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Remote Sensing","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"![](https://github.com/carlos-alberto-silva/rGEDI/blob/master/readme/fig1.png)\u003cbr/\u003e\n\n[![R-CMD-check](https://github.com/carlos-alberto-silva/rGEDI/actions/workflows/r.yml/badge.svg?branch=master)](https://github.com/carlos-alberto-silva/rGEDI/actions/workflows/r.yml)\n[![CRAN](https://www.r-pkg.org/badges/version/rGEDI)](https://cran.r-project.org/package=rGEDI)\n![Github](https://img.shields.io/badge/Github-0.1.12-green.svg)\n![licence](https://img.shields.io/badge/Licence-GPL--3-blue.svg) \n![Downloads](https://cranlogs.r-pkg.org/badges/grand-total/rGEDI)\n\n**rGEDI: An R Package for NASA's Global Ecosystem Dynamics Investigation (GEDI) Data Visualizing and Processing.**\n\nAuthors: 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  \n\nThe rGEDI package provides functions for i) downloading, ii) visualizing, iii) clipping, iv) gridding, iv) simulating and v) exporting GEDI data.\n\n# Getting Started\n\n## Installation\n```{r}\n# The CRAN version:\ninstall.packages(\"rGEDI\")\n\n# The development version:\ninstall.packages('rGEDI', repos = c('https://carlos-alberto-silva.r-universe.dev', 'https://cloud.r-project.org'))\n\n# loading rGEDI package\nlibrary(rGEDI)\n\n```    \n\n## Find GEDI data within your study area (GEDI finder tool)\n```{r}\n# Study area boundary box coordinates\nul_lat\u003c- -44.0654\nlr_lat\u003c- -44.17246\nul_lon\u003c- -13.76913\nlr_lon\u003c- -13.67646\n\n# Specifying the date range\ndaterange=c(\"2019-07-01\",\"2020-05-22\")\n\n# Get path to GEDI data\ngLevel1B\u003c-gedifinder(product=\"GEDI01_B\",ul_lat, ul_lon, lr_lat, lr_lon,version=\"002\",daterange=daterange)\ngLevel2A\u003c-gedifinder(product=\"GEDI02_A\",ul_lat, ul_lon, lr_lat, lr_lon,version=\"002\",daterange=daterange)\ngLevel2B\u003c-gedifinder(product=\"GEDI02_B\",ul_lat, ul_lon, lr_lat, lr_lon,version=\"002\",daterange=daterange)\n```\n## Downloading GEDI data\n```{r}\n# Set output dir for downloading the files\noutdir=getwd()\n\n# Downloading GEDI data\ngediDownload(filepath=gLevel1B,outdir=outdir)\ngediDownload(filepath=gLevel2A,outdir=outdir)\ngediDownload(filepath=gLevel2B,outdir=outdir)\n\n#######\n# Herein, we are using only a GEDI sample dataset for this tutorial.\n#######\n# downloading zip file\ndownload.file(\"https://github.com/carlos-alberto-silva/rGEDI/releases/download/datasets/examples.zip\",destfile=file.path(outdir, \"examples.zip\"))\n\n# unzip file \nunzip(file.path(outdir,\"examples.zip\"))\n\n```\n\n## Reading GEDI data\n```{r}\n# Reading GEDI data\ngedilevel1b\u003c-readLevel1B(level1Bpath = file.path(outdir,\"GEDI01_B_2019108080338_O01964_T05337_02_003_01_sub.h5\"))\ngedilevel2a\u003c-readLevel2A(level2Apath = file.path(outdir,\"GEDI02_A_2019108080338_O01964_T05337_02_001_01_sub.h5\"))\ngedilevel2b\u003c-readLevel2B(level2Bpath = file.path(outdir,\"GEDI02_B_2019108080338_O01964_T05337_02_001_01_sub.h5\"))\n```\n\n## Get GEDI Pulse Geolocation (GEDI Level1B)\n```{r}\nlevel1bGeo\u003c-getLevel1BGeo(level1b=gedilevel1b,select=c(\"elevation_bin0\"))\nhead(level1bGeo)\n\n##           shot_number latitude_bin0 latitude_lastbin longitude_bin0 longitude_lastbin elevation_bin0\n##  1: 19640002800109382     -13.75903        -13.75901      -44.17219         -44.17219       784.8348\n##  2: 19640003000109383     -13.75862        -13.75859      -44.17188         -44.17188       799.0491\n##  3: 19640003200109384     -13.75821        -13.75818      -44.17156         -44.17156       814.4647\n##  4: 19640003400109385     -13.75780        -13.75777      -44.17124         -44.17124       820.1437\n##  5: 19640003600109386     -13.75738        -13.75736      -44.17093         -44.17093       821.7012\n##  6: 19640003800109387     -13.75697        -13.75695      -44.17061         -44.17061       823.2526\n\n# Converting shot_number as \"integer64\" to \"character\"\nlevel1bGeo$shot_number\u003c-as.character(level1bGeo$shot_number)\n\n# Converting level1bGeo as data.table to sf\nlevel1bGeo_spdf \u003c-\n  sf::st_as_sf(\n    level1bGeo,\n    coords = c(\"longitude_bin0\", \"latitude_bin0\"),\n    crs = \"epsg:4326\")\n\n# Exporting level1bGeo as ESRI Shapefile\nsf::st_write(level1bGeo_spdf, file.path(outdir,\"GEDI01_B_2019108080338_O01964_T05337_02_003_01_sub.shp\"))\n```\n\u003cimg align=\"right\" src=\"https://github.com/carlos-alberto-silva/rGEDI/blob/master/readme/fig2.PNG\"  width=\"400\"\u003e\n\n```\nlibrary(leaflet)\nlibrary(leafsync)\n\nleaflet() %\u003e%\n  addCircleMarkers(level1bGeo$longitude_bin0,\n                   level1bGeo$latitude_bin0,\n                   radius = 1,\n                   opacity = 1,\n                   color = \"red\")  %\u003e%\n  addScaleBar(options = list(imperial = FALSE)) %\u003e%\n  addProviderTiles(providers$Esri.WorldImagery) %\u003e%\n  addLegend(colors = \"red\", labels= \"Samples\",title =\"GEDI Level1B\")\n\n\n```\n\n## Get GEDI Full-waveform (GEDI Level1B)\n```{r}\n# Extracting GEDI full-waveform for a giving shotnumber\nwf \u003c- getLevel1BWF(gedilevel1b, shot_number=\"19640521100108408\")\n\npar(mfrow = c(1,2), mar=c(4,4,1,1), cex.axis = 1.5)\n\nplot(wf, relative=FALSE, polygon=TRUE, type=\"l\", lwd=2, col=\"forestgreen\",\n     xlab=\"Waveform Amplitude\", ylab=\"Elevation (m)\")\ngrid()\nplot(wf, relative=TRUE, polygon=FALSE, type=\"l\", lwd=2, col=\"forestgreen\",\n     xlab=\"Waveform Amplitude (%)\", ylab=\"Elevation (m)\")\ngrid()\n```\n![](https://github.com/carlos-alberto-silva/rGEDI/blob/master/readme/fig3.png)\n\n## Get GEDI Elevation and Height Metrics (GEDI Level2A)\n```{r}\n# Get GEDI Elevation and Height Metrics\nlevel2AM\u003c-getLevel2AM(gedilevel2a)\nhead(level2AM[,c(\"beam\",\"shot_number\",\"elev_highestreturn\",\"elev_lowestmode\",\"rh100\")])\n\n##          beam       shot_number elev_highestreturn elev_lowestmode rh100\n##  1: BEAM0000 19640002800109382           740.7499        736.3301  4.41\n##  2: BEAM0000 19640003000109383           756.0878        746.7614  9.32\n##  3: BEAM0000 19640003200109384           770.3423        763.1509  7.19\n##  4: BEAM0000 19640003400109385           775.9838        770.6652  5.31\n##  5: BEAM0000 19640003600109386           777.8409        773.0841  4.75\n##  6: BEAM0000 19640003800109387           778.7181        773.6990  5.01\n\n# Converting shot_number as \"integer64\" to \"character\"\nlevel2AM$shot_number\u003c-as.character(level2AM$shot_number)\n\n# Converting Elevation and Height Metrics as data.table to sf\nlevel2AM_spdf \u003c- sf::st_as_sf(\n  level2AM,\n  coords = c(\"lon_lowestmode\", \"lat_lowestmode\"),\n  crs = \"epsg:4326\"\n)\n\n# Exporting Elevation and Height Metrics as ESRI Shapefile\nsf::write_sf(level2AM_spdf,file.path(outdir,\"GEDI02_A_2019108080338_O01964_T05337_02_001_01_sub.shp\"))\n```\n\n## Plot