Estimating the girth distribution of rubber trees using support and relevance vector machines

Trisasongko, Bambang Hendro; Panuju, Dyah Retno; Sholihah, Rizqi I’anatus; Karyati, Nur Etika

doi:10.1007/s12518-024-00550-1

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…Machine learning techniques have been proven to have great potential in handling a large number of parameters for building non-linear models [34,35]. Currently, machine learning algorithms such as Random Forest (RF), Extreme Gradient Boosting Regressor (XGBR), Support Vector Regression (SVR), and K Nearest Neighbors Regression (KNNR) are widely used for forest dynamic monitoring, including research directions such as forest cover and land use change, forest health, and pest monitoring [36][37][38]. They are also widely applied in biomass estimation studies, such as estimating the AGB of mangroves [39] and forests [40], nitrogen nutrition status in winter wheat [41], and predicting corn yield [42].…”

Section: Introductionmentioning

confidence: 99%

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

Fu,

Tan,

Kou

et al. 2024

Forests

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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.

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