital Terrain Model (DTM) is available, DAP can produce photogrammetric Canopy Height Model (pCHM) which describes the tree canopy height. While this potential seems pretty obvious, few studies have investigated the quality of regional pCHM based on aerial stereo images acquired by standard official aerial surveys. Our study proposes to evaluate the quality of pCHM individual tree height estimates based on raw images acquired following such protocol using a reference filed-measured tree height database. To further ensure the replicability of the approach, the pCHM tree height estimates benchmarking only relied on public forest inventory (FI) information and the photogrammetric protocol was based on low-cost and widely used photogrammetric software. Moreover, our study investigates the relationship between the pCHM tree height estimates based on the neighboring forest parameter provided by the FI program.Our results highlight the good agreement of tree height estimates provided by pCHM using DAP with both field measured and ALS tree height data. In terms of tree height modeling, our pCHM approach reached similar results than the same modeling strategy applied to ALS tree height estimates. Our study also identified some of the drivers of the pCHM tree height estimate error and found forest parameters like tree size (diameter at breast height) and tree type (evergreenness/deciduousness) as well as the terrain topography (slope) to be of higher importance than image survey parameters like the variation of the overlap or the sunlight condition in our dataset. In combination with the pCHM tree height estimate, the terrain slope, the Diameter at Breast Height (DBH) and the evergreenness factor were used to fit a multivariate model predicting the field measured tree height. This model presented better performance than the model linking the pCHM estimates to the field tree height estimates in terms of r² (0.90 VS 0.87) and root mean square error (RMSE, 1.78 VS 2.01 m). Such aspects are poorly addressed in literature and further research should focus on how pCHM approaches could integrate them to improve forest characterization using DAP and pCHM. Our promising results can be used to encourage the use of regional aerial orthophoto surveys archive to produce large scale quality tree height data at very low additional costs, notably in the context of updating national forest inventory programs.     

Estimation of aboveground biomass of Robinia pseudoacacia forest in the Yellow River Delta based on UAV and Backpack LiDAR point clouds

Forest plantations are an important source of terrestrial carbon sequestration. The forest of Robinia pseudoacacia in the Yellow River Delta (YRD) is the largest artificial ecological protection forest in China. However, more than half of the forest has appeared different degrees of dieback and even death since the 1990s. Timely and accurate estimation of the forest aboveground biomass (AGB) is a basis for studying the carbon cycle of forests. Light Detecting and Ranging (LiDAR) has been proved to be one of the most powerful methods for forest biomass estimation. However, because of an irregular and overlapping shape of the broadleaved forest canopy in a growing season, it is difficult to segment individual trees and estimate the tree biomass from airborne LiDAR data. In this study, a new method was proposed to solve this problem of individual tree detection in the Robinia pseudoacacia forest based on a combination of the Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) with the Backpack-LiDAR. The proposed method mainly consists of following steps: (i) at a plot level, trees in the UAV-LiDAR data were detected by seed points obtained by an individual tree segmentation (ITS) method from the Backpack-LiDAR data; (ii) height and diameter at breast height (DBH) of an individual tree would be extracted from UAV and Backpack LiDAR data, respectively; (iii) the individual tree AGB would be calculated through an allometric equation and the forest AGB at the plot level was accumulated; and (iv) the plot-level forest AGB was taken as a dependent variable, and various metrics extracted from UAV-LiDAR point cloud data as independent variables to estimate forest AGB distribution in the study area by using both multiple linear regression (MLR) and random forest (RF) models. The results demonstrate that: (1) the seed points extracted from Backpack-LiDAR could significantly improve the overall accuracy of individual tree detection (F = 0.99), and thus increase the forest AGB estimation accuracy; (2) compared with MLR model, the RF model led to a higher estimation accuracy (p < 0.05); and (3) LiDAR intensity information selected by both MLR and RF models and laser penetration rate (LP) played an important role in estimating healthy forest AGB.     

全站仪在森林生态系统大样地定位中的应用

本文介绍全站仪在森林大样地内进行放样测量、树高测量及胸径测量的原理与方法,通过误差传播定律分析其测量的精度,结果表明:全站仪在森林生态调查定位中是可行的.放样距控制点300m的方格顶点的精度可达到1/100,树高测量的精度可达到1/250,胸径测量的精度可达到1/50,可满足林业调查规范的要求.对于日后森林生态系统大样...     

