Mixed-effects modeling for tree height prediction models of Oriental beech in the Hyrcanian forests

Height–diameter relationships are essential elements of forest assessment and modeling efforts.In this work,two linear and eighteen nonlinear height–diameter equations were evaluated to find a local model for Oriental beech（Fagus orientalis Lipsky） in the Hyrcanian Forest in Iran.The predictive performance of these models was first assessed by different evaluation criteria： adjusted R^2（R^2_（adj））,root mean square error（RMSE）,relative RMSE（%RMSE）,bias,and relative bias（%bias） criteria.The best model was selected for use as the base mixed-effects model.Random parameters for test plots were estimated with different tree selection options.Results show that the Chapman–Richards model had better predictive ability in terms of adj R^2（0.81）,RMSE（3.7 m）,%RMSE（12.9）,bias（0.8）,%Bias（2.79） than the other models.Furthermore,the calibration response,based on a selection of four trees from the sample plots,resulted in a reduction percentage for bias and RMSE of about 1.6–2.7%.Our results indicate that the calibrated model produced the most accurate results.

Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height

Background:Deep Learning Algorithms(DLA)have become prominent as an application of Artificial Intelligence(Al)Techniques since 2010.This paper introduces the DLA to predict the relationships between individual tree height(ITH)and the diameter at breast height(DBH).Methods:A set of 2024 pairs of individual height and diameter at breast height measurements,originating from 150 sample plots located in stands of even aged and pure Anatolian Crimean Pine(Pinus nigra J.F.Arnold ssp.pallasiana(Lamb.)Holmboe)in Konya Forest Enterprise.The present study primarily investigated the capability and usability of DLA models for predicting the relationships between the ITH and the DBH sampled from some stands with different growth structures.The 80 different DLA models,which involve different the alternatives for the numbers of hidden layers and neuron,have been trained and compared to determine optimum and best predictive DLAs network structure.Results:It was determined that the DLA model with 9 layers and 100 neurons has been the best predictive network model compared as those by other different DLA,Artificial Neural Network,Nonlinear Regression and Nonlinear Mixed Effect models.The alternative of 100#neurons and 9#hidden layers in deep learning algorithms resulted in best predictive ITH values with root mean squared error(RMSE,0.5575),percent of the root mean squared error(RMSE%,4.9504%),Akaike information criterion(AIC,-998.9540),Bayesian information criterion(BIC,884.6591),fit index(Fl,0.9436),average absolute error(AAE,0.4077),maximum absolute error(max.AE,2.5106),Bias(0.0057)and percent Bias(Bias%,0.0502%).In addition,these predictive results with DLAs were further validated by the Equivalence tests that showed the DLA models successfully predicted the tree height in the independent dataset.Conclusion:This study has emphasized the capability of the DLA models,novel artificial intelligence technique,for predicting the relationships between individual tree height and the diameter at breast height that can be required information for the management of forests.

New approach to calculating tree height at the regional scale

Background:Determining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research.The geometric-optical mutual shadowing(GOMS)model can be used to invert the forest canopy structural parameters at the regional scale.However,this method can obtain only the ratios among the horizontal canopy diameter(CD),tree height,clear height,and vertical CD.In this paper,we used a semi-variance model to calculate the CD using high spatial resolution images and expanded this method to the regional scale.We then combined the CD results with the forest canopy structural parameter inversion results from the GOMS model to calculate tree heights at the regional scale.Results:The semi-variance model can be used to calculate the CD at the regional scale that closely matches(mainly with in a range from-1 to 1 m)the CD derived from the canopy height model(CHM)data.The difference between tree heights calculated by the GOMS model and the tree heights derived from the CHM data was small,with a root mean square error(RMSE)of 1.96 for a 500-m area with high fractional vegetation cover(FVC)(i.e.,forest area coverage index values greater than 0.8).Both the inaccuracy of the tree height derived from the CHM data and the unmatched spatial resolution of different datasets will influence the accuracy of the inverted tree height.And the error caused by the unmatched spatial resolution is small in dense forest.Conclusions:The semi-variance model can be used to calculate the CD at the regional scale,together with the canopy structure parameters inverted by the GOMS model,the mean tree height at the regional scale can be obtained.Our study provides a new approach for calculating tree height and provides further directions for the application of the GOMS model.

