基于无人机可见光影像的华山松人工林计测参数提取

针对无人机在森林资源监测中的便携性特点,利用无人机RGB三波段影像进行森林计测参数(株数、树高及蓄积量)的提取及精度验证。以华山松人工林为研究对象,以无人机RGB影像为主要信息源,在前期进行5块0.08hm~2华山松人工林标准地单木定位的基础上,采用冠层高度模型(CHM)最大值法和点云分割方法,提取华山松人工林计测参数,建立无人机RGB影像的华山松人工林单木二元材积模型。研究结果表明:1)采用CHM最大值分割法较点云分割方法精度高,单木株数分割精度分别为87.17%和80.79%;提取得到的树高与其地面实测所得树高的R~2相比较,使用CHM方法,R~2为0.71;而使用点云算法,R~2为0.69。2)基于CHM最大值法提取的单株冠幅和树高所建立的二元材积模型,其决定系数(R~2)为0.94,均方根误差(RMSE)为0.033 8m~3;与基于云南省华山松人工林二元材积表的标准地实测蓄积量调查结果相比,基于无人机RGB数据的5块标准地蓄积量监测精度分别为79.72%,81.64%,83.57%,82.49%,80.28%,平均精度达81.54%。基于无人机RGB影像的华山松人工林在森林计测参数提取中,CHM最大值分割法优于点云分割,所建立的树高和冠幅二元材积模型,可为华山松单层人工林无人机遥感监测提供参考。     

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。     

基于无人机影像的树顶点和树高提取及其影响因素分析

对基于无人机影像生成的树冠高度模型(Canopy Height Model,CHM),采用局部最大值算法进行树顶点和树高提取的可行性进行了探讨。此外,还探讨了分辨率、窗口大小对于树顶点提取的影响。以密集的针阔混交林为样地,利用SfM(Structure from Motion)算法结合无人机影像对研究区进行三维重建,得到点云、数字表面模型(Digital Surface Model,DSM)、数字高程模型(Digital Elevation Model,DEM)等一系列三维数据并生成CHM。然后,对不同分辨率的CHM使用不同的平滑窗口大小、移动窗口大小组合进行树顶点的提取并对结果进行精度评价。当CHM分辨率为0.4m,平滑窗口大小为3×3像元,移动窗口大小为3×3像元时,树顶点的提取精度最高,F测度为77.08%。将基于该组合提取正确的37个树顶点对应的提取树高与实地测量得到的树高对比,R~2为0.966 9,RMSE为1.411 4m,rRMSE=10.69%。研究结果表明:利用无人机影像可以较好地提取复杂树林的树顶点和树高;基于局部最大值算法提取树顶点,需要根据实际情况确定CHM的分辨率、平滑窗口大小和移动窗口大小,以获得最佳提取结果。     

基于机载LiDAR的单木结构参数及林分有效冠的提取

【目的】基于机载激光雷达(LiDAR)数据提取单木树冠三维结构参数(树冠顶点位置、树高、冠幅和冠长),并在此基础上对林分有效冠进行提取,为进一步研究林分尺度上的有效冠结构及其动态提供依据,以更好掌握并改进林业经营措施。【方法】采用一定规则下的局部最大值窗口搜索树冠顶点,进行单木树冠顶点探测和单木树高提取;以树冠顶点为标记,利用标记控制分水岭分割算法提取单木冠幅;采用垂直方向点云高程检测方法获取枝下高位置,提取冠长;在标记控制分水岭分割出的树冠边界,提取树冠接触高,取平均值作为该样地的林分有效冠高。【结果】树冠分割正确率为88.5%;结合样地实测参数对提取值进行相关性分析,树高R~2=0.886 2,冠幅R~2=0.786 4,冠长R~2=0.800 0,树高、冠幅和冠长精度分别为90.34%、86.80%和89.90%;同一林分内单木接触高相对比较稳定,对提取的林分有效冠高进行单因素方差分析,无显著差异。【结论】基于机载LiDAR数据,采用可变大小的动态窗口搜索局部最大值点,能提高单木结构参数的提取精度;利用树冠顶点标记控制分水岭算法,将高空间分辨率航片作为辅助数据,可完成较高精度的单木冠幅提取;垂直方向点云高程检测方法可提取单木冠长;LiDAR点云数据可对林分有效冠进行提取,在同一林分中,不同样本数量对接触高提取的变异性影响不大,有效冠高大致相同。机载LiDAR数据具有良好的单木树冠三维结构参数提取能力,能够满足现代林业调查对单木结构参数提取的需要,实现对林分有效冠的提取。     

