rGEDI: An R Package for NASA's Global Ecosystem Dynamics Investigation (GEDI) Data Visualizing and Processing.

Authors: Carlos Alberto Silva, Caio Hamamura, Ruben Valbuena, Steven Hancock, Adrian Cardil, Eben N. Broadbent, Danilo R. A. de Almeida, Celso H. L. Silva Junior and Carine Klauberg

The rGEDI package provides functions for i) downloading, ii) visualizing, iii) clipping, iv) gridding, iv) simulating and v) exporting GEDI data.

Getting Started

Installation

```{r}

The CRAN version:

install.packages("rGEDI")

The development version:

install.packages('rGEDI', repos = c('https://carlos-alberto-silva.r-universe.dev', 'https://cloud.r-project.org'))

loading rGEDI package

library(rGEDI)

```

Find GEDI data within your study area (GEDI finder tool)

```{r}

Study area boundary box coordinates

ullat<- -44.0654 lrlat<- -44.17246 ullon<- -13.76913 lrlon<- -13.67646

Specifying the date range

daterange=c("2019-07-01","2020-05-22")

Get path to GEDI data

gLevel1B<-gedifinder(product="GEDI01B",ullat, ullon, lrlat, lrlon,version="002",daterange=daterange) gLevel2A<-gedifinder(product="GEDI02A",ullat, ullon, lrlat, lrlon,version="002",daterange=daterange) gLevel2B<-gedifinder(product="GEDI02B",ullat, ullon, lrlat, lr_lon,version="002",daterange=daterange) ```

Downloading GEDI data

```{r}

Set output dir for downloading the files

outdir=getwd()

Downloading GEDI data

gediDownload(filepath=gLevel1B,outdir=outdir) gediDownload(filepath=gLevel2A,outdir=outdir) gediDownload(filepath=gLevel2B,outdir=outdir)

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"))

```

Reading GEDI data

```{r}

Reading GEDI data

gedilevel1b<-readLevel1B(level1Bpath = file.path(outdir,"GEDI01B2019108080338O01964T053370200301sub.h5")) gedilevel2a<-readLevel2A(level2Apath = file.path(outdir,"GEDI02A2019108080338O01964T053370200101sub.h5")) gedilevel2b<-readLevel2B(level2Bpath = file.path(outdir,"GEDI02B2019108080338O01964T053370200101sub.h5")) ```

Get GEDI Pulse Geolocation (GEDI Level1B)

```{r} level1bGeo<-getLevel1BGeo(level1b=gedilevel1b,select=c("elevation_bin0")) head(level1bGeo)

shotnumber latitudebin0 latitudelastbin longitudebin0 longitudelastbin elevationbin0

1: 19640002800109382 -13.75903 -13.75901 -44.17219 -44.17219 784.8348

2: 19640003000109383 -13.75862 -13.75859 -44.17188 -44.17188 799.0491

3: 19640003200109384 -13.75821 -13.75818 -44.17156 -44.17156 814.4647

4: 19640003400109385 -13.75780 -13.75777 -44.17124 -44.17124 820.1437

5: 19640003600109386 -13.75738 -13.75736 -44.17093 -44.17093 821.7012

6: 19640003800109387 -13.75697 -13.75695 -44.17061 -44.17061 823.2526

Converting shot_number as "integer64" to "character"

level1bGeo$shotnumber<-as.character(level1bGeo$shotnumber)

Converting level1bGeo as data.table to sf

level1bGeospdf <- sf::stassf( level1bGeo, coords = c("longitudebin0", "latitude_bin0"), crs = "epsg:4326")

Exporting level1bGeo as ESRI Shapefile

sf::stwrite(level1bGeospdf, file.path(outdir,"GEDI01B2019108080338O01964T053370200301sub.shp")) ```

``` library(leaflet) library(leafsync)

leaflet() %>% addCircleMarkers(level1bGeo$longitudebin0, level1bGeo$latitudebin0, radius = 1, opacity = 1, color = "red") %>% addScaleBar(options = list(imperial = FALSE)) %>% addProviderTiles(providers$Esri.WorldImagery) %>% addLegend(colors = "red", labels= "Samples",title ="GEDI Level1B")

```

Get GEDI Full-waveform (GEDI Level1B)

