Discovering forest height changes based on spaceborne lidar data of ICESat-1 in 2005 and ICESat-2 in 2019:a case study in the Beijing-Tianjin-Hebei region of China

Background:The assessment of change in forest ecosystems,especially the change of canopy heights,is essential for improving global carbon estimates and understanding effects of climate change.Spaceborne lidar systems provide a unique opportunity to monitor changes in the vertical structure of forests.NASA’s Ice,Cloud and Land Elevation Satellites,ICESat-1 for the period 2003 to 2009,and ICESat-2(available since 2018),have collected elevation data over the Earth’s surface with a time interval of 10 years.In this study,we tried to discover forest canopy changes by utilizing the global forest canopy height map of 2005(complete global coverage with 1 km resolution)derived from ICESat-1 data and the ATL08 land and vegetation products of 2019(sampling footprints with 17 m diameter)from ICESat-2.Results:Our study revealed a significant increase in forest canopy heights of China’s Beijing-Tianjin-Hebei region.Evaluations of unchanging areas for data consistency of two products show that the bias values decreased significantly from line-transect-level(−8.0 to 6.2 m)to site-level(^(−1).5 to 1.1 m),while RMSE values are still relatively high(6.1 to 15.2 m,10.2 to 12.0 m).Additionally,58%of ATL08 data are located in‘0m’pixels with an average height of 7.9 m,which are likely to reflect the ambitious tree planting programs in China.Conclusions:Our study shows that it is possible,with proper calibrations,to use ICESat-1 and-2 products to detect forest canopy height changes in a regional context.We expect that the approach presented in this study is potentially suitable to derive a fine-scale map of global forest change.

Forest height mapping using inventory and multi-source satellite data over Hunan Province in southern China

Background:Accurate mapping of forest canopy heights at a fine spatial resolution over large geographical areas is challenging.It is essential for the estimation of forest aboveground biomass and the evaluation of forest ecosystems.Yet current regional to national scale forest height maps were mainly produced at coarse-scale.Such maps lack spatial details for decision-making at local scales.Recent advances in remote sensing provide great opportunities to fill this gap.Method:In this study,we evaluated the utility of multi-source satellite data for mapping forest heights over Hunan Province in China.A total of 523 plot data collected from 2017 to 2018 were utilized for calibration and validation of forest height models.Specifically,the relationships between three types of in-situ measured tree heights(maximum-,averaged-,and basal area-weighted-tree heights)and plot-level remote sensing metrics(multispectral,radar,and topo variables from Landsat,Sentinel-1/PALSAR-2,and SRTM)were analyzed.Three types of models(multilinear regression,random forest,and support vector regression)were evaluated.Feature variables were selected by two types of variable selection approaches(stepwise regression and random forest).Model parameters and model performances for different models were tuned and evaluated via a 10-fold cross-validation approach.Then,tuned models were applied to generate wall-to-wall forest height maps for Hunan Province.Results:The best estimation of plot-level tree heights(R2 ranged from 0.47 to 0.52,RMSE ranged from 3.8 to 5.3 m,and rRMSE ranged from 28%to 31%)was achieved using the random forest model.A comparison with existing forest height maps showed similar estimates of mean height,however,the ranges varied under different definitions of forest and types of tree height.Conclusions:Primary results indicate that there are small biases in estimated heights at the province scale.This study provides a framework toward establishing regional to national scale maps of vertical forest structure.

Forest biomass is strongly shaped by forest height across boreal to tropical forests in China

Forest height is a major factor shaping geographic biomass patterns,and there is a growing dependence on forest height derived from satellite light detecting and ranging(LiDAR)to monitor large-scale biomass patterns.However,how the relationship between forest biomass and height is modulated by climate and biotic factors has seldom been quantified at broad scales and across various forest biomes,which may be crucial for improving broad-scale biomass estimations based on satellite LiDAR.Methods We used 1263 plots,from boreal to tropical forest biomes across China,to examine the effects of climatic(energy and water avail-ability)and biotic factors(forest biome,leaf form and leaf phenol-ogy)on biomass-height relationship,and to develop the models to estimate biomass from forest height in China.Important Findings(i)Forest height alone explained 62%of variation in forest biomass across China and was far more powerful than climate and other biotic factors.(ii)However,the relationship between biomass and forest height were significantly affected by climate,forest biome,leaf phenology(evergreen vs.deciduous)and leaf form(needleleaf vs.broadleaf).among which,the effect of climate was stronger than other factors.The intercept of biomass-height relationship was more affected by precipitation while the slope more affected by energy availability.(iii)When the effects of climate and biotic factors were considered in the models,geographic biomass patterns could be well predicted from forest height with an r2 between 0.63 and 0.78(for each forest biome and for all biomes together).For most biomes,forest biomass could be well predicted with simple models includ-ing only forest height and climate.(iv)We provided the first broad-scale models to estimate biomass from forest height across China,which can be utilized by future LiDAR studies.(v)our results suggest that the effect of climate and biotic factors should be carefully considered in models estimating broad-scale forest biomass patterns with satellite LiDAR.

