Estimating Forest Aboveground Biomass in Tropical Zones by Integrating LiDAR and Sentinel-2B Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Field Sampling
2.2. UAV–LiDAR and Sentinel-2 Image Acquisition
2.3. UAV–LiDAR and Sentinel-2 Image-Derived Feature Selection Using SPA
- (1)
- For the 98 UAV LiDAR-derived features, 69 Sentinel-2 image-derived features (12 bands and 57 VIs), and their coupled data (167 features), the 49 sample plots were randomly divided into training (70%) and test (30%) subsets through 200 stratified sampling iterations. Each training subset maintained a consistent distribution, containing data from 19 plots in Diaoluoshan forest park, 7 plots in Qingpilin reserve, and 8 plots in Fengmu forest farm.
- (2)
- The SPA was executed for each of the 200 training and test subset pairs, resulting in 200 distinct sets of selected features.
- (3)
- We subsequently analyzed the frequency of feature selection across all iterations. Features with higher selection frequencies were considered as more sensitive and reliable for accurate AGB estimation.
2.4. RF Model Development and Evaluation
- (1)
- The features were first ranked based on their selection frequency in the SPA from highest to lowest. The regression model then sequentially selected the top k features (k = 1, 2, 3, …, n, where n is the total number of features selected by the SPA across the 200 iterations) as the new independent variables, resulting in n combinations of independent variables. This step-wise approach allowed us to systematically evaluate how incremental additions of features affected the model performance.
- (2)
- For each combination of independent variables, we paired the selected features with the AGB measurements from the 49 sampling plots. Each dataset was randomly divided into training (70%) and testing (30%) subsets, repeating this partitioning 200 times following the methodology described in Section 2.3. The RF models were trained with 500 trees, as this number typically ensures prediction error stabilization while maintaining computational efficiency.
- (3)
- An RF model was established and cross-validated with a training subset in Matlab 2019 using the following specifications: a minimum leaf size of 5 observations to prevent overfitting and an unlimited tree depth to ensure complete node purity. Each model was independently validated with the corresponding test set. The determination coefficient of cross-validation and independent validation (R2CV and R2Val) and residual prediction deviation (RPD) values were calculated to evaluate the performance of each model. Hence, a total of 200 R2CV, R2Val, and RPD values were generated for each combination of independent variables.
3. Results
3.1. Statistics of Aboveground Biomass
3.2. Relationship Between UAV–LiDAR/Sentinel-2B Image-Derived Features and Aboveground Biomass
3.3. Sensitive Features for Aboveground Biomass Estimation
3.4. Model Performance for Aboveground Biomass Estimation
4. Discussion
5. Conclusions
- (1)
- UAV–LiDAR data served as a reliable foundation for tropical forest AGB estimation, capturing crucial structural information;
- (2)
- LiDAR-derived metrics consistently outperformed Sentinel-2B data in estimation accuracy, highlighting the importance of three-dimensional canopy characterization;
- (3)
- The SPA proved highly effective in identifying optimal metric combinations by minimizing collinearity while retaining biologically meaningful predictors;
- (4)
- The sensor fusion strategy yielded superior results, with the combined LiDAR and Sentinel-2B approach outperforming either dataset used independently.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Feature ID | Feature Name | Description | Formula |
---|---|---|---|
elevation_1 | Height variance | ||
elevation_2 | Height standard deviation | ||
elevation_3 | Height quadratic mean | ||
