An approach to estimate tree height using PolInSAR data constructed by the Sentinel-1 dual-pol SAR data and RVoG model

We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.

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.

Ecoregional height-diameter models for Scots pine in Turkiye

Ecoregion-based height-diameter models were developed in the present study for Scots pine(Pinus sylves-tris L.)stands in Turkiye and included several ecological factors derived from a pre-existing ecoregional classification system.The data were obtained from 2831 sample trees in 292 sample plots.Ten generalized height–diameter models were developed,and the best model(HD10)was selected according to statistical criteria.Then,nonlinear mixed-effects modeling was applied to the best model.The R2 for the generalized height‒diameter model(Richards function)modified by Sharma and Parton is 0.951,and the final model included number of trees,dominant height,and diameter at breast height,with a random parameter associated with each ecoregion attached to the inverse of the mean basal area.The full model predictions using the nonlinear mixed-effects model and the reduced model(HD10)predictions were compared using the nonlinear sum of extra squares test,which revealed significant differences between ecore-gions;ecoregion-based height–diameter models were thus found to be suitable to use.In addition,using these models in appropriate ecoregions was very important for achieving reliable predictions with low prediction errors.

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.

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.

Complex Least Squares Adjustment to Improve Tree Height Inversion Problem in PolInSAR

At present,the principal data processing methods involving complex observations are based on two strategies according to characteristics of the observation process,i.e.,step-by-step and direct resolution.However,these strategies have some limitations,e.g.they cannot consider statistical observation error information,redundant observations and so on.This paper applies least squares methods to complex data processing to extend surveying adjustment theory from real to complex number space.We compared the two adjustment criteria for a complex domain in a quantitative way.In order to understand the effectiveness of complex least squares,tree height inversion from PolInSAR data is taken as an example.We firstly established both a complex adjustment function model and a stochastic model for PolInSAR tree height inversion,and then applied the complex least squares method to estimate tree height.Results show that the complex least squares approach is reliable and outperforms other classic tree height retrieval methods;the method is simple and easy to implement.

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.

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.

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.

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.

Effects of climate factors on the height increment of poplar protec-tion forest in the riverbank field

Based on the data of stand investigation and stem analysis, the effects of climate factors on the poplar protection forest increment in the riverbank field of the Dalinghe and Xiaolinghe rivers of Liaoning Province, China were studied by step-wise regression procedure and grey system theories and methods. A regression model reflecting the correlation between the height increment of poplar protection forest and climatic factors was developed. The order of grey relevance for the effect of climatic factors on the height increment of poplar protection forest is: light>water>heat, and it could be interpreted that the poplar increment was mainly influenced by light factor, water factor, and heat factor. This result will provide scientific basis for the in-tensive cultivation and regeneration of the poplar protection forest in riverbank field in similar regions in China.

适于GlobalAllomeTree国际数据平台的标准化中国主要树种树高-胸径方程研建

GlobalAllomeTree作为共享异速方程的国际网络平台,逐渐受到全球高度关注。当前,为促进该项国际合作,针对当前该平台缺乏中国主要树种生长异速方程的现状,系统性更新标准化中国主要树种树高-胸径方程。由于树冠和下部灌木及草丛遮挡,树高相对于胸径测量具有一定的难度,因此需要使用数学工具进行计算。选取了36个树种为材料构建树高-胸径关系方程,以全国主要树种的二元材积模型、各地区一元材积表为基础材料,以取样径阶为1 cm间隔所生成1692组树高-胸径数据作为建立方程样本,1238组外业调查数据为验证样本。建模结果表明:36个主要树种的1692组树高-胸径数据建立的全国通用性树高-胸径方程拟合相关系数(R2)为0.801,方程拟合结果较好,说明可以通过测定胸径,带入树高(H,m)-胸径(D,cm)方程(H=aDb)预估树高;对36个主要树种的树高-胸径方程进行拟合,决定系数R2值均大于0.916,平均误差(ME)、平均绝对误差(MAE)和均方根误差(RMSE)相对较小,方程整体精度较高,可广泛推广;将外业采集的1238组树高-胸径数据,根据36个主要树种树高-胸径方程拟合公式及参数估计值a、b进行方程精度验证,方程预测的平均相对误差为16.86%,在误差允许范围内,并且模型形式规范,可为GlobalAllomeTree平台用户提供科学参考。

