Accurate leaf area index estimation for Eucalyptus grandis using machine learning method with GF-6 WFV—A case study for Huangmian town, China - Zhou, Xiangjun; Liang, Bin; He, Jianan; He, Wen (Frontiers in Forests and Global Change, 2024)
Spectral and texture features play important roles in plantation leaf area index (LAI) estimation, and their combination may enhance LAI inversion accuracy. Furthermore, research on the impact of different machine learning (ML) models on their hyperparameter combinations and splitting ratios remains challenging. In our study, experiments based on spectral and textural features of GF-6 WFV data were conducted on Eucalyptus grandis plantation forests in Huangmian Town, Guangxi, China. ML methods such as multiple stepwise regression (MSR), random forest (RF), back-propagation neural network (BPNN), and support vector regression (SVR) were mainly utilized to perform model hyper-parameter tuning and split-ratio analysis in order to estimate the LAI. The results of the study showed that spectral and gray level co-occurrence matrix (GLCM) texture features were very sensitive to changes in Eucalyptus grandis LAI. The accuracy of combining the two was 10% higher than when they were not combined. Furthermore, it was found that the nonlinear methods (RF, BPNN, and SVR) outperformed the linear method (MSR), with the average Rmax2 of the nonlinear model being 26% higher than that of the linear model, and the RMSE value being 29% lower than that of the linear model. In addition, by analyzing different combinations of features, model hyperparameter fine-tuning, and splitting ratios in the nonlinear model, it was found that the splitting ratios of different combinations of model hyperparameters have a great impact on the accuracy of the model. A total of 12 out of 21 data sets showed high accuracy and stability at a split ratio of 8.5:1.5 (ratio of 0.85), with the best-performing RF model differing from the lowest by 91% for Rmax2 and 39% for Rstd2. Combining spectral and texture features provides highly accurate inversion data. Model hyper-parameter fine-tuning and segmental scale tuning can facilitate the application of inversion data to fully utilize the optimal performance of the ML model.