IEEE Geoscience and Remote Sensing Magazine - September 2023 - 36

(a)
(b)
(c)
(d)
FIGURE 18. Noisy 3D returns caused by (a) smoke, (b) unknown flying objects, (c) cloud, and (d) solar background noise or rain.
of data voids (refer to the " Occlusions/Shadows " section).
Instantaneous rainfall or snow storms during airborne lidar
surveys generate random scattered returns, which are
challenging to remove manually. Similar scenarios have
been reported in photon-counting lidar systems, such as
the Leica SPL-100, where the system's detector is sensitive
to solar background photons [7]. As a result, the solar background
noise contaminates the collected point clouds. Unlike
the previously mentioned unwanted returns caused by
clouds or smoke, random noisy returns are challenging to
remove manually.
There are two ways to remove the outliers when
one faces the preceding situations. Users can perform denoising
on the point cloud, based on different 3D spatial
filters [102]; clustering approaches, such as principal
component analysis [103] and density-based spatial clustering
of applications with noise [104]; or deep learning
approaches [105]. Unlike those point clouds collected by
low-cost sensors, the density of outliers, in most cases, is
significantly lower than the point cloud itself. Therefore,
one may simply investigate the distance between any pair
of closest points or construct a voxel grid to assess the local
density [7] so as to look for the outliers. If end users
are unable to have the original point clouds on hand, a
despiking algorithm, such as the despike virtual deforestation
algorithm [106] or spike-free algorithm [107], can
be applied to the triangular area network (TIN) for removing
the spiky features and negative blunders as well
as smoothening the terrain.
PITS AND SPIKES IN CANOPY HEIGHT MODELS
The penetrating property of lasers and the ability of lidar
systems to probe and reconstruct environments in
3D have allowed scientists to adopt airborne lidar data
to retrieve/understand forest structures and properties.
Although airborne lidar systems have been proved to be
superior to optical imaging sensors in fine-scale forestry
studies [108], [109], the collected datasets have certain
imperfections disrupting the retrieval of forest inventory
parameters. Aside from the occlusion effects caused by
foliage and upper canopies, as mentioned in the " Occlusions/Shadows "
section, the retrieved CHMs often suffer
from randomly distributed data pits and spikes [see Figure
19(a)]. These unnatural pits cause a high fluctuation
of elevations in the CHMs, which may influence the estimation
of tree heights, crown boundaries, basal areas,
and stand volumes or induce errors in treetop detection
[107], [110], [111].
Due to the natural variation of canopies and irregu(a)
(b)
FIGURE
19. (a) A CHM suffering from data pits. (b) The CHM after
the data pits are removed.
36
larities in tree heights, the emitted laser beams usually
produce a number of returns from the upper canopies,
tree trunks, branches, and foliage before reaching the
understory or even hitting the ground [see Figure 2(b)].
Occasionally, laser beams may not interact with any
overstory and simply penetrate deeply into the crown,
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE SEPTEMBER 2023
IEEE Geoscience and Remote Sensing Magazine - September 2023

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