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+---
+title: "Exploring the potential of aerial lidar: introduction to lidR"
+description: "This tutorial provides an introduction for processing ALS (Airborne
+Laser Scanning) point cloud data using `lidR` and spatial analysis
+packages in R. Our team (biotope diversity) is currently looking more closely into the
+opportunities and challenges presented by this type of data (and the
+models) so that we are prepared for the new research questions that will
+undoubtedly arise once the new aerial LiDAR campaign (which is currently
+being carried out) is complete. The data and products are planned to be ready in 2028: [digitaal hoogtemodel Vlaanderen](https://www.vlaanderen.be/digitaal-vlaanderen/nieuws/dit-jaar-start-de-vernieuwing-van-het-digitaal-hoogtemodel)."
+author: "Sebastiaan Verbesselt"
+date: "2026-10-23"
+categories: "Remote Sensing"
+tags: ["Remote Sensing", "Aerial lidar", "R"]
+output:
+  md_document:
+    preserve_yaml: true
+    variant: gfm+footnotes
+---
+
+
+# 1. Introduction & Acknowledgments
+The workflow integrates: 1. **Custom workflow script** based on regional
+LiDAR data (e.g., Open Data Vlaanderen) pre-classified with deep
+learning models (3D SegFormer). 2. **Individual Tree Segmentation
+(ITS)** techniques adapted from the [lidRtutorial by Tristan
+Goodbody](https://tgoodbody.github.io/lidRtutorial/06_its.html) ([GitHub
+repository](https://github.com/tgoodbody/lidRtutorial/blob/master/R/06_its.R)). 
+3. **Advanced tree metrics and point cloud processing** concepts
+inspired by Liam Irwin’s
+[CompTreeR](https://liamirwin.github.io/CompTreeR/). The code was compiled and documented with the support of Gemini.
+
+------------------------------------------------------------------------
+
+# 2. Setup & Data Preparation
+
+## 2.1 Package Installation & Environment
+
+First, ensure all required R packages are installed.
+
+``` r
+# Core LiDAR processing package
+if (!requireNamespace("lidR", quietly = TRUE)) install.packages("lidR")
+
+# Optional ground classification algorithm support
+if (!requireNamespace("RCSF", quietly = TRUE)) install.packages("RCSF")
+
+# Spatial handling and raster processing
+if (!requireNamespace("sf", quietly = TRUE)) install.packages("sf")
+if (!requireNamespace("terra", quietly = TRUE)) install.packages("terra")
+if (!requireNamespace("stars", quietly = TRUE)) install.packages("stars")
+if (!requireNamespace("alphashape3d", quietly = TRUE)) install.packages("alphashape3d")
+if (!requireNamespace("knitr", quietly = TRUE)) install.packages("knitr")
+
+# Load libraries
+library(lidR)
+library(sf)
+library(terra)
+library(stars)
+library(alphashape3d)
+library(knitr)
+```
+
+## 2.2 External Data Workflow (QGIS Pre-processing)
+The output of the following steps can be downloaded from the [github page of this tutorial](https://github.com/inbo/tutorials/tree/main/content/tutorials/r_lidr).
+1.  **Download Data**: Download `.laz` tiles matching your area of
+    interest (e.g., via [OpenLidar
+    Vlaanderen](https://remotesensing.vlaanderen.be/apps/openlidar/#collapseDataDownload)).
+
+![](./media/Schermafbeelding%202026-10-21%20194444.png)
+
+1.  **Merge Tiles**: Merge overlapping `.laz` files using the QGIS
+    **Merge** tool.
+
+2.  **AI Classification**: Pre-classify ground, vegetation, buildings,
+    poles, wires and transmission towers (not necessary in natural
+    environments but comes in handy for complex urban areas) using the
+    **SegFormer 3D Airborne model (30cm)** via the LiDAR AI Classifier
+    plugin in QGIS.