基于无人机遥感影像的DSM及遥感数据林分平均高提取

以河北省丰宁县西南部山区为研究区域,利用无人机遥感立体像对提取数字表面模型(DSM)。通过目视判读,在影像中无植被的区域设置校正点,利用校正点处DSM与DEM的值拟合校正因子对DSM进行高程校正,然后结合数字高程模型(DEM)提取树高并利用林地"一张图"数据求得各林地图斑的树高平均值。结果表明:经过高程校正并结合利用林地"一张图"数据的林分平均树高提取方法求得的结果可以直接作为林地变更调查的补充,能实现对林分平均树高、树高等级等因子进行实时更新。     

无人机遥感影像速生桉林分参数自动化提取研究

在实景三维建模软件支持下,使用无人机遥感技术快速获取广西扶绥县龙头乡将军屯速生桉林影像,通过Pix4d软件对航摄数据进行自动化内业处理,获取数字正射影像成果(DOM)和数字地表模型(DSM)以及树冠高度模型。基于此树冠高度模型,提取速生桉林的株数、树高、郁闭度等森林参数,并进行精度分析。结果表明:株数精度验证指标(株数探测率、株数准确率、F参数)较优;树高估测值与树高实测值存在较强相关性;郁闭度参数准确率高达92.85%。该林分参数自动化提取方法,能够达到相关实践要求,可以在一定程度上替代人工实测,在人工林中具有广阔应用前景。     

基于地基激光雷达胸径提取的单木位置精确测量

[目的 ]为将单木位置匹配至更精确的树干中心处,本研究发展了一种基于地基激光雷达提取胸径中心位置的空间校正方法。[方法 ]对高密度地基激光雷达点云数据,使用霍夫变换的方法提取单木胸径及圆心点,以外业调查的胸径数据作为精度控制基础,再用空间点校正方法将外业测量的样地单木相对空间位置匹配至提取的单木胸径中心点处,最后通过数字冠层高度模型特征分析实现单木位置的地理位置匹配。[结果 ]以黑龙江佳木斯孟家岗林场为研究区,对比分析3种不同株数密度的落叶松样地,提取胸径误差在1 cm之内,高、中、低株数密度样地单木位置在空间点校正时胸径误差2 cm误差范围内位置正确匹配率分别为91%、92.5%、98.6%。全部匹配的外业调查单木相对空间位置误差控制在1 m之内,且与机载激光雷达数字冠层模型影像地理位置匹配误差在0.5 m内。[结论 ]基于地基激光雷达提取胸径匹配单木空间位置的方法,极大地提高了单木空间位置测量精度。此方法的发展,不仅为局部样地单木分割等提供精确位置信息,也为大范围遥感数据提供可靠地面基础位置验证数据,是可靠的单木位置测量和多源数据匹配方法。     

基于无人机影像与Mask R-CNN的单木树冠检测与分割

对无人机遥感影像中单木树冠进行检测与分割并获取树冠冠幅与树冠面积参数,可以为城市中不同场景下的林业资源调查提供高效快捷的途径。以银杏树为研究对象,创建基于无人机遥感影像的银杏单木树冠数据集,并使用卷积神经网络Mask R-CNN算法结合正射影像图对城市中不同场景下的树冠进行检测和树冠边界勾绘以获取相关树冠参数。结果表明,加入无人机银杏树冠影像数据集训练后的网络模型,可以较好地适用于城市不同场景下的银杏单木树冠检测与分割。在4个测试场景下的86棵银杏单木树冠目标总体查准率达到93.90%,召回率达到89.53%,F1-score为91.66%,平均精度均值为90.86%,且可以提取到较为准确的银杏单木树冠的冠幅值与树冠面积,预测冠幅的平均相对误差与均方根误差分别为7.50%和0.55,预测树冠面积的平均相对误差与均方根误差分别为11.15%和2.48。将无人机影像与深度学习算法结合应用到城市林业资源调查中,可以得到较为准确的树冠检测与轮廓分割结果,有效地提高城市林业资源调查效率。     