14 nm工艺下基于H-Tree和clock mesh混合时钟树的研究与实现

在数字集成电路设计中,时钟信号是数据传输的基准,时钟信号作为数字芯片内部转换频率最高和布线距离最长的信号,也是数字芯片功耗的重要组成部分。为了优化数字芯片的功耗、功能和稳定性,在GF14 nm工艺下对时钟树进行优化设计,提出一种H-Tree和clock mesh相结合的混合时钟树结构的设计方法,通过clock mesh和clock spine的布局优化整体时钟树的性能和稳定性。仿真结果表明,该混合时钟树能够结构显著提升时钟树性能,有效减少布线长度、时钟偏移以及传播延迟,降低PVT等环境参数的影响。

Cloth simulation-based construction of pitfree canopy height models from airborne LiDAR data

Background:The universal occurrence of randomly distributed dark holes(i.e.,data pits appearing within the tree crown)in LiDAR-derived canopy height models(CHMs)negatively affects the accuracy of extracted forest inventory parameters.Methods:We develop an algorithm based on cloth simulation for constructing a pit-free CHM.Results:The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details.Our pitfree CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms,as evidenced by the lowest average root mean square error(0.4981 m)between the reference CHMs and the constructed pit-free CHMs.Moreover,our pit-free CHMs show the best performance overall in terms of maximum tree height estimation(average bias=0.9674 m).Conclusion:The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

Ecological Structure and Prediction Equations for Estimating Tree Age, and Dendometric Parameters of <i>Acacia senegal</i>in the Senegalese Semi-Arid Zone—Ferlo

The development of equations to predict tree height, crown diameter, crown depth from stem diameter of a tree species enables arborists, researchers, and urban forest managers to model costs and benefits, analyze alternative management scenarios, and determine the best management practices for sustainable forests. The objective of this study was to develop regression prediction models for tree age, tree height, crown diameter, crown ratio and crown depth for A. senegal growing in Ferlo, in the northern Senegal. Four plantations of different years old (ISRA, 10 years old plantations, Ndodj, 8 years old plantations, Boulal, 5 years old plantations and Déali, 4 years old plantations) were selected. The following dendometric variables: crown height, crown diameter, stem diameter at the breast height, stem basal diameter (at 0.30 m) and the height from the tree base to first branch were measured on a total of 489 trees. The results suggested that the ecological structure of the different year old A. Senegal plantation revealed a bell-shaped form with left dissymmetric distribution indicating a predominance of individuals with small diameter at breast height. Allometry study of A. Senegal showed highly significant positive correlations (p = 0.00) between stem diameter at breast height, stem basal diameter, tree height, crown diameter and crown depth. Positive correlations were also found between crown diameter, tree height and crown height. Prediction models derived from these relationships can be used to estimate the tree height, stem diameter at breast height and crown depth from crown diameter with greater precision. As for A. Senegal age estimation, the established model is not strong as it can explain only 49.1% of the age variation.

Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes

Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing datasets.However,combining the advantages of active and passive data sources to improve estimation accuracy remains challenging.Here,we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral information.Over 60 km^(2) of drone light detection and ranging(LiDAR)data and 1,370 field plot measurements,covering the four major forest types of China(coniferous forest,sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and tropical broadleaf forest),were collected together with Sentinel-2 images to evaluate the proposed approach.The results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum values.Compared with structural attributes used alone,combining structural and spectral information can improve the estimation accuracy of AGB,increasing R^(2) by about 10%and reducing the root mean square error by about 22%;the accuracy of the proposed approach can yield a R^(2) of 0.7 in different forests types.The proposed approach performs the best in coniferous forest,followed by sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and then tropical broadleaf forest.Furthermore,the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression,artificial neural networks,and Random Forest.The proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.

