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This `R` tutorial presents a workflow to compute both object-oriented and pixel-based metrics to describe the forest structure from Airborne Laser Scanning (lidar remote sensing) data. Extraction of different types of metrics is exemplified and code for parallelized processing of several ALS data files is provided.
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The following files are provided:
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* [Rmarkdown source](https://gitlab.irstea.fr/jean-matthieu.monnet/lidartree_tutorials/-/blob/master/gaps.edges.detection.Rmd),
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* [**Forest gaps and edges detection tutorial in html**](https://gitlab.irstea.fr/jean-matthieu.monnet/lidartree_tutorials/-/raw/master/export/gaps.edges.detection.html?inline=false),
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* [Forest gaps and edges detection tutorial in pdf](https://gitlab.irstea.fr/jean-matthieu.monnet/lidartree_tutorials/-/blob/master/export/gaps.edges.detection.pdf),
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* a [bibliography file](https://gitlab.irstea.fr/jean-matthieu.monnet/lidartree_tutorials/-/blob/master/bib/bibliography.bib)
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* datasets required to run the source code are also available on the repository.
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Change log
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* Jan 2021: updated to comply with lidR 3.1.0 and lidaRtRee 3.0.0
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* Oct, 2020: checked compatibility with lidR 3.0.3
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* May, 2020: added edge metrics, gaps computed using option `gapReconstruct=TRUE`, output resolution changed to 10 m for better consistency of tree metrics
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* July, 2019: use of `doFuture` package instead of `doParallel` for parallelization; tested with lidR 2.1.0
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* Feb, 2019: updated for compatibility with package lidR 2.0.0 and lidaRtRee 2.0.0
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* July, 2018: initial version |
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\ No newline at end of file |