基于FCM和分水岭算法的无人机影像中林分因子提取

【目的】研究高精度小型无人机获取林分调查因子方法,将林分调查因子在低空无人机影像上识别并提取出来,获取树高、冠径等测树因子,建立林分因子测量方法,实现经济、高效、快捷、精准的森林资源调查和监测,及时掌握森林资源及相关林分因子的时空变化特征。【方法】以东北林业大学城市林业示范基地樟子松人工林为研究对象,以多旋翼无人机影像为数据源,基于FCM聚类算法和分水岭分割算法以及形态学运算、阈值分割、图像平滑、灰度化、二值化等一系列数字图像处理技术,提取樟子松人工林林分因子。FCM聚类算法和阈值分割法用于提取树梢标记图像,分水岭分割算法对树梢标记图像进行迭代处理从而获得单木树冠分割图像,根据单木树冠分割结果提取单木特征进而计算各林分因子值。【结果】在林地提取中,根据影像的颜色特征绿度分割成功地将林地部分与非林地部分分离开来,确定单木树冠分割范围。在单木树冠分割中,阈值分割法和FCM聚类算法均可有效将树梢标记从林地图像中提取出来;将基于标记的分水岭分割算法用于单木树冠分割取得较好效果,大多数单木树冠被单独分割出来,但某些区域仍然存在一定的欠分割或过分割问题。在林分因子提取中,提取的林分因子包括林分郁闭度、林地面积、立木株数和平均冠幅,其中林分郁闭度的测量精度为96.67%,林地面积的测量精度为81.23%,立木株数和平均冠幅的测量精度与单木树冠分割中的树梢提取方法(阈值分割法和FCM聚类算法)及分水岭分割中的2个参数(形态学腐蚀的结构元素大小和中值滤波的窗口大小)有关。针对2种树梢提取方法,分别进行参数组合试验,结果显示2种树梢提取方法使用适当参数组合所得各林分因子测量精度均在80%以上,平均测量精度均在90%以上,其中阈值分割法的最高平均测量精度为94.49%,FCM聚类算法的最高平均测量精度为93.17%。【结论】利用无人机拍摄的人工林影像进行森林资源调查,将先进的计算机科学技术和无人机技术应用到林业领域中,可有效提高森林资源调查的效率和精度。本研究提出的林分因子提取方法适用于高郁闭度林分,测量精度满足实际需求。     

天山云杉林无人机可见光影像树冠信息提取方法研究

高精度轻小型无人机森林树冠参数信息提取方法是森林资源监测和生态功能评估的重要基础。以新疆山地森林优势树种天山云杉为研究对象,在南山实习林场采集积雪背景下无人机遥感影像并进行预处理,对比分析3种方法(以光谱为特征空间的面向对象法、以光谱+纹理为特征空间的面向对象法、随机森林法)提取天山云杉林树冠参数信息的精度。结果表明:3种方法提取天山云杉林分郁闭度精度均高于93%,其中以光谱为特征空间的面向对象法最优,精度可达93.73%;3种方法自动提取单木树冠面积与目视解译结果的R~2均大于0.91,虽然随机森林法的统计指标最优,但面向对象法可获得完整的单木树冠闭合曲线,故以光谱+纹理为特征空间的面向对象法效果较优;面向对象法的最优分割尺度为29,纹理特征的加入会导致郁闭度提取精度降低0.66%,但会优化单木树冠面积提取效果。     

Multiple attribute decision making for individual tree detection using high-resolution laser scanning

