return

<p>A dataset containing the return waveforms with intensity along the path. The zero padding was used
to make sure every waveforms has the same length. In this case, 0 means no recorded intensity.</p>

datasets

A wealth of Full Waveform (FW) Light Detection and Ranging (LiDAR) data are available to
the public from different sources, which is poised to boost the extensive
application of FW LiDAR data. However, we lack a handy and open source tool
that can be used by ecological and remote sensing communities for processing and analyzing FW LiDAR
data. To this end, we introduce 'waveformlidar', an R package dedicated to
FW LiDAR processing, analysis and visualization as a solution to the constraint.
Specifically, this package provides several commonly used waveform processing methods
such as Gaussian, adaptive Gaussian and Weibull decompositions, and
deconvolution approaches (Gold and Richard-Lucy (RL)) with customized settings.
In addition, we also develop some functions to derive commonly used waveform metrics
for characterizing vegetation structure. Moreover, a new way to directly visualize
FW LiDAR data is developed through converting waveforms into points to form the Hyper
Point cloud (HPC), which can be easily adopted and subsequently analyzed with existing
discrete-return LiDAR processing tools such as 'LAStools' and 'FUSION'. Basic explorations of
the HPC such as 3D voxelization of the HPC and conversion from original waveforms to
composite waveforms are also available in this package. All of these functions are
developed based on small-footprint FW LiDAR data, but they can be easily transplanted to
the large footprint FW LiDAR data such as Geoscience Laser Altimeter System (GLAS)
and Global Ecosystem Dynamics Investigation (GEDI) data analysis. References: Zhou et al. (2017a) <doi:10.1016/j.isprsjprs.2017.04.021>;
Zhou et al. (2017b) <doi:10.1016/j.rse.2017.08.012>; Zhou et al. (2018a) <doi:10.3390/rs10010039>;Zhou et al. (2018b) <doi:10.3390/rs10121949>.

Tan Zhou

waveformlidar

Waveform LiDAR Data Processing and Analysis

return function

<p>A data frame with 500 rows and 208 variables:</p>

Format

500 sample LiDAR waveforms from Harvard Forest provided by National Ecological Observatory Networks (NEON).
More details can be found in	Tan Zhou*, Sorin C. Popescu, Keith Krause, Ryan D. Sheridan, and Eric Putman, 2017.
Gold-A novel deconvolution algorithm with optimization for waveform LiDAR processing. ISPRS Journal of Photogrammetry and Remote Sensing 129 (2017): 131-150. https://doi.org/10.1016/j.isprsjprs.2017.04.021 — return

500 sample LiDAR waveforms from Harvard Forest provided by National Ecological Observatory Networks (NEON).
More details can be found in	Tan Zhou*, Sorin C. Popescu, Keith Krause, Ryan D. Sheridan, and Eric Putman, 2017.
Gold-A novel deconvolution algorithm with optimization for waveform LiDAR processing. ISPRS Journal of Photogrammetry and Remote Sensing 129 (2017): 131-150. https://doi.org/10.1016/j.isprsjprs.2017.04.021

Description

Usage

Arguments

Format