联合星−空激光雷达和Sentinel-2数据的森林地上生物量估测方法

doi:10.11707/j.1001-7488.LYKX20250054

Abstract

Abstract:

Objective: This study aims to explore the adaptability of the joint retrieval of regional forest aboveground biomass (AGB) by the new generation of ice, cloud, and land elevation satellite (ICESat-2), airborne LiDAR and Sentinel-2 multi-source data, providing scientific basis for large-scale forest resource management and dynamic monitoring. Method: Wangyedian Forest Farm in Chifeng City was selected as the research area, and a high-precision AGB estimation model was established based on airborne LiDAR data and sample plot measured data. The AGB of the sample plot was extended from discrete “points” to “surface” data on a large regional scale to overcome the problem of difficulty in matching sample points with spaceborne points. On this basis, ICESat-2 and Sentinel-2 remote sensing data were combined for AGB inversion, and the optimal variable combination and optimal inversion model were finally selected to draw the spatial distribution map of forest AGB in the study area. Result: 1) The three-dimensional structural information extracted from airborne LiDAR was highly correlated with forest AGB, and the inversion results of the random forest model had the highest accuracy with r of 0.91, root mean squared error (RMSE) of 17.00 t·hm^?2, and estimation accuracy (EA) of 88.90%. 2) After combining the ICESat-2 and Sentinel-2 variables, the accuracy of the inversion model was further improved (R²=0.74, RMSE=27.44 t·hm^?2, EA=69.32%), with R² and EA increasing by 30.26% and 14.18%, respectively. 3) The spatial distribution results of forest AGB in the study area showed that the forest AGB in the southeast was relatively low (with an average of 97.13 t·hm^?2), while that in the mid-east and northeast was relatively high (with an average of 117.03 t·hm^?2), which was consistent with the actual distribution. Conclusion: The AGB inverted from airborne LiDAR data has high accuracy, which can be used as an intermediate parameter to connect measured data and satellite data. Combining airborne LiDAR, Sentinel-2 and ICESat-2 data can not only further improve the estimation accuracy of forest AGB, but also provide a new reference for large-scale forest resource management and dynamic detection in the future.

Key words: forest aboveground biomass, machine learning, ICESat-2, UAV-LiDAR, Sentinel-2

CLC Number:

Sheng Zhou,Fugen Jiang,Shuai Chen,Yi Long,Binbin Wang,Zige Song,Hua Sun. Estimation of Forest Aboveground Biomass Using Joint Spaceborne-UAV LiDAR and Sentinel-2 Data[J]. Scientia Silvae Sinicae, 2026, 62(3): 74-87.

Figures/Tables 13

Fig.1

Table 1

Table 2

Spectral remote sensing variables used in the study"

变量名称 Variable name	计算公式 Calculation formula	变量名称 Variable name	计算公式 Calculation formula
大气阻抗植被指数 Atmospherically resistant vegetation index (ARVI)	$ \dfrac{\left[{{\mathrm{NIR-(2Red-Blue)}}}\right]}{\left[{{\mathrm{NIR+(2Red-Blue)}}}\right]} $	绿色归一化差异植被指数 Green normalized difference vegetation index (GNDVI)	$ \dfrac{{{\mathrm{NIR-Green}}}}{{{\mathrm{NIR+Green}}}} $
差异植被指数 Difference vegetation index (DVI)	$ {{\mathrm{NIR-Red}}} $	绿色比值植被指数 Green ratio vegetation index (GRVI)	$ \dfrac{{{\mathrm{NIR}}}}{{{\mathrm{Green}}}} $
增强植被指数 Enhanced vegetation index (EVI)	$ {2.5\times }\dfrac{{{\mathrm{NIR-Red}}}}{{{\mathrm{NIR+6\times Red-7.5\times Blue+1}}}} $	归一化植被指数 Normalized difference vegetation index (NDVI)	$ \dfrac{{{\mathrm{NIR-Red}}}}{{{\mathrm{NIR+Red}}}} $
绿色大气阻力指数 Green atmospherically resistant index (GARI)	$ \dfrac{{{\mathrm{NIR}}-}\left[{{\mathrm{Green}}-1.7\times }\left({{\mathrm{Blue}}-{\mathrm{Red}}}\right)\right]}{{{\mathrm{NIR}}+}\left[{{\mathrm{Green}}+1.7\times }\left({{\mathrm{Blue-Red}}}\right)\right]} $	红边简单比值 Red edge simple ratio (RESR)	$ \dfrac{{{\mathrm{NIR}}}}{{{\mathrm{Red Edge}}1}} $
绿差植被指数 Green difference vegetation index (GDVI)	$ {{\mathrm{NIR-Green}}} $	红边叶绿素指数 Red edge chlorophyll index (RECI)	$ \dfrac{{{\mathrm{NIR}}}}{{{\mathrm{Red Edge}}1}}{-1} $
红边归一化差值植被指数a Red edge normalized difference vegetation index a (RENDVI_a)	$ \dfrac{{{\mathrm{NIR-Red Edge}}1}}{{{\mathrm{NIR+Red Edge}}1}} $	红边归一化差值植被指数b Red edge normalized difference vegetation index b (RENDVI_b)	$ \dfrac{{{\mathrm{NIR-Red Edge}}2}}{{{\mathrm{NIR+Red Edge}}2}} $
单波段反射率 Original band reflectance	$ B_i=\mathrm{Band}_i,\ i=1,2,\ldots,8 $	灰度差分向量 Gray level difference vector (GLDV)	$ {\mathrm{GLDV}}\_ i\_ j $
灰度共生矩阵 Gray level co-occurrence matrix (GLCM)	$ {\mathrm{GLCM}}\_ i\_ j $	和差直方图 Sum and difference histogram (SADH)	$ {\mathrm{SADH}}\_ i\_ j $