Comparing Tree Heights among Montane Forest Blocks of Kenya Using LiDAR Data from GLAS

This study was designed to use LiDAR data to research tree heights in montane forest blocks of Kenya. It uses a completely randomised block design to asses if differences exist in forest heights: 1) among montane forest blocks, 2) among Agro ecological zones (AEZ) within each forest block and 3) between similar AEZ in different forest blocks. Forest height data from the Geoscience Laser Altimeter System (GLAS) on the Ice Cloud and Land Elevation Satellite (ICE-SAT) for the period 2003-2009 was used for 2146 circular plots, of 0.2 - 0.25 ha in size. Results indicate that, tree height is largely influenced by Agro ecological conditions and the wetter zones have taller trees in the upper, middle and lower highlands. In the upper highland zones of limited human activity, tree heights did not vary among forest blocks. Variations in height among forest blocks and within forest blocks were exaggerated in regions of active human intervention.

Formulae of Tree Height Curve and Volume Curve Derived from Theory of Column Buckling

In this paper, the new formulae of tree height curve and volume cdrie were derived from the theory of column buckling. They were applied to artificial Pine (Pinus sylvestris var. mongolica) and Larch (Larix principis rupprechtii). The results demonsed that the new formulae wee more effeCtive and precise than conventional formulae of height curve and volume curve.

Predicting the Growth in Tree Height for Building Sunshine in Residential District

Residential greening constitutes a significant portion of the urban environment. Trees, as the largest entities in the tree-shrub-herb greening system, are the best choice for residential afforestation. Hence, tree arrangement in green space between buildings is significant, for which may exert negative impact on building sunshine. This study takes He Qingyuan residential area in Beijing as a case study to predict the growth in tree height between buildings to meet good sunshine requirements. The procedures were draw as follows: 1) models including building layout and trees were built using computer-aided design (Auto CAD). Afterwards, according to tree crown shape, tree height limits were determined for the same building layout;2) and after that, the growth in tree height was predicted using the nonlinear height-diameter functions to meet the good sunshine requirements. The results allow us to determine which trees to plant between buildings in that the designers can predict the effects of future tree growth on building sunshine.

Estimating Pinus palustris tree diameter and stem volume from tree height,crown area and stand-level parameters

Accurate and efficient estimation of forest growth and live biomass is a critical element in assessing potential responses to forest management and environmental change. The objective of this study was to develop models to predict longleaf pine tree diameter at breast height （dbh） and merchantable stem volume （V） using data obtained from field measurements. We used longleaf pine tree data from 3,376 planted trees on 127 permanent plots located in the U.S. Gulf Coastal Plain region to fit equations to predict dbh and V as functions of tree height （H） and crown area （CA）. Prediction of dbh as a function of H improved when CA was added as an additional independent variable. Similarly, predic- tions of V based on H improved when CA was included. Incorporation of additional stand variables such as age, site index, dominant height, and stand density were also evaluated but resulted in only small improvements in model performance. For model testing we used data from planted and naturally-regenerated trees located inside and outside the geographic area used for model fitting. Our results suggest that the models are a robust alternative for dbh and V estimations when H and CA are known on planted stands with potential for naturally-regenerated stands, across a wide range of ages. We discuss the importance of these models for use with metrics derived from remote sensing data.

Extraction and analysis of tree canopy height information in high-voltage transmission-line corridors by using integrated optical remote sensing and LiDAR

Traditional inspection methods cannot quickly and accurately monitor tree barriers and safeguard the transmission lines.To solve these problems,in this study,we proposed a rapid canopy height information extraction method using optical remote sensing and LiDAR,and used UAV optical imagery with LiDAR to monitor the height of trees in a university and a high-voltage transmission line corridor in the Ningxia region.The results showed that the relative error of tree height extraction using UAV optical images was less than 5%,and the lowest relative error was 0.11%.The determination coefficient R^(2) between the optical image tree height extraction results and the measured tree height was 0.97,thus indicating a high correlation for both.In the field of tree barrier monitoring,the determination coefficient R^(2) of tree height extracted using airborne LiDAR point cloud,and canopy height model(CHM)and of the measured tree height were 0.947 and 0.931,respectively.The maximum and minimum relative error in tree height extraction performed using point cloud was 2.91%and 0.2%,respectively,with an extraction accuracy of over 95%.The experimental results demonstrated that it is feasible to use UAV optical remote sensing and LiDAR in monitoring tree barriers and tree height information extraction quickly and accurately,which is of great significance for the risk assessment and early warning of tree barriers in transmission-line corridors.

Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery

Forests of the Sierra Nevada(SN)mountain range are valuable natural heritages for the region and the country,and tree height is an important forest structure parameter for understanding the SN forest ecosystem.There is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial resolution.In this study,we presented a method to map wall-to-wall forest tree height(defined as Lorey’s height)across the SN at 70-m resolution by fusing multi-source datasets,including over 1600 in situ tree height measurements and over 1600 km^(2) airborne light detection and ranging(LiDAR)data.Accurate tree height estimates within these airborne LiDAR boundaries were first computed based on in situ measurements,and then these airborne LiDAR-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System(GLAS)footprints.Finally,the random forest algorithm was used to model the SN tree height from these GLAS tree heights,optical imagery,topographic data,and climate data.The results show that our fine-resolution SN tree height product has a good correspondence with field measurements.The coefficient of determination between them is 0.60,and the root-mean-squared error is 5.45 m.

基于无人机低空遥感数据的时序动态生物量计算研究

为解决林分自然生长和人工剪伐修枝引起的单木特征参数变化所造成的生物量变化问题,采用地基激光雷达数据(TLS)和无人机激光雷达数据(UAV-LS)为数据源,通过单木分割的方法,以地基激光雷达数据提供的高精度数字高程模型为基础,提升无人机激光雷达数据的单木召回率;基于无人机激光雷达数据进行单木树高的提取及一致性评定,通过优化的生物量模型,利用树高参数计算2022年和2023年各树种单木生物量。结果表明,联合地面激光雷达数据可以将无人机激光雷达数据的单木召回率从60.0%提升至73.1%;对2022年、2023年树高参数提取得到近两年树木自然生长、修剪状况;对树高一致性评定得到一致性相关系数(Concordance correlation coefficient,CCC)为0.98,均方根误差(RMSE)为1.12 m;对生物量计算得到近两年各树种单木生物量、林分生物量,2022年、2023年单位面积生物量分别为77.39、81.56 t/hm^(2)。研究证实在研究区通过无人机低空遥感数据获取树高时序动态计算各树种单木生物量可行,可以掌握林分自然生长和人工修剪引起的生物量变化。

含竞争指标的广义可加混合效应树高-胸径模型

广义可加混合效应模型(GAMM)兼具参数模型与非参数模型的优点,同时适于处理多层次分组数据。通过运用广义可加混合效应模型模拟胸径及树高之间关系,加入竞争因子作为辅助变量,并与传统非线性混合效应模型进行比较,能够为建立树高曲线及提高模型精度提供新方法。根据吉林省汪清林业局金沟岭林场2块100 m×100 m次生混交林样地中的实测单木数据,按照7∶3比例随机划分建模与验证数据。随机效应设定为林木分级,辅助变量选择大于对象木胸高断面积之和(B_(AL))或简单竞争指数(Hegyi指数,H_(EG)),根据随机效应的设定位置共构建15个广义可加混合效应模型,对照模型以Logistic及Richard方程为基础模型,共构建6个非线性混合效应树高-胸径模型。结果表明:所有广义可加混合效应模型均能较好地描述自变量与树高之间的关系,决定系数(R^(2))为0.8897~0.8998,相对均方根误差(R_(RMSE))为17.87%~18.74%,平均绝对误差(M_(AE))为1.7881~1.8745 m,赤池信息量(A_(IC))为4120.42~4162.23,均优于相同自变量下的非线性混合模型,R^(2)平均提高0.005,相对均方根误差、平均绝对误差、赤池信息量分别平均降低0.46%、0.0587 m、41.49。对于验证数据的预测可以看出,模型5具有最小的预测相对均方根误差,为20.28%,同时具有最小的预测平均绝对误差,为2.1038 m。但部分广义可加混合效应模型的预测表现略差于非线性混合模型。综合考虑参数与非参数估计显著性、模型估计精度及预测能力,所有模型中的最优模型为模型5,即以B_(AL)为辅助变量,考虑唯一全局平滑函数并以具有相同扭曲程度的分组水平平滑函数为基础添加随机效应。竞争因子选择B AL作为辅助变量能够提升树高模型的精度,而选择Hegyi指数为辅助变量的促进效果不明显。研究建立的广义可加混合效应树高胸径模型相较于传统非线性混合效应模型具有更高的估计精度及预测效果,B AL适宜作为树高模型的辅助变量来反映林木竞争状况的影响。

