HyperHeight Lidar Compressive Sampling and Machine Learning Reconstruction of Forested Landscapes

Abstract

Low-altitude airborne lidars deliver high spatial resolution swath mapping using dense laser footprint sampling but only in limited areas, while satellite lidars offer global sampling but are hampered by low resolution due to sparse footprints. This work presents a novel approach to satellite lidar remote sensing designed to address the low spatial resolution by leveraging the principles of compressive sensing and machine learning applied to a highly efficient, adaptive lidar capable of dense footprint sampling. Compressive sensing enables the distribution of footprints across a swath with a density appropriate to recover the features of interest, without unnecessarily oversampling the terrain. Machine learning techniques are employed to reconstruct the compressive lidar measurements, leading to high-resolution, dense coverage, and a broad field-of-view per swath pass. HyperHeight Data Cubes are introduced, which offer a wealth of information about the 3D structure of a scene, including digital surface models, canopy height models and the internal organization of canopies. Training data was obtained from NASA's G-LiHT airborne lidar, and simulations of satellite observations performed on various forest types across the US demonstrate the efficacy of the new lidar imaging approach.