Combining airborne LiDAR data and optical Imagery for improved national-scale beach topography estimation: a case study in New Zealand

Accurate beach topography mapping is crucial for understanding coastal dynamics and mitigating climate change impacts. However, traditional methods such as airborne LiDAR have limitations, leading to substantial gaps in national-scale elevation data. This study presents an innovative framework to reconstruct missing elevation data along New Zealand's (NZL) coastline by integrating airborne LiDAR, Sentinel-2 optical imagery, and geometric features (distance) using machine learning (ML) methods. Our results show that artificial neural network (ANN) emerged as the best model (test set: R² =0.79, root mean squared error (RMSE) = 0.91 m; validation set: 0.79, RMSE = 0.93 m), outperforming other models in accuracy. The produced 10-m digital elevation model (DEM) for national-scale sandy beaches expands area coverage by 286.6% (114.15 km²), filling gaps in 1249 beaches, including remote areas such as Stewart Island. This novel framework offers a scalable solution for improving the comprehensiveness and accuracy of beach topography. It provides essential support for inundation prediction, habitat management, and the development of climate adaptation strategies, thereby facilitating more informed decision-making in coastal zone management and climate change mitigation efforts.