Sub-Meter Tree Height Mapping of California using Aerial Images and
  LiDAR-Informed U-Net Model
Fabien H Wagner Sophia Roberts Alison L Ritz Griffin Carter Ricardo Dalagnol Samuel Favrichon Mayumi CM Hirye Martin Brandt Philipe Ciais Sassan Saatchi
Tree canopy height is one of the most important indicators of forest biomass,
productivity, and species diversity, but it is challenging to measure
accurately from the ground and from space. Here, we used a U-Net model adapted
for regression to map the canopy height of all trees in the state of California
with very high-resolution aerial imagery (60 cm) from the USDA-NAIP program.
The U-Net model was trained using canopy height models computed from aerial
LiDAR data as a reference, along with corresponding RGB-NIR NAIP images
collected in 2020. We evaluated the performance of the deep-learning model
using 42 independent 1 km$^2$ sites across various forest types and landscape
variations in California. Our predictions of tree heights exhibited a mean
error of 2.9 m and showed relatively low systematic bias across the entire
range of tree heights present in California. In 2020, trees taller than 5 m
covered ~ 19.3% of California. Our model successfully estimated canopy heights
up to 50 m without saturation, outperforming existing canopy height products
from global models. The approach we used allowed for the reconstruction of the
three-dimensional structure of individual trees as observed from nadir-looking
optical airborne imagery, suggesting a relatively robust estimation and mapping
capability, even in the presence of image distortion. These findings
demonstrate the potential of large-scale mapping and monitoring of tree height,
as well as potential biomass estimation, using NAIP imagery.