waveform with RH metrics\n```{r}\nshot_number = \"19640521100108408\"\n\npng(\"fig8.png\", width = 8, height = 6, units = 'in', res = 300)\nplotWFMetrics(gedilevel1b, gedilevel2a, shot_number, rh=c(25, 50, 75, 90))\ndev.off()\n```\n![](https://github.com/carlos-alberto-silva/rGEDI/blob/master/readme/fig8.png)\n## Get GEDI Vegetation Biophysical Variables (GEDI Level2B)\n```{r}\nlevel2BVPM\u003c-getLevel2BVPM(gedilevel2b)\nhead(level2BVPM[,c(\"beam\",\"shot_number\",\"pai\",\"fhd_normal\",\"omega\",\"pgap_theta\",\"cover\")])\n\n##          beam       shot_number         pai fhd_normal omega pgap_theta       cover\n##   1: BEAM0000 19640002800109382 0.007661204  0.6365142     1  0.9961758 0.003823273\n##   2: BEAM0000 19640003000109383 0.086218357  2.2644432     1  0.9577964 0.042192958\n##   3: BEAM0000 19640003200109384 0.299524575  1.8881851     1  0.8608801 0.139084846\n##   4: BEAM0000 19640003400109385 0.079557180  1.6625489     1  0.9609926 0.038997617\n##   5: BEAM0000 19640003600109386 0.018724868  1.5836401     1  0.9906789 0.009318732\n##   6: BEAM0000 19640003800109387 0.017654873  1.2458609     1  0.9912092 0.008788579\n\n# Converting shot_number as \"integer64\" to \"character\"\nlevel2BVPM$shot_number\u003c-as.character(level2BVPM$shot_number)\n\n# Converting GEDI Vegetation Profile Biophysical Variables as data.table to sf\nlevel2BVPM_spdf\u003c-sf::st_as_sf(\n  level2BVPM,\n  coords = c(\"longitude_lastbin\",\"latitude_lastbin\"),\n  crs = \"epsg:4326\"\n)\n# Exporting GEDI Vegetation Profile Biophysical Variables as ESRI Shapefile\nsf::st_write(level2BVPM_spdf,file.path(outdir,\"GEDI02_B_2019108080338_O01964_T05337_02_001_01_sub_VPM.shp\"))\n\n```\n\n## Get Plant Area Index (PAI) and Plant Area Volume Density (PAVD) Profiles (GEDI Level2B)\n```{r}\nlevel2BPAIProfile\u003c-getLevel2BPAIProfile(gedilevel2b)\nhead(level2BPAIProfile[,c(\"beam\",\"shot_number\",\"pai_z0_5m\",\"pai_z5_10m\")])\n\n##          beam       shot_number   pai_z0_5m   pai_z5_10m\n##   1: BEAM0000 19640002800109382 0.007661204 0.0000000000\n##   2: BEAM0000 19640003000109383 0.086218357 0.0581122264\n##   3: BEAM0000 19640003200109384 0.299524575 0.0497199222\n##   4: BEAM0000 19640003400109385 0.079557180 0.0004457365\n##   5: BEAM0000 19640003600109386 0.018724868 0.0000000000\n##   6: BEAM0000 19640003800109387 0.017654873 0.0000000000\n\nlevel2BPAVDProfile\u003c-getLevel2BPAVDProfile(gedilevel2b)\nhead(level2BPAVDProfile[,c(\"beam\",\"shot_number\",\"pavd_z0_5m\",\"pavd_z5_10m\")])\n\n##          beam       shot_number  pavd_z0_5m  pavd_z5_10m\n##   1: BEAM0000 19640002800109382 0.001532241 0.0007661204\n##   2: BEAM0000 19640003000109383 0.005621226 0.0086218351\n##   3: BEAM0000 19640003200109384 0.049960934 0.0299524590\n##   4: BEAM0000 19640003400109385 0.015822290 0.0079557188\n##   5: BEAM0000 19640003600109386 0.003744974 0.0018724868\n##   6: BEAM0000 19640003800109387 0.003530974 0.0017654872\n\n# Converting shot_number as \"integer64\" to \"character\"\nlevel2BPAIProfile$shot_number\u003c-as.character(level2BPAIProfile$shot_number)\nlevel2BPAVDProfile$shot_number\u003c-as.character(level2BPAVDProfile$shot_number)\n\n# Converting PAI and PAVD Profiles as data.table to sf\nlevel2BPAIProfile_spdf \u003c- sf::st_as_sf(\n  level2BPAIProfile,\n  coords = c(\"lon_lowestmode\", \"lat_lowestmode\"),\n  crs = \"epsg:4326\"\n)\nlevel2BPAVDProfile_spdf \u003c- sf::st_as_sf(\n  level2BPAVDProfile,\n  