+
+![](./media/Schermafbeelding%202026-10-21%20194739.png)
+
+![](./media/Schermafbeelding%202026-10-21%20194846.png)
+
+![](./media/Schermafbeelding%202026-10-21%20194931.png)
+
+![](./media/Schermafbeelding%202026-10-21%20194952.png)
+
+------------------------------------------------------------------------
+
+# 3. Data Loading & Quality Control
+
+## 3.1 Loading Classified LAS Data
+
+``` r
+# Load the pre-classified LAS/LAZ file
+las_path <- "./classified_3dsegformer.laz"
+classified <- readLAS(las_path)
+
+# Verify CRS and structure
+print(classified)
+```
+
+## 3.2 Data Inspection (`las_check`)
+
+It is crucial to inspect point cloud validity (e.g., duplicated points,
+scan angle missing values, normalization status).
+
+``` r
+# Check data integrity and attributes
+las_check(classified) # 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).
+```
+
+## 3.3 Initial 3D Visualization
+
+Quickly inspect point cloud attributes in 3D using `lidR::plot()`.
+
+``` r
+# Visualization colored by elevation
+plot(classified, color = "Z")
+
+# Color by intensity
+#plot(classified, color = "Intensity")
+
+# Color by AI classification values
+plot(classified, color = "Classification")
+
+# Color by RGB (if available in point cloud attributes)
+plot(classified, color = "RGB")
+```
+
+------------------------------------------------------------------------
+
+# 4. Height Normalization & Transect Analysis
+
+Point cloud elevation (*Z*) reflects absolute elevation above sea level.
+Height normalization converts *Z* to height above ground surface.
+
+## 4.1 Ground Normalization
+
+``` r
+# Height normalization using a Triangular Irregular Network (TIN) ground model
+norm <- normalize_height(classified, tin())
+```
+
+## 4.2 Cross-Section Visualizations (Transects)
+
+Extracting cross-sections allows visual validation of height
+normalization and canopy structure.
+
+``` r
+# Define transect endpoints (Coordinates in local projection, e.g., Belge 1972 / Lambert 72)
+X1 <- 149800; Y1 <- 180514
+X2 <- 150000; Y2 <- 180514
+
+# Slice 2-meter wide transects for raw and normalized point clouds
+slice <- clip_transect(classified, p1 = c(X1, Y1), p2 = c(X2, Y2), width = 2)
+slice_norm <- clip_transect(norm, p1 = c(X1, Y1), p2 = c(X2, Y2), width = 2) 
+
+# Compare raw vs. normalized profile plots
+par(mfrow = c(2, 1))
+plot(slice$X, slice$Z, col = slice$Z, pch = 19, main = "Raw Elevation Profile", xlab = "Distance", ylab = "Elevation (m)")
+plot(slice_norm$X, slice_norm$Z, col = slice_norm$Z, pch = 19, main = "Normalized Height Profile", xlab = "Distance", ylab = "Height above ground (m)")
+```
+
+![](lidR_totarial_files/figure-markdown_github/transect-analysis-1.png)
+
+``` r
+par(mfrow = c(1, 1))
+```
+
+------------------------------------------------------------------------
+
+# 5. Individual Tree Segmentation (ITS)
+
+This section combines canopy model generation and tree detection
+techniques adapted from [Goodbody
+(lidRtutorial)](https://tgoodbody.github.io/lidRtutorial/06_its.html)
+and [Irwin (CompTreeR)](https://liamirwin.github.io/CompTreeR/).
+
+## 5.1 Canopy Height Model (CHM) & Tree Top Detection
+
+``` r
+# Filter normalized point cloud to high vegetation (ASPRS class 5)
+high_veg <- filter_poi(norm, Classification == 5)
+
+# Generate a 0.5m Canopy Height Model using Pit-free or Point-to-Raster algorithms
+chm <- rasterize_canopy(high_veg, res = 0.5, algorithm = p2r(subcircle = 0.15))
+
+# Locate individual tree tops using a Local Maximum Filter (LMF)
+# Variable window size (ws) can also be used: e.g., function(z) { z * 0.1 + 1 }
+ttops <- locate_trees(high_veg, lmf(ws = 5)) # This algorithm work both on normalized point clouds, as dsm (but results can be slightly different).
