联合UAV-LiDAR和GEDI数据的区域森林地上生物量估算

doi:10.11707/j.1001-7488.LYKX20240818

林业科学 ›› 2025, Vol. 61 ›› Issue (8): 142-153.doi: 10.11707/j.1001-7488.LYKX20240818

联合UAV-LiDAR和GEDI数据的区域森林地上生物量估算

熊晓燕^1,^2,³,李彩霞^1,^2,^3,*(),柴国奇⁴,陈龙⁴,贾翔⁵,雷令婷^1,^2,³,张晓丽^1,^2,^3,*()

1. 北京林业大学　林木资源高效生产全国重点实验室　北京 100083
2. 北京林业大学林学院　精准林业北京市重点实验室　北京 100083
3. 北京林业大学　森林培育与保护教育部重点实验室　北京 100083
4. 中国林业科学研究院资源信息研究所　北京 100091
5. 河南省科学院地理研究所　郑州 450052

收稿日期:2024-12-31 出版日期:2025-08-25 发布日期:2025-09-02
通讯作者: 李彩霞,张晓丽 E-mail:licaixia179@163.com;zhang-xl@263.net
基金资助:
中欧对地观测合作森林监测技术与示范应用（2021YFE0117700-3）。

Estimation of Aboveground Biomass in Regional Forests by Using Integrating UAV-LiDAR and GEDI Data

Xiaoyan Xiong^1,^2,³,Caixia Li^1,^2,^3,*(),Guoqi Chai⁴,Long Chen⁴,Xiang Jia⁵,Lingting Lei^1,^2,³,Xiaoli Zhang^1,^2,^3,*()

1. Beijing Forestry University　State Key Laboratory of Efficient Production of Forest Resources　Beijing 100083
2. College of Forestry, Beijing Forestry University　Beijing Key Laboratory of Precision Forestry　Beijing 100083
3. Beijing Forestry University　Key Laboratory of Forest Cultivation and Protection, Ministry of Education　Beijing 100083
4. Institute of Forest Resource Information Techniques, Chinese Academy of Forestry　Beijing 100091
5. Institute of Geographical Sciences, Henan Academy of Sciences　Zhengzhou 450052

Received:2024-12-31 Online:2025-08-25 Published:2025-09-02
Contact: Caixia Li,Xiaoli Zhang E-mail:licaixia179@163.com;zhang-xl@263.net

摘要/Abstract

摘要：

目的: 结合无人机激光雷达（UAV-LiDAR）和全球生态系统动态调查（GEDI）数据，构建“样地?局部?区域”估算框架估算高峰林场森林地上生物量（AGB），为森林碳储量监测提供新路径。方法: 以林场内样地实测数据为基础，评估多元线性回归（MLR）、随机森林（RF）和支持向量回归（SVR）3种模型在估算UAV-LiDAR区域AGB中的性能。为扩增区域尺度样本数量，利用GEDI光斑处的UAV-LiDAR区域AGB，结合筛选的GEDI光斑关键特征，构建光斑尺度AGB估算模型，预测林场内的光斑AGB。联合UAV-LiDAR局部AGB与光斑AGB，采用经验贝叶斯克里金（EBK）法实现森林AGB空间插值；对关键光斑特征进行EBK插值，并结合UAV-LiDAR估算的AGB构建模型，实现AGB空间分布反演。结果: 与MLR和SVR模型相比，RF模型在估算UAV-LiDAR区域AGB中表现更优异，R²高达0.95，RMSE为9.96 Mg?hm^?2，rRMSE为9.79%。利用RF估算的光斑AGB与UAV-LiDAR区域AGB的拟合较好，R²为0.93，RMSE为5.93 Mg?hm^?2，rRMSE为5.84%。采用UAV-LiDAR局部AGB和光斑AGB协同插值的预测精度R²为0.78，RMSE为22.30 Mg?hm^?2，MAE为16.99 Mg?hm^?2。与基于插值关键特征（fhd、rh₉₆、cover、pt₄和pai）的AGB反演结果相比，获得的研究区AGB空间范围更合理（49.26~193.27 Mg?hm^?2）。结论: 以“样地?局部?区域”AGB估算框架为基础，并采用随机森林算法和空间插值法，有效结合UAV-LiDAR和GEDI数据，克服了实测样地数量有限和遥感数据空间不连续的问题，验证了光斑样本在森林区域AGB估算中的可行性，实现了高峰林场AGB估算，为森林碳储量评估和可持续管理提供了数据支撑。

