waveformlidar (version 1.2.0)

geotransform: geotransform

Description

The function allows you to convert parameters from decomposition to point cloud by using georeference data (generally it should come with waveform data). Detailed description of how to calculate can refer to Zhou, T., Popescu, S.C., Krause, K., Sheridan, R.D., Putman, E., 2017. Gold - a noveldeconvolution algorithm with optimization for waveform LiDAR processing.ISPRS Journal of Photogrammetry and Remote Sensing 129 (2017): 131-150. For the direct decomposition method, three kinds of position are calculated: leading edge, peak and trail edge. For the deconvolution and decomposition method, only the peak position is used to calculate the position. In additional, the uncertianty of the point cloud was provided based on detected peaks' 95 you need to preprocess data to the same format or have the same required information or datsets.

Usage

geotransform(decomp, geo, source = "decomposition")

Arguments

decomp

the object from the decomposition or after deconvolution and decomposition.

geo

the reference geolocation that is generally coming with waveform data and provided by the data provider.

source

is determined by input data. If estimated parameters are from dcomposition, source = "decomposition". Otherwise will be deconvolution and decomposition. Default is decomposition.

Value

A dataframe with columns. For the direct decompostion method, we will have 29 columns and for the deconvolution and decomposition method. 17 columns were generated: index,pi,t,sd,pise,tse,sdse,px,py,pz,uncerUXpeak,uncerUYpeak,uncerUZpeak,uncerLXpeak,uncerLYpeak,uncerLZpeak,rn.

index

The index of waveform.

pi

The estimated amplitude of an waveform componment.

t

The estimated peak location of an waveform componment.

sd

The estimated echo width of an waveform componment.

pise

The standard error of the estimated amplitude.

tse

The standard error of the estimated peak location.

sdse

The standard error of the estimated echo width.

px

Desired x position using peak locations.

py

Desired y position using peak locations.

pz

Desired y position using peak locations.

lowx

Desired x position using leading edge locations.

lowy

Desired y position using leading edge locations.

lowz

Desired z position using leading edge locations.

upx

Desired x position using trailing edge locations.

upy

Desired y position using trailing edge locations.

upz

Desired z position using trailing edge locations.

uncerUXpeak

Upper bound of 95th confidence interval of px.

uncerUYpeak

Upper bound of 95th confidence interval of py.

uncerUZpeak

Upper bound of 95th confidence interval of pz.

uncerLXpeak

Lower bound of 95th confidence interval of px.

uncerLYpeak

Lower bound of 95th confidence interval of py.

uncerLZpeak

Lower bound of 95thconfidence interval of pz.

uncerUXleading

Upper bound of 95th confidence interval of lowx.

uncerUYleading

Upper bound of 95th confidence interval of lowy.

uncerUZleading

Upper bound of 95th confidence interval of lowz.

uncerLXleading

Lower bound of 95th confidence interval of lowx.

uncerLYleading

Lower bound of 95th confidence interval of lowy.

uncerLZleading

Lower bound of 95th confidence interval of lowz.

rn

The number of return for each waveform.

By combining xyz, the users can get waveform-based point cloud using leading edge, peak and trail edge positions, and parameter uncertainty of the point cloud.

References

Zhou, Tan*, 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

Examples

# NOT RUN {
data(geo)
data(decom_result)
data(decon_result)
##used part of data to show the results
decomp<-decom_result[1:80,]
geo<-geo[1:80]
deconp<-decon_result[1:80]

##the follwoing steps are reuired to conduct the geotransformation,
##we need assign exactly same column names
geoindex=c(1:9,16)
colnames(geo)[geoindex]<-c("index","orix","oriy","oriz","dx","dy","dz","outref","refbin","outpeak")
##for decomposition results

geor<-geotransform(decomp,geo)

########for deconvolution and decomposition
decongeo<-geotransform(decomp=deconp,geo,source="deconvolution")

# }