Data from: From lidar waveforms to vegetation products: 7380 km2 of high-resolution airborne and simulated GEDI data over Sierra Nevada, California

Vegetated ecosystems have complex three-dimensional (3D) canopy structures
with diverse plant type assemblages, crown architectures and historical
disturbances. Vegetation structure changes dramatically with strong
topographic, edaphic and climate gradients. Airborne lidar remote sensing
has the potential to measure fine-scale 3D proprieties of forests with
limited temporal and spatial coverage limitations due to prohibitively
cost. Here, we present a composite high-resolution airborne lidar dataset
covering 7380 km2 over the Sierra Nevada, California, that has been
calculated from a time-series (2014-2017) collected by the NASA-JPL
Airborne Snow Observatory (ASO). We coherently merge low resolution (~1 pt
m-2) ASO lidar measurements, primarily acquired to quantify snow volume
and dynamics, to produce high-resolution point clouds that better describe
forest structure and underlying topography (Ferraz et al., 2016). The
methodology addresses the removal of lidar points corresponding to snow
cover as well as the spatial bias in multi-temporal data due to
uncertainties in platform trajectory and motion. We then derived many
user-friendly raster products on 3D forest structure (relative heights,
plant area index, foliage height diversity), forest functional diversity
(richness, evenness, beta diversity) and topographic indexes (topographic
wetness index, terrain ruggedness, slope) with spatial resolutions varying
from 10 m to 1 km. Our dataset enables the study of landscape-scale
studies on a myriad of applications (carbon storage, habitat niche and
quality, hydrology, fire hazard and behavior,) over a large region rich in
forest types diversity (e.g. alpine, montane, sub-montane forests), with
contrasting land uses (e.g. protected vs managed forest) and well
documented historical disturbances (e.g. fire).