关键词: 无人机激光雷达, 全球生态系统动态调查, 随机森林, 森林地上生物量, 经验贝叶斯克里金法

Abstract:

Objective: By integrating unmanned aerial vehicle light detection and ranging (UAV-LiDAR) data and global ecosystem dynamics investigation (GEDI) data, a“plot-local-region”estimation framework was constructed to estimate the aboveground biomass (AGB) of Gaofeng Forest Farm, providing a new approach for forest carbon storage monitoring. Method: Based on plot-level field measured data within the forest farm, this study evaluated the performance of three models: multiple linear regression (MLR), random forest (RF), and support vector regression (SVR), in estimating UAV-LiDAR-derived regional aboveground biomass (AGB). To augment the sample size at the regional scale, UAV-LiDAR-derived AGB estimated at GEDI footprint locations was combined with selected key GEDI footprint metrics to develop a footprint-level AGB estimation model. This model was subsequently employed to predict footprint-level AGB across the entire forest farm. The spatial interpolation of forest AGB was achieved by integrating UAV-LiDAR-derived local AGB with footprint-level AGB using empirical Bayesian Kriging (EBK) method. The inversion of AGB spatial distribution was implemented through EBK interpolation of key footprint metrics combined with UAV-LiDAR estimated AGB for model construction. Result: RF model demonstrated superior performance in estimating UAV-LiDAR-derived regional AGB compared to both MLR and SVR, achieving an R² of 0.95 with RMSE = 9.96 Mg·hm^?2 and rRMSE = 9.79%. The footprint-level AGB estimated by RF showed strong agreement with UAV-LiDAR regional AGB (R2 = 0.93, RMSE = 5.93 Mg·hm^?2, rRMSE = 5.84%). The synergistic interpolation of UAV-LiDAR local AGB and GEDI footprint AGB achieved a prediction accuracy of R² = 0.78, RMSE = 22.30 Mg·hm^?2, and MAE = 16.99 Mg·hm^?2 Compared with the AGB inversion results based on interpolated key features (fhd, rh₉₆, cover, pt₄and pai), the AGB ranges in the study area obtained were more reasonable (49.26–193.27 Mg·hm^?2). Conclusion: This study is based on the“plot-local-region”AGB estimation framework, employing random forest algorithms and spatial interpolation methods to effectively integrate UAV-LiDAR and GEDI data. It overcomes the limitations of scarce field plot measurements and the spatial discontinuity of remote sensing data, and verifies the feasibility of using footprint samples for forest AGB estimation. The research achieves accurate AGB estimation in Gaofeng Forest Farm, providing essential data support for forest carbon storage assessment and sustainable management.

Key words: unmanned aerial vehicle light detection and ranging (UAV-LiDAR), global ecosystem dynamics investigation (GEDI), random forest, forest aboveground biomass, empirical Bayesian Kriging

中图分类号:

S771.8
P237

熊晓燕,李彩霞,柴国奇,陈龙,贾翔,雷令婷,张晓丽. 联合UAV-LiDAR和GEDI数据的区域森林地上生物量估算[J]. 林业科学, 2025, 61(8): 142-153.

Xiaoyan Xiong,Caixia Li,Guoqi Chai,Long Chen,Xiang Jia,Lingting Lei,Xiaoli Zhang. Estimation of Aboveground Biomass in Regional Forests by Using Integrating UAV-LiDAR and GEDI Data[J]. Scientia Silvae Sinicae, 2025, 61(8): 142-153.

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样地类别 Plots class	样地数量 Plots number	平均胸径 Mean DBH/cm	平均树高 Average tree height/m	林分密度 Stand density/ (tree?hm^?2)	异速生长方程 Allometric models	AGB（均值） AGB (mean)/ (Mg?hm^?2)
桉树Eucalyptus	40	8.39~28.13	10.96~29.06	128~1744	$ {{W}} = 0.082\;784 \times {{{D}}^{{\text{2}}{\text{.0831 1}}}} \times {{{H}}^{{\text{0}}{\text{.489 97}}}} $	34.82~213.30（94.57）
杉木Cunninghamia lanceolata	9	14.97~26.93	11.98~20.16	208~1520	${{W}} = 0.045\;31 \times {D^{1.915}} \times {H^{0.712\;0}}$	36.63~99.95（64.25）
红锥Castanopsis hystrix	8	15.03~23.00	10.47~21.13	528~1056	$ W=W\mathrm{_S}+W\mathrm{_L}+W\mathrm{_B} $；$ W\mathrm{_S}=0.0641\; 1(D^2H)^{0.869\; 9} $；$ W\mathrm{_L}=0.000\; 002\; 8(D^2H)^{1.605\; 2} $；$ W\mathrm{_B}=0.000\; 11(D^2H)^{0.1.394\; 9} $	79.90~259.52（180.13）

