chmloader

The goal of chmloader is to download the Canopy Height Model (CHM) data from this recent work by Tolan et al. (2024). A high-level summary of this work can be found here. The data is downloaded from AWS s3 storage - further details on the bucket can be found here

Installation

You can install chmloader like so:

# install.packages("pak")
pak::pkg_install("TESS-Laboratory/chmloader")

Example

This is a basic example which shows you how to download some data. The download_chm function uses gdalwarp (via sf::gdal_utils) to efficiently retrieve only the required data from multiple tiles - the default resolution is 1 m but this can be reprojected as needed using the res argument.

library(chmloader)

parana_cuiana <- sf::st_point(c(-61.89, -4.12)) |>
  sf::st_sfc(crs = 4326) |>
  sf::st_buffer(3000)

pc_chm <- download_chm(
  parana_cuiana,
  filename = tempfile(fileext = ".tif")
)
terra::plot(pc_chm, col = hcl.colors(256, "viridis"))

This package also provides a simple function to create plots for comparing different CHMs. The intention of this function is to enable simple and robust evaluation of the Tolan et al. (2024) CHM data with LiDAR-based models and other ML-derived products. The chmloader package comes with a small set of LiDAR-based CHM example datasets, derived from the English Environment Agency’s Vegetation Object Model dataset Below is an example using one of these example datasets from Fingle Woods, Devon, UK:

fingle_woods <- reference_data("fingle_woods")

compare_models(fingle_woods, aggregate = 10, drop_zeros = TRUE)
#> ℹ meta/WRI CHM not provided, downloading now...
#> ✔ CHM downloaded successfully!

Note in this example, the aggregate argument is used to reduce the resolution of both the reference and Meta/WRI CHM by a factor of 10 (resulting in a 10 m model) and test both this coarser scale model in addition to the original 1 m model. This functionality may help to reveal what the true resolution of the Meta/WRI CHM is, and how it compares to the LiDAR-based model across scales.

Also, the drop_zeros argument is used to remove zero values from the both the 2d density plot and the derived statistics, where values from the reference/benchmark data and the Meta/WRI CHM are both zero. This is particularly useful where the main interest is to evaluate the tree canopy rather than the absence of trees and/or where tree cover is sparse; however, the default drop_zeros value is FALSE.