FANR5640/7640 – Global Mapper Pixels to Points


Pixels To Points Help

In this lab, you will be processing drone data acquired on February 07, 2020 at the terraced section of the UGA Botanical Garden.  The photos were captured with the DJI Phantom 4 Professional using the DJI Ground Station software running on an iPAD mini; the drone was set to fly at a height of 75 meters above ground level with a sidelap of 80% and an endlap of 80%.  The mission resulted in 84 photos.

The You will use the LiDAR toolset in Global Mapper to generate an orthophoto, a bare ground “terrain” model and a “surface” model (includes trees, stumps, buildings, etc).

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Pre-processing data management: Data management is a necessity.  Sooner or later, you will need to develop a data management strategy – one that allows you to quickly browse your data directory and know what is there and where it was captured.  I try to create a project folder with separate subfolders to store the unprocessed drone photos, outputs, and any other existing GIS data.

  • Create a working directory on the E:\ Drive called BotGarden_Feb072020.  Also create a folder called InputPhotos and another folder called OutputData
  • Download lab data (HERE) and copy it over to your working directory.  The compressed file size is 706,494MB.
  • Unzip your data file.  Move photos ‘DJI_0029.JPG’ – ‘DJI_0104.JPG’ to the InputPhotos folder.  You can delete the other photos.

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Open Global Mapper v21.0

Pixels To Points Blue Marble Geo Help

  • Set Coordinate System: Tools > Configure > Projection > UTM/Zone17N/NAD83/METERS
  • Save your project to your working directory
    • File > Save Workspace As…

Convert UAV photos to point cloud using the Pixels to Points tool

  • File > Open Data Files load UAV photos
  • For context, load a basemap
    • File > Download Online Imagery…
    • Select World Imagery
  • Save your workspace
  • Load Pixels-To-Points (PTP) tool (located on the LiDAR toolbar, last icon on the right)
  • From the PTP tool
    • Input Image Files: right-click > Add Loaded Pictures…
    • Point Cloud/Orthoimage/Mesh/Log Output: Save “BG072020_pointcloud” (in GMP Global Mapper format) in your <working directory>\OutputData folder
    • Orthoimage: Save “BG072020_ortho” in your OutputData folder
    • Reduce Image Size: (use 8 to speed processing for lab) down-samples the original images which decreases processing time
    • Analysis Method: means by which matching points are located on UAV photos
      • Incremental: The Incremental method starts from two images, and progressively adds more, recalculating the parameters and locations of the points to minimize the error.
      • Global: considers the keypoints across all images at the same time.

Output of Pixels To Points tool

Remove the individual drone photos (shift-select > right-click > remove…)

Save your outputs in a common GIS-ready format:

  • Right-click on the layer you want to export from Global Mapper > EXPORT… > select your file type > name your output & hit Apply/OK
    • Save your point cloud as a LAS file
    • Save your other raster layers as an ERDAS Imagine File

Create surface and terrain models

Explore the Analysis > ‘Create Elevation Grid from 3D Vector/Lidar Data’ tool…

  • Grid Method ‘Maximum Value – DSM’ to create a surface model
  • Grid Method ‘Minimum Value – DTM’ to create a terrain model

Explore the Analysis > ‘Combine/Compare Terrain Layers’ tool…


We will work on this in a couple weeks…

CONTINUATION of Monday’s lab

Global Mapper is the second software that you have seen this semester that allows you to create an orthophoto and a point cloud from a series of photos (ReCap Pro is the other).  While it appears that Global Mapper does have quite a bit of GIS functionality, we’ll be analyzing the output in ArcGIS and R.

Think back to Lab 7 and the Lab 7 Follow-up.  The simplified locate-tree workflow we followed was:

  • Focal Minimum to find the ground surface
  • Difference original surface model and the focal minimum output
  • Focal Maximum on output to find tree apex
  • Generate slope
  • Reclassify 0-slope as 1 (YES tree)
  • Convert to polygon then polygon-to-point

Lets try this approach on Monday’s data…  (In-class Demo)…


Now, in R…

https://cran.r-project.org/web/packages/lidR/lidR.pdf

#####load required R libraries
library(lidR)
library(raster)
#####Install Bioconductor
source("https://bioconductor.org/biocLite.R")
biocLite("EBImage")
library(EBImage)

#####specify my working directory
setwd("G:/UAV/SouthernGrowers/SmallSubset/out/")

#####read LAS file generated in Agisoft Photoscan, Global Mapper, etc...
my_data1<- readLAS("clpSoutheasternGrowers_PointCloud.las")
summary(my_data1)
my_data1bak<- my_data1  #####back up the original data file

#####classify ground layer
#####sequences used in the lasground command
ws = seq(0.75,3, 0.75) th = seq(0.1, 1.1, length.out = length(ws))
lasground(my_data1, "pmf", ws, th)
plot(my_data1, color = "Classification")

my_data1grnd<- my_data1  #####back up ground classification
####writeLAS(my_data1,"xxy.las")

#####compute DTM
dtm1 = grid_terrain(my_data1, res=0.9, method="knnidw", k=10, keep_lowest=TRUE)
dtm1r = as.raster(dtm1)
plot(dtm1)

#####normalize point cloud
##lnorm = lasnormalize(my_data1, method="kriging", k=10L)
lasnormalize(my_data1, dtm1)
#####my_data1 now contains the normalized heights
summary(my_data1)
plot(my_data1)
plot(dtm1)


#####create grid canopy
chm = grid_canopy(my_data1, res=0.25, subcircle = 0.2)
chm2 = grid_canopy(my_data1, 0.15, subcircle = 0.2)
chm2 = grid_canopy(my_data1, 0.05, subcircle = 0.025)
plot(chm2)
chm2 = as.raster(chm2)

#####smoothing post-process (2x mean)
kernel<- matrix(1,3,3)
chm2a = raster::focal(chm2, w=kernel, fun=mean)
chm2a = raster::focal(chm2, w=kernel, fun=mean)
raster::plot(chm2a, col=height.colors(50))

#####segmentation
crowns = lastrees(my_data1, "watershed", chm2a, th=0.25, extra=TRUE)
contour = rasterToPolygons(crowns, dissolve=TRUE)
tree = lasfilter(my_data1, !is.na(treeID))
##plot(tree, color="treeID", colorPalette=pastel.colors(200))

plot(chm2, col=height.colors(50))
plot(contour, add=T)

plot(tree)
#####Output new raster file called "ttyz.img"
raster::writeRaster(my_trees, filename="ttyz.img", format="HFA", overwrite=TRUE)
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