Underlying topography and forest height estimation from SAR tomography based on a nonparametric spectrum estimation method with low sidelobes

The underlying topography and forest height play an indispensable role in many fields,including geomorphology,civil engineering construction,forest investigation,and the modeling of natural disasters.As a new microwave remote sensing technology with three-dimensional imaging capability,synthetic aperture radar(SAR)tomography(TomoSAR)has already been proven to be an important tool for underlying topography and forest height estimation.Many spectrum estimation methods have now been proposed for TomoSAR.However,most of the commonly used methods are susceptible to noise and inevitably produce sidelobes,resulting in a reduced accuracy for the inversion of forest structural parameters.In this paper,to solve this problem,a nonparametric spectrum estimation method with low sidelobes-the G-Pisarenko method-is introduced.This method performs a logarithmic operation on the covariance matrix to obtain the main scattering characteristics of the objects of interest while suppressing the noise as much as possible.The effectiveness of the proposed method is demonstrated by the use of both simulated data and P-band airborne SAR data from a tropical forest region in Gabon,Africa.The results show that the proposed method can reduce the sidelobes and improve the estimation accuracy for the underlying topography and forest height.

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.

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.

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。

基于遥感的森林碳储量显式计算模型

面向国家“双碳”目标和国际碳交易市场需求,陆地生态系统的固碳现状和未来固碳潜力亟须研究。森林是陆地生态系统中重要的碳库,目前基于地面观测的清查方法工作量大且抽样统计结果难以评价,基于卫星遥感反演的方法缺乏理论解释且普适性差。本文从单木级森林碳储量模型出发,提出一种基于遥感的森林碳储量显式计算模型。首先使用图像分辨率、森林郁闭度和森林高度3个关键变量构建森林碳储量显式计算模型,并对模型变量和参数进行误差分析;然后利用单木的生长特性,仿真不同饱和度的森林场景,从理论上解算不同树种的模型参数,并检验模型参数的精度与稳定性;最后验证模型在不同空间尺度、饱和度森林场景下的精度、稳健性和适用性。本文提出的森林碳储量模型解决了现有卫星遥感反演缺乏理论解释性和适用性差的难题,可实现大范围森林碳储量高分辨率制图和全球森林碳汇动态监测。

基于多源遥感数据的福建省海岸带县域森林生态质量评价

【目的】森林生态质量是从生态角度反映森林质量的内涵,对森林的生态功能和生态服务、生长状况以及自我调节功能进行综合测度,以期提高森林改善生态环境、维护生态平衡的能力。【方法】利用中、高分辨率多源遥感数据,获取大范围尺度下能表征森林生态质量的关键指标信息,在此基础上,分析福建省海岸带40个县域的森林生态质量状况。首先,基于2016年2 m分辨率多源遥感数据为主要数据源,利用双层尺度集模型选定最佳分割尺度,多分类器集成算法集,自动选择最优分类算法进行森林类型提取,并结合2020年Sentinel遥感数据及森林分类产品,更新2020年福建省海岸带森林类型精细分布图;其次,利用LandTrendr算法衍生的干扰开始时间特征推算现存森林年龄,通过GEDI冠层高度产品获取海岸带森林冠层高度分布图;在以上关键森林质量指标提取基础上,对遥感手段获取的8项森林生态质量评价指标进行主成分分析,获得福建省海岸带县域森林生态质量综合评价结果。【结果】2020年福建海岸带40个县域约50%的县域森林生态质量处于优良水平,其中仙游县、闽侯县、南安市、霞浦县、柘荣县及厦门海沧区、思明区、集美区、同安区等森林生态质量为优;森林生态质量较差的县域有惠安县、秀屿区、石狮市、福安市、平潭实验区、诏安县。【结论】结合中、高分辨率多源遥感数据,能够发挥遥感大范围监测优点,客观评价福建省海岸带40个县域的森林生态质量;研究结果表明2020年福建沿海县域森林生态质量还存在较大的提升空间,需要针对存在的问题采取相应森林管理措施提升森林生态质量。