A canopy height model (CHM) is a standard LiDAR-derived product for deriving relevant forest inventory information, including individual tree positions, crown boundaries and plant density. Several image-processing techniques for individual tree detection from LiDAR data have been extensively described in literature. Such methods show significant performance variability depending on the vegetation characteristics of the monitored forest. Moreover, over regions of high vegetation density, existing algorithms for individual tree detection do not perform well for overlapping crowns and multi-layered forests. This study presents a new time and cost-efficient procedure to automatically detect the best combination of the morphological analysis for reproducing the monitored forest by estimating tree positions, crown boundaries and plant density from LiDAR data. The method needs an initial calibration phase based on multi attribute decision making-simple additive weighting (MADM-SAW). The model is tested over three different vegetation patterns: two riparian ecosystems and a small watershed with sparse vegetation. The proposed approach allows exploring the dependences between CHM filtering and segmentation procedures and vegetation patterns. The MADM architecture is able to self calibrate, automatically finding the most accurate de-noising and segmentation processes over any forest type. The results show that the model performances are strongly related to the vegetation characteristics. Good results are achieved over areas with a ratio between the average plant spacing and the average crown diameter (TCI) greater than 0.59, and plant spacing larger than the remote sensing data spatial resolution. The proposed algorithm is thus shown a cost effective tool for forest monitoring using LiDAR data that is able to detect canopy parameters in complex broadleaves forests with high vegetation density and overlapping crowns and with consequent significant reduction of the field surveys, limiting them over only the calibration site.     

Mapping of snow-damaged trees based on bitemporal airborne LiDAR data

The use of multitemporal LiDAR data in forest-monitoring applications has been so far largely unexplored. In this work, we aimed to develop and test a simple method for the detection of snow-induced canopy changes by employing bitemporal LiDAR data acquired in 2006–2010. Our study area was located in southern Finland (62°N, 24°E), where snow-induced damage occurred in 10 permanent Scots pine (Pinus sylvestris)-dominated plots in winter 2009–2010. For the detection of snow-damaged crowns, we developed a ?CHM method by contrasting bitemporal LiDAR canopy height models (CHMs) and analyzing the resulting difference image, using binary image operations to extract the damaged crowns. Furthermore, we examined the structural and spatial factors that could explain snow damage at the individual tree level. The ?CHM method developed is based on two threshold parameters, i.e., the required height difference in the contrasted CHMs and the minimum plausible area of damage. When testing the performance of ?CHM method, we found that the plot-level omission error rates were 19–75%, while the commission error rates were 0–21%. Furthermore, the relative estimation accuracy of the damaged crown projection area (DCPA) ranged from ?16.4 to 5.4%. The observed damage could be explained at tree level by stem tapering, relative tree size, and local stand density. To conclude, ?CHM method developed constitutes a potential tool for the monitoring of structural canopy changes in the dominant tree layer if dense multitemporal LiDAR data are available.     

Vegetation and weather explain variation in crown damage within a large mixed-severity wildfire

The 2002 Biscuit Fire burned through more than 200,000 ha of mixed-conifer/evergreen hardwood forests in southwestern Oregon and northwestern California. The size of the fire and the diversity of conditions through which it burned provided an opportunity to analyze relationships between crown damage and vegetation type, recent fire history, geology, topography, and regional weather conditions on the day of burning. We measured pre- and post-fire vegetation cover and crown damage on 761 digital aerial photo-plots (6.25 ha) within the unmanaged portion of the burn and used random forest and regression tree models to relate patterns of damage to a suite of 20 predictor variables. Ninety-eight percent of plots experienced some level of crown damage, but only 10% experienced complete crown damage. The median level of total crown damage was 74%; median damage to conifer crowns was 52%. The most important predictors of total crown damage were the percentage of pre-fire shrub-stratum vegetation cover and average daily temperature. The most important predictors of conifer damage were average daily temperature and “burn period,” an index of fire weather and fire suppression effort. The median level of damage was 32% within large conifer cover and 62% within small conifer cover. Open tree canopies with high levels of shrub-stratum cover were associated with the highest levels of tree crown damage, while closed canopy forests with high levels of large conifer cover were associated with the lowest levels of tree crown damage. Patterns of damage were similar within the area that burned previously in the 1987 Silver Fire and edaphically similar areas without a recent history of fire. Low-productivity sites on ultramafic soils had 92% median crown damage compared to 59% on non-ultramafic sites; the proportion of conifer cover damaged was also higher on ultramafic sites. We conclude that weather and vegetation conditions — not topography — were the primary determinants of Biscuit Fire crown damage.     

