机器学习在森林地上生物量估测中的应用OACHSSCD

Accurate estimation of forest aboveground biomass(AGB)is essential for assessing forest carbon sequestration and underpinning sustainable forest management.The choice of machine learning models depends heavily on data quality and scenario features.This paper reviews the application of machine learning in forest AGB estimation:1)For small-to-medium samples and the scenarios with explicit engineered features,it evaluates the strengths and limitations of traditional models such as random forest,support vector machine,and gradient boosting trees.2)For the needs of high-dimensional features and hyperparameter optimization,it assesses the effectiveness of global optimization methods,including Bayesian optimization,particle swarm optimization,and genetic algorithms,in improving model accuracy,along with their trade-offs in computational cost and convergence stability.3)For massive raw remote sensing data,it examines the strong potential of deep learning models,exemplified by convolutional neural networks,in end-to-end feature learning.Based on these analyses,the paper proposes the future research directions in intelligent feature engineering,data fusion with synergistic inversion,and improved interpretability of deep learning models,with the aim of providing a reference for science-based selection and optimization of machine learning models under varying data regimes,and advancing remote sensing-based forest AGB estimation towards higher accuracy and intelligence.