classInt, data.table (>= 1.12.0), glue, grDevices, lazyeval,
raster, Rcpp (>= 1.0.3), rgl, rlas (>= 1.5.0), sf, sp, stats,
stars, terra, tools, utils
EBImage, future, geometry, gstat, RCSF, RMCC, rgdal, mapview,
mapedit, progress, testthat (>= 2.1.0), knitr, rmarkdown

classInt, data.table (>= 1.12.0), glue, grDevices, lazyeval,
raster, Rcpp (>= 1.0.3), rgl, rlas (>= 1.5.0), sf, sp, stats,
stars, terra (>= 1.5-17), tools, utils

Forest Analysis with Airborne Laser Scanning (LiDAR) Data
Provides functions for forest analysis using airborne laser scanning (LiDAR remote sensing) data: tree detection (method 1 in Eysn et al. (2015) <doi:10.3390/f6051721>) and segmentation; forest parameters estimation and mapping with the area-based approach. It includes complementary steps for forest mapping: co-registration of field plots with LiDAR data (Monnet and Mermin (2014) <doi:10.3390/f5092307>); extraction of both physical (gaps, edges, trees) and statistical features from LiDAR data useful for e.g. habitat suitability modeling (Glad et al. (2020) <doi:10.1002/rse2.117>); model calibration with ground reference, and maps export.
graphics, stats, methods, grDevices, sp, raster, imager,
leaps, gvlma, car, reldist, lidR (>= 3.1.0)

c(
person("Jean-Matthieu", "Monnet", email = "jean-matthieu.monnet@inrae.fr", role = c("aut", "cre")),
person("Pascal", "Obstétar", email = "pascal.obstetar@onf.fr", role = c("ctb")))
Jean-Matthieu Monnet [aut, cre],
Pascal Obstétar [ctb]

c(
person("Jean-Matthieu", "Monnet", email = "jean-matthieu.monnet@inrae.fr", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9948-9891")),
person("Pascal", "Obstétar", email = "pascal.obstetar@onf.fr", role = c("ctb"), comment = c(ORCID = "0000-0002-2811-7548")))
Jean-Matthieu Monnet [aut, cre]
(<https://orcid.org/0000-0002-9948-9891>),
Pascal Obstétar [ctb] (<https://orcid.org/0000-0002-2811-7548>)

graphics, stats, methods, grDevices, terra, sf, imager, leaps,
gvlma, car, reldist, lidR (>= 4.0.0)

Provides functions for forest analysis using airborne laser scanning (LiDAR remote sensing) data:
tree detection (method 1 in Eysn et al. (2015) <doi:10.3390/f6051721>) and segmentation;
forest parameters estimation and mapping with the area-based approach. It includes complementary steps for forest mapping:
co-registration of field plots with LiDAR data (Monnet and Mermin (2014) <doi:10.3390/f5092307>);
extraction of both physical (gaps, edges, trees) and statistical features from LiDAR data useful
for e.g. habitat suitability modeling (Glad et al. (2020) <doi:10.1002/rse2.117>) and forest maturity mapping (Fuhr et al. (2022) <doi:10.1002/rse2.274>);
model calibration with ground reference, and maps export.