```{r}

Extracting GEDI full-waveform for a giving shotnumber

wf <- getLevel1BWF(gedilevel1b, shot_number="19640521100108408")

par(mfrow = c(1,2), mar=c(4,4,1,1), cex.axis = 1.5)

plot(wf, relative=FALSE, polygon=TRUE, type="l", lwd=2, col="forestgreen", xlab="Waveform Amplitude", ylab="Elevation (m)") grid() plot(wf, relative=TRUE, polygon=FALSE, type="l", lwd=2, col="forestgreen", xlab="Waveform Amplitude (%)", ylab="Elevation (m)") grid() ```

Get GEDI Elevation and Height Metrics (GEDI Level2A)

```{r}

Get GEDI Elevation and Height Metrics

level2AM<-getLevel2AM(gedilevel2a) head(level2AM[,c("beam","shotnumber","elevhighestreturn","elev_lowestmode","rh100")])

beam shotnumber elevhighestreturn elev_lowestmode rh100

1: BEAM0000 19640002800109382 740.7499 736.3301 4.41

2: BEAM0000 19640003000109383 756.0878 746.7614 9.32

3: BEAM0000 19640003200109384 770.3423 763.1509 7.19

4: BEAM0000 19640003400109385 775.9838 770.6652 5.31

5: BEAM0000 19640003600109386 777.8409 773.0841 4.75

6: BEAM0000 19640003800109387 778.7181 773.6990 5.01

Converting shot_number as "integer64" to "character"

level2AM$shotnumber<-as.character(level2AM$shotnumber)

Converting Elevation and Height Metrics as data.table to sf

level2AMspdf <- sf::stassf( level2AM, coords = c("lonlowestmode", "lat_lowestmode"), crs = "epsg:4326" )

Exporting Elevation and Height Metrics as ESRI Shapefile

sf::writesf(level2AMspdf,file.path(outdir,"GEDI02A2019108080338O01964T053370200101sub.shp")) ```

Plot waveform with RH metrics

```{r} shot_number = "19640521100108408"

png("fig8.png", width = 8, height = 6, units = 'in', res = 300) plotWFMetrics(gedilevel1b, gedilevel2a, shot_number, rh=c(25, 50, 75, 90)) dev.off() ```

Get GEDI Vegetation Biophysical Variables (GEDI Level2B)

```{r} level2BVPM<-getLevel2BVPM(gedilevel2b) head(level2BVPM[,c("beam","shotnumber","pai","fhdnormal","omega","pgap_theta","cover")])

beam shotnumber pai fhdnormal omega pgap_theta cover

1: BEAM0000 19640002800109382 0.007661204 0.6365142 1 0.9961758 0.003823273

2: BEAM0000 19640003000109383 0.086218357 2.2644432 1 0.9577964 0.042192958

3: BEAM0000 19640003200109384 0.299524575 1.8881851 1 0.8608801 0.139084846

4: BEAM0000 19640003400109385 0.079557180 1.6625489 1 0.9609926 0.038997617

5: BEAM0000 19640003600109386 0.018724868 1.5836401 1 0.9906789 0.009318732

6: BEAM0000 19640003800109387 0.017654873 1.2458609 1 0.9912092 0.008788579

Converting shot_number as "integer64" to "character"

level2BVPM$shotnumber<-as.character(level2BVPM$shotnumber)

Converting GEDI Vegetation Profile Biophysical Variables as data.table to sf

level2BVPMspdf<-sf::stassf( level2BVPM, coords = c("longitudelastbin","latitude_lastbin"), crs = "epsg:4326" )

Exporting GEDI Vegetation Profile Biophysical Variables as ESRI Shapefile

sf::stwrite(level2BVPMspdf,file.path(outdir,"GEDI02B2019108080338O01964T053370200101sub_VPM.shp"))

