基于机载激光雷达的崇礼冬奥核心区林分地上生物量反演

doi:10.11707/j.1001-7488.20221004

Abstract

Abstract:

Object: This study was implemented to develop the stand aboveground biomass model with stable structure based on light detection and ranging(LiDAR) data, considering the ordinary least squares, mixed effects and Bayesian parameter estimation method, and then discussed the selection of the optimal biomass prediction model with the aims to provide a scientific basis for biomass modeling method and biomass estimation and to provide a technical support for achieving the "double carbon" target and biomass model calculation in the core area of the Winter Olympics. Method: Based on the LiDAR data and field measurement of 62 sample plots distributed across the Larix principis-rupprechtii and Betula platyphylla forest stands in the core areas of the Winter Olympics, ordinary least squares (OLS), mixed effects and Bayesian biomass models were established by variable screening.Determination coefficient (R²), root mean square error(RMSE), residual error and total relative error(TRE) were used to evaluate the model, and reserve-one crossover method was used to verify the accuracies of the models. Result: A total of 20 LiDAR variables with high correlations were filtered out, and 3 independent variables were finally entered into the models. The best fitting was the Logistic mixed effect model(RMSE = 22.99 t·hm^-2, R² = 0.768, TRE = 6.08%). After establishing the model by tree species, the accuracy of larch model was improved(RMSE = 22.92 t·hm^-2, R² = 0.795, TRE = 7.45%), and the accuracy of birch model decreased(RMSE = 23.34 t·hm^-2, R² = 0.440, TRE = 4.35%). Using the trained model, the biomass of Chongli Winter Olympic core area was predicted and mapped. Conclusion: As for the estimation of the stand aboveground biomass based on the LiDAR and field measurement data, the nonlinear model was superior to the linear model. The nonlinear mixed effect model with age group as random effects might have the highest prediction accuracy of biomass. Bayesian estimation may be greatly affected by prior conditions and might have further discussion values although with a small sample size in this study.

Key words: arborne LiDAR, forest aboveground biomass, mixed effect model, Bayesian model

CLC Number:

Xingjing Chen,Linyan Feng,Yuchao Zhang,Qingwang Liu,Zhaohui Yang,Liyong Fu,Jinhua Bai. Inversion of Aboveground Biomass in the Core Area of Chongli Winter Olympics Based on Airborne LiDAR[J]. Scientia Silvae Sinicae, 2022, 58(10): 35-46.

Figures/Tables 13

Table 1

Fig.1

Table 附表

Airborne LiDAR variable meter"