类别 Class	变量 Variable	描述 Description	类别 Class	变量 Variable	描述 Description
高度变量 Height variable	H_X	高度百分位 Height percentile	强度变量 Intensity variable	I_X	强度百分位 Intensity percentile
	H_mean	平均值 Mean		I_mean	平均值 Mean
	H_max	最大值 Maximum (max)		I_max	最大值 Maximum (max)
	H_min	最小值 Minimum (min)		I_min	最小值 Minimum (min)
	H_median	中位数 Median		I_median	中位数 Median
	H_std	标准差 Standard deviation (std)		I_std	标准差 Standard deviation (std)
	H_cv	变异系数 Coefficient of variation (cv)		I_cv	变异系数 Coefficient of variation (cv)
	H_ske	偏斜度 Skewness (ske)		I_ske	偏斜度 Skewness (ske)
	H_kur	峰度 Kurtosis (kur)		I_kur	峰度 Kurtosis (kur)
	H_sqrt	二次幂平均 Quadratic mean (sqrt)		I_var	方差 Variance (var)
	H_cur	三次幂平均 Cubic mean (cur)		—	—
	H_var	方差 Variance (var)		—	—
密度变量 Density variable	D₀， …， D₉	密度变量Density variable	冠层特征Canopy feature	CC	郁闭度 Canopy cover (CC)
				GF	孔隙率 Gap fraction (GF)
				CRR	冠层起伏度 Canopy relief ratio (CRR)

产品 Product	变量 Variable	缩写 Abbreviation	描述 Description
L2A	Rh	rh₁， rh₂， …， rh₁₀₀	相对高度指标，以1%为间隔 Relative height metrics at 1% intervals (m)
L2B	Pai	pai	植物面积指数 Plant area index
	fhd_normal	fhd	叶高多样性指数 Leaf-height diversity index
	Cover	cover	总冠层覆盖度 Total canopy cover percentage
	modis_treecover	mt	MODIS数据的树木覆盖率 Tree cover fraction from MODIS
	modis_nonvegetated	mn	MODIS数据的非植被百分比 Non-Vegetated percentage in MODIS
	pgap_theta_aN	pt_N	森林冠层间隙概率 Forest canopy gap probability
	height_lastbin	hl	相对森林冠层间隙误差的地面高度 Ground height relative to canopy gap error
	Rg	rg_N	选定波形中地面分量的积分 Integrated ground return energy (of selected waveform)
	Rv	rv_N	选定波形中植被分量的积分 Integrated vegetation return energy
	rx_energy_aN	re_N	去除平均噪声后接收到的波形总能量 Total waveform energy (noise-corrected)

特征Features	R²	RMSE	MAE
fhd	0.74	0.47	0.34
rh₉₆	0.80	8.00	6.38
cover	0.53	0.28	0.23
pt₄	0.59	0.28	0.24
pai	0.63	1.96	1.61

试验 Experiment	样本类别 Sample class	样本值Sample value/(Mg?hm^?2)	估算值Estimation results/(Mg?hm^?2)	AGB均值 Mean AGB/(Mg?hm^?2)	精度评价Accuracy assessment
试验 Experiment	样本类别 Sample class	样本值Sample value/(Mg?hm^?2)	估算值Estimation results/(Mg?hm^?2)	AGB均值 Mean AGB/(Mg?hm^?2)	R²	RMSE/(Mg?hm^?2)	MAE/(Mg?hm^?2)
I	a	47.17~213.25	51.03~174.69	101.33	0.62	27.66	21.91
	b	65.42~168.66	77.99~130.94	98.43	0.69	15.55	10.25
	c	47.17~213.25	49.26~193.27	101.03	0.78	22.30	16.99
Ⅱ	d	52.71~188.87	71.08~161.34	91.91	0.73	15.53	13.26

联合UAV-LiDAR和GEDI数据的区域森林地上生物量估算

Estimation of Aboveground Biomass in Regional Forests by Using Integrating UAV-LiDAR and GEDI Data

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