激光雷达森林参数反演研究进展

激光雷达通过发射激光能量和接收返回信号的方式,来获取高精度的森林空间结构和林下地形信息。全波形激光雷达通过记录返回信号的全部能量,得到亚米级植被垂直剖面;离散回波激光雷达记录的单个或多个回波,表示来自不同冠层的回波信号。星载激光雷达一般采用全波形或光子计数激光剖面系统,仅能获取卫星轨道下方的单波束或多波束数据,用于区域/全球范围的森林垂直结构及变化观测。机载激光雷达多采用离散回波或全波形激光扫描系统,能够获取飞行轨迹下方特定视场范围内的扫描数据,用于林分/区域范围的森林结构观测。地基激光雷达多采用离散回波激光扫描系统,获取以测站为中心的球形空间内扫描数据,用于单木/样地范围的森林结构观测。激光雷达单木因子估测方法可分为CHM单木法、NPC单木法和体元单木法3类。CHM单木法通过局部最大值识别树冠顶点,采用区域生长或图像分割算法识别树冠边界或树冠主方向,NPC单木法一般通过空间聚类或形态学算法识别单木,体元单木法在3维体元空间采用区域生长或空间聚类算法识别树冠。根据激光雷达冠层高度分布可以估测林分因子,冠层高度分布特征来自于离散点云或全波形。多时相激光雷达可用于森林生长量、生物量变化等监测,以及森林采伐、灾害等引起的结构变化监测。随着激光雷达技术的发展,它将在森林调查、生态环境建模等生产与科学研究领域中得到更为广泛的应用。     

机载激光雷达平均树高提取研究

为了研究机载激光雷达(LiDAR)树高提取技术,以山东省泰安市徂徕山林场为实验区,于2005年5月进行了机载LiDAR数据获取和外业测量.通过对LiDAR点云数据的分类处理,分别得到了试验区的地面点云子集、植被点云子集和高程归一化的植被点云子集.基于高程归一化的植被点云子集计算了上四分位数处的高度,与实地测量的数据进行了比较,并结合中国森林调查规程进行了实用性分析.结果表明:对于较低密度的点云数据,使用分位数法可以较好地进行林分平均高的估计;机载激光雷达技术对树高估计是可行的,精度都高于87%,总体平均精度为90.59%,其中阔叶树的精度高于针叶树.该试验精度可以满足中国二类森林调查规程中平均树高因子的一般商品林和生态公益林的精度要求,对国有商品林小班的调查精度要求(5%)存在一点差距,需要在国有商品林区进一步开展验证工作.对本试验区而言,已经可以满足其作为森林公园生态公益林的调查要求.     

The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity

Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence.     

Estimates of forest structure parameters from GLAS data and multi-angle imaging spectrometer data

Quantitative estimates of forest vertical and spatial distribution using remote sensing technology play an important role in better understanding forest ecosystem function, forest carbon storage and the global carbon cycle. Although most remote sensing systems can provide horizontal distribution of canopies, information concerning the vertical distribution of canopies cannot be detected. Fortunately, laser radars have become available, such as GLAS (Geoscience laser altimeter system). Because laser radar can penetrate foliage, it is superior to other remote sensing technologies for detecting vertical forest structure and has higher accuracy. GLAS waveform data were used in this study to retrieve average tree height and biomass in a GLAS footprint area in Heilongjiang Province. However, GLAS data are not spatially continuous. To fill the gaps, MISR (multi- angle imaging spectrometer) spectral radiance was chosen to predict the regional continuous tree height by developing a multivariate linear regression model. We compared tree height estimated by the regression model and GLAS data. The results confirmed that estimates of tree height and biomass based on GLAS data are considerably more accurate than estimates based on traditional methods. The accuracy is approximately 90%. MISR can be used to estimate tree height in continuous areas with a robust regression model. The R², precision and root mean square error of the regression model were 0.8, 83% and 1 m, respectively. This study provides an important reference for mapping forest vertical parameters.     

Single tree biomass modelling using airborne laser scanning

Accurate forest biomass mapping methods would provide the means for e.g. detecting bioenergy potential, biofuel and forest-bound carbon. The demand for practical biomass mapping methods at all forest levels is growing worldwide, and viable options are being developed. Airborne laser scanning (ALS) is a promising forest biomass mapping technique, due to its capability of measuring the three-dimensional forest vegetation structure. The objective of the study was to develop new methods for tree-level biomass estimation using metrics derived from ALS point clouds and to compare the results with field references collected using destructive sampling and with existing biomass models. The study area was located in Evo, southern Finland. ALS data was collected in 2009 with pulse density equalling approximately 10 pulses/m². Linear models were developed for the following tree biomass components: total, stem wood, living branch and total canopy biomass. ALS-derived geometric and statistical point metrics were used as explanatory variables when creating the models. The total and stem biomass root mean square error per cents equalled 26.3% and 28.4% for Scots pine (Pinus sylvestris L.), and 36.8% and 27.6% for Norway spruce (Picea abies (L.) H. Karst.), respectively. The results showed that higher estimation accuracy for all biomass components can be achieved with models created in this study compared to existing allometric biomass models when ALS-derived height and diameter were used as input parameters. Best results were achieved when adding field-measured diameter and height as inputs in the existing biomass models. The only exceptions to this were the canopy and living branch biomass estimations for spruce. The achieved results are encouraging for the use of ALS-derived metrics in biomass mapping and for further development of the models.