coords = c(\"lon_lowestmode\", \"lat_lowestmode\"),\n  crs = \"epsg:4326\"\n)\n\n# Exporting PAI and PAVD Profiles as ESRI Shapefile\nsf::write_sf(level2BPAIProfile_spdf,file.path(outdir,\"GEDI02_B_2019108080338_O01964_T05337_02_001_01_sub_PAIProfile.shp\"))\nsf::write_sf(level2BPAVDProfile_spdf,file.path(outdir,\"GEDI02_B_2019108080338_O01964_T05337_02_001_01_sub_PAVDProfile.shp\"))\n\n```\n\n## Plot Plant Area Index (PAI) and Plant Area Volume Density (PAVD) Profiles \n```{r}\n#specify GEDI beam\nbeam=\"BEAM0101\"\n\n# Plot Level2B PAI Profile\ngPAIprofile\u003c-plotPAIProfile(level2BPAIProfile, beam=beam, elev=TRUE)\n\n# Plot Level2B PAVD Profile\ngPAVDprofile\u003c-plotPAVDProfile(level2BPAVDProfile, beam=beam, elev=TRUE)\n\n```\n![](https://github.com/carlos-alberto-silva/rGEDI/blob/master/readme/fig9.png)\n\n\n## Clip GEDI data (h5 files; gedi.level1b, gedi.level2a and gedi.level2b objects)\n```{r}\n## Clip GEDI data by coordinates\n# Study area boundary box\nxmin = -44.15036\nxmax = -44.10066\nymin = -13.75831\nymax = -13.71244\n\n## clipping GEDI data within boundary box\nlevel1b_clip_bb \u003c- clipLevel1B(gedilevel1b, xmin, xmax, ymin, ymax,output=file.path(outdir,\"level1b_clip_bb.h5\"))\nlevel2a_clip_bb \u003c- clipLevel2A(gedilevel2a, xmin, xmax, ymin, ymax, output=file.path(outdir,\"level2a_clip_bb.h5\"))\nlevel2b_clip_bb \u003c- clipLevel2B(gedilevel2b, xmin, xmax, ymin, ymax,output=file.path(outdir,\"level2b_clip_bb.h5\"))\n\n## Clipping GEDI data by geometry\n# specify the path to shapefile for the study area\npolygon_filepath \u003c- system.file(\"extdata\", \"stands_cerrado.shp\", package=\"rGEDI\")\n\n# Reading shapefile as sf object\npolygon_spdf \u003c- sf::st_read(polygon_filepath)\nhead(polygon_spdf) # column id name \"id\"\nsplit_by \u003c- \"id\"\n\n# Clipping h5 files\nlevel1b_clip_gb \u003c- clipLevel1BGeometry(gedilevel1b,polygon_spdf,output=file.path(outdir,\"level1b_clip_gb.h5\"), split_by=split_by)\nlevel2a_clip_gb \u003c- clipLevel2AGeometry(gedilevel2a,polygon_spdf,output=file.path(outdir,\"level2a_clip_gb.h5\"), split_by=split_by)\nlevel2b_clip_gb \u003c- clipLevel2BGeometry(gedilevel2b,polygon_spdf,output=file.path(outdir,\"level2b_clip_gb.h5\"), split_by=split_by)\n```\n## Clip GEDI data (data.table objects)\n```{r}\n## Clipping GEDI data within boundary box\nlevel1bGeo_clip_bb \u003c-clipLevel1BGeo(level1bGeo, xmin, xmax, ymin, ymax)\nlevel2AM_clip_bb \u003c- clipLevel2AM(level2AM, xmin, xmax, ymin, ymax)\nlevel2BVPM_clip_bb \u003c- clipLevel2BVPM(level2BVPM, xmin, xmax, ymin, ymax)\nlevel1BPAIProfile_clip_bb \u003c- clipLevel2BPAIProfile(level2BPAIProfile, xmin, xmax, ymin, ymax)\nlevel2BPAVDProfile_clip_bb \u003c- clipLevel2BPAVDProfile(level2BPAVDProfile, xmin, xmax, ymin, ymax)\n\n## Clipping GEDI data by geometry\nlevel1bGeo_clip_gb \u003c- clipLevel1BGeoGeometry(level1bGeo,polygon_spdf, split_by=split_by)\nlevel2AM_clip_gb \u003c- clipLevel2AMGeometry(level2AM,polygon_spdf, split_by=split_by)\nlevel2BVPM_clip_gb \u003c- clipLevel2BVPMGeometry(level2BVPM,polygon_spdf, split_by=split_by)\nlevel1BPAIProfile_clip_gb \u003c- clipLevel2BPAIProfileGeometry(level2BPAIProfile,polygon_spdf, split_by=split_by)\nlevel2BPAVDProfile_clip_gb \u003c- clipLevel2BPAVDProfileGeometry(level2BPAVDProfile,polygon_spdf, split_by=split_by)\n\n\n## View GEDI clipped data by bbox\nm1\u003c-leaflet() %\u003e%\n  addCircleMarkers(level2AM$lon_lowestmode,\n                   