河南栾川老君山麻栎林群落结构与多样性动态研究

在河南老君山国家级自然保护区的麻栎林设置样地,分别于2018年、2023年进行了群落结构特征及其多样性调查.结果表明:5 a间,麻栎林的乔木密度下降,种类组成由52科86属110种减少至41科72属102种,常见种数量由70增加至79,物种丰富度指数增加,但各层次优势种、建群种相对稳定,群落具有一定相似性;胸径级与树高级的个体分布均表现出倒“J”形;多样性指数以灌木层的最大,灌木层与草本层均表现为增加,而乔木层表现为下降.研究表明,5 a间,群落结构特征与多样性发生了一定程度的变化,但总体上表现出较好的稳定性.

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。

结合修正后的全球生态系统动态调查冠层高度的森林地上生物量模型优化——以福建省为例

森林地上生物量(Above Ground Biomass,AGB)是衡量森林生态系统碳存储、能量流动和生物多样性的关键指标,对于气候变化研究和森林资源管理至关重要。福建省地处多云多雨的亚热带,地形和森林类型复杂,森林地上生物量估算难度大。为提升森林地上生物量估算效果,将最新星载激光雷达数据全球生态系统动态调查(GEDI)、Landsat以及Sentinel系列卫星等多源遥感数据进行集成和综合利用,通过Landsat影像计算的林龄对GEDI_V27冠层高度产品进行优化,结合优化后的MGEDI_V27冠层高度产品,建立传统遥感特征结合冠层高度的极端梯度提升模型(XGBoost)生物量反演模型,实现了福建省森林地上生物量的有效估算与制图。研究结果表明:(1)通过林龄优化后的GEDI冠层高度精度评价结果为R^(2)=0.67,RMSE=2.24m;(2)通过递归特征消除算法对三种森林类型进行特征优选,得到10个遥感特征,其中,三种森林类型最重要的遥感特征均为森林冠层高度,并且对比评价了在包含传统遥感特征因子的情况下有无冠层高度对于模型精度的影响,结果表明,在冠层高度因子参加特征构建时,森林AGB回归分析的精度明显提高,证实了冠层高度在生物量估算中具有显著的重要性;(3)研究得到的福建省森林AGB范围为0.001—363.331Mg/hm^(2),整体精度评价结果为R^(2)=0.75,RMSE=17.34Mg/hm^(2),2020年全省AGB总量为8.22亿Mg,平均值为101.24Mg/hm^(2)。通过优化GEDI中的森林冠层高度,并且结合传统遥感特征,可以实现对福建省森林地上生物量的精确估算和监测,研究成果有助于区域森林碳汇的评估。

利用星载激光雷达数据校正森林区DEM误差

在植被区,通过光学遥感或InSAR技术生产的DEM产品不能反映真实林下地形高度。森林区DEM误差主要是植被高引起的系统偏差,植被覆盖度和地形是森林区DEM的主要误差来源。新一代星载激光雷达可以提供大量高精度林下地形控制点产品,为森林区DEM误差的纠正提供了新的契机。鉴于此,文章提出基于机器学习框架下顾及植被覆盖及地形因素的林区DEM误差校正方法。首先,获取高精度星载激光雷达地形控制点与DEM的地形残差;其次,利用光学遥感数据、SAR遥感数据及DEM产品数据计算与植被覆盖和地形有关的特征参数;最后,联合这些特征参数与获取的地形残差点分别建立不同类型DEM产品误差校正模型。选取位于美国田纳西州和北卡罗来纳州交界处的山地林区作为本研究的实验区。研究结果表明,相对原始DEM,校正高程误差后的DEM精度提升超过40%,有效校正了林区DEM误差。

