03_aba.R

#' ---
#' title: "Area-based metrics"
#' ---
#' 
#' ## Relevant Resources
#' 
#' -   [Code](https://github.com/tgoodbody/lidRtutorial/blob/master/R/03_aba.R)
#' -   [lidRbook metrics section](https://r-lidar.github.io/lidRbook/modeling-aba.html)
#' -   [lidRbook modelling section](https://r-lidar.github.io/lidRbook/modeling-aba.html)
#' 
#' ## Overview
#' 
#' This code demonstrates an example of the area-based approach for LiDAR data. Basic usage involves computing mean and max height of points within 10x10 m pixels and visualizing the results. The code shows how to compute multiple metrics simultaneously and use predefined metric sets. Advanced usage introduces user-defined metrics for more specialized calculations.
#' 
#' ## Environment
#' 

# Clear environment
rm(list = ls(globalenv()))

# Load packages
library(lidR)
library(sf)

#' 
#' ## Basic Usage
#' 
#' We'll cover the basic usage of the `lidR` package to compute metrics from LiDAR data.
#' 

# Load LiDAR data, excluding withheld flag points
las <- readLAS(files = "data/MixedEucaNat_normalized.laz", select = "*",  filter = "-set_withheld_flag 0")

#' 
#' The `pixel_metrics()` function calculates structural metrics within a defined spatial resolution (`res`).

# Compute the mean height of points within 10x10 m pixels
hmean <- pixel_metrics(las = las, func = ~mean(Z), res = 10)
hmean
plot(hmean, col = height.colors(50))

# Compute the max height of points within 10x10 m pixels
hmax <- pixel_metrics(las = las, func = ~max(Z), res = 10)
hmax
plot(hmax, col = height.colors(50))

#' 
#' You can specify that multiple metrics should be calculated by housing them in a `list()`.
#' 

# Compute several metrics at once using a list
metrics <- pixel_metrics(las = las, func = ~list(hmax = max(Z), hmean = mean(Z)), res = 10)
metrics
plot(metrics, col = height.colors(50))

#' 
#' Pre-defined metric sets are available, such as `.stdmetrics_z`. See more [here](https://github.com/r-lidar/lidR/wiki/stdmetrics).
#' 

# Simplify computing metrics with predefined sets of metrics
metrics <- pixel_metrics(las = las, func = .stdmetrics_z, res = 10)
metrics
plot(metrics, col = height.colors(50))

# Plot a specific metric from the predefined set
plot(metrics, "zsd", col = height.colors(50))

#' 
#' ## Advanced Usage with User-Defined Metrics
#' 
#' ::: callout-tip
#' ## [3rd party metric packages](https://r-lidar.github.io/lidRbook/metrics.html#metrics-3rd-party)
#' 
#' `lidR` provides flexibility for users to define custom metrics. Check out 3rd party packages like [`lidRmetrics`](https://github.com/ptompalski/lidRmetrics) for suites of metrics.
#' :::
#' 
#' We can also create our own user-defined metric functions. This demonstrates the flexibility of the `lidR` package!
#' 

# Generate a user-defined function to compute weighted mean
f <- function(x, weight) { sum(x*weight)/sum(weight) }

# Compute grid metrics for the user-defined function
X <- pixel_metrics(las = las, func = ~f(Z, Intensity), res = 10)

# Visualize the output
plot(X, col = height.colors(50))

#' 
#' ## Exercises and Questions
#' 
#' Using:
#' 

las <- readLAS("data/MixedEucaNat_normalized.laz", select = "*",  filter = "-set_withheld_flag 0")
 
#' #### E1.
#' 
#' Assuming that biomass is estimated using the equation `B = 0.5 * mean Z + 0.9 * 90th percentile of Z` applied on first returns only, map the biomass.
#' 
#' #### E2.
#' 
#' Map the density of ground returns at a 5 m resolution with `pixel_metrics(filter = ~Classification == LASGROUND)`.
#' 
#' #### E3.
#' 
#' Map pixels that are flat (planar) using `stdshapemetrics`. These could indicate potential roads.
#' 
#' ## Conclusion
#' 
#' In this tutorial, we covered basic usage of the `lidR` package for computing mean and max heights within grid cells and using predefined sets of metrics. Additionally, we explored the advanced usage with the ability to define user-specific metrics for grid computation.