```

Get Plant Area Index (PAI) and Plant Area Volume Density (PAVD) Profiles (GEDI Level2B)

```{r} level2BPAIProfile<-getLevel2BPAIProfile(gedilevel2b) head(level2BPAIProfile[,c("beam","shotnumber","paiz05m","paiz5_10m")])

beam shotnumber paiz05m paiz5_10m

1: BEAM0000 19640002800109382 0.007661204 0.0000000000

2: BEAM0000 19640003000109383 0.086218357 0.0581122264

3: BEAM0000 19640003200109384 0.299524575 0.0497199222

4: BEAM0000 19640003400109385 0.079557180 0.0004457365

5: BEAM0000 19640003600109386 0.018724868 0.0000000000

6: BEAM0000 19640003800109387 0.017654873 0.0000000000

level2BPAVDProfile<-getLevel2BPAVDProfile(gedilevel2b) head(level2BPAVDProfile[,c("beam","shotnumber","pavdz05m","pavdz5_10m")])

beam shotnumber pavdz05m pavdz5_10m

1: BEAM0000 19640002800109382 0.001532241 0.0007661204

2: BEAM0000 19640003000109383 0.005621226 0.0086218351

3: BEAM0000 19640003200109384 0.049960934 0.0299524590

4: BEAM0000 19640003400109385 0.015822290 0.0079557188

5: BEAM0000 19640003600109386 0.003744974 0.0018724868

6: BEAM0000 19640003800109387 0.003530974 0.0017654872

Converting shot_number as "integer64" to "character"

level2BPAIProfile$shotnumber<-as.character(level2BPAIProfile$shotnumber) level2BPAVDProfile$shotnumber<-as.character(level2BPAVDProfile$shotnumber)

Converting PAI and PAVD Profiles as data.table to sf

level2BPAIProfilespdf <- sf::stassf( level2BPAIProfile, coords = c("lonlowestmode", "latlowestmode"), crs = "epsg:4326" ) level2BPAVDProfilespdf <- sf::stassf( level2BPAVDProfile, coords = c("lonlowestmode", "latlowestmode"), crs = "epsg:4326" )

Exporting PAI and PAVD Profiles as ESRI Shapefile

sf::writesf(level2BPAIProfilespdf,file.path(outdir,"GEDI02B2019108080338O01964T053370200101subPAIProfile.shp")) sf::writesf(level2BPAVDProfilespdf,file.path(outdir,"GEDI02B2019108080338O01964T053370200101subPAVDProfile.shp"))

```

Plot Plant Area Index (PAI) and Plant Area Volume Density (PAVD) Profiles

```{r}

specify GEDI beam

beam="BEAM0101"

Plot Level2B PAI Profile

gPAIprofile<-plotPAIProfile(level2BPAIProfile, beam=beam, elev=TRUE)

Plot Level2B PAVD Profile

gPAVDprofile<-plotPAVDProfile(level2BPAVDProfile, beam=beam, elev=TRUE)

```

Clip GEDI data (h5 files; gedi.level1b, gedi.level2a and gedi.level2b objects)

```{r}

Clip GEDI data by coordinates

Study area boundary box

xmin = -44.15036 xmax = -44.10066 ymin = -13.75831 ymax = -13.71244

clipping GEDI data within boundary box

level1bclipbb <- clipLevel1B(gedilevel1b, xmin, xmax, ymin, ymax,output=file.path(outdir,"level1bclipbb.h5")) level2aclipbb <- clipLevel2A(gedilevel2a, xmin, xmax, ymin, ymax, output=file.path(outdir,"level2aclipbb.h5")) level2bclipbb <- clipLevel2B(gedilevel2b, xmin, xmax, ymin, ymax,output=file.path(outdir,"level2bclipbb.h5"))

Clipping GEDI data by geometry

specify the path to shapefile for the study area

polygonfilepath <- system.file("extdata", "standscerrado.shp", package="rGEDI")

Reading shapefile as sf object

polygonspdf <- sf::stread(polygonfilepath) head(polygonspdf) # column id name "id" split_by <- "id"

Clipping h5 files

level1bclipgb <- clipLevel1BGeometry(gedilevel1b,polygonspdf,output=file.path(outdir,"level1bclipgb.h5"), splitby=splitby) level2aclipgb <- clipLevel2AGeometry(gedilevel2a,polygonspdf,output=file.path(outdir,"level2aclipgb.h5"), splitby=splitby) level2bclipgb <- clipLevel2BGeometry(gedilevel2b,polygonspdf,output=file.path(outdir,"level2bclipgb.h5"), splitby=split_by) ```

Clip GEDI data (data.table objects)

