Forest height plays a crucial role in various fields, such as forest ecology, resource management, natural disaster management, and environmental protection. In order to obtain accurate and efficient measurements of forest height over large areas, in this study, Terra Synthetic Aperture Radar-X and the TerraSAR-X Add-on for Digital Elevation Measurement (TerraSAR-X/TanDEM-X), Sentinel-2A, and Shuttle Radar Topography Mission (SRTM) data were used, and various feature combinations were established in conjunction with measurements from Light Detection and Ranging (LiDAR). Classification and regression tree (CART), gradient-boosting decision tree (GBDT), random forest (RF), and support vector machine (SVM) algorithms were employed to estimate forest height in the study area. Independent validation on the basis of LiDAR forest height samples showed the following results: (1) Regarding feature combinations, the combination of coherence and decorrelation of volume scattering provided by TerraSAR-X/TanDEM-X data outperformed the combination of backscatter coefficient and local incidence angle, as well as the combination of coherence, decorrelation of volume scattering, backscatter coefficient, and local incidence angle. The best results (R² = 0.67, RMSE = 2.89 m) were achieved with the combination of coherence and decorrelation of volume scattering using the GBDT and RF algorithms. (2) In terms of machine learning methods, the GBDT algorithm proved suitable for estimating forest height. The most effective approach for forest height mapping involved combining the GBDT algorithm with coherence, decorrelation of volume scattering, and a small amount of LiDAR forest height data, used as training data.

Ice, Cloud, and land Elevation Satellite (ICESat-2) carries the Advanced Topographic Laser Altimeter System (ATLAS), which enhancing along-track sampling density but introduces substantial noise in photon point cloud data. Therefore, this study establishes a denoising and classification feature parameter system grounded in the three-dimensional spatial distribution characteristics of photon point clouds. Modeling is conducted in two layers: one layer for upper noise photons and canopy signal photons, and another layer for lower noise photons and ground signal photons. Machine learning and neural network algorithms are utilized to denoise and classify the original photon point clouds from ICESat-2, aiming to obtain a transferable and universally applicable supervised classification model for denoising photon point clouds. Recall, Precision, and the harmonic mean of Recall and Precision (F1 Score) are used as evaluation metrics to verify the accuracy of local, transfer, and global models. The results indicate that under various forest types and external conditions, the proposed photon point cloud Layered Denoising and Classification Model (LDCM) outperforms the Differential Regressive and Gaussian Adaptive Nearest Neighbor (DRAGANN, ICESat-2 ATL08 production algorithm), Ordering Points to Identify the Clustering Structure (OPTICS), and Adaptive Elevation Difference Thresholding (AEDTA) algorithms in terms of accuracy. Compared to the DRAGANN algorithm, the maximum accuracy improvement is 60%, with an average improvement of approximately 20%; compared to the OPTICS algorithm, the maximum accuracy improvement is 36%, with an average improvement of about 28%; compared to the AEDTA algorithm, the maximum accuracy improvement is 27%, with an average improvement of about 14%. The F1 Score for the validation set of the machine learning and neural network algorithms is above 0.94, with the Categorical Boosting (CatBoost) algorithm achieving the best performance. Both the transfer model and the global model have F1 Scores above 0.90. Therefore, the proposed photon point cloud LDCM not only demonstrates excellent classification accuracy but also exhibits good transferability and general applicability.