+
+# Plot CHM with detected tree tops
+plot(chm, main = "Canopy Height Model with Detected Treetops")
+plot(sf::st_geometry(ttops), add = TRUE, col = "red", pch = 3, cex = 0.8)
+```
+
+![](lidR_totarial_files/figure-markdown_github/chm-and-ttops-1.png)
+
+If you would do the same on the norm laz file (without filtering for
+high vegetation), convert it to a chm. The locate_trees algorithm will
+confuse building or other urban structures with trees.
+
+``` r
+# Generate a 0.5m Canopy Height Model using Pit-free or Point-to-Raster algorithms
+chm2 <- rasterize_canopy(norm, res = 0.5, algorithm = p2r(subcircle = 0.15))
+
+# Locate individual tree tops using a Local Maximum Filter (LMF)
+# Variable window size (ws) can also be used: e.g., function(z) { z * 0.1 + 1 }
+ttops2 <- locate_trees(norm, lmf(ws = 5))
+
+# Plot CHM with detected tree tops
+plot(chm2, main = "Canopy Height Model with Detected Treetops")
+plot(sf::st_geometry(ttops2), add = TRUE, col = "red", pch = 3, cex = 0.8)
+```
+
+![](lidR_totarial_files/figure-markdown_github/chm-and-ttops%202-1.png)
+
+``` r
+nrow(ttops)
+nrow(ttops2)
+```
+
+## 5.2 Tree Segmentation Algorithms
+
+`lidR` provides multiple segmentation algorithms: `li2012()`,
+`dalponte2016()`, `silva2016()`, and `watershed()`. You could do tree
+segementation on point clouds, but when you work with with large laz
+files, it it faster to convert your point layer to a chm and do the
+segementation on your chm.
+
+``` r
+# Point-to-point tree segmentation using Li et al. (2012)
+segmented_las <- segment_trees(high_veg, li2012()) # +/- 3 min. computation time
+
+# Plot the segmented point cloud where colors represent individual tree IDs
+plot(segmented_las, color = "treeID")
+
+ttops_seg <- tree_metrics(segmented_las, func = .stdmetrics) %>% st_as_sf()
+kable(summary(ttops_seg[,c( "zmax", "zmean","zsd","zq95","area")]%>% st_drop_geometry()))
+
+
+hist(ttops_seg$zmax,plot = T, main = "Height Distribrution (m)",xlab =   "Height (m)",ylab = "Frequency")
+```
+
+![](lidR_totarial_files/figure-markdown_github/segmentation-1.png)
+
+## 5.3 Transect Inspection of Tree Clusters
+
+``` r
+# Clip transect of segmented trees
+slice_tree <- clip_transect(segmented_las, p1 = c(X1, Y1), p2 = c(X2, Y2), width = 2)
+
+# Plot profile colored by segmented tree IDs
+plot(slice_tree$X, slice_tree$Z, col = slice_tree$treeID, pch = 19, 
+     xlab = "Distance", ylab = "Height", main = "Individual Tree Segmentation Cross-Section")
+```
+
+![](lidR_totarial_files/figure-markdown_github/tree-transect-1.png)
+
+------------------------------------------------------------------------
+
+# 6. Metrics Extraction
+
+## 6.1 Individual Tree Level Metrics
+
+Extract metrics for isolated tree crowns.
+
+``` r
+# Extract a single tree
+tree_1 <- filter_poi(segmented_las, treeID == 1)
+plot(tree_1)
+
+# Extract maximum tree height directly
+max_height <- max(tree_1$Z)
+cat("Tree 1 Max Height:", max_height, "m\n")
+
+# Standard crown metrics
+crown_m <- crown_metrics(tree_1, .stdtreemetrics)
+summary(crown_m)
+
+# Standard height metrics
+cloud_m <- cloud_metrics(tree_1, .stdmetrics_z)
+print(cloud_m)
+```
+
+Computing crown volume (for one tree). See
+<https://liamirwin.github.io/CompTreeR/> to calcluate across the whole
+dataset.
