Estimation of Rubber Plantation Biomass Based on Variable Optimization from Sentinel-2 Remote Sensing Imagery

The rapid, accurate, and non-destructive estimation of rubber plantation aboveground biomass (AGB) is essential for producers to forecast rubber yield and carbon storage. To enhance the estimation accuracy, an increasing number of remote sensing variables are incorporated into the development of mul...

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Bibliographic Details
Published inForests Vol. 15; no. 6; p. 900
Main Authors Fu, Yanglimin, Tan, Hongjian, Kou, Weili, Xu, Weiheng, Wang, Huan, Lu, Ning
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2024
Subjects
aboveground biomass
Accuracy
Algorithms
Biomass
Boruta
Carbon sequestration
Costs
Imagery
Lasers
Machine learning
Optimization
Parameter estimation
Plantations
Rankings
Regression
Remote sensing
Rubber
Rubber goods
Rubber industry
satellite
Support vector machines
Unmanned aerial vehicles
variable optimization
Variables
Vegetation
China
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Summary:The rapid, accurate, and non-destructive estimation of rubber plantation aboveground biomass (AGB) is essential for producers to forecast rubber yield and carbon storage. To enhance the estimation accuracy, an increasing number of remote sensing variables are incorporated into the development of multi-parameter models, which makes its practical application and the potential impact on predictive precision challenging due to the inclusion of non-essential or redundant variables. Therefore, this study systematically evaluated the performance of different parameter combinations derived from Sentinel-2 imagery, using variable optimization approaches with four machine learning algorithms (Random Forest Regression, RF; XGBoost Regression, XGBR; K Nearest Neighbor Regression, KNNR; and Support Vector Regression, SVR) for the estimation of the AGB of rubber plantations. The results indicate that RF achieved the best estimation accuracy (R2 = 0.86, RMSE = 15.77 Mg/ha) for predicting rubber plantation AGB when combined with Boruta-selected variables, outperforming other combinations (variable combinations obtained based on importance ranking, univariate combinations, and multivariate combinations). Our research findings suggest that the consideration of parameter-optimized remote sensing variables is advantageous for improving the estimation accuracy of forest biophysical parameters, when utilizing a large number of parameters for estimation.
ISSN:1999-4907
1999-4907
DOI:10.3390/f15060900