变量 Variable	名称 Name	原理 Principle
x₁	高度平均绝对偏差 Average absolute deviation of height	$v=\frac{\sum_\limits{i=1}^n\left(\left\|z_i-\bar{z}\right\|\right)}{n}$，其中，z_i为每一统计单元内第i个点的高度值，${\bar z}$为每一统计单元内所有点的平均高度，n为每一统计单元内的总点数 $v=\frac{\sum_\limits{i=1}^n\left(\left\|z_i-\bar{z}\right\|\right)}{n}$, where z_i is the height of the i point in each statistical unit, ${\bar z}$ is the average height of all points in each statistical unit, n is the total number of points in each statistical unit
x₂	冠层高度起伏率 Canopy height fluctuation rate	$v=\frac{\operatorname{mean}-\min }{\max -\min }$，其中，mean为每一统计单元内所有点的平均高度，min为每一统计单元内所有点的最小高度值，max为每一统计单元内所有点的最大高度值 $v=\frac{\operatorname{mean}-\min }{\max -\min }$，where, mean is the average height of all points in each statistical unit, min is the minimum height of all points in each statistical unit, and max is the maximum height of all points in each statistical unit
x₃-x₁₇	累计高度百分位数(AIH) Cumulative height percentile(AIH)	某一统计单元内，将其内部所有归一化的激光雷达点云按高度进行排序并计算所有点的累积高度，每一统计单元内x%的点所在的累积高度，即为该统计单元的累积高度百分位数。 In a statistical unit, all normalized LiDAR point clouds within it are sorted by height and the cumulative height of all points is calculated. The cumulative height of x% points in each statistical unit is the percentile of the cumulative height of the statistical unit.
x₁₈	累积高度百分位数四分位数间距 Cumulative height percentile quartile spacing	V=AIH75%-AIH25%
x₁₉	高度三次幂平均值 Average height of cubic power	$V=\sqrt[3]{\frac{\sum_\limits{i=1}^n Z_i^3}{n}}$, 其中，z_i为每一统计单元内第i个点的高度值，n为每一统计单元内的总点数 $V=\sqrt[3]{\frac{\sum_\limits{i=1}^n Z_i^3}{n}}$, where z_i is the height of point i in each statistical unit and n is the total number of points in each statistical unit
x₂₀	变异系数 Coefficient of variation	$V=\frac{Z_{\text {std }}}{Z_{\text {mean }}} \times 100 \%$, 其中，Z_std为每一统计单元内所有点高度值的标准差，Z_mean为每一统计单元内所有点的平均高度 $V=\frac{Z_{\text {std }}}{Z_{\text {mean }}} \times 100 \%$, where Z_std is the standard deviation of the height of all points in each statistical unit, and Z_mean is the average height of all points in each statistical unit
x₂₁	高度百分位数四分位数间距 Height percentile quartile spacing	V=Elcv75%-Elcv25%
x₂₂	高度峰度 Height kurtosis	$\text { Kurtosis }=\frac{\frac{1}{n} \sum_\limits{i=1}^n\left(z_i-\bar{z}\right)^4}{\sigma^4}$, 其中，z_i为每一统计单元内第i个点的高度值，${\bar z}$为每一统计单元内所有点的平均高度，n为每一统计单元内的总点数，σ为统计单元内点云高度分布的标准差 $\text { Kurtosis }=\frac{\frac{1}{n} \sum_\limits{i=1}^n\left(z_i-\bar{z}\right)^4}{\sigma^4}$，where z_iis the height of the first point in each statistical unit, ${\bar z}$ is the average height of all points in each statistical unit, n is the total number of points in each statistical unit, σ is the standard deviation of the height distribution of point clouds in each statistical unit
x₂₃	高度中位数绝对偏差的中位数 Median of absolute deviation of height median	高度中位数绝对偏差的中位数 The median of absolute deviation of height median