立地环境和林分结构驱动的辽东栎林生长与模拟

【目的】定量研究宁夏六盘山区辽东栎(Quercus mongolica)林的树高和胸径生长对气候、地形、土壤、林分结构等多种因子变化的响应规律,在此基础上构建多因子耦合模型,准确模拟和预测气候等因子对森林生长的影响。【方法】依据宁夏六盘山区环境梯度,2021—2022年对79块辽东栎天然次生林样地进行每木调查,利用上外包线法确定林分平均树高和平均胸径对立地环境和林分结构等因子的响应关系和函数类型,通过连乘构建多因子耦合模型框架并基于实测数据拟合模型参数,用于模拟气候变化对辽东栎林生长的影响。【结果】同时影响六盘山区辽东栎林平均树高和平均胸径生长的主要因子是林龄、林分密度、年均降水量、年均气温、土壤厚度。平均树高和平均胸径随林龄增大呈缓慢−快速−缓慢的生长变化,随林分密度增大呈先稳定后加速下降的变化,随土壤厚度和年均降水量增大呈先快速增加后渐趋平稳的变化,随年均气温升高呈先快速上升后快速下降的单峰变化趋势。根据林分平均树高和平均胸径对各单因子的响应函数构建了二者的多因子耦合模型,预测精度较高(R2>0.70)。对年均气温升高1.5℃的情景模拟结果表明,整个六盘山区的辽东栎林平均树高和平均胸径生长速率都会降低,其降低幅度在气候冷湿的六盘山区南部分别为10%和7%,在气候暖湿的六盘山区中部分别为15%和12%,在气候暖干的六盘山区北部分别为18%和13%。【结论】宁夏六盘山区辽东栎次生林的平均树高和平均胸径生长同时受气候、林分结构、立地环境等多因子的影响,建立反映多个主要因子(年均降水量、年均气温、林龄、林分密度、土壤厚度)影响的耦合生长模型可以准确预测气候变化和密度调控对树高和胸径生长的影响。未来的气温升高将会降低平均树高和平均胸径的生长,但降低幅度存在区域差异,表现为气温越高的区域降低幅度越大,需要分区采取不同的森林管理应对措施。研究结果可为预测气候变化影响和确定森林管理措施提供理论与技术支持。

广西-东盟多源遥感数据的森林高度反演及应用

森林生态系统在调节生态气候与碳循环方面发挥着重要作用,森林高度是衡量森林生态系统功能的重要参数。利用单一遥感数据获取森林冠层高度会受到多种制约。因此,本文使用星载激光雷达ICESat-2提供的高质量离散森林冠层高度点,结合Sentinel-1、Landsat 8及地形数据,采用随机森林方法建立不同影像特征组合森林冠层高度的回归模型,并分析各特征对森林高度反演的影响,最后将模型应用于广西森林冠层高度制图。试验结果表明,多源遥感数据可有效提高森林冠层高度反演精度,在所利用遥感数据中,特征重要性从大到小依次为光学特征、地形特征、SAR特征,“L8+SRTM+Sentinel-1+邻域均值”特征组合的反演精度最高,加入邻域均值特征进行森林冠层高度反演效果最佳,随机森林模型能精确绘制森林冠层高度。

湘西龙山县华桑人工林直径与树高结构研究

为掌握华桑中幼林直径和树高结构规律,给营林生产提供技术参考,在龙山县砂子坡国有林场跑马坪分场华桑10年生人工林中设置3块20 m×30 m的样地,调查样地内乔木树种的直径、树高等因子。结果表明:该林分是以华桑为优势树种、6个树种伴生而成的针阔混交林,结构合理;华桑顶端具有双主梢生长特性,需在前期进行修枝整形;林分直径分布呈左偏的近似正态分布曲线,为典型的同龄林直径结构特征,林分处于中幼林阶段,需加强培育管理,直径分布服从Weibull分布和一元正态分布;林分树高分布呈左偏的近似正态分布曲线,树高分布服从Weibull分布和一元正态分布;树高与径阶之间存在正相关关系,即林分树高随着径阶的增加而增加,树高-径阶曲线可用y=2.729+0.659 d-0.017 d 2+0.0003 d 3表示。该林分为较为理想的中幼林林分结构,研究结果可为华桑人工林林分结构调整优化提供理论依据。

基于机载激光雷达和机器学习的林分平均胸径遥感估测

为探究不同模型对林分平均胸径的预测精度,使用贵州省桂花国有林场马厂工区同步获取的机载激光雷达点云数据和地面实测样地数据,通过提取样地水平的点云特征变量,采用方差膨胀因子分析和皮尔逊相关性检验进行自变量选择,建立机器学习模型估测样地平均胸径。结果表明:1)点云特征变量,如平均冠层高度和高度偏态与林分平均胸径有很强的相关性。2)机器学习模型(随机森林、支持向量机、最近邻算法)优于多元线性回归模型,其中,随机森林的拟合效果最好。随机森林的决定系数(R^(2))为0.71,均方根误差(RMSE)为2.50。3)通过柳杉纯林、针叶混交林、针阔混交林、马尾松纯林4种森林类型的林分平均胸径预测值与实际值差值,进一步证实随机森林模型精度最高,拟合效果最好。利用机载激光雷达点云数据提取点云特征变量,并构建基于机器学习算法的林分平均胸径估测模型是可行的,该方法的精度能满足森林资源调查的应用需求,可作为辅助林业调查工作的技术手段。