基于混合效应和分位数回归的温带针阔混交林树高与胸径关系研究

【目的】基于非线性回归和广义模型构建不同分位数回归和混合效应的树高预测方程,并对比分析非线性模型、不同分位点(τ=0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9)模型、广义模型及非线性混合效应模型的拟合效果和预测精度,为研究林分生长和收获提供理论依据。【方法】本研究以吉林蛟河地区针阔混交林的主要树种(红松、色木槭、紫椴和水曲柳)为研究对象,基于21.12 hm2样地数据,首先在11个广泛使用的树高方程基础模型中选定基础模型;其次探究林分变量对树高的影响并构建含林分变量的广义模型;最后在基础模型和广义模型的基础上,构建分位数模型,同时考虑样方效应对树高的影响,构建混合效应模型。【结果】(1)各树种均以Richards模型拟合精度更高,且具有生物学意义,选定为基础模型;考虑林分变量与树高的相关性以及模型收敛性,加入优势木高建立的广义模型能显著提高拟合效果。(2)各树种均为中位数τ=0.5时模型拟合效果最佳,且与非线性回归预测精度相近,红松、色木槭、紫椴和水曲柳最高R^(2)值分别为0.811、0.809、0.724和0.617,广义中位数回归预测能力得到进一步提高,R^(2)值分别为0.891、0.874、0.858和0.627。(3)混合效应模型相对其他模型能显著提高预测精度,其中基础混合模型略优于广义混合模型,4个树种R^(2)值达到0.937、0.919、0.906和0.643,表明包含样方效应的混合模型能得到更准确更稳定的预测结果。【结论】与传统方法建立的基础模型和广义模型以及两者的中位数回归模型相较,基于非线性混合效应构建的树高-胸径模型预测精度更高,其中基于基础混合效应构建的吉林蛟河地区混交林树高-胸径模型更具优越性和稳定性。

不同类型肥料对重庆地区“长林”系列油茶3个品种幼树生长的影响

[目的]为获得重庆地区油茶幼树生长的最佳肥料配方,对定植后的2年生油茶“长林”系列3个无性系幼树进行为期3年的施肥试验,筛选出适宜油茶幼树生长的最佳施肥比例,为提高油茶经济效益奠定基础。[方法]设置4个处理,即每年11月施用复合肥(C_(0.5),0.50 kg/株),复合肥+油茶专用有机肥(C_(0.25)O_(0.5),无机肥0.25 kg/株,有机肥0.50 kg/株),油茶专用有机肥(O_(1),1.00 kg/株)以及空白对照(CK),测定油茶幼树的树高、地径及冠幅等指标来比较和分析肥料类型间的差异。[结果]施肥促进油茶高生长,但影响不显著(P>0.05),其中以复合肥+油茶专用有机肥效用最好,与CK相比,长林3号、长林4号、长林40号增长量分别为38.46%、10.77%、22.22%;施肥显著影响油茶地径、冠幅生长(P<0.05),以复合肥+油茶专用有机肥处理增长量最大,与CK相比,地径增长量分别为125.33%、47.76%、12.87%,冠幅增长量分别为30.30%、13.00%、46.75%。[结论]复合肥与油茶专用有机肥配施对“长林”系列油茶提质增效具有积极作用。