x₂₄	高度最大值 Maximum height	某一统计单元内，所有点的Z值的最大值 In a statistical unit, the maximum Z value of all points
x₂₅	高度最小值 Minimum height	某一统计单元内，所有点的Z值的最小值 Minimum Z value for all points in a statistical unit
x₂₆	高度平均值 Average height	某一统计单元内，所有点的Z值的平均值 In a statistical unit, the average Z value of all points
x₂₇	高度中位数 Height median	某一统计单元内，所有点的Z值的中位数 In a statistical unit, the median of Z value of all points
x₂₈-x₄₂	高度百分位数(Elcv) Height percentile (Elcv)	某一统计单元内，将其内部所有归一化的激光雷达点云按高度进行排序，然后计算每一统计单元内x%的点所在的高度，即为该统计单元的高度百分位数 In a statistical unit, all normalized LiDAR point clouds inside it are sorted by height, and then the height of x% points in each statistical unit is calculated, which is the height percentile of the statistical unit
x₄₃	高度偏斜度(偏态) Height skewness (skewness)	某一统计单元内，所有点的Z值分布的对称性, $\text { Skewness }=\frac{\frac{1}{n} \sum_\limits{i=1}^n\left(z_i-\bar{z}\right)^3}{\sigma^3}$, 其中，z_i为每一统计单元内第i个点的高度值，n为每一统计单元内的总点数 The symmetry of Z-value distribution of all points in a statistical unit, $\text { Skewness }=\frac{\frac{1}{n} \sum_\limits{i=1}^n\left(z_i-\bar{z}\right)^3}{\sigma^3}$, where z_i is the height of point i in each statistical unit and n is the total number of points in each statistical unit
x₄₄	高度二次幂平均值 Average value of height quadratic power	$V=\sqrt[2]{\frac{\sum_\limits{i=1}^n Z_i{ }^2}{n}}$, 其中，z_i为每一统计单元内第i个点的高度值，${\bar z}$为每一统计单元内所有点的平均高度, n为每一统计单元内的总点数 $V=\sqrt[2]{\frac{\sum_\limits{i=1}^n Z_i{ }^2}{n}}$, where z_i is the height of the i point in each statistical unit, ${\bar z}$ is the average height of all points in each statistical unit, n is the total number of points in each statistical unit
x₄₅	高度标准差 Height standard deviation	某一统计单元内，所有点的Z值的标准差 The standard deviation of Z value of all points in a statistical unit
x₄₆	高度方差 Height variance	某一统计单元内，所有点的Z值的方差 In a statistical unit, the variance of Z value of all points
x₄₇-x₅₆	密度变量 Density variable	将点云数据从低到高分成十个相同高度的切片，每层回波数的比例就是相应的密度变量 The point cloud data is divided into ten slices of the same height from low to high, and the ratio of echo number per layer is the corresponding density variable
x₅₇	强度平均绝对偏差 Average absolute deviation of intension	$v=\frac{\sum_\limits{i=1}^n\left(\left\|I_i-\bar{I}\right\|\right)}{n}$，其中，I_i为每一统计单元内第i个点的高度值，${\bar I}$为每一统计单元内所有点的平均高度，n为每一统计单元内的总点数 $v=\frac{\sum_\limits{i=1}^n\left(\left\|I_i-\bar{I}\right\|\right)}{n}$，where I_iis the height of the i point in each statistical unit, ${\bar I}$ is the average height of all points in each statistical unit, and n is the total number of points in each statistical unit
x₅₈	强度变异系数 Tension variance	$V=\frac{I_{\mathrm{std}}}{I_{\text {mean }}} \times 100 \%$, 其中，I_std为每一统计单元内所有点高度值的标准差，I_mean为每一统计单元内所有点的平均高度 $V=\frac{I_{\mathrm{std}}}{I_{\text {mean }}} \times 100 \%$, where I_std is the standard deviation of the height of all points in each statistical unit, and I_mean is the average height of all points in each statistical unit