Leaf traits in relation to crown development, light interception and growth of elite families of loblolly and slash pine

Crown architecture and size influence leaf area distribution within tree crowns and have large effects on the light environment in forest canopies. The use of selected genotypes in combination with silvicultural treatments that optimize site conditions in forest plantations provide both a challenge and an opportunity to study the biological and environmental determinants of forest growth. We investigated tree growth, crown development and leaf traits of two elite families of loblolly pine (Pinus taeda L.) and one family of slash pine (P. elliottii Mill.) at canopy closure. Two contrasting silvicultural treatments -- repeated fertilization and control of competing vegetation (MI treatment), and a single fertilization and control of competing vegetation treatment (C treatment) -- were applied at two experimental sites in the West Gulf Coastal Plain in Texas and Louisiana. At a common tree size (diameter at breast height), loblolly pine trees had longer and wider crowns, and at the plot-level, intercepted a greater fraction of photosynthetic photon flux than slash pine trees. Leaf-level, light-saturated assimilation rates (A(max)) and both mass- and area-based leaf nitrogen (N) decreased, and specific leaf area (SLA) increased with increasing canopy depth. Leaf-trait gradients were steeper in crowns of loblolly pine trees than of slash pine trees for SLA and leaf N, but not for A(max). There were no species differences in A(max), except in mass-based photosynthesis in upper crowns, but the effect of silvicultural treatment on A(max) differed between sites. Across all crown positions, A(max) was correlated with leaf N, but the relationship differed between sites and treatments. Observed patterns of variation in leaf properties within crowns reflected acclimation to developing light gradients in stands with closing canopies. Tree growth was not directly related to A(max), but there was a strong correlation between tree growth and plot-level light interception in both species. Growth efficiency was unaffected by silvicultural treatment. Thus, when coupled with leaf area and light interception at the crown and canopy levels, A(max) provides insight into family and silvicultural effects on tree growth.     

基于航空影像的树冠提取方法研究

用高空间分辨率遥感影像对单木树冠进行自动提取和轮廓描绘是近年来林业遥感领域研究的热点。本文以覆盖实验区的航空影像为例,采用面向对象的多尺度分割方法,逐级对植被对象、树冠对象和分树种模式进行分割,有效地提取了影像中的树冠大小信息。     

Spatial partitioning of biomass and diversity in a lowland Bolivian forest: Linking field and remote sensing measurements

Large-scale inventories of forest biomass and structure are necessary for both understanding carbon dynamics and conserving biodiversity. High-resolution satellite imagery is starting to enable structural analysis of tropical forests over large areas, but we lack an understanding of how tropical forest biomass links to remote sensing. We quantified the spatial distribution of biomass and tree species diversity over 4 ha in a Bolivian lowland moist tropical forest, and then linked our field measurements to high-resolution Quickbird satellite imagery. Our field measurements showed that emergent and canopy dominant trees, being those directly visible from nadir remote sensors, comprised the highest diversity of tree species, represented 86% of all tree species found in our study plots, and contained the majority of forest biomass. Emergent trees obscured 1–15 trees with trunk diameters (at 1.3 m, diameter at breast height (DBH)) ≥20 cm, thus hiding 30–50% of forest biomass from nadir viewing. Allometric equations were developed to link remotely visible crown features to stand parameters, showing that the maximum tree crown length explains 50–70% of the individual tree biomass. We then developed correction equations to derive aboveground forest biomass, basal area, and tree density from tree crowns visible to nadir satellites. We applied an automated tree crown delineation procedure to a high-resolution panchromatic Quickbird image of our study area, which showed promise for identification of forest biomass at community scales, but which also highlighted the difficulties of remotely sensing forest structure at the individual tree level.     

