A Dual-Variable Selection Framework for Enhancing Forest Aboveground Biomass Estimation via Multi-Source Remote Sensing
Integrating multi-source remote sensing can improve the accuracy of forest aboveground biomass (AGB) estimation. However, the accuracy and stability of the forest AGB estimation results are affected by multiple remote sensing feature variables as well as parameter tuning of machine learning algorith...
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| Päätekijät: | , , , , , , , , , |
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| Aineistotyyppi: | Artikkeli |
| Kieli: | englanti |
| Julkaistu: |
MDPI AG
2025-07-01
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| Sarja: | Remote Sensing |
| Aiheet: | |
| Linkit: | https://www.mdpi.com/2072-4292/17/14/2493 |
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| Yhteenveto: | Integrating multi-source remote sensing can improve the accuracy of forest aboveground biomass (AGB) estimation. However, the accuracy and stability of the forest AGB estimation results are affected by multiple remote sensing feature variables as well as parameter tuning of machine learning algorithms. To this end, this study employed six types of remote sensing data—Landsat 8 OLI, Sentinel-2A, GEDI, ICESat-2, ALOS-2, and SAOCOM. A dual-variable selection strategy based on SHapley Additive exPlanations (SHAP) was developed, and a genetic algorithm (GA) was used to optimize the parameters of five machine learning models—elastic net (EN), least absolute shrinkage and selection operator (Lasso), support vector regression (SVR), Random Forest (RF), and Categorical Boosting (CatBoost)—to estimate the AGB of <i>Pinus kesiya</i> var. <i>langbianensis</i> forest in Wuyi Village, Zhenyuan County. The dual-variable selection strategy integrates SHAP with the Pearson correlation coefficient (PC), RF, EN, and Lasso to enhance feature screening robustness and interpretability. The results of the study showed that Lasso-SHAP dual-variate screening was more stable than SHAP univariate screening. In particular, the Lasso-SHAP strategy improved the average R<sup>2</sup> from 0.59 (using SHAP alone) to above 0.70, achieving an enhancement of 11%. Among GA-optimized parametric machine learning models, the linear GA-Lasso achieved the best performance, with an R<sup>2</sup> of 0.91 and an RMSE of 12.94 Mg/ha, followed by the GA-EN model (R<sup>2</sup> = 0.89, RMSE = 14.46 Mg/ha). For nonlinear models, GA-SVR performed the best (R<sup>2</sup> = 0.74, RMSE = 22.07 Mg/ha), surpassing the GA-CatBoost model (R<sup>2</sup> = 0.64, RMSE = 25.88 Mg/ha). In summary, the Lasso-SHAP dual-variable selection strategy effectively improves the estimation accuracy of AGB for <i>Pinus kesiya</i> var. <i>langbianensis</i> forests, while GA-optimized machine learning models demonstrate excellent performance, providing strong support for regional-scale forest resource monitoring and carbon stock assessment. |
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| ISSN: | 2072-4292 |