This worksheet covers the transformation of selected information from 3D LiDAR point clouds to 2D raster datasets.
After completing this worksheet you should know how to subset LiDAR point clouds and select problem guided rules for the transformation into raster datasets.
The following tasks will be used as examples of potentially useful LiDAR information. While we will not come back to these datasets within this module, it will be analyzed as part of the Advanced GIS and the upcoming in-depth modules on environmental information systems.
Given all the software and hardware constraints when dealing with LiDAR datasets (see W01-1: LiDAR - LAS I/O, we will restrict today's work to a minimum.
As always, please add these entries to your today's learning log in the beginning of your Rmd file you will use to push to your GitHub classroom.
As today's special, please complete the following assignment:
If this is the first time that you use your Git repository provided by LogMOC, the learning log space of Marburg Open Courseware on GitHub, you have to get yourself a GitHub account and use the link provided by the course lecturers afterwards in order to create your personal repository.
If not done yet, please create your personal learning log repository using the link provided by the course lecturers and clone it to your local computer.
Once you have your repository cloned to your local computer, create the following two folders inside the repository:
remote-sensing/rs-ws-01-1. Please note that
rs-ws-01-1 is a sub-folder of
remote_sensing which in turn is a sub-folder of your main repository folder (i.e.
msc-phygeo-class-of-<your year>-<your github name>). Do not use any spaces within your folder or filenames. Never.
Please create the folder structure described above.
In the following we will transform 3D LiDAR point clouds to 2D raster surfaces. This also comprises the determination of a fixed spatial resolution as well as a mathematical function which controls the spatial transformation.
Define a common resolution applicable to all of the following datasets and compute the respective raster data layers for the entire Marburg University forest study region.
Create a Rmd file with html output in your GitHub classroom repository and include the name of the dataset you have created followed by a screen shot of the raster dataset (e.g. showing it as layer in ArcGIS). Knitr your Rmd file, update (i.e. commit) it in your local repository and publish (i.e. push) it to the GitHub classroom. Make sure that the created html file is also part of your git repository.
rs-ws-01-1(i.e. the worksheet shortname) also for the title and the filename. Store it in the
rs-ws-01-1folder just created. You can commit/push as many versions as you like during your work but once you have reached your final version, please use “Final version” as comment for the commit.
In order to include an image, saved on your local hard drive at e.g. “D:\studies\remote-sensing\01.jpg”, please add
![A test image](D:\studies/remote-sensing/01.jpg) to your markdown file outside a code block. This is a markdown command, not an R command! Please also make sure that you only use a backslash (“\”) for separating the driver letter from the rest. All other folders must be separated by a slash (“/”).
Pleae also make sure that you create a “standalone” hmtl output. This is the default setting of knit html. To check this setting from within RStudio you can click on the gear-wheel symbol next to the kniter symbol and then on the “Advanced” tab.
Please also make sure that the knitred html file is actually put into version control, i.e. it has to show up not only in your local repository folder but also on your GitHub master branch. If it won't show up on GitHub, it is likely that
.html is part of a
.gitignore file in your repository. For a quick'n'dirty solution, just delete that file if it shows up until you are more familiar with Git.