基于Landsat 8 OLI和资源3号立体像对数据的桉树森林蓄积量估测

【目的】探索Landsat8 OLI数据和立体数据在估算桉树森林蓄积量(forest stock volume,FSV)中的潜力,并且准确地估计桉树的FSV。【方法】以3幅Landsat8 OLI图像和资源3号立体数据为遥感数据源,并且结合少量地面调查数据实现了桉树FSV的遥感估计。研究中提取了三类遥感特征用于估计桉树FSV:第一类是包括植被指数和单波段反射率在内的光谱特征;第二类是基于Landsat 8 OLI图像的单波段提取的8种纹理特征;第三类是基于资源3号立体像对数据和开源的数字高程模型(digital elevation model,DEM)提取的冠层高度模型(canopy height model,CHM)。利用Boruta算法对三类遥感特征进行提取,之后建立了随机森林(random forest,RF)、K-最近邻(K-nearest neighbor,KNN)和支持向量机(support vector machine,SVM)3种机器学习模型以及传统的多源线性回归模型(multiple linear regression,MLR),并以决定系数(R2)、均方根误差(root mean square error,RMSE)和相对均方根误差(relative root mean square error,rRMSE)作为评价指标对模型结果进行评估。【结果】基于ZY-3立体像对数据和开源的DEM数据提取的CHM与桉树的FSV具有很强的相关性,Pearson相关系数达到了0.71。仅仅利用基于Landsat 8 OLI图像提取的光谱和纹理特征难以准确2地估计桉树的FSV,估测模型的R为0.29~0.38,rRMSE为35.65%~43.30%,存在严重的数据饱和问题。2当变量集中加入CHM后,模型的估测精度明显提高,R达到了0.64~0.66,rRMSE为25.74%~26.41%。【结论】使用Landsat 8 OLI数据估算桉树FSV时存在严重的数据饱和问题,并且使用空间分辨率为30 m的纹理特征难以有效地改善森林蓄积量的估计精度。利用资源3号立体像对数据和开源的DEM数据可以提取较为准确的CHM,并且所提取的CHM可以解决改善光学数据的饱和问题,从而提高桉树FSV的估计精度。

不同来源尾巨桉苗木造林生长的比较研究

为明确不同来源良种尾巨桉(Eucalyptus urophylla×E.grandis)无性系DH32-29苗木造林后幼龄阶段(1-3 a)的生长差异,为优质苗木的选择和提质增产增效提供理论依据,本研究以来源清楚的尾巨桉无性系DH32-29苗木(BG)和来源不清的尾巨桉无性系DH32-29苗木(NBG)造林林分为研究对象,比较研究广西西北部、中部及南部3个区域不同苗木造林林分的胸径、树高、生物量、碳储量及生产力的差异性。结果表明:林龄为2、3 a时,尾巨桉BG林分的平均胸径、单株生物量、单株碳储量及单株生产力均显著高于NBG林分(P<0.05);BG林分乔木层生物量、碳储量及生产力整体高于NBG林分,在林龄为1 a时差异较小,林龄为2 a生时差异增大,但未达显著水平(P>0.05),林龄为3 a生时差异达极显著水平(P<0.01)。因此,采用来源清楚的优质苗木造林增产效果更明显。

Models and form factors for stand volume estimation in natural forest ecosystems: a case study of Katarniaghat Wildlife Sanctuary (KGWS), Bahraich District, India

In view of the difficulties in stand volume estimation in natural forests, we derived real form factors and models for volume estimation in these types of forest ecosystems, using Katarniaghat Wildlife Sanctuary as a case study. Tree growth data were obtained for all trees （dbh 〉10 cm） in 4 plots （25 × 25 m） randomly located in each of three strata selected in the forest. The form factor calculated for the stand was 0.42 and a range of 0.42 0.57 was estimated for selected species （density 〉10）. The parameters of model variables were consistent with general growth trends of trees and each was statistically significant. There was no significant difference （p〉0.05） between the observed and predicted volumes for all models and there was very high correlation between observed and predicted volumes. The output of the performance statistics and the logical signs of the regression coefficients of the models demonstrated that they are useful for volume estimation with minimal error. Plotting the biases with respect to considerable regressor variables showed no meaningful and evident trend of bias values along with the independent variables. This showed that the models did not violate regression assumptions and there were no heteroscedacity or multiculnarity problems. We recommend use of the form factors and models in this ecosystem and in similar ones for stand and tree volume estimation.