红松半同胞家系生长性状变异及优良家系和单株的筛选

在辽宁省本溪满族自治县清河城实验林场国家红松良种基地,以2007年营建的15年生红松半同胞子代测定林为研究对象,选择参试家系171个(其中包括当地生产苗作为对照),造林株行距2 m×3 m,设计为10株小区,9次重复;2022年11月份,测定试验林所有存活木的树高、胸径,以树高、胸径、单株材积等生长性状为评价指标,计算各指标的变异系数、家系遗传力、遗传增益、现实增益;采用多目标决策法、隶属函数法筛选优良家系和优良单株。结果表明:参试家系各测定指标差异均达极显著水平(P<0.01),各指标变异系数变化范围为9.69%~35.01%、家系各指标遗传力变化范围为0.64~0.78、家系单株遗传力变化范围为0.14~0.45,说明参试家系具有较大的性状变异和较高的遗传力。利用隶属函数法筛选出优良家系17个,入选家系的胸径均值5.69 cm、树高均值12.92 m、单株材积均值0.04 m^(3),分别是当地生产苗(对照)胸径的1.14倍、树高的1.13倍、单株材积的1.44倍;17个优良家系的现实遗传增益,胸径为3.21%、树高为4.54%、单株材积为9.37%。在优良家系的基础上筛选出50株优良单株,入选单株胸径平均值为6.6 cm、树高平均值为15.1 m、单株材积平均值为0.0573 m^(3)。

电子森林罗盘仪的研发与试验

树高和树木位置是森林经营决策中重要的指标,常用于估计森林生长量、树龄、材积、生物量和碳储量等立木参数,其精度对立木质量的评价及森林生长的预测分析影响较大。为解决当前森林调查仪器价格昂贵、移动不便、测量周期长、人力耗损大等问题,基于三维电子罗盘和激光测距雷达研发了一种电子森林罗盘仪。该仪器设计了嵌入式软硬件,开发了与之配套的Android端应用,实现了树高和树木位置数据测量的电子化和数字化。为验证仪器的精准度与效率,选取59株立木进行试验。结果表明:在精准度上,方位角的绝对误差均值为4.5°,树高的相对精度为96.95%;在作业效率上,该仪器约为全站仪的2倍。该仪器携带方便、制造成本低、内外业一体化,能够满足国家森林资源连续清查中的测量精度要求,在森林资源调查中具有广阔的应用前景。

基于机载激光雷达点云数据和Catboost算法的杉木单木蓄积量估测研究

选取福建顺昌县洋口国有林场6块杉木标准地内200株杉木的激光雷达点云数据和地面调查数据,基于机载激光雷达点云数据生成的冠层高度模型,运用局域极大值算法检测树冠顶点,提取树高;采用标记极值的分水岭算法估测冠幅面积,将估测的树高和冠幅面积结合单木蓄积量真值,构建基于Catboost算法的单木蓄积量估测模型。结果表明:使用局域极大值算法估测树高,R^(2)为0.91,RMSE为0.81 m;采用标记极值的分水岭算法估测冠幅面积,R^(2)为0.81,RMSE为1.18 m^(2);采用Catboost算法构建单木蓄积量估测模型R^(2)为0.934。因此,机载激光雷达点云数据可以有效估测树高和树冠面积,采用Catboost算法能够实现杉木单木蓄积量的估测,为高精度反演森林蓄积量提供新的思路。

南方典型红壤侵蚀区马尾松林立木生物量无人机遥感估测

以南方典型红壤侵蚀区长汀县河田镇为例,结合无人机与激光雷达产生的点云数据优势,通过局部最大值和分水岭算法反演单木树高(H)和冠层半径(R_(c)),拟合以H和R_(c)为变量组合的异速生长方程,得到以新冠层参数为底的马尾松立木生物量模型。结果表明:提取树高的决定系数(R^(2))和均方根误差(RMSE)分别为0.93和0.49 m;计算冠层半径的R2和RMSE分别为0.88和0.64 m;估算立木生物量的R^(2)和RMSE分别为0.89和3.37 kg。本研究通过无人机遥感影像定量参数并构建的异速生长方程中,以组合(H+R_(c))为底的异速生长方程估测马尾松林立木生物量的精度较高,可以有效估测马尾松林立木生物量,可为南方典型红壤侵蚀区马尾松林立木生物量准确估测提供参考。

