Abstract
Near earth sensing from unmanned aerial vehicles or UAVs has emerged as
a potential approach for fine-scale environmental monitoring. These
systems provide a cost-effective and repeatable means to acquire
remotely sensed images in unprecedented spatial detail and high
signal-to-noise ratio. It is becoming increasingly possible to obtain
both physiochemical and structural insights of the environment using
state-of-art light detection and ranging (LiDAR) sensors integrated onto
UAVs. Monitoring of sensitive environments, such as swamp vegetation in
longwall mining areas is important, yet challenging due to their
inherent complexities. Current practices for monitoring these remote and
difficult environments are primarily ground-based. This is partly due to
an absent framework and challenges of using UAV-based sensor systems in
monitoring such sensitive environments. This research addresses the
related challenges in the development of a LiDAR system including a
workflow for mapping and potentially monitoring highly heterogeneous and
complex environments. This involves the amalgamation of several design
components, which include hardware integration, calibration of sensors,
mission planning, and designing of a processing chain to generate usable
datasets. It also includes the creation of new methodologies and
processing routines to establish a pipeline for efficient data retrieval
and generation of usable products. The designed systems and methods were
applied on a peat swamp environment to obtain accurate geo-spatialised
LiDAR point cloud. Performance of the LiDAR data was tested against
ground-based measurements on various aspects including visual assessment
for generation LiDAR metrices maps, canopy height model, and fine-scale
mapping.