Stand structure and height-diameter relationship of a degraded Populus euphratica forest in the lower reaches of the Tarim River, Northwest China

Understanding stand structure and height-diameter relationship of trees provides very useful information to establish appropriate countermeasures for sustainable management of endangered forests. Populus euphratica, a dominant tree species along the Tarim River watershed, plays an irreplaceable role in the sustainable development of regional ecology, economy and society. However, as the result of climate changes and human activities, the natural riparian ecosystems within the whole river basin were degraded enormously, particularly in the lower reaches of the river where about 320 km of the riparian forests were either highly degraded or dead. In this study, we presented one of the main criteria for the assessment of vitality of P. euphrafica forests by estimating the defoliation level, and analyzed forest structure and determined the height-diameter （height means the height of a tree and diameter means the diameter at breast height （DBH） of a tree） relationship of trees in different vitality classes （i.e. healthy, good, medium, senesced, dying, dead and fallen）. Trees classified as healthy and good ac- counted for approximately 40% of all sample trees, while slightly and highly degraded trees took up nearly 60% of total sample trees. The values of TH （tree height） and DBH ranged from 0-19 m and 0-125 cm, respectively. Trees more than 15 m in TH and 60 cm in DBH appeared sporadically. Trees in different vitality classes had different distribution patterns. Healthy trees were mainly composed more of relatively younger trees than of degraded tress. The height-diameter relationships differed greatly among tress in different vitality classes, with the coefficients ranging from 0.1653 to 0.6942. Correlation coefficients of TH and DBH in healthy and good trees were higher than those in trees of other vitality classes. The correlation between TH and DBH decreased with the decline of tree vitality. Our results suggested that it might be able to differentiate degraded P. euphratica trees from healthy trees by determining the height-diameter correlation coefficient, and the coefficient would be a new parameter for detecting degradation and assessing sustainable management of floodplain forests in arid regions. In addition, tree vitality should be taken into account to make an accurate height-diameter model for tree height prediction.

HRM: H-tree based reconfiguration mechanism in reconfigurable homogeneous PE array

In order to accommodate the variety of algorithms with different performance in specific application and improve power efficiency,reconfigurable architecture has become an effective methodology in academia and industry.However,existing architectures suffer from performance bottleneck due to slow updating of contexts and inadequate flexibility.This paper presents an H-tree based reconfiguration mechanism(HRM)with Huffman-coding-like and mask addressing method in a homogeneous processing element(PE)array,which supports both programmable and data-driven modes.The proposed HRM can transfer reconfiguration instructions/contexts to a particular PE or associated PEs simultaneously in one clock cycle in unicast,multicast and broadcast mode,and shut down the unnecessary PE/PEs according to the current configuration.To verify the correctness and efficiency,we implement it in RTL synthesis and FPGA prototype.Compared to prior works,the experiment results show that the HRM has improved the work frequency by an average of 23.4%,increased the updating speed by 2×,and reduced the area by 36.9%;HRM can also power off the unnecessary PEs which reduced 51%of dynamic power dissipation in certain application configuration.Furthermore,in the data-driven mode,the system frequency can reach 214 MHz,which is 1.68×higher compared with the programmable mode.

Survivorship, attained diameter, height and volume of three Paulownia species after 9 years in the southern Appalachians, USA

Little is known of the tree and stand dynamics of varied species of planted Paulownia left unmanaged until harvest in the southeastern United States.We sought to remedy this lack of information needed by land managers to make informed decisions by investigating diff erences in survivorship,attained diameter breast height(DBH),diameter at ground level,total height,tree volume and standlevel volume yields of planted P.elongata,P.fortunei,and P.tomentosa in the cool-moist environment of the southern Appalachian Mountains.After 9 years,combined-species survivorship was only 27.3%.Low survivorship was likely related to several inclement weather events.P.fortunei was signifi cantly smaller in DBH and total height.Three combined-species stem(bole)volume models were developed as functions of(1)DBH squared,(2)the product DBH squared and total height,and(3)the product diameter ground line squared and total height.Mean total volume production of unmanaged stands was greatest for P.elongata and P.fortunei 4 years after planting;by the 9th year,total volume of P.elongata was greater than the other two species.Results of our study provide managers information on productivity of three species of Paulownia that can be used for estimating plantation yields.