Enhancing Mangrove Aboveground Biomass Estimation with UAV-LiDAR: A Novel Mutual Information-Based Feature Selection Approach
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition and Processing
2.2.1. Field Data Acquisition and Processing
2.2.2. UAV-LiDAR Data Acquisition and Processing
2.3. Research Methodology
2.3.1. Technical Routes
2.3.2. Variable Selection Methods
- Zero-Importance Feature Selection
- 2.
- Zero-Importance Feature Selection Based on Mutual Information
2.3.3. Machine Learning Algorithms
- Random Forest Algorithm
- 2.
- Extreme Gradient Boosting Algorithm
- 3.
- Support Vector Machine Algorithm
2.3.4. Model Accuracy Assessment
3. Results
3.1. Mangrove Identification Result
3.2. Assessment of the Importance of Variables
- Zero-Importance Feature Selection
- 2.
- Zero-Importance Feature Selection Based on Mutual Information
3.3. Modeling Accuracy Evaluation and Comparison
3.4. Spatial Distribution of Mangrove Aboveground Biomass
4. Discussion
4.1. Data Selection and Variable Screening Methods
4.2. Inversion Model Selection
4.3. Research Deficiencies and Future Research Directions
- (1)
- The zero-importance feature selection method based on mutual information has certain advantages in feature selection, but it also has some drawbacks and limitations. This method relies on accurate estimation of mutual information, which is sensitive to data distribution and sample size. Particularly in high-dimensional data or small sample scenarios, this can lead to inaccurate estimations, thereby affecting the reliability of feature selection. Furthermore, the method has a high computational complexity, especially when handling large-scale data, as the time and space costs of computing mutual information are substantial, which restricts its scalability in practical applications.
- (2)
- Current UAV-LiDAR-based AGB inversion studies are predominantly region-specific, with models optimized for particular areas. These models may not maintain comparable accuracy when applied to different regions due to variations in mangrove growth conditions, such as climate, soil properties, and nutrient availability, leading to potential declines in estimation precision during cross-regional applications.
5. Conclusions
- (1)
- The SVM model exhibited the highest fitting ability for mangrove AGB estimation; with the addition of a zero-importance feature selection method based on mutual information, the test set accuracy reached an R2 of 0.8853 and RMSE of 0.4766 kg/m2, while the test set accuracy was R2 of 0.7548 and RMSE of 0.6074 kg/m2 when combined with the zero-importance feature variable selection method. Application of the mutual information-based zero-importance feature selection method to the RF and XGBoost models resulted in less impressive test set accuracies. RF achieved R2 = 0.8277 and RMSE = 0.4340 kg/m2, while XGBoost resulted in R2 = 0.3896 and RMSE = 1.3140 kg/m2. Compared with SVM, the fitting capability is relatively less prominent, and the prediction accuracy indicates that XGBoost is less suitable for estimating mangrove AGB in this region. These results underscore the effectiveness of the mutual information-based zero-importance feature selection method in identifying key variables and highlight the superior performance of the SVM model for mangrove AGB estimation. This study substantially enhances accuracy of mangrove AGB inversion and provides innovative methods for future research.
- (2)
- Predicted AGB values for mangrove forests in the study area ranged from a low value of 1.97 kg/m2 to a high value of 5.23 kg/m2, with an average value of 3.83 kg/m2. This distribution reflects relatively uniform growth conditions in the study area, primarily due to the pristine state of the mangrove ecosystem and minimal human disturbance.
- (3)
- Canopy height, a key LiDAR-derived feature, played a critical part in improving the accuracy of mangrove AGB estimation. The inclusion of canopy height features in the model remarkably enhanced predictive capability compared with intensity features, also derived from LiDAR data. These results highlight the importance of vertical structure in accurately modeling AGB in mangrove ecosystems.