level2AM$lat_lowestmode,\n                   radius = 1,\n                   opacity = 1,\n                   color = \"red\")  %\u003e%\n  addCircleMarkers(level2AM_clip_bb$lon_lowestmode,\n                   level2AM_clip_bb$lat_lowestmode,\n                   radius = 1,\n                   opacity = 1,\n                   color = \"green\")  %\u003e%\n  addScaleBar(options = list(imperial = FALSE)) %\u003e%\n  addProviderTiles(providers$Esri.WorldImagery)  %\u003e%\n  addLegend(colors = c(\"red\",\"green\"), labels= c(\"All samples\",\"Clip bbox\"),title =\"GEDI Level2A\") \n\n## View GEDI clipped data by geometry\n# color palette\npal \u003c- colorFactor(\n  palette = c('blue', 'green', 'purple', 'orange',\"white\",\"black\",\"gray\",\"yellow\"),\n  domain = level2AM_clip_gb$poly_id\n)\n\nm2\u003c-leaflet() %\u003e%\n  addCircleMarkers(level2AM$lon_lowestmode,\n                   level2AM$lat_lowestmode,\n                   radius = 1,\n                   opacity = 1,\n                   color = \"red\")  %\u003e%\n  addCircleMarkers(level2AM_clip_gb$lon_lowestmode,\n                   level2AM_clip_gb$lat_lowestmode,\n                   radius = 1,\n                   opacity = 1,\n                   color = pal(level2AM_clip_gb$poly_id))  %\u003e%\n  addScaleBar(options = list(imperial = FALSE)) %\u003e%\n  addPolygons(data=polygon_spdf,weight=1,col = 'white',\n              opacity = 1, fillOpacity = 0) %\u003e%\n  addProviderTiles(providers$Esri.WorldImagery) %\u003e%\n  addLegend(pal = pal, values = level2AM_clip_gb$poly_id,title =\"Poly IDs\" ) \n\nsync(m1, m2)\n```\n![](https://github.com/carlos-alberto-silva/rGEDI/blob/master/readme/fig4.png)\n\n## Compute descriptive statistics of GEDI Level2A and Level2B data\n```{r}\n# Define your own function\nmySetOfMetrics = function(x)\n{\nmetrics = list(\n    min =min(x), # Min of x\n    max = max(x), # Max of x\n    mean = mean(x), # Mean of x\n    sd = sd(x)# Sd of x\n  )\n  return(metrics)\n}\n\n# Computing the maximum of RH100 stratified by polygon\nrh100max_st\u003c-polyStatsLevel2AM(level2AM_clip_gb,func=max(rh100), id=\"poly_id\")\nhead(rh100max_st)\n\n##    poly_id   max\n## 1:       2 12.81\n## 2:       1 12.62\n## 3:       5  9.96\n## 4:       6  8.98\n## 5:       4 10.33\n## 6:       8  8.72\n\n# Computing a serie statistics for GEDI metrics stratified by polygon\nrh100metrics_st\u003c-polyStatsLevel2AM(level2AM_clip_gb,func=mySetOfMetrics(rh100),\nid=\"poly_id\")\nhead(rh100metrics_st)\n\n##    poly_id  min   max     mean       sd\n## 1:       2 4.08 12.81 5.508639 1.452143\n## 2:       1 3.78 12.62 5.514930 1.745507\n## 3:       5 4.12  9.96 5.100122 1.195272\n## 4:       6 4.64  8.98 5.595294 1.024171\n## 5:       4 4.38 10.33 7.909500 1.757200\n## 6:       8 4.45  8.72 6.136471 1.097468\n\n# Computing the max of the Total Plant Area Index\npai_max\u003c-polyStatsLevel2BVPM(level2BVPM_clip_gb,func=max(pai), id=NULL)\npai_max\n\n##          max\n#   1: 1.224658\n\n# Computing a serie of statistics of Canopy Cover stratified by polygon\ncover_metrics_st\u003c-polyStatsLevel2BVPM(level2BVPM_clip_gb,func=mySetOfMetrics(cover),\nid=\"poly_id\")\nhead(cover_metrics_st)\n\n##     poly_id          min       max       mean         sd\n##  1:       2 0.0010017310 0.3479594 0.05156159 0.05817241\n##  2:       1 0.0003717059 0.3812594 0.04829096 0.06346548\n##  3:       5 0.0020242794 0.4262614 0.03577852 0.06407325\n##  