abstract={Abstract Habitat suitability models (HSMs) are widely used to plan actions for species of conservation interest. Models that will be turned into conservation actions need predictors that are both ecologically pertinent and fit managers’ conceptual view of ecosystems. Remote sensing technologies such as light detection and ranging (LiDAR) can describe landscapes at high resolution over large spatial areas and have already given promising results for modeling forest species distributions. The point-cloud (PC) area-based LiDAR variables are often used as environmental variables in HSMs and have more recently been complemented by object-oriented (OO) metrics. However, the efficiency of each type of variable to capture structural information on forest bird habitat has not yet been compared. We tested two hypotheses: (1) the use of OO variables in HSMs will give similar performance as PC area-based models; and (2) OO variables will improve model robustness to LiDAR datasets acquired at different times for the same area. Using the case of a locally endangered forest bird, the capercaillie (Tetrao urogallus), model performance and predictions were compared between the two variable types. Models using OO variables showed slightly lower discriminatory performance than PC area-based models (average ΔAUC = −0.032 and −0.01 for females and males, respectively). OO-based models were as robust (absolute difference in Spearman rank correlation of predictions ≤ 0.21) or more robust than PC area-based models. In sum, LiDAR-derived PC area-based metrics and OO metrics showed similar performance for modeling the distribution of the capercaillie. We encourage the further exploration of OO metrics for creating reliable HSMs, and in particular testing whether they might help improve the scientist–stakeholder interface through better interpretability.},
Glad, Anouk, Björn Reineking, Marc Montadert, Alexandra Depraz, and Jean-Matthieu Monnet. 2020. <span>“Assessing the Performance of Object-Oriented LiDAR Predictors for Forest Bird Habitat Suitability Modeling.”</span> <em>Remote Sensing in Ecology and Conservation</em> 6 (1): 5–19. https://doi.org/<a href="https://doi.org/10.1002/rse2.117">https://doi.org/10.1002/rse2.117</a>.
Glad, Anouk, Björn Reineking, Marc Montadert, Alexandra Depraz, and Jean-Matthieu Monnet. 2020. <span>“Assessing the Performance of Object-Oriented LiDAR Predictors for Forest Bird Habitat Suitability Modeling.”</span> <em>Remote Sensing in Ecology and Conservation</em> 6 (1): 5–19. <a href="https://doi.org/10.1002/rse2.117">https://doi.org/10.1002/rse2.117</a>.
The R code below presents a forest structure metrics computation workflow from Airborne Laser Scanning (ALS) data. Workflow is based on functions from packages `lidaRtRee` and `lidR`, packages `vegan` and `foreach` are also required. Metrics are computed for each cell of a grid defined by a resolution. Those metrics are designed to describe the 3D structure of forest.
Different types of metrics are computed:
* 1D height metrics
+ 2D metrics of the canopy height model (CHM)
+ tree segmentation metrics
+ forest gaps and edges metrics
+ tree segmentation metrics (see also [tree segmentation tutorial](https://gitlab.irstea.fr/jean-matthieu.monnet/lidartree_tutorials/-/blob/master/tree.detection.Rmd))
+ forest gaps and edges metrics (see also [gaps and edges detection tutorial](https://gitlab.irstea.fr/jean-matthieu.monnet/lidartree_tutorials/-/blob/master/gaps.edges.detection.Rmd))
The forest structure metrics derived from airborne laser scanning can be used for habitat suitability modelling and mapping. This workflow has been applied to compute the metrics used in the modeling and mapping of the habitat of Capercaillie (*Tetrao urogallus*)(@Glad20). For more information about tree segmentation and gaps detection, please refer to the corresponding tutorials.
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@@ -126,7 +126,7 @@ The next step is to compute the canopy height model (CHM). It will be used to de
* 2D canopy height metrics related to multi-scale vertical heterogeneity (mean and standard deviation of CHM, smoothed at different scales)
+ tree metrics from tree top segmentation
+ gaps and edges metrics from gap extraction
+ gaps and edges metrics from gap segmentation
The CHM is computed and NA values are replaced by 0. A check is performed to make sure low or high points are not present.
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@@ -351,8 +351,10 @@ Some outputs are displayed hereafter.
par(mfrow=c(1,3))
raster::plot(metrics.trees$Tree.meanH, main="Mean (detected) tree height (m)")
points(trees$x, trees$y, cex=trees$h/40)
raster::plot(metrics.trees$TreeSup20.density, main="Density of (detected) trees > 20m (/ha)")
raster::plot(metrics.trees$Tree.meanCrownVolume, main="Mean crown volume of detected trees (m3)")
raster::plot(metrics.trees$TreeSup20.density,
main="Density of (detected) trees > 20m (/ha)")
raster::plot(metrics.trees$Tree.meanCrownVolume,
main="Mean crown volume of detected trees (m3)")
```
### Point cloud (1D) metrics
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@@ -394,8 +396,6 @@ Based on those metrics, additional metrics are computed (Simpson index, relative
raster::plot(metrics.1d$H.p50, main="Percentile 50 of heights")
raster::plot(metrics.1d$H.nb2_5ratio, main="Penetration ratio 2-5 m")
raster::plot(metrics.1d$H.nb2_5ratio, main="Interception ratio 2-5 m")
```
## Batch processing
The following code uses parallel processing to handle multiple files and arranges all metrics in a single dataframe. Code in the "parameters" section has to be run beforehand.
The following code uses parallel processing to handle multiple files and arranges all metrics in a raster stack. Code in the "parameters" section has to be run beforehand.