x₅₉-x₇₃	累计强度百分位数 Cumulative intension percentiles	某一统计单元内，将其内部所有归一化的激光雷达点云按强度进行排序并计算所有点的累积强度，每一统计单元内x%的点的累积强度，即为该统计单元的累积强度百分位数 All normalized LiDAR point clouds within a statistical unit are sorted by intension and the cumulative intension of all points is calculated. The cumulative intension of x% points in each statistical unit is the percentile of the cumulative intension of the statistical unit
x₇₄	强度峰度 Peak intension	$\text { Kurtosis }=\frac{\frac{1}{n} \sum_\limits{i=1}^n\left(I_i-\bar{I}\right)^4}{\sigma^4}$, 其中，I_i为每一统计单元内第i个点的高度值，${\bar I}$为每一统计单元内所有点的平均高度，n为每一统计单元内的总点数，σ为统计单元内点云高度分布的标准差 $\text { Kurtosis }=\frac{\frac{1}{n} \sum_\limits{i=1}^n\left(I_i-\bar{I}\right)^4}{\sigma^4}$，where I_i is the height of the i point in each statistical unit, ${\bar I}$ is the average height of all points in each statistical unit, n is the total number of points in each statistical unit, σ is the standard deviation of the height distribution of point clouds in each statistical unit
x₇₅	强度中位数绝对偏差的中位数 Median of absolute deviation of median intension	强度中位数绝对偏差的中位数 Median of absolute deviation of median intension
x₇₆	强度最大值 Intension maximums	某一统计单元内，所有点的强度值的最大值 In a statistical unit, the maximum intension value of all points
x₇₇	强度平均值 Average intension	某一统计单元内，所有点的强度值的平均值 Average intension of all points in a statistical unit
x₇₈	强度中位数 Intension median	某一统计单元内，所有点的强度值的中位数 Median intensit of all points in a statistical unit
x₇₉	强度最小值 Minimum intension	某一统计单元内，所有点的强度值的最小值 Minimum intension of all points in a statistical unit
x₈₀	强度偏斜度 Intension skewness	某一统计单元内，所有点的强度值分布的对称性, $\text { Skewness }=\frac{\frac{1}{n} \sum_\limits{i=1}^n\left(I_i-\bar{I}\right)^3}{\sigma^3}$, 其中，I_i为每一统计单元内第i个点的高度值，${\bar I}$为每一统计单元内所有点的平均强度n为每一统计单元内的总点数 Symmetry of intension distribution of all points in a statistical unit, $\text { Skewness }=\frac{\frac{1}{n} \sum_\limits{i=1}^n\left(I_i-\bar{I}\right)^3}{\sigma^3}$, where I_i is the height of the i point in each statistical unit, and ${\bar I}$ is the average intension n of all points in each statistical unit
x₈₁	强度方差 Variance of intension	某一统计单元内，所有点的强度值的方差 Variance of intension values of all points in a statistical unit
x₈₂	强度标准差 Standard deviation of intension	某一统计单元内，所有点的强度值的标准差 Standard deviation of intension values of all points in a statistical unit
x₈₃-x₉₇	强度百分位数 Intension percentiles	某一统计单元内，将其内部所有归一化的激光雷达点云按强度进行排序，然后计算每一统计单元内x%的点的强度，即为该统计单元的强度百分位数 In a statistical unit, all normalized laser radar point clouds inside it are sorted by intension, and then the intension of x% points in each statistical unit is calculated as the intension percentile of the statistical unit
x₉₈	强度百分位数四分位数间距 Intension percentile quartile spacing	V=Int75%-Int25%