elevation_4 | Height skewness | ||
elevation_5–19 | In a specific statistical unit, the internally normalized LiDAR point cloud is sorted by height, and then the height of x% of points within each statistical unit is calculated. This represents the height percentile of that unit. x ∈ (1, 5, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99) | ||
elevation_20 | Median height value | ||
elevation_21 | Height mean | ||
elevation_22 | Height maximum | ||
elevation_23 | Median of the height median absolute deviation | ||
elevation_24 | Height kurtosis | ||
elevation_25 | Height coefficient of variation | ||
elevation_26 | height cubic mean | ||
elevation_27 | Height canopy relief ratio | ||
elevation_28 | Height mean absolute deviation | ||
elevation_29 | Height percentile interquartile range | ||
elevation_30 | Cumulative height percentile interquartile range | ||
elevation_31–46 | In a given statistical unit, the normalized LiDAR point cloud is sorted by height, and the cumulative height of all points is calculated. The cumulative height at which x% of points within each statistical unit are located represents the cumulative height percentile of that unit. x ∈ (1, 5, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99) | ||
elevation_47–56 | Height density variable of the xth slice, x ∈ (0, 1, 2, 3, 4, 5, 6, 7, 8, 9). | Slicing the point cloud data into ten equal-height layers from low to high, the proportion of returns in each layer represents the corresponding density variable | |
intensity_1 | Intensity variance | ||
intensity_2 | Intensity standard deviation | ||
intensity_3 | Skewness of intensity | ||
intensity_4–18 | In a specific statistical unit, the internally normalized LiDAR point cloud is sorted by intensity, and then the intensity of x% of points within each statistical unit is calculated. This represents the intensity percentile of that unit. x ∈ (1, 5, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99) | ||
intensity_19 | Minimum intensity value | ||
intensity_20 | Median intensity value | ||
intensity_21 | Mean intensity value | ||
intensity_22 | Maximum intensity value | ||
intensity_23 | Median absolute deviation median | ||
intensity_24 | Kurtosis of intensity | ||
intensity_25 | Coefficient of intensity variation | ||
intensity_26 | Intensity percentile quartile spacing | ||
intensity_27 | Mean absolute deviation of intensity | ||
intensity_28–42 | In a specific statistical unit, the internally normalized LiDAR point cloud is sorted by intensity, and the cumulative intensity of all points is calculated. The cumulative intensity at which x% of points within each statistical unit are located represents the cumulative intensity percentile of that unit. x ∈ (1, 5, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99) |
VI ID | VI Name | Formula | VI ID | VI Name | Formula |
---|---|---|---|---|---|
VI_1 | Anthocyanin Reflectance Index 1 (ARI1) | VI_2 | Anthocyanin Reflectance Index 2 (ARI2) | ||
VI_3 | Atmospherically Resistant Vegetation Index (ARVI) | VI_4 | Burn Area Index (BAI) | ||
VI_5 | Clay Minerals (CM) | VI_6 | Difference Vegetation Index (DVI) | ||
VI_7 | Enhanced Vegetation Index (EVI) | VI_8 | Ferrous Minerals (FM) | ||
VI_9 | Global Environmental Monitoring Index (GEMI) | VI_10 | Green Atmospherically Resistant Index (GARI) | ||
VI_11 | Green Chlorophyll Index (GCI) | VI_12 | Green Difference Vegetation Index (GDVI) | ||
VI_13 | Green Leaf Index (GLI) | VI_14 | Green NDVI (GNDVI) | ||
VI_15 | Green OSAVI (GOSAVI) | VI_16 | Green Ratio Vegetation Index (GRVI) | ||
VI_17 | Green SAVI (GSAVI) | VI_18 | Green Vegetation Index (GVI) | ||
VI_19 | Infrared Percentage Vegetation Index (IPVI) | VI_20 | Iron Oxide (IO) | ||
VI_21 | Leaf Area Index (LAI) | VI_22 | Modified Chlorophyll Absorption Ratio Index (MCARI) | ||
VI_23 | MCARI-Improved (MCARI2) | VI_24 | Non-Linear Index (NLI) | ||