+
+``` r
+get_crown_attributes <- function(X, Y, Z, alpha = c(Inf, 1)) {
+  if (length(X) <= 3 || length(Y) <= 3 || length(Z) <= 3) {
+    message("Cannot compute a 3D hull from 3 or fewer points")
+    return(NULL)
+  }
+  
+  # Prepare centered matrix for stable alpha shape computation
+  mean_x <- mean(X)
+  mean_y <- mean(Y)
+  
+  a3d_centered <- cbind(X - mean_x, Y - mean_y, Z)
+  
+  top_x <- X[which.max(Z)]
+  top_y <- Y[which.max(Z)]
+  
+  # Compute alpha shape
+  ashape <- alphashape3d::ashape3d(x = a3d_centered, alpha = alpha, pert = TRUE)
+  
+  # Calculate crown metrics
+  df <- data.frame(
+    Zmax = max(Z),
+    Zq999 = as.numeric(quantile(Z, 0.999)),
+    Zq99 = as.numeric(quantile(Z, 0.990)),
+    Z_mean = mean(Z),
+    n_points = length(Z),
+    vol_convex = alphashape3d::volume_ashape3d(ashape, indexAlpha = 1),
+    vol_concave = alphashape3d::volume_ashape3d(ashape, indexAlpha = 2),
+    CV_Z = sd(Z) / mean(Z),
+    CRR = (mean(Z) - min(Z)) / (max(Z) - min(Z)),
+    X = top_x,
+    Y = top_y
+  )
+  
+  # Return both metrics and the fitted hull object along with shift offsets
+  return(list(
+    metrics = df, 
+    ashape = ashape, 
+    centered_points = a3d_centered
+  ))
+}
+
+# Run function
+result <- get_crown_attributes(
+  tree_1@data[["X"]],
+  tree_1@data[["Y"]],
+  tree_1@data[["Z"]],
+  alpha = c(Inf, 1)
+)
+
+# Extract components
+metrics <- result$metrics
+ashape_obj <- result$ashape
+pts_centered <- result$centered_points
+
+# --- Plot 1: Convex Hull (alpha = Inf) ---
+plot(ashape_obj, indexAlpha = 1, transparency = 0.3, axes = TRUE)
+rgl::points3d(pts_centered, color = "black", size = 4)
+
+# --- Plot 2: Concave Hull (alpha = 1) ---
+plot(ashape_obj, indexAlpha = 2, transparency = 0.4, axes = TRUE)
+rgl::points3d(pts_centered, color = "black", size = 4)
+```
+
+## 6.2 Crown Hull Geometry Options
+
+Computing crown geometries (polygons/hulls) using different algorithms:
+
+``` r
+# Using example dataset for multi-tree crown geometries
+LASfile <- system.file("extdata", "MixedConifer.laz", package = "lidR")
+trees <- readLAS(LASfile, filter = "-drop_z_below 0")
+
+# Convex Hull
+metrics_convex <- crown_metrics(trees, .stdtreemetrics, geom = "convex")
+
+# Concave Hull
+metrics_concave <- crown_metrics(trees, .stdtreemetrics, geom = "concave")
+
+# Bounding Box
+metrics_bbox <- crown_metrics(trees, .stdtreemetrics, geom = "bbox")
+
+# Visualize concave hulls
+par(mfrow=c(1,3))
+plot(sf::st_geometry(metrics_concave))
+plot(sf::st_geometry(metrics_convex))
+plot(sf::st_geometry(metrics_bbox))
+```
+
+![](lidR_totarial_files/figure-markdown_github/crown-geometries-1.png)
+
+``` r
+par(mfrow=c(1,1))     
+```
+
+## 6.3 Custom Metric Functions
+
+Defining custom statistical metrics for LiDAR returns:
+
+``` r
+# Define custom height & intensity metric function
+myMetrics <- function(z, i) {
+  metrics <- list(
+    zwimean = sum(z * i) / sum(i), # Intensity-weighted mean elevation
+    zimean  = mean(z * i),         # Mean product of height and intensity
+    zsqmean = sqrt(mean(z^2))      # Quadratic mean height (RMS)
+  )
+  return(metrics)
+}
+
+# Apply custom metrics over a stars spatial grid template
+template_stars <- stars::st_as_stars(st_bbox(segmented_las), dx = 10, dy = 10)
+m_custom <- template_metrics(segmented_las, myMetrics(Z, Intensity), template_stars)