4:       6 0.0028748326 0.2392146 0.03094646 0.05577988\n##  5:       4 0.0022404396 0.3501986 0.11343149 0.09354305\n##  6:       8 0.0050588539 0.1457105 0.04784596 0.04427151\n```\n\n## Compute Grids with descriptive statistics of GEDI-derived Elevation and Height Metrics (Level2A)\n\n\u003cimg align=\"right\" src=\"https://github.com/carlos-alberto-silva/rGEDI/blob/master/readme/fig5.png\" width=\"300\"\u003e\n\n```{r}\n# Computing a serie of statistics of GEDI RH100 metric\nrh100metrics\u003c-gridStatsLevel2AM(level2AM = level2AM, func=mySetOfMetrics(rh100), res=0.005)\n\n# View maps\nlibrary(rasterVis)\nlibrary(viridis)\n\nrh100maps\u003c-levelplot(rh100metrics,\n                     layout=c(1, 4),\n                     margin=FALSE,\n                     xlab = \"Longitude (degree)\", ylab = \"Latitude (degree)\",\n                     colorkey=list(\n                       space='right',\n                       labels=list(at=seq(0, 18, 2), font=4),\n                       axis.line=list(col='black'),\n                       width=1),\n                     par.settings=list(\n                       strip.border=list(col='gray'),\n                       strip.background=list(col='gray'),\n                       axis.line=list(col='gray')\n                     ),\n                     scales=list(draw=TRUE),\n                     col.regions=viridis,\n                     at=seq(0, 18, len=101),\n                     names.attr=c(\"rh100 min\",\"rh100 max\",\"rh100 mean\", \"rh100 sd\"))\n\n# Exporting maps \npng(\"fig6.png\", width = 6, height = 8, units = 'in', res = 300)\nrh100maps\ndev.off()\n\n\n\n```\n\n## Compute Grids with descriptive statistics of GEDI-derived Canopy Cover and Vertical Profile Metrics (Level2B)\n\n\u003cimg align=\"right\" src=\"https://github.com/carlos-alberto-silva/rGEDI/blob/master/readme/fig6.png\" width=\"300\"\u003e\n\n```{r}\n# Computing a serie of statistics of Total Plant Area Index\nlevel2BVPM$pai[level2BVPM$pai==-9999]\u003c-NA # assing NA to -9999\npai_metrics\u003c-gridStatsLevel2BVPM(level2BVPM = level2BVPM, func=mySetOfMetrics(pai), res=0.005)\n\n# View maps\npai_maps\u003c-levelplot(pai_metrics,\n                    layout=c(1, 4),\n                    margin=FALSE,\n                    xlab = \"Longitude (degree)\", ylab = \"Latitude (degree)\",\n                    colorkey=list(\n                      space='right',\n                      labels=list(at=seq(0, 1.5, 0.2), font=4),\n                      axis.line=list(col='black'),\n                      width=1),\n                    par.settings=list(\n                      strip.border=list(col='gray'),\n                      strip.background=list(col='gray'),\n                      axis.line=list(col='gray')\n                    ),\n                    scales=list(draw=TRUE),\n                    col.regions=viridis,\n                    at=seq(0, 1.5, len=101),\n                    names.attr=c(\"PAI min\",\"PAI max\",\"PAI mean\", \"PAI sd\"))\n\n# Exporting maps \npng(\"fig7.png\", width = 6, height = 8, units = 'in', res = 300)\npai_maps\ndev.off()\n\n\n\n\n```\n\n## Simulating GEDI full-waveform data from Airborne Laser Scanning (ALS) 3-D point cloud and extracting canopy derived metrics\n```{r}\n# Specifying the path to ALS data\nlasfile_amazon \u003c- file.path(outdir, \"Amazon.las\")\nlasfile_savanna \u003c- file.path(outdir, \"Savanna.las\")\n\n# Reading and plot ALS