Table 附表

Table 2

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Fig.2

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Table 8

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References 0

	符利勇, 唐守正, 张会儒, 等. 基于多水平非线性混合效应蒙古栎林单木断面积模型. 林业科学研究, 2015, 28 (1): 23- 31. doi: 10.13275/j.cnki.lykxyj.2015.01.004
	Fu L Y , Tang S Z , Zhang H R , et al. Multilevel nonlinear mixed-effects basal area models for individual trees of Quercus mongolica. Forest Research, 2015, 28 (1): 23- 31. doi: 10.13275/j.cnki.lykxyj.2015.01.004
	符利勇, 张会儒, 唐守正. 基于非线性混合模型的杉木优势木平均高. 林业科学, 2012, 48 (7): 66- 71.
	Fu L Y , Zhang H R , Tang S Z . Dominant height for chinese fir plantation using nonlinear mixed effects model based on linearization algorithm. Scientia Silvae Sinicae, 2012, 48 (7): 66- 71.
	国家林业局. 中国森林资源报告2009—2013. 北京: 中国林业出版社, 2014.
	State Forestry Administration . Report on China's forest resources: 2009—2013. Beijing: China Forestry Publishing House, 2014.
	国家林业局. 立木生物量模型及碳计量参数: 落叶松(LY/T 2654—2016). 北京: 中国标准出版社, 2017a.
	State Forestry Administration . Tree biomass models and related parameters to carbon accounting for Larix gmelinii(LY/T 2654—2016). Beijing: China Standards Publishing House, 2017a.
	国家林业局. 立木生物量模型及碳计量参数: 桦树(LY/T 2659—2016). 北京: 中国标准出版社, 2017b.
	State Forestry Administration . Tree biomass models and related parameters to carbon accounting for Betula platyphylla(LY/T 2659—2016). Beijing: China Standards Publishing House, 2017b.
	何潇, 雷渊才, 薛春泉, 等. 广东省木荷碳密度及其不确定性估计. 林业科学, 2019, 55 (11): 163- 171. doi: 10.11707/j.1001-7488.20191118
	He X , Lei Y C , Xue C Q , et al. Carbon density uncertainty estimates for Schima superba in Guangdong Province. Scientia Silvae Sinicae, 2019, 55 (11): 163- 171. doi: 10.11707/j.1001-7488.20191118
	李春明, 张会儒. 利用非线性混合模型模拟杉木林优势木平均高. 林业科学, 2010, 46 (3): 89- 95. doi: 10.3969/j.issn.1672-8246.2010.03.020
	Li C M , Zhang H R . Modeling dominant height for chinese fir plantation using a nonlinear mixed-effects modeling approach. Scientia Silvae Sinicae, 2010, 46 (3): 89- 95. doi: 10.3969/j.issn.1672-8246.2010.03.020
	李桂林, 王红, 宋音. 基于无人机机载激光雷达估算黄河三角洲孤岛刺槐林地上生物量. 中国农学通报, 2018, 34 (26): 52- 57.
	Li G L , Wang H , Song Y . Estimating aboveground biomass of Robinia pseudoacacia in the Yellow River Delta based on UAV airborne laser radar. Chinese Agricultural Science Bulletin, 2018, 34 (26): 52- 57.
	理查德·麦克尔里思. 统计反思: 用R和Stan例解贝叶斯方法. 北京: 机械工业出版社, 2019.
	Richard M . Statistical rethinking of R and Stan examples for Solving Bayesian methods. Beijing: China Machine Press, 2019.
	刘清旺, 谭炳香, 胡凯龙, 等. 机载激光雷达和高光谱组合系统的亚热带森林估测遥感试验. 高技术通讯, 2016, (3): 264- 274. doi: 10.3772/j.issn.1002-0470.2016.03.006
	Liu Q W , Tan B X , Hu K L , et al. The remote sensing experiment on airborne LiDAR and hyperspectral integrated system for subtropical forest estimation. Chinese High Technology Letters, 2016, (3): 264- 274. doi: 10.3772/j.issn.1002-0470.2016.03.006
	庞勇, 李增元. 基于机载激光雷达的小兴安岭温带森林组分生物量反演. 植物生态学报, 2012, 36 (10): 1095- 1105.
	Pang Y , Li Z Y . Inversion of biomass components of the temperate forest using airborne LiDAR technology in Xiaoxing'an Mountains, northeastern of China. Chinese Journal of Plant Ecology, 2012, 36 (10): 1095- 1105.
	曲苑婷, 汪垚, 刘观潮, 等. 基于GLAS激光雷达反演森林生物量. 测绘通报, 2014, (11): 73- 77.
	Qu Y T , Wang Y , Liu G C , et al. The inversion of forest biomass based on GLAS laser radar. Bulletin of Surveying and Mapping, 2014, (11): 73- 77.
	曾伟生, 孙乡楠, 王六如, 等. 基于机载激光雷达数据的森林蓄积量模型研建. 林业科学, 2021, 57 (2): 31- 38.
	Zeng W S , Sun X N , Wang L R , et al. Development of forest stand volume models based on airborne laser scanning data. Scientia Silvae Sinicae, 2021, 57 (2): 31- 38.
	曾伟生, 唐守正. 立木生物量模型的优度评价和精度分析. 林业科学, 2011, 47 (11): 106- 113. doi: 10.11707/j.1001-7488.20111117
	Zeng W S , Tang S Z . Goodness evaluation and precision analysis of tree biomass equations. Scientia Silvae Sinicae, 2011, 47 (11): 106- 113. doi: 10.11707/j.1001-7488.20111117
	唐守正, 张会儒, 胥辉. 相容性生物量模型的建立及其估计方法研究. 林业科学, 2000, 36 (S1): 19- 27.
	Tang S Z , Zhang H R , Xu H . Study on establish and estimate method of compatible biomass model. Scientia Silvae Sinicae, 2000, 36 (S1): 19- 27.
	尤号田, 邢艳秋, 王萌, 等. 小光斑激光雷达数据估测森林生物量研究进展. 森林工程, 2014, 30 (3): 39- 42.
	You H T , Xing Y Q , Wang M , et al. Research progress in estimation of forest biomass from small spot LiDAR data. Forest Engineering, 2014, 30 (3): 39- 42.
