rGEDISimulator: GEDI simulator extension for rGEDI
Authors: Caio Hamamura, Carlos Alberto Silva, Ruben Valbuena, Steven Hancock, Adrian Cardil, Eben N. Broadbent, Danilo R. A. de Almeida, Celso H. L. Silva Junior and Carine Klauberg
The rGEDIsimulator will provide fullwaveform GEDI data simulation and calculates metrics based on aerial lidar systems data (ALS).
First we need to install rGEDI itself:
# Install from github
devtools::install_github("carlos-alberto-silva/rGEDI", dependencies = TRUE)
library(rGEDI)Then we can install the rGEDI simulator
# Install from github
devtools::install_github("caiohamamura/Rgedisimulator", dependencies = TRUE)
library(rGEDIsimulator)Simulating GEDI full-waveform data from Airborne Laser Scanning (ALS) 3-D point cloud and extracting canopy derived metrics
outdir=getwd()
#######
# Herein, we are using only a GEDI sample dataset for this tutorial.
#######
# downloading zip file
download.file("https://github.com/carlos-alberto-silva/rGEDI/releases/download/datasets/examples.zip",destfile=file.path(outdir, "examples.zip"))
# unzip file
unzip(file.path(outdir,"examples.zip"))
# Specifying the path to ALS data
lasfile_amazon <- file.path(outdir, "Amazon.las")
lasfile_savanna <- file.path(outdir, "Savanna.las")
# Reading and plot ALS file
library(lidR)
library(plot3D)
las_amazon<-readLAS(lasfile_amazon)
las_savanna<-readLAS(lasfile_savanna)
# Extracting plot center geolocations
xcenter_amazon = mean(st_bbox(las_amazon)[c(1, 3)])
ycenter_amazon = mean(st_bbox(las_amazon)[c(2, 4)])
xcenter_savanna = mean(st_bbox(las_savanna)[c(1, 3)])
ycenter_savanna = mean(st_bbox(las_savanna)[c(2, 4)])
# Simulating GEDI full-waveform
wf_amazon<-gediWFSimulator(input=lasfile_amazon,output=file.path(getwd(),"gediWF_amazon_simulation.h5"),coords = c(xcenter_amazon, ycenter_amazon))
wf_savanna<-gediWFSimulator(input=lasfile_savanna,output=file.path(getwd(),"gediWF_savanna_simulation.h5"),coords = c(xcenter_savanna, ycenter_savanna))
# Plotting ALS and GEDI simulated full-waveform
png("gediWf.png", width = 8, height = 6, units = 'in', res = 300)
par(mfrow=c(2,2), mar=c(4,4,0,0), oma=c(0,0,1,1),cex.axis = 1.2)
scatter3D(las_amazon@data$X,las_amazon@data$Y,las_amazon@data$Z,pch = 16,colkey = FALSE, main="",
cex = 0.5,bty = "u",col.panel ="gray90",phi = 30,alpha=1,theta=45,
col.grid = "gray50", xlab="UTM Easting (m)", ylab="UTM Northing (m)", zlab="Elevation (m)")
# Simulated waveforms shot_number is incremental beggining from 0
shot_number = 0
simulated_waveform_amazon = getLevel1BWF(wf_amazon, shot_number)
plot(simulated_waveform_amazon, relative=TRUE, polygon=TRUE, type="l", lwd=2, col="forestgreen",
xlab="", ylab="Elevation (m)", ylim=c(90,140))
grid()
scatter3D(las_savanna@data$X,las_savanna@data$Y,las_savanna@data$Z,pch = 16,colkey = FALSE, main="",
cex = 0.5,bty = "u",col.panel ="gray90",phi = 30,alpha=1,theta=45,
col.grid = "gray50", xlab="UTM Easting (m)", ylab="UTM Northing (m)", zlab="Elevation (m)")
shot_number = 0
simulated_waveform_savanna = getLevel1BWF(wf_savanna, shot_number)
plot(simulated_waveform_savanna, relative=TRUE, polygon=TRUE, type="l", lwd=2, col="green",
xlab="Waveform Amplitude (%)", ylab="Elevation (m)", ylim=c(815,835))
grid()
dev.off()
wf_amazon_metrics<-gediWFMetrics(input=wf_amazon,
outRoot=file.path(getwd(), "amazon"))
wf_savanna_metrics<-gediWFMetrics(input=wf_savanna,
outRoot=file.path(getwd(), "savanna"))
metrics<-rbind(wf_amazon_metrics,wf_savanna_metrics)
rownames(metrics)<-c("Amazon","Savanna")
head(metrics[,1:8])
# wave ID true ground true top ground slope ALS cover gHeight maxGround inflGround
#Amazon gedi.BEAM0000.0 -1e+06 133.25 -1e+06 -1 94.93 99.95 95.16