```{r}

Clipping GEDI data within boundary box

level1bGeoclipbb <-clipLevel1BGeo(level1bGeo, xmin, xmax, ymin, ymax) level2AMclipbb <- clipLevel2AM(level2AM, xmin, xmax, ymin, ymax) level2BVPMclipbb <- clipLevel2BVPM(level2BVPM, xmin, xmax, ymin, ymax) level1BPAIProfileclipbb <- clipLevel2BPAIProfile(level2BPAIProfile, xmin, xmax, ymin, ymax) level2BPAVDProfileclipbb <- clipLevel2BPAVDProfile(level2BPAVDProfile, xmin, xmax, ymin, ymax)

Clipping GEDI data by geometry

level1bGeoclipgb <- clipLevel1BGeoGeometry(level1bGeo,polygonspdf, splitby=splitby) level2AMclipgb <- clipLevel2AMGeometry(level2AM,polygonspdf, splitby=splitby) level2BVPMclipgb <- clipLevel2BVPMGeometry(level2BVPM,polygonspdf, splitby=splitby) level1BPAIProfileclipgb <- clipLevel2BPAIProfileGeometry(level2BPAIProfile,polygonspdf, splitby=splitby) level2BPAVDProfileclipgb <- clipLevel2BPAVDProfileGeometry(level2BPAVDProfile,polygonspdf, splitby=split_by)

View GEDI clipped data by bbox

m1<-leaflet() %>% addCircleMarkers(level2AM$lonlowestmode, level2AM$latlowestmode, radius = 1, opacity = 1, color = "red") %>% addCircleMarkers(level2AMclipbb$lonlowestmode, level2AMclipbb$latlowestmode, radius = 1, opacity = 1, color = "green") %>% addScaleBar(options = list(imperial = FALSE)) %>% addProviderTiles(providers$Esri.WorldImagery) %>% addLegend(colors = c("red","green"), labels= c("All samples","Clip bbox"),title ="GEDI Level2A")

View GEDI clipped data by geometry

color palette

pal <- colorFactor( palette = c('blue', 'green', 'purple', 'orange',"white","black","gray","yellow"), domain = level2AMclipgb$poly_id )

m2<-leaflet() %>% addCircleMarkers(level2AM$lonlowestmode, level2AM$latlowestmode, radius = 1, opacity = 1, color = "red") %>% addCircleMarkers(level2AMclipgb$lonlowestmode, level2AMclipgb$latlowestmode, radius = 1, opacity = 1, color = pal(level2AMclipgb$polyid)) %>% addScaleBar(options = list(imperial = FALSE)) %>% addPolygons(data=polygonspdf,weight=1,col = 'white', opacity = 1, fillOpacity = 0) %>% addProviderTiles(providers$Esri.WorldImagery) %>% addLegend(pal = pal, values = level2AMclipgb$poly_id,title ="Poly IDs" )

