decon_result

This dataset contained the results using deconvolution and then decompostion method as described in the
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

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

decon_result function

A data frame with 1000 rows and 7 variables<dl class="dl-horizontal">
 <dt>index</dt><dd>index of waveform, which can tell which rows or results belongs to specific waveform</dd>
 <dt>A</dt><dd>the amplitude of one waveform componment (A)</dd>
 <dt>u</dt><dd>the time location corresponds to the amplitude for one waveform componment</dd>
 <dt>sigma</dt><dd>the echo width of one waveform componment</dd>
 <dt>A_std</dt><dd>the standard error of A, which can be used for uncertainty analysis</dd>
 <dt>u_std</dt><dd>the standard error of u, which can be used for uncertainty analysis</dd>
 <dt>sig_std</dt><dd>the standard error of sigma, which can be used for uncertainty analysis</dd>
</dl>

Format

Results of using the deconvolution and decomposition method. — decon_result

A data frame with 1000 rows and 7 variables<dl class='dl-horizontal'>
 <dt>index</dt><dd>index of waveform, which can tell which rows or results belongs to specific waveform</dd>
 <dt>A</dt><dd>the amplitude of one waveform componment (A)</dd>
 <dt>u</dt><dd>the time location corresponds to the amplitude for one waveform componment</dd>
 <dt>sigma</dt><dd>the echo width of one waveform componment</dd>
 <dt>A_std</dt><dd>the standard error of A, which can be used for uncertainty analysis</dd>
 <dt>u_std</dt><dd>the standard error of u, which can be used for uncertainty analysis</dd>
 <dt>sig_std</dt><dd>the standard error of sigma, which can be used for uncertainty analysis</dd>
</dl>

decon_result: Results of using the deconvolution and decomposition method.

Description

Usage

Arguments

Format