Collection Overview:
This dataset was collected to provide high-density, contemporary ground-truth data on 3D vegetation structure in the fire-prone montane ecosystems of Southern California. It served as a benchmark for developing and validating deep learning models designed to enhance sparse, historical airborne light detection and ranging (lidar) data by fusing it with multi-temporal optical National Agriculture Imaging Program (NAIP) and L-band synthetic aperture radar (UAVSAR) imagery for applications in ecological modeling and land management.