Abstract

Mapping forest canopy height is critical for climate modeling and forest management, and tropical forests present unique challenges for remote sensing due to their dense vegetation and complex structure. The advent of ICESat-2 and GEDI, two advanced lidar datasets, offers new opportunities for improving canopy height estimation. In this study, we used footprint-level canopy height products from ICESat-2 and GEDI, combined with features extracted from Landsat-8, PALSAR-2, and FABDEM products. The AutoGluon stacking ensemble learning algorithm was employed to construct inversion models, generating 30 m resolution continuous canopy height maps for the tropical forests of Puerto Rico. Accuracy validation was performed using the high-resolution G-LiHT airborne lidar products. Results show that tropical forest canopy height inversion remains challenging, with all models yielding relative root mean square errors (rRMSE) exceeding 0.30. The stacking ensemble model outperformed all base learners, and the GEDI-based map had slightly higher accuracy than the ICESat-2-based map, with RMSE values of 4.81 and 4.99 m, respectively. Both models showed systematic biases, but the GEDI-based model exhibited less underestimation for taller canopies, making it more suitable for biomass estimation. The proposed approach can be applied to other forest ecosystems, enabling fine-resolution canopy height mapping and enhancing forest conservation efforts.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.