Affiliation:
1. College of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming 650223, China
2. Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, Kunming 650223, China
Abstract
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.
Funder
the National Natural Science Foundation of China
the Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, Southwest Forestry University
the Joint Special Project for Agriculture of Yunnan Province
Ten Thousand Talents Program Special Project for Young Top-notch Talents of Yunnan Province
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