In MalditoBarbudo/lfcdata: Access to the LFC Databases

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(lfcdata)
library(stars)

LiDAR database

LiDAR database provides the eco-physiological variables data obtained by LiDAR flights from the ICGC in combination with the Spanish National Forest Inventory data.

Access the database with the lidar() function:

lidardb <- lidar()
lidardb

Access precalculated and aggregated data

Preacalculated data for different levels of administrative divisions are available with the lidar_get_data function. To see the available tables use lidar_avail_tables:

lidar_avail_tables(lidardb)
lidar_get_data(lidardb, 'lidar_provinces')
lidar_get_data(lidardb, 'lidar_enpes')

Access the low resolution rasters (400x400m)

Low resolution rasters can be retrieved with lidar_get_lowres_raster:

lidar_avail_tables(lidardb)
lidar_describe_var(lidardb, 'AB')
ab_raster <- lidar_get_lowres_raster(lidardb, 'AB', 'stars')
ab_raster

Data formats

Raster data can be retrieved in two formats, stars (default) and RasterLayer:

lidar_get_lowres_raster(lidardb, 'AB', 'stars') |>
  class()

lidar_get_lowres_raster(lidardb, 'AB', 'raster') |>
  class()

More than one table can be accessed at the same time, resulting in a RasterBrick object or an stars object with attributes for each table:

lidar_get_lowres_raster(lidardb, c('AB', 'DBH'), 'stars')
lidar_get_lowres_raster(lidardb, c('AB', 'DBH'), 'raster')

Access the raw raster data

Raw rasters (20x20m) are available for data extraction and calculations only, no for download

Extract point values

Given a set of points (sf object), lidar_point_value extracts the raster values for the desired variables:

# get a set of points, we can use the national forest inventory data
point_set <-
  nfi()$get_data('plots', spatial = TRUE) |>
  dplyr::slice(1:25) |>
  dplyr::select(plot_id)
# and extract the values
lidardb |>
  lidar_point_value(point_set, 'plot_id', c('AB', 'DBH', 'REC'))

Clip and stats

Given a set of polygons (sf object), lidar_clip_and_stats calculate the raster stats for each polygon:

# we get the provinces polygons
polygon_set <-
  lidardb |>
  lidar_get_data('lidar_municipalities', 'AB') |>
  dplyr::select(poly_id, geometry) |>
  dplyr::slice(1:2)
# we calculate the stats
lidardb |>
  lidar_clip_and_stats(polygon_set, 'poly_id', c('AB', 'DBH', 'REC'))