#Savanna gedi.BEAM0000.0 -1e+06 831.47 -1e+06 -1 822.18 822.17 822.25
wf_amazon_metrics_noise<-gediWFMetrics(input=wf_amazon,
outRoot=file.path(getwd(), "amazon"),
linkNoise= c(3.0103,0.95),
maxDN= 4096,
sWidth= 0.5,
varScale= 3)
wf_savanna_metrics_noise<-gediWFMetrics(
input=wf_savanna,
outRoot=file.path(getwd(), "savanna"),
linkNoise= c(3.0103,0.95),
maxDN= 4096,
sWidth= 0.5,
varScale= 3)
metrics_noise<-rbind(wf_amazon_metrics_noise,wf_savanna_metrics_noise)
rownames(metrics_noise)<-c("Amazon","Savanna")
head(metrics_noise[,1:8])
# #wave ID true ground true top ground slope ALS cover gHeight maxGround inflGround
# Amazon 0 -1e+06 133.29 -1e+06 -1 99.17 99.99 95.39
# Savanna 0 -1e+06 831.36 -1e+06 -1 822.15 822.21 822.18
close(wf_amazon)
close(wf_savanna)
close(gedilevel1b)
close(gedilevel2a)
close(gedilevel2b)
Dubayah, R., Blair, J.B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S., Hofton, M., Hurtt, G., Kellner, J., Luthcke, S., & Armston, J. (2020) The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography. Science of Remote Sensing, p.100002. https://doi.org/10.1016/j.srs.2020.100002
Hancock, S., Armston, J., Hofton, M., Sun, X., Tang, H., Duncanson, L.I., Kellner, J.R. and Dubayah, R., 2019. The GEDI simulator: A large-footprint waveform lidar simulator for calibration and validation of spaceborne missions. Earth and Space Science. https://doi.org/10.1029/2018EA000506
Silva, C. A.; Saatchi, S.; Alonso, M. G. ; Labriere, N. ; Klauberg, C. ; Ferraz, A. ; Meyer, V. ; Jeffery, K. J. ; Abernethy, K. ; White, L. ; Zhao, K. ; Lewis, S. L. ; Hudak, A. T. (2018) Comparison of Small- and Large-Footprint Lidar Characterization of Tropical Forest Aboveground Structure and Biomass: A Case Study from Central Gabon. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, p. 1-15. https://ieeexplore.ieee.org/document/8331845
GEDI webpage. Accessed on February 15 2020 https://gedi.umd.edu/
GEDI01_Bv001. Accessed on February 15 2020 https://lpdaac.usgs.gov/products/gedi01_bv001/
GEDI02_Av001. Accessed on February 15 2020 https://lpdaac.usgs.gov/products/gedi02_av001/
GEDI02_Bv001. Accessed on February 15 2020 https://lpdaac.usgs.gov/products/gedi02_bv001/
GEDI Finder. Accessed on February 15 2020 https://lpdaacsvc.cr.usgs.gov/services/gedifinder
The University of Maryland and NASA's Goddard Space Flight Center for developing GEDI mission.
We gratefully acknowledge funding from NASA’s Carbon Monitoring Systems, grant NNH15ZDA001N-CMS. Project entitled "Future Mission Fusion for High Biomass Forest Carbon Accounting" led by Dr. Laura Duncanson (lduncans@umd.edu, University of Maryland) and Dr. Lola Fatoyinbo (lola.fatoyinbo@nasa.gov, NASA's Goddard Space Flight Center).
The Brazilian National Council for Scientific and Technological Development (CNPq) for funding the project entitled "Mapping fuel load and simulation of fire behaviour and spread in the Cerrado biome using modeling and remote sensing technologies" and leaded by Prof. Dr. Carine Klauberg (carine_klauberg@hotmail.com) and Dr. Carlos Alberto Silva (carlos_engflorestal@outlook.com).
Hamamura,C.; Silva,C.A; Hancock,S.; Valbuena, R.; Cardil,A.; Broadbent, E. N.; Almeida,D.R.A.; Silva Junior, C.H.L; Klauberg, C. NASA's Global Ecosystem Dynamics Investigation (GEDI) Simulator for ALS Data. version 0.1.1, accessed on April. 04 2024, available at: https://CRAN.R-project.org/package=rGEDIsimulator
rGEDI package has not been developted by the GEDI team. It comes with no guarantee, expressed or implied, and the authors hold no responsibility for its use or reliability of its outputs.