树干高度耦合壁面热效应对城市街谷内污染扩散的影响研究

在半封闭的街道峡谷内,交通排放和二次污染物容易在通风不良的区域积聚,严重威胁人们的健康。在影响街道峡谷流场和污染物扩散特性的诸多因素中,太阳辐射引起的壁面热浮力以及不同树干高度对空气动力学的影响一直没有得到足够的重视。通过设置5种树干高度(0.18H、0.40H、0.62H、0.84H、1.06H,H为建筑高度)耦合4种壁面加热配置,研究不同树干高度(耦合树木遮阴效应)和墙体加热条件对城市街道峡谷内气流流动和污染物扩散的影响。结果表明,不同树干高度及壁面热效应对城市街道峡谷内气流流动和污染物扩散有显著影响。当树干高度低于建筑物高度时,壁面加热产生的热浮力作用会降低街谷内污染物浓度并增强通风性能;当树干高度超过建筑物高度时,迎风面加热所产生的热浮力会对污染物扩散造成阻碍。采用全壁面加热能够实现更低的污染物积累。研究结果可为城市绿色设施的优化设计,实现对局部微气候环境和空气质量精准调控提供技术指导。

基于多视角三维航摄影像的树高提取方法比较

树高是一个重要的立木调查因子,传统树高测量方法是利用测高器人工测量,该方法调查速度慢,效率低下.重叠的立体像对,可以反映地物的高度信息,为大面积树高信息提取提供了可能.本研究采用大疆Phantom 4 RTK无人机获取研究区航摄影像并构建立体像对,分别从不同视角采用三维模型直接测量法、倾斜摄影点云提取法、冠层高度模型局部最大值法进行了树高信息提取.研究结果表明,三种方法林木检测率依次为:82.5%、76.8%、71.1%,测得的树高结果精度分别为:R2=0.730,RMSE=1.699;R2=0.804,RMSE=1.459;R2=0.766,RMSE=1.548.三维模型测量法获取的树高提取率最高,倾斜摄影点云提取的树高值与实测值的拟合度最高.研究结果利用航摄影像能够高效地提取树高信息,为林业研究调查提供一种低成本、高效率的方法.

黔中马尾松木荷混交林树高-胸径模型

【目的】建立马尾松Pinus massoniana-木荷Schima superba混交林树高-胸径模型,将树种作为哑变量引入模型,考虑模型残差空间自相关和异质性,为混交林树高-胸径模型构建和科学经营提供理论依据。【方法】基于贵州省开阳县马尾松-木荷混交林727组树高-胸径调查数据,构建普通最小二乘法模型(OLS)、广义可加模型(GAM)、线性混合模型(LMM)、地理加权回归模型(GWR)和地理加权回归克里格模型(GWRK)的树高-胸径全林木模型,在此基础上,将树种作为哑变量引入,选择全局莫兰指数(Moran’I)、局域Moran’I和组内方差分析5种模型残差空间自相关与空间异质性,并采用决定系数(R 2)、均方误差(MSE)和赤池信息准则(AIC)对模型进行评价。【结果】①马尾松-木荷混交林全林木基础模型的拟合精度从低到高依次为OLS、GAM、LMM、GWR、GWRK。②将树种作为哑变量引入模型后,各模型拟合精度均高于全林木基础模型。③OLS和GAM模型残差的全局Moran’I在α=0.05水平下显著(Z>1.96),局域Moran’I分布图中存在较多热点,表现出强烈的空间自相关。而LMM、GWR和GWRK模型残差全局Moran’I在α=0.05水平下不显著(−1.96≤Z≤1.96),且在局域Moran’I分布图中存在较多冷点,说明模型残差空间自相关已被消除。④5种模型残差的组内方差均表现随着滞后距离增大而增大的趋势,但GWR和GWRK模型具有更小的组内方差,能较好地降低模型残差空间的异质性。【结论】OLS和GAM模型拟合精度不高,并且不能消除模型残差空间自相关和异质性,因此不是用来建立树高-胸径模型的最佳选择。LMM、GWR和GWRK模型在提高模型拟合精度和降低空间自相关性方面表现良好,但GWR和GWRK模型在降低空间异质性方面显著,是最适合的树高-胸径模型。