Measuring loblolly pine crowns with drone imagery through deep learning

In modeling forest stand growth and yield,crown width,a measure for stand density,is among the parameters that allows for estimating stand timber volumes.However,accurately measuring tree crown size in the field,in particu-lar for mature trees,is challenging.This study demonstrated a novel method of applying machine learning algorithms to aerial imagery acquired by an unmanned aerial vehi-cle (UAV) to identify tree crowns and their widths in two loblolly pine plantations in eastern Texas,USA.An ortho mosaic image derived from UAV-captured aerial photos was acquired for each plantation (a young stand before canopy closure,a mature stand with a closed canopy).For each site,the images were split into two subsets:one for training and one for validation purposes.Three widely used object detection methods in deep learning,the Faster region-based convolutional neural network (Faster R-CNN),You Only Look Once version 3 (YOLOv3),and single shot detection(SSD),were applied to the training data,respectively.Each was used to train the model for performing crown recogni-tion and crown extraction.Each model output was evaluated using an independent test data set.All three models were successful in detecting tree crowns with an accuracy greater than 93％,except the Faster R-CNN model that failed on the mature site.On the young site,the SSD model performed the best for crown extraction with a coefficient of determination(R2) of 0.92,followed by Faster R-CNN (0.88) and YOLOv3(0.62).As to the mature site,the SSD model achieved a R2 as high as 0.94,follow by YOLOv3 (0.69).These deep leaning algorithms,in particular the SSD model,proved to be successfully in identifying tree crowns and estimat-ing crown widths with satisfactory accuracy.For the pur-pose of forest inventory on loblolly pine plantations,using UAV-captured imagery paired with the SSD object deten-tion application is a cost-effective alternative to traditional ground measurement.     

Crown plasticity in mixed forests—Quantifying asymmetry as a measure of competition using terrestrial laser scanning

Interspecific competition is a key process determining the dynamics of mixed forest stands and influencing the yield of multispecies tree plantations. Trees can respond to competitive pressure from neighbors by crown plasticity, thereby avoiding competition. We employed a high-resolution ground-based laser scanner to analyze the 3-dimensional extensions and shape of the tree crowns in a near-natural broad-leaved mixed forest in order to quantify the direction and degree of crown asymmetry of 15 trees (Fagus sylvatica, Fraxinus excelsior, Carpinus betulus) in detail. We also scanned the direct neighbors and analyzed the distance of their crown centres and the crown shape with the aim to predict the crown asymmetry of the focal tree from competition-relevant attributes of its neighbors. It was found that the combination of two parameters, one summarizing the size of the neighbor (DBH) and one describing the distance to the neighbor tree (HD), was most suitable for characterizing the strength of the competitive interaction exerted on a target tree by a given neighbor. By summing up the virtual competitive pressure of all neighbors in a single competitive pressure vector, we were able to predict the direction of crown asymmetry of the focal tree with an accuracy of 96° on the full circle (360°).The competitive pressure model was equally applicable to beech, ash and hornbeam trees and may generate valuable insight into competitive interactions among tree crowns in mixed stands, provided that sufficiently precise data on the shape and position of the tree crowns is available. Multiple-aspect laser-scanning proved to be an accurate and practicable approach for analyzing the complex 3-dimensional shape of the tree crowns, needed to quantify the plasticity of growth processes in the canopy. We conclude that the laser-based analysis of crown plasticity offers the opportunity to achieve a better understanding of the dynamics of canopy space exploration and also may produce valuable advice for the silvicultural management of mixed stands.     

基于树高和树冠因子的立木材积与地上生物量相容模型研究

【目的】无人机机载激光雷达能够准确地测定单木、林分乃至大尺度森林结构参数(树高和树冠因子)。为应用无人机激光雷达技术准确估测森林蓄积量、生物量和碳储量提供计量依据和技术支撑。【方法】以150株实测马尾松生物量样本数据为研究对象,采用非线性回归估计方法和度量误差联立方程组方法,分析立木材积和地上生物量与树高、树冠因子的相关性,并在此基础上研究建立基于树高和树冠因子的立木材积与地上生物量相容模型。【结果】单株材积和地上生物量与树高因子的相关性最为紧密,其次才是树冠因子;基于树高和冠幅因子的二元材积和地上生物量模型预估精度较高,达到92%以上,再考虑冠长因子的三元模型预估精度改进不大;基于树高和冠幅因子的二元立木材积与地上生物量相容模型估计效果更好,相对于一元相容模型系统而言,二元相容模型拟合效果有较大幅度提高,预估精度达到92%以上。【结论】采用度量误差联立方程组方法可以有效解决基于树高和树冠因子的立木材积与地上生物量相容问题,并且预估精度达到92%以上,所建二元立木材积与地上生物量相容模型可为应用激光雷达技术反演森林蓄积量和生物量提供计量依据。