sync(m1, m2) ```

Compute descriptive statistics of GEDI Level2A and Level2B data

```{r}

Define your own function

mySetOfMetrics = function(x) { metrics = list( min =min(x), # Min of x max = max(x), # Max of x mean = mean(x), # Mean of x sd = sd(x)# Sd of x ) return(metrics) }

Computing the maximum of RH100 stratified by polygon

rh100maxst<-polyStatsLevel2AM(level2AMclipgb,func=max(rh100), id="polyid") head(rh100max_st)

poly_id max

1: 2 12.81

2: 1 12.62

3: 5 9.96

4: 6 8.98

5: 4 10.33

6: 8 8.72

Computing a serie statistics for GEDI metrics stratified by polygon

rh100metricsst<-polyStatsLevel2AM(level2AMclipgb,func=mySetOfMetrics(rh100), id="polyid") head(rh100metrics_st)

poly_id min max mean sd

1: 2 4.08 12.81 5.508639 1.452143

2: 1 3.78 12.62 5.514930 1.745507

3: 5 4.12 9.96 5.100122 1.195272

4: 6 4.64 8.98 5.595294 1.024171

5: 4 4.38 10.33 7.909500 1.757200

6: 8 4.45 8.72 6.136471 1.097468

Computing the max of the Total Plant Area Index

paimax<-polyStatsLevel2BVPM(level2BVPMclipgb,func=max(pai), id=NULL) paimax

max

1: 1.224658

Computing a serie of statistics of Canopy Cover stratified by polygon

covermetricsst<-polyStatsLevel2BVPM(level2BVPMclipgb,func=mySetOfMetrics(cover), id="polyid") head(covermetrics_st)

poly_id min max mean sd

1: 2 0.0010017310 0.3479594 0.05156159 0.05817241

2: 1 0.0003717059 0.3812594 0.04829096 0.06346548

3: 5 0.0020242794 0.4262614 0.03577852 0.06407325

4: 6 0.0028748326 0.2392146 0.03094646 0.05577988

5: 4 0.0022404396 0.3501986 0.11343149 0.09354305

6: 8 0.0050588539 0.1457105 0.04784596 0.04427151

```

Compute Grids with descriptive statistics of GEDI-derived Elevation and Height Metrics (Level2A)

```{r}

Computing a serie of statistics of GEDI RH100 metric

rh100metrics<-gridStatsLevel2AM(level2AM = level2AM, func=mySetOfMetrics(rh100), res=0.005)

View maps

library(rasterVis) library(viridis)

rh100maps<-levelplot(rh100metrics, layout=c(1, 4), margin=FALSE, xlab = "Longitude (degree)", ylab = "Latitude (degree)", colorkey=list( space='right', labels=list(at=seq(0, 18, 2), font=4), axis.line=list(col='black'), width=1), par.settings=list( strip.border=list(col='gray'), strip.background=list(col='gray'), axis.line=list(col='gray') ), scales=list(draw=TRUE), col.regions=viridis, at=seq(0, 18, len=101), names.attr=c("rh100 min","rh100 max","rh100 mean", "rh100 sd"))

Exporting maps

png("fig6.png", width = 6, height = 8, units = 'in', res = 300) rh100maps dev.off()

```

Compute Grids with descriptive statistics of GEDI-derived Canopy Cover and Vertical Profile Metrics (Level2B)

```{r}

Computing a serie of statistics of Total Plant Area Index

level2BVPM$pai[level2BVPM$pai==-9999]<-NA # assing NA to -9999 pai_metrics<-gridStatsLevel2BVPM(level2BVPM = level2BVPM, func=mySetOfMetrics(pai), res=0.005)

View maps

paimaps<-levelplot(paimetrics, layout=c(1, 4), margin=FALSE, xlab = "Longitude (degree)", ylab = "Latitude (degree)", colorkey=list( space='right', labels=list(at=seq(0, 1.5, 0.2), font=4), axis.line=list(col='black'), width=1), par.settings=list( strip.border=list(col='gray'), strip.background=list(col='gray'), axis.line=list(col='gray') ), scales=list(draw=TRUE), col.regions=viridis, at=seq(0, 1.5, len=101), names.attr=c("PAI min","PAI max","PAI mean", "PAI sd"))

Exporting maps

png("fig7.png", width = 6, height = 8, units = 'in', res = 300) pai_maps dev.off()