+plot(m_custom)
+```
+
+![](lidR_totarial_files/figure-markdown_github/custom-metrics-1.png)
+
+``` r
+plot(chm)
+```
+
+![](lidR_totarial_files/figure-markdown_github/custom-metrics-2.png)
+
+## 6.4 Pixel, Plot, and Voxel Metrics
+
+### Pixel Metrics & Filtering Strategies
+
+``` r
+# 1. Basic approach
+first_returns <- filter_poi(segmented_las, ReturnNumber == 1)
+m_pixel_basic <- pixel_metrics(first_returns, ~mean(Z), res = 20)
+
+# 2. Optimized approach (Inline filtering - memory efficient)
+m_pixel_opt <- pixel_metrics(segmented_las, ~mean(Z), res = 20, filter = ~ReturnNumber == 1)
+
+# 3. Echo-type breakdown
+echo_types <- c("all", "first", "multiple")
+func_echo <- ~list(avgI = mean(Intensity))
+m_echo <- pixel_metrics(segmented_las, func_echo, res = 20, by_echo = echo_types)
+plot(m_echo)
+```
+
+![](lidR_totarial_files/figure-markdown_github/pixel-metrics-1.png)
+
+### Plot Metrics (Inventory Matching)
+
+``` r
+# Load sample forest inventory plot data
+LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
+las <- readLAS(LASfile, filter = "-keep_random_fraction 0.5")
+plot(las)
+shpfile <- system.file("extdata", "efi_plot.shp", package = "lidR")
+inventory <- sf::st_read(shpfile, quiet = TRUE)
+inventory # contains an ID and a Value Of Interest (VOI) per plot
+plot(inventory[,1]) # Point shapefile dataset.
+```
+
+![](lidR_totarial_files/figure-markdown_github/plot-metrics-1.png)
+
+``` r
+# Calculate metrics per circular plot (radius = 11.28m, ~400 m² plot)
+fun_q85 <- ~list(q85 = quantile(Z, probs = 0.85))
+m_plot <- plot_metrics(las, fun_q85, inventory, radius = 11.28)
+plot(header(las))
+plot(m_plot["q85"], pch = 19, cex = 3, add = TRUE)
+```
+
+![](lidR_totarial_files/figure-markdown_github/plot-metrics-2.png)
+
+``` r
+# Buffer inventory points into explicit polygon objects
+inventory_poly <- sf::st_buffer(inventory, 11.28)
+m_poly <- plot_metrics(las, .stdmetrics_z, inventory_poly)
+
+# Works with polygons as well
+inventory <- sf::st_buffer(inventory, 11.28)
+plot(header(las))
+plot(sf::st_geometry(inventory), add = TRUE)
+m <- plot_metrics(las, .stdmetrics_z, inventory)
+plot(m["zq85"], pch = 19, cex = 3, add = TRUE)
+```
+
+![](lidR_totarial_files/figure-markdown_github/plot-metrics-3.png)
+
+### Voxel Metrics (3D Grid Summaries)
+
+``` r
+# Generate 8m voxel counts and mean intensity
+v_count <- voxel_metrics(las, length(Z), res = 8)
+v_intensity <- voxel_metrics(las, mean(Intensity), res = 8)
+
+# 3D Voxel rendering
+plot(v_intensity, color = "V1", colorPalette = heat.colors(50), trim = 60, voxel = TRUE)
+
+# 3D Voxel rendering
+plot(v_count, color = "V1", colorPalette = heat.colors(50), trim = 60, voxel = TRUE)
+```
+
+------------------------------------------------------------------------
+
+# 7. References & Further Reading
+
+1.  **lidR Official Documentation**: Roussel et al., *lidR: Airborne
+    LiDAR Data Manipulation and Visualization for Forestry
+    Applications*. [CRAN lidR
+    Package](https://cran.r-project.org/package=lidR)
+2.  **Goodbody, T.** *lidRtutorial: Individual Tree Segmentation (ITS)*.
+    [Online Tutorial &
+    Code](https://tgoodbody.github.io/lidRtutorial/06_its.html)
+3.  **Irwin, L.** *CompTreeR: Computational Tree Analysis in R*.
+    [CompTreeR Guide](https://liamirwin.github.io/CompTreeR/)
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