file\nlibrary(lidR)\nlibrary(plot3D)\nlas_amazon\u003c-readLAS(lasfile_amazon)\nlas_savanna\u003c-readLAS(lasfile_savanna)\n\n# Extracting plot center geolocations\nxcenter_amazon = mean(st_bbox(las_amazon)[c(1, 3)])\nycenter_amazon = mean(st_bbox(las_amazon)[-c(1, 3)])\nxcenter_savanna = mean(st_bbox(las_savanna)[c(1, 3)])\nycenter_savanna = mean(st_bbox(las_savanna)[-c(1, 3)])\n\n# The gedi simulator has been moved separately in rGEDIsimulator as following\ndevtools::install_git(\"https://github.com/caiohamamura/Rgedisimulator\", dependencies = TRUE)\nlibrary(rGEDIsimulator)\n\n# Simulating GEDI full-waveform\nwf_amazon\u003c-gediWFSimulator(input=lasfile_amazon,output=file.path(getwd(),\"gediWF_amazon_simulation.h5\"),coords = c(xcenter_amazon, ycenter_amazon))\nwf_savanna\u003c-gediWFSimulator(input=lasfile_savanna,output=file.path(getwd(),\"gediWF_savanna_simulation.h5\"),coords = c(xcenter_savanna, ycenter_savanna))\n\n# Plotting ALS and GEDI simulated full-waveform\npng(\"gediWf.png\", width = 8, height = 6, units = 'in', res = 300)\n\npar(mfrow=c(2,2), mar=c(4,4,0,0), oma=c(0,0,1,1),cex.axis = 1.2)\nscatter3D(las_amazon@data$X,las_amazon@data$Y,las_amazon@data$Z,pch = 16,colkey = FALSE, main=\"\",\n          cex = 0.5,bty = \"u\",col.panel =\"gray90\",phi = 30,alpha=1,theta=45,\n          col.grid = \"gray50\", xlab=\"UTM Easting (m)\", ylab=\"UTM Northing (m)\", zlab=\"Elevation (m)\")\n\n# Simulated waveforms shot_number is incremental beggining from 0\nshot_number = 0\nsimulated_waveform_amazon = getLevel1BWF(wf_amazon, shot_number)\nplot(simulated_waveform_amazon, relative=TRUE, polygon=TRUE, type=\"l\", lwd=2, col=\"forestgreen\",\n     xlab=\"\", ylab=\"Elevation (m)\", ylim=c(90,140))\ngrid()\nscatter3D(las_savanna@data$X,las_savanna@data$Y,las_savanna@data$Z,pch = 16,colkey = FALSE, main=\"\",\n          cex = 0.5,bty = \"u\",col.panel =\"gray90\",phi = 30,alpha=1,theta=45,\n          col.grid = \"gray50\", xlab=\"UTM Easting (m)\", ylab=\"UTM Northing (m)\", zlab=\"Elevation (m)\")\n\nshot_number = 0\nsimulated_waveform_savanna = getLevel1BWF(wf_savanna, shot_number)\nplot(simulated_waveform_savanna, relative=TRUE, polygon=TRUE, type=\"l\", lwd=2, col=\"green\",\nxlab=\"Waveform Amplitude (%)\", ylab=\"Elevation (m)\", ylim=c(815,835))\ngrid()\ndev.off()\n```\n![](https://github.com/carlos-alberto-silva/rGEDI/blob/master/readme/fig7.png)\n\n## Extracting GEDI full-waveform derived metrics without adding noise to the full-waveform\n```\nwf_amazon_metrics\u003c-gediWFMetrics(input=wf_amazon,\n                                outRoot=file.path(getwd(), \"amazon\"))\nwf_savanna_metrics\u003c-gediWFMetrics(input=wf_savanna,\n                                outRoot=file.path(getwd(), \"savanna\"))\n\nmetrics\u003c-rbind(wf_amazon_metrics,wf_savanna_metrics)\nrownames(metrics)\u003c-c(\"Amazon\",\"Savanna\")\nhead(metrics[,1:8])\n\n#                wave ID true ground true top ground slope ALS cover gHeight maxGround inflGround\n#Amazon  gedi.BEAM0000.0      -1e+06   133.25       -1e+06        -1   94.93     99.95      95.16\n#Savanna gedi.BEAM0000.0      -1e+06   831.47       -1e+06        -1  822.18    822.17     822.25\n```\n## Extracting GEDI full-waveform derived metrics after adding noise to the full-waveform\n```\nwf_amazon_metrics_noise\u003c-gediWFMetrics(input=wf_amazon,\n                         outRoot=file.path(getwd(), \"amazon\"),\n                         