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Remote sensing inversion of forest aboveground carbon storage based on GAC feature selection and AST regression algorithm
- “Researchers proposed a stacked ensemble learning regression model AST, which combines genetic algorithm and CatBoost feature selection method GAC to screen remote sensing variables. Combined with the Hyperopt hyperparameter optimization framework, four machine learning models, AdaBoost, CatBoost, Random Forest, and LightGBM, were stacked and integrated to achieve high-precision estimation of aboveground carbon storage in high-altitude pine forests, providing new technical support for regional scale forest carbon storage inversion.”
- Vol. 30, Issue 8, Pages: 2463-2485(2026)
DOI:10.11834/jrs.20265246
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