VI_25 | Modified NDWI (MNDWI) | VI_26 | Modified Red Edge NDVI (MRENDVI) | ||
VI_27 | Modified Red Edge Simple Ratio (MRESR) | VI_28 | Modified Simple Ratio (MSR) | ||
VI_29 | Modified SAVI2 (MSAVI2) | VI_30 | Triangular Greenness Index (TGI) | ||
VI_31 | Modified Triangular Vegetation Index (MTVI) | VI_32 | MTVI-Improved (MTVI2) | ||
VI_33 | Normalized Burn Ratio (NBR) | VI_34 | Normalized Difference Built-Up Index (NDBI) | ||
VI_35 | Normalized Difference Mud Index (NDMI) | VI_36 | Normalized Difference Snow Index (NDSI) | ||
VI_37 | NDVI | VI_38 | Optimized SAVI (OSAVI) | ||
VI_39 | Plant Senescence Reflectance Index (PSRI) | VI_40 | Red Edge NDVI (RENDVI) | ||
VI_41 | Red Edge Position Index (REPI) | VI_42 | Red Green Ratio Index (RGRI) | ||
VI_43 | Renormalized DVI (RDVI) | VI_44 | Simple Ratio (SR) | ||
VI_45 | Soil Adjusted VI (SAVI) | VI_46 | Structure Insensitive Pigment Index (SIPI) | ||
VI_47 | Sum Green Index (SGI) | VI_48 | Transformed Chlorophyll Absorption Reflectance Index (TCARI) | ||
VI_49 | Transformed Difference VI (TDVI) | VI_50 | Triangular Vegetation Index (TVI) | ||
VI_51 | Triangular Vegetation Index (TVI) | VI_52 | Visible Atmospherically Resistant Index (VARI) | ||
VI_53 | Wide Dynamic Range VI (WDRVI) | VI_54 | WorldView Built-Up Index (WV-BI) | ||
VI_55 | WorldView Improved VI (WV-VI) | VI_56 | WorldView Non-Homogeneous Feature Diff (WV-NHFD | ||
VI_57 | WorldView Water Index (WV-WI) |
Study Area | Number of Plots | Aboveground Biomass (Unit: t/ha) | |||
---|---|---|---|---|---|
Min | Max | Mean | CV * (%) | ||
Diaoluoshan forest park | 27 | 110.408 | 169.424 | 144.135 | 9.91 |
Qingpilin reserve | 10 | 90.223 | 144.168 | 119.946 | 13.08 |
Fengmu forest farm | 12 | 89.162 | 185.878 | 153.010 | 23.94 |
All | 49 | 89.162 | 185.878 | 141.372 | 17.30 |
Data | Number of Features | R2CV | R2Val | RPD | |||
---|---|---|---|---|---|---|---|
Mean | SD * | Mean | SD | Mean | SD | ||
UAV–LiDAR | 6 (SPA) | 0.628 | 0.149 | 0.670 | 0.230 | 1.752 | 0.432 |
98 (all) | 0.585 | 0.172 | 0.617 | 0.233 | 1.701 | 0.444 | |
Sentinel-2B imagery | 13 (SPA) | 0.466 | 0.128 | 0.522 | 0.174 | 1.416 | 0.214 |
69 (all) | 0.439 | 0.146 | 0.507 | 0.209 | 1.376 | 0.228 | |
UAV–LiDAR + Sentinel-2B imagery | 7 (SPA) | 0.697 | 0.095 | 0.749 | 0.154 | 2.101 | 0.661 |
167 (all) | 0.691 | 0.126 | 0.681 | 0.252 | 1.945 | 0.809 |
Study Area | UAV–LiDAR | Sentinel-2B Imagery | Coupled Data | |||
---|---|---|---|---|---|---|
SPA–RF | RF | SPA–RF | RF | SPA–RF | RF | |
Diaoluoshan forest park | 0.042 | 0.049 | 0.090 | 0.083 | 0.045 | 0.047 |
Qingpilin reserve | 0.158 | 0.099 | 0.147 | 0.142 | 0.079 | 0.130 |
Fengmu forest farm | 0.095 | 0.146 | 0.139 | 0.171 | 0.124 | 0.074 |
All | 0.081 | 0.083 | 0.114 | 0.117 | 0.071 | 0.073 |
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Chen, Z.; Yang, X.; Pan, X.; Wu, T.; Lei, J.; Chen, X.; Li, Y.; Chen, Y. Estimating Forest Aboveground Biomass in Tropical Zones by Integrating LiDAR and Sentinel-2B Data. Sustainability 2025, 17, 3631. https://doi.org/10.3390/su17083631
Chen Z, Yang X, Pan X, Wu T, Lei J, Chen X, Li Y, Chen Y. Estimating Forest Aboveground Biomass in Tropical Zones by Integrating LiDAR and Sentinel-2B Data. Sustainability. 2025; 17(8):3631. https://doi.org/10.3390/su17083631
Chicago/Turabian StyleChen, Zongzhu, Xiaobo Yang, Xiaoyan Pan, Tingtian Wu, Jinrui Lei, Xiaohua Chen, Yuanling Li, and Yiqing Chen. 2025. "Estimating Forest Aboveground Biomass in Tropical Zones by Integrating LiDAR and Sentinel-2B Data" Sustainability 17, no. 8: 3631. https://doi.org/10.3390/su17083631
APA StyleChen, Z., Yang, X., Pan, X., Wu, T., Lei, J., Chen, X., Li, Y., & Chen, Y. (2025). Estimating Forest Aboveground Biomass in Tropical Zones by Integrating LiDAR and Sentinel-2B Data. Sustainability, 17(8), 3631. https://doi.org/10.3390/su17083631