  Forest land plays a vital role in global climate, ecosystems, farming and
human living environments. Therefore, forest biomass estimation methods are
necessary to monitor changes in the forest structure and function, which are
key data in natural resources research. Although accurate forest biomass
measurements are important in forest inventory and assessments, high-density
measurements that involve airborne light detection and ranging (LiDAR) at a low
flight height in large mountainous areas are highly expensive. The objective of
this study was to quantify the aboveground biomass (AGB) of a plateau
mountainous forest reserve using a system that synergistically combines an
unmanned aircraft system (UAS)-based digital aerial camera and LiDAR to
leverage their complementary advantages. In this study, we utilized digital
aerial photogrammetry (DAP), which has the unique advantages of speed, high
spatial resolution, and low cost, to compensate for the deficiency of forestry
inventory using UAS-based LiDAR that requires terrain-following flight for
high-resolution data acquisition. Combined with the sparse LiDAR points
acquired by using a high-altitude and high-speed UAS for terrain extraction,
dense normalized DAP point clouds can be obtained to produce an accurate and
high-resolution canopy height model (CHM). Based on the CHM and spectral
attributes obtained from multispectral images, we estimated and mapped the AGB
of the region of interest with considerable cost efficiency. Our study supports
the development of predictive models for large-scale wall-to-wall AGB mapping
by leveraging the complementarity between DAP and LiDAR measurements. This work
also reveals the potential of utilizing a UAS-based digital camera and LiDAR
synergistically in a plateau mountainous forest area.