	于颖, 范文义, 李明泽, 等. 利用大光斑激光雷达数据估测树高和生物量. 林业科学, 2010, 46 (9): 84- 87.
	Yu Y , Fan W Y , Li M Z , et al. Estimation of forest tree heights and biomass from GLAS data. Scientia Silvae Sinicae, 2010, 46 (9): 84- 87.
	Andersen H E , Reutebuch S E , McGaughey R J . A rigorous assessment of tree height measurements obtained using airborne liDAR and conventional field methods. Canadian Journal of Remote Sensing, 2006, 32 (5): 355- 366.
	Bouvier M , Durrieu S , Fournier R A , et al. Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. Remote Sensing of Environment, 2015, 156, 322- 334.
	Dubayah R O , Drake J B . Lidar remote sensing for forestry. Journal of Forestry, 2000, 98 (6): 44- 46.
	Ellison A M . Bayesian inference in ecology. Ecology Letters, 2004, 7 (6): 209- 520.
	Ferraz A , Saatchi S , Mallet C , et al. Airborne LiDAR estimation of aboveground forest biomass in the absence of field inventory. Remote Sensing, 2016, 8 (8): 653.
	Field C B , Raupach M R , Victoria R . The global carbon cycle: integrating humans, climate and the natural world. Global Carbon Cycle Integrating Humans Climate & the Natural World, 2004, 6, 2389- 2390.
	Fu L Y , Liu Q W , Sun H , et al. Developing a system of compatible individual tree diameter and aboveground biomass prediction models using error-in-variable regression and airborne LiDAR data. Remote Sensing, 2018, 10 (2): 325.
	González-Ferreiro E , Diéguez-Aranda U , Miranda D . Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities. Forestry: an International Journal of Forest Research, 2012, 85 (2): 281- 292.
	He Q , Chen E , An R , et al. Above-ground biomass and biomass components estimation using LiDAR data in a coniferous forest. Forests, 2013, 4 (4): 984- 1002.
	Jachowski N , Quak M , Friess D A , et al. Mangrove biomass estimation in southwest Thailand using machine learning. Applied Geography, 2013, 45, 311- 321.
	Lim K S , Treitz P M . Estimation of aboveground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Scandinavian Journal of Forest Research, 2004, 19, 558- 570.
	MacLean G A , Krabill W B . Gross merchantable timber volume estimation using an airbome LiDAR system. Canadian Journal of Remote Sensing, 1986, 12, 7- 18.
	Maltamo M , Eerikainen K , Pitkanen J , et al. Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions. Remote Sensing of Environment, 2004, 90, 319- 330.
	Næsset E . Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, 1997, 52, 49- 56.
	Nelson R , Krabill W , MacLean G . Determining forest canopy characteristics using airbome laser data. Remote Sensing of Environment, 1984, 15, 201- 212.
	Nelson R , Krabill W , Tonelli J . Estimating forest biomass and voILune using airborne laser data. Remote Sensing of Environment, 1988, 24, 247- 267.
	Nelson R , Oderwald R , Gregoire T G . Separating the ground and airborne laser sampling phases to estimate tropical forest basal area, volume, and biomass. Remote Sensing of Environment, 1997, 60, 311- 326.
	Parresol B R . Assessing tree and stand biomass: a review with examples and critical comparisons. Forest Science, 1999, 45 (4): 573- 593.
	Pinheiro J C , Bates D M . Mixed-effects models in S and S-plus. New York: Spring Verlag, 2000.
	Popescu S C . Estimating biomass of individual pine trees using airborne LiDAR. Biomass and Bioenergy, 2007, 31, 646- 655.
	Tian X , Su Z B , Chen E X , et al. Estimation of forest above-ground biomass using multiparameter remote sensing data over a cold and arid area. International Journal of Applied Earth Observation and Geoinformation, 2012, 14 (1): 160- 168.
	Xiao W , Xu S , Elberink S O , et al. Individual tree crown modeling and change detection from airborne lidar data. IEEE journal of selected topics in applied earth observations and remote sensing, 2016, 9 (8): 3467- 3477.
	Ye Q L , Huang P , Zhang Z , et al. Multiview learning with robust double-side twin SVM. IEEE Transactions on Cybernetics, 2021,
	Zhang X Q , Cao Q V , Duan A G , et al. Modeling tree mortality in relation to climate, initial planting density and competition in Chinese fir plantations using a Bayesian logistic multilevel method. Canadian Journal of Forest Research, 2017, 47 (9): 1278- 1285.
	Zhao K G , Popescu S , Nelson R . LiDAR remote sensing of forest biomass: a scale-invariant estimation approach using airborne Lasers. Remote Sensing of Environment, 2009, 113, 182- 196.