linkNoise= c(3.0103,0.95),\n                         maxDN= 4096,\n                         sWidth= 0.5,\n                         varScale= 3)\n\nwf_savanna_metrics_noise\u003c-gediWFMetrics(\n                        input=wf_savanna,\n                        outRoot=file.path(getwd(), \"savanna\"),\n                        linkNoise= c(3.0103,0.95),\n                        maxDN= 4096,\n                        sWidth= 0.5,\n                        varScale= 3)\n\nmetrics_noise\u003c-rbind(wf_amazon_metrics_noise,wf_savanna_metrics_noise)\nrownames(metrics_noise)\u003c-c(\"Amazon\",\"Savanna\")\nhead(metrics_noise[,1:8])\n\n#         #wave ID true ground true top ground slope ALS cover gHeight maxGround inflGround\n# Amazon         0      -1e+06   133.29       -1e+06        -1   99.17     99.99      95.39\n# Savanna        0      -1e+06   831.36       -1e+06        -1  822.15    822.21     822.18\n\n```\n\n## Always close gedi objects, so HDF5 files won't be blocked!\n```{r cleanup, echo=TRUE, results=\"hide\", error=TRUE}\nclose(wf_amazon)\nclose(wf_savanna)\nclose(gedilevel1b)\nclose(gedilevel2a)\nclose(gedilevel2b)\n```\n\n\n# References\nDubayah, R., Blair, J.B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S., Hofton, M., Hurtt, G.,         Kellner, J., Luthcke, S., \u0026 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\n\nHancock, S., Armston, J., Hofton, M., Sun, X., Tang, H., Duncanson, L.I., Kellner,\n       J.R. and Dubayah, R., 2019. The GEDI simulator: A large-footprint waveform lidar simulator\n       for calibration and validation of spaceborne missions. Earth and Space Science.\n       https://doi.org/10.1029/2018EA000506\n\nSilva, 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.\n      https://ieeexplore.ieee.org/document/8331845\n\nGEDI webpage. Accessed on September 23 2025 https://gedi.umd.edu/   \nGEDI L1B Geolocated Waveform Data Global Footprint Level V002. Accessed on September 23 2025 https://doi.org/10.5067/GEDI/GEDI01_B.002\nGEDI L2A Elevation and Height Metrics Data Global Footprint Level V002. Accessed on September 23 2025 https://doi.org/10.5067/GEDI/GEDI02_A.002\nGEDI L2B Canopy Cover and Vertical Profile Metrics Data Global Footprint Level V002. Accessed on September 23 2025 https://doi.org/10.5067/GEDI/GEDI02_B.002\nNASA EarthData search API. Accessed on September 23 2025 https://cmr.earthdata.nasa.gov/search/site/docs/search/api.html\n\n# Acknowledgements\nThe University of Maryland and NASA's Goddard Space Flight Center for developing GEDI mission.\n\nWe 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).\n\nThe 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\n(carlos_engflorestal@outlook.com).\n\n# Getting Help\nThe best place to get help from community is StackExchange:\n\u003chttps://gis.stackexchange.com/questions/tagged/gedi\u003e. \nBefore posting there, make sure your question hasn't already been answered.\nAlso don't forget to add relevant tags such as `gedi` and `rgedi`.\n\n# Reporting BugsIssues \nPlease report any issue regarding the rGEDI package herein https://github.com/carlos-alberto-silva/rGEDI/issues\n\n# Citing rGEDI\nSilva,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.\nversion 0.1.9, accessed on October. 22 2020, available at: \u003chttps://CRAN.R-project.org/package=rGEDI\u003e\n\n# Disclaimer\n**rGEDI package has not been developted by the GEDI team. 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