- (4)
- The estimation of aboveground biomass in mangrove forests provides crucial references for the protection and restoration of mangrove ecosystems in the region, contributing to the sustainable development of the local ecosystem.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | Variables | Variable Description |
---|---|---|
Height Metrics | elev_per/elev_AIH01, 05, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99 | Height percentile corresponding to all point clouds at 1%, 5%, 10%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80%, 90%, 95%, and 99% quantile/cumulative height percentile |
elev _mean, elev _median, elev _max, elev _min, elev _madmedian | Mean, median, maximum, minimum and median absolute deviation of heights | |
elev_stddev, elev_var, elev_cv, elev_kurtosis, elev_skewness, elev_crr. | Standard deviation, variance, coefficient of variation, kurtosis, skewness, canopy undulation rate of height | |
elev_sqrt_mean_sq, elev_curt_mean_cube, elev_IQ, elev_AIH_IQ | Generalized mean of quadratic and cubic (root mean square/cubic root of height), interquartile spacing of heights, cumulative interquartile spacing of heights | |
Density Metrics | elev_density1, 2, 3, 4, 5, 6, 7, 8, 9,10 | Ratio of total number of point clouds in each layer of the point cloud from bottom to top |
Intensity Metrics | int_per01, 05, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99 | Corresponding intensity percentiles at the 1%, 5%, 10%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80%, 90%, 95%, and 99% quartiles of all point clouds |
int _mean, int _median, int _max, int_min, int _madmedian | Mean, median, maximum, minimum and median absolute deviation of intensity | |
int _std, int _var, int _cv,int_kurtosis, int _skewness, | Standard deviation, variance, coefficient of variation, kurtosis, skewness for intensity | |
int_IQ | Intensity quartile spacing |
Tree Species | Statistic | Plant Height (m) | Diameter at Breast Height (cm) | Single Plant AGB/(kg) |
---|---|---|---|---|
Rhizophora stylosa | Average value | 3.19 | 4.43 | 5.3 |
Maximum values | 6 | 5.67 | 7.62 | |
Minimum value | 1.8 | 3 | 2.39 |
Variables | Overall Rating | Variables | Overall Rating | Variables | Overall Rating |
---|---|---|---|---|---|
elev_AIH25 | 0.9092 | elev_per10 | 0.8483 | elev_AIH70 | 0.8169 |
elev_AIH90 | 0.8983 | elev_per20 | 0.8438 | elev_AIH95 | 0.8100 |
density4 | 0.8935 | elev_AIH40 | 0.8433 | elev_per25 | 0.8038 |
elev_skewness | 0.8836 | elev_per50 | 0.8388 | elev_AIH50 | 0.7992 |
elev_AIH30 | 0.8696 | elev_per90 | 0.8289 | elev_per40 | 0.7990 |
elev_AIH20 | 0.8681 | elev_AIH60 | 0.8283 | elev_per70 | 0.7953 |
elev_AIH10 | 0.8662 | elev_per05 | 0.8278 | elev_sqrt_mean_sq | 0.7892 |
density5 | 0.8501 | elev_per80 | 0.82676 | elev_AIH05 | 0.7873 |
elev_per75 | 0.8491 | elev_kurtosis | 0.8182 |
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Huang, S.; Zhang, Z.; Sun, Y.; Song, W.; Meng, J. Enhancing Mangrove Aboveground Biomass Estimation with UAV-LiDAR: A Novel Mutual Information-Based Feature Selection Approach. Sustainability 2025, 17, 3004. https://doi.org/10.3390/su17073004
Huang S, Zhang Z, Sun Y, Song W, Meng J. Enhancing Mangrove Aboveground Biomass Estimation with UAV-LiDAR: A Novel Mutual Information-Based Feature Selection Approach. Sustainability. 2025; 17(7):3004. https://doi.org/10.3390/su17073004
Chicago/Turabian StyleHuang, Shan, Zhiwei Zhang, Yonggen Sun, Weilong Song, and Jianing Meng. 2025. "Enhancing Mangrove Aboveground Biomass Estimation with UAV-LiDAR: A Novel Mutual Information-Based Feature Selection Approach" Sustainability 17, no. 7: 3004. https://doi.org/10.3390/su17073004
APA StyleHuang, S., Zhang, Z., Sun, Y., Song, W., & Meng, J. (2025). Enhancing Mangrove Aboveground Biomass Estimation with UAV-LiDAR: A Novel Mutual Information-Based Feature Selection Approach. Sustainability, 17(7), 3004. https://doi.org/10.3390/su17073004