树种 Species	异速生长方程 Allometric growth equation	来源 Source
华北落叶松 Larix principis-rupprechtii	AGB=0.027 7×DBH^{2.793 26}	国家林业局，2017a; 2017b State Forestry Administration，2017a; 2017b
白桦 Betula platyphylla	AGB=0.1905×DBH^{2.243 22}

树种 Species	样地数 Number of plots	平均胸径DBH/cm		平均树高Mean tree height/m		生物量Biomass/(t·hm^-2)
树种 Species	样地数 Number of plots	最小值Min.	最大值Max.	最小值Min.	最大值Max.	最小值Min.	最大值Max.
华北落叶松Larix principis-rupprechtii	40	2	41.4	7.2	16.8	4.91	197.62
白桦Betula platyphylla	22	2.3	41.2	6.4	14.3	26.32	165.15

序号 No.	模型 Model	模型来源 Model source
Ⅰ	AGB=b₀+b₁X₁+b₂X₂+b₃X₃+…+b_nX_n+ε_AGB	Linear
Ⅱ	AGB=b₀/[1+b₁exp(-b₂X₁-b₃X₂-b₄X₃-…-b_n+1X_n)]+ε_AGB	Logistic
Ⅲ	AGB=b₀exp(-b₁X₁-b₂X₂-b₃X₃-…-b_nX_n)+ε_AGB	Exponential
Ⅳ	AGB=b₀X₁^b₁X₂^b₂X₃^b₃…X_n^b_n+ε_AGB	Empirical

变量 Variable	相关系数 Correlation coefficient	变量 Variable	相关系数 Correlation coefficient	变量 Variable	相关系数 Correlation coefficient	变量 Variable	相关系数 Correlation coefficient
x₁₂	0.889 52	x₁₆	0.883 15	x₂₆	0.877 50	x₅₀	-0.696 09
x₁₃	0.888 53	x₁₉	0.882 68	x₁₇	0.875 29	x₄₇	-0.706 26
x₁₄	0.887 56	x₄₄	0.880 84	x₄₂	0.873 65	x₄₉	-0.761 40
x₁₅	0.886 27	x₁₀	0.879 22	x₉	0.871 94	x₄₈	-0.800 81
x₁₁	0.885 50	x₂₇	0.879 18	x₂₄	0.871 90	x₄₃	-0.819 34

模型 Model	参数 Parameter	解释变量 Explainable variable	参数估计值 Parameter estimate	P
Ⅰ	b₁	x₄₆	8.82	1.84E-07
	b₂	x₄₇	-298.89	1.84E-07
	b₃	x₅₆	300.42	0.040 4
Ⅱ	b₀		152.27	8.30E-11
	b₁		3.35	0.005 948
	b₂	x₄₆	0.32	0.000 253
	b₃	x₄₇	-11.87	0.000 472
	b₄	x₅₆	10.70	0.112 488
Ⅲ	b₀		14.80	0.06
	b₁	x₄₆	0.71	1.07E-06
	b₂	x₄₇	-0.23	1.72E-05
	b₃	x₅₆	0.05	0.456 8
Ⅳ	b₀		59.98	6.51E-11
	b₁	x₄₆	-0.11	3.31E-07
	b₂	x₄₇	6.52	2.93E-06
	b₃	x₅₆	-1.13	0.467 0

Inversion of Aboveground Biomass in the Core Area of Chongli Winter Olympics Based on Airborne LiDAR

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模型Model	最小二乘Ordinary least squares			贝叶斯Bayesian
模型Model	均方根误差 RMSE/(t·hm^-2)	确定系数 R²	总体相对误差 TRE(%)	均方根误差 RMSE/(t·hm^-2)	确定系数 R²	总体相对误差 TRE(%)
LinearⅠ	22.49	0.780	5.69	33.19	0.545	13.55
LogisticⅡ	21.66	0.798	5.26	24.29	0.732	7.31
EmpiricalⅢ	21.74	0.796	5.30	21.58	0.796	5.32
Exponential Ⅳ	24.41	0.743	6.78	25.21	0.743	6.82

模型Model	训练集Training set			验证集Verification sets
模型Model	均方根误差 RMSE/(t·hm^-2)	确定系数 R²	总体相对误差 TRE(%)	均方根误差 RMSE/(t·hm^-2)	确定系数 R²	总体相对误差 TRE(%)
LinearⅠ	19.28	0.840	4.13	23.46	0.759	6.32
LogisticⅡ	18.64	0.850	3.86	22.99	0.768	6.08
EmpiricalⅢ	21.73	0.796	5.89	23.52	0.757	6.30
Exponential Ⅳ	21.97	0.792	5.45	27.93	0.658	8.94

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