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| ``` r | ||
| # Check data integrity and attributes | ||
| las_check(classified) |
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Is it worth printing the result of this command in the tutorial and explaining why you ignore certain warnings? When I apply it, I get warnings on duplicate and degenerate points. Later on in the tutorial, the function get_crown_attributes() also issues a warning that duplicate points have been removed.
- Checking absence of NAs... ✓
- Checking duplicated points...
⚠ 5829552 points are duplicated and share XYZ coordinates with other points
- Checking degenerated ground points...
⚠ There were 3503496 degenerated ground points. Some X Y Z coordinates were repeated
⚠ There were 2273 degenerated ground points. Some X Y coordinates were repeated but with different Z coordinates
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It is okay for me to print out the get_crown_attributes(). We have duplicates since the tiles of laz files overlap to a certain degree (flight paths of the plane with the lidar sensor onboard overlap). In these overlapping zones, you can have dubble the amount of points. You can remove them before analysis (for example, remove the overlapping zones), but most functions inside the package will detect them automatically and remove them (but for for statistical analysis/metrics, it is indeed better to remove duplicates). The degenerated ground points can be caused by small errors in the sensor, or by "moving" objects on the ground during lidar scanning (For example, if there is wind, tree crowns move slightly, causing different z-values for the same locations).
sverbesselt01
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I agree to your changes and provided an answer to your question :)
Added explanation about duplicates in lidar data and their impact on analysis.
bserrien
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Thanks for your contribution to the INBO tutorials website!
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tutorials/content/index.md. In case of an Rmarkdown tutorial I have knitted myindex.Rmdtoindex.md(both files are pushed to the repo).yamlheader:authorsyaml tag, using[MY_AUTHOR_ID]. An author information file exists in<tutorials>/data/authors/<author>.toml.categoriesto the YAML header and my category tags are from the list of categories.tags(i.e. keywords) in the YAML header to improve the visibility of the new tutorial (see the tags listed in the tutorials website side bar).dateis in formatYYYY-MM-DDand adjusted.Previewing the pull request
Thanks to GitHub Actions, an artifact (=zip file) of the rendered website is automatically created for each pull request.
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Downloads/tutorials_preview.httpsites on your local machine. One such option is theservrpackage in R:& '\C:\Program Files\R\R-4.4.2\bin\Rscript.exe' -e "servr::httd('./tutorials_preview')" -p8887(make sure to adjust the path to yourRscript.exe; on Linux, simply useRscript -e [...]).Note: for step 3, you can use any other simple HTTP server to serve the current directory, e.g. Python
http.server:python -m http.server 8887 --bind localhost --directory path/to/tutorials_previewAlternative: Locally Building the Site
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