  Computational models for understanding and predicting fire in wildland and
managed lands are increasing in impact. Data characterizing the fuels and
environment is needed to continue improvement in the fidelity and reliability
of fire outcomes. This paper addresses a gap in the characterization and
population of mid-story fuels, which are not easily observable either through
traditional survey, where data collection is time consuming, or with remote
sensing, where the mid-story is typically obscured by forest canopy. We present
a methodology to address populating a mid-story using a generative model for
fuel placement that captures key concepts of spatial density and heterogeneity
that varies by regional or local environmental conditions. The advantage of
using a parameterized generative model is the ability to calibrate (or `tune')
the generated fuels based on comparison to limited observation datasets or with
expert guidance, and we show how this generative model can balance information
from these sources to capture the essential characteristics of the wildland
fuels environment. In this paper we emphasize the connection of terrestrial
LiDAR (TLS) as the observations used to calibrate of the generative model, as
TLS is a promising method for supporting forest fuels assessment. Code for the
methods in this paper is available.