```

Simulating GEDI full-waveform data from Airborne Laser Scanning (ALS) 3-D point cloud and extracting canopy derived metrics

```{r}

Specifying the path to ALS data

lasfileamazon <- file.path(outdir, "Amazon.las") lasfilesavanna <- file.path(outdir, "Savanna.las")

Reading and plot ALS file

library(lidR) library(plot3D) lasamazon<-readLAS(lasfileamazon) lassavanna<-readLAS(lasfilesavanna)

Extracting plot center geolocations

xcenteramazon = mean(stbbox(lasamazon)[c(1, 3)]) ycenteramazon = mean(stbbox(lasamazon)[-c(1, 3)]) xcentersavanna = mean(stbbox(lassavanna)[c(1, 3)]) ycentersavanna = mean(stbbox(lassavanna)[-c(1, 3)])

The gedi simulator has been moved separately in rGEDIsimulator as following

devtools::install_git("https://github.com/caiohamamura/Rgedisimulator", dependencies = TRUE) library(rGEDIsimulator)

Simulating GEDI full-waveform

wfamazon<-gediWFSimulator(input=lasfileamazon,output=file.path(getwd(),"gediWFamazonsimulation.h5"),coords = c(xcenteramazon, ycenteramazon)) wfsavanna<-gediWFSimulator(input=lasfilesavanna,output=file.path(getwd(),"gediWFsavannasimulation.h5"),coords = c(xcentersavanna, ycentersavanna))

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(lasamazon@data$X,lasamazon@data$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

shotnumber = 0 simulatedwaveformamazon = getLevel1BWF(wfamazon, shotnumber) plot(simulatedwaveformamazon, relative=TRUE, polygon=TRUE, type="l", lwd=2, col="forestgreen", xlab="", ylab="Elevation (m)", ylim=c(90,140)) grid() scatter3D(lassavanna@data$X,lassavanna@data$Y,lassavanna@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)")

shotnumber = 0 simulatedwaveformsavanna = getLevel1BWF(wfsavanna, shotnumber) plot(simulatedwaveform_savanna, relative=TRUE, polygon=TRUE, type="l", lwd=2, col="green", xlab="Waveform Amplitude (%)", ylab="Elevation (m)", ylim=c(815,835)) grid() dev.off() ```

Extracting GEDI full-waveform derived metrics without adding noise to the full-waveform

``` wfamazonmetrics<-gediWFMetrics(input=wfamazon, outRoot=file.path(getwd(), "amazon")) wfsavannametrics<-gediWFMetrics(input=wfsavanna, outRoot=file.path(getwd(), "savanna"))

metrics<-rbind(wfamazonmetrics,wfsavannametrics) 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

```

Extracting GEDI full-waveform derived metrics after adding noise to the full-waveform

``` wfamazonmetricsnoise<-gediWFMetrics(input=wfamazon, outRoot=file.path(getwd(), "amazon"), linkNoise= c(3.0103,0.95), maxDN= 4096, sWidth= 0.5, varScale= 3)

wfsavannametricsnoise<-gediWFMetrics( input=wfsavanna, outRoot=file.path(getwd(), "savanna"), linkNoise= c(3.0103,0.95), maxDN= 4096, sWidth= 0.5, varScale= 3)

metricsnoise<-rbind(wfamazonmetricsnoise,wfsavannametricsnoise) rownames(metricsnoise)<-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

```

Always close gedi objects, so HDF5 files won't be blocked!

{r cleanup, echo=TRUE, results="hide", error=TRUE} close(wf_amazon) close(wf_savanna) close(gedilevel1b) close(gedilevel2a) close(gedilevel2b)

References

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/
GEDI01Bv001. Accessed on February 15 2020 https://lpdaac.usgs.gov/products/gedi01bv001/
GEDI02Av001. Accessed on February 15 2020 https://lpdaac.usgs.gov/products/gedi02av001/
GEDI02Bv001. Accessed on February 15 2020 https://lpdaac.usgs.gov/products/gedi02bv001/
GEDI Finder. Accessed on February 15 2020 https://lpdaacsvc.cr.usgs.gov/services/gedifinder

Acknowledgements

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 (carineklauberg@hotmail.com) and Dr. Carlos Alberto Silva (carlosengflorestal@outlook.com).

Reporting Issues

Please report any issue regarding the rGEDI package herein https://groups.yahoo.com/neo/groups/rGEDI

Citing rGEDI

Silva,C.A; Hamamura,C.; Valbuena, R.; Hancock,S.; Cardil,A.; Broadbent, E. N.; Almeida,D.R.A.; Silva Junior, C.H.L; Klauberg, C. rGEDI: NASA's Global Ecosystem Dynamics Investigation (GEDI) Data Visualization and Processing. version 0.1.9, accessed on October. 22 2020, available at: https://CRAN.R-project.org/package=rGEDI

Disclaimer

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.