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\begin{document}

\title{Addressing Underestimation in Global Forest Structure Mapping}



\author[1]{Jake Lee}%
\author[1]{Samuel Favrichon}%
\author[1]{Steffen Mauceri}%
\author[1]{Yan Yang}%
\author[2]{John Armston}%
\author[1]{Sassan Saatchi}%
\affil[1]{Jet Propulsion Laboratory, California Institute of Technology}%
\affil[2]{Dept. of Geographical Sciences, University of Maryland}%


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  \date{June 10, 2024}


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\begin{abstract}
Mapping global vertical vegetation structure (VS) is critical for the
quantification of global carbon stocks. While orbital LIDAR measurements
of NASA's Global Ecosystem Dynamics Investigation (GEDI) mission provide
direct estimates of vertical VS, they only cover 4\% of the global land
surface. Recent works have produced global contiguous maps of canopy
height using convolutional neural network (CNN) models and satellite
imagery. However, these models faced some challenges estimating high
canopy heights (\textgreater{}30 m) and identified some tiling artifacts
(Lang et al., 2022; Potapov et al., 2020).

We present various methods to address these limitations. First, we
remove tiling artifacts by using overlapping tiles when producing maps
and removing zero-padding from CNN architectures. Second, we compare the
benefits and limitations of a few different methods to improve high
canopy height estimation. Among the methods is histogram matching
predicted heights to nearby LIDAR measurements. Another is learning a
calibration model to correct each pixel based on similar known
measurements nearby. Finally, we borrow recent advances in deep depth
completion from the autonomous driving field to create an integrated
model that uses known values to improve the prediction map.~

To demonstrate these methods, we map global vertical VS with a CNN model
at 1km resolution using observations from Landsat, L-band synthetic
aperture radar observations from the Advanced Land Observing Satellite
(ALOS) PALSAR-2, and surface topography. For model training, GEDI
relative height metrics are filtered and aggregated into 1km grids. We
also use measurements from the Ice, Cloud and land Elevation Satellite 2
(ICESat-2), inter-calibrated with GEDI using co-located measurements.
Finally, we apply the aforementioned corrective methods to the product,
reporting global RMSE and MAE metrics, as well as visual qualitative
observations. These results open the path for unsaturated global
vertical VS products at higher 500 m, 200 m, and 100 m resolutions.%
\end{abstract}%



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