Table 2

Fig.2

Fig.3

Table 3

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Table 4

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数据类型 Data type	变量名称 Variable name	缩写 Abbreviation
无人机激光雷达 UAV LiDAR	密度特征变量Density metrics	den
	高度特征变量Elevation metrics	ele
	强度特征变量Intensity metrics	int
星载激光雷达 Spaceborne LiDAR	表观地表反射率Apparent surface reflectance	asr
	最小冠层高度Minimum height	h_min
	平均冠层高度Mean height	h_mean
	中位数冠层高度Median height	h_mid
	最大冠层高度Maximum height	h_max
	地形最合适高度Terrain height best fit	h_dem
	冠层高度百分位数Canopy height percentile	h_p (p=25, 50, 60, 70, 75, 80, 85, 90, 95, 98)

参数 Parameters	系数 Coefficient	显著性 Significance
常数Constant	–30.49	0.03
ele_p_80	8.88	0
ele_kurto	–8.94	0
ele_crr	134.37	0.01

数据源 Data source	组合 Combination	变量 Variables	决定系数 R²	均方根误差 RMSE/ ( t?hm^?2)	相对均方根误差 rRMSE (%)	估测精度 EA (%)
Sentinel-2	变量组合1 Combination 1	b2, EVI, b3, GDVI, b4	0.44	35.21	36.88	59.26
	变量组合2 Combination 2	b2, GLCM_3_ent, b3, GLCM_2_sec, GLCM_3_sec, GLCM_2_ent, GLCM_7_var, GLCM_5_ent, GLCM_1_var, GLCM_7_con	0.53	34.37	35.99	59.20
	变量组合3 Combination 3	b2, GLDV_8_sec, b3, GLDV_5_ent, GLDV_2_var, GLDV_2_ent, GLDV_4_ent, GLDV_2_mea, GLDV_7_var, GLDV_2_con, GLDV_4_sec	0.51	33.74	35.33	61.46
	变量组合4 Combination 4	b2, SADH_8_cor, SADH_1_sec, b3, SADH_6_var, SADH_7_con, SADH_3_sec, SADH_2_sec, SADH_4_cor, SADH_7_var, SADH_7_hom, SADH_4_mea	0.54	33.46	35.04	60.78
	变量组合5 Combination 5	b2, GLDV_8_var, GLCM_4_mea, GLDV_8_ent, GLCM_7_dis, b3, GLCM_2_ent, GLDV_8_sec, GLDV_2_var	0.56	33.02	34.58	60.71
Sentinel-2+ ICESat-2	变量组合6 Combination 6	h_85, NDVI, h_max, GLCM_3_ent, h_dem, GLCM_3_cor, GLCM_6_ent, GLDV_1_ent, h_70, h_mean, RENDVI_a, ARVI, GLCM_1_cor	0.74	27.44	28.74	69.32

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Estimation of Forest Aboveground Biomass Using Joint Spaceborne-UAV LiDAR and Sentinel-2 Data

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