Learn R Programming

⚠️There's a newer version (4.0.8) of this package.Take me there.

lidaRtRee (version 4.0.5)

Forest Analysis with Airborne Laser Scanning (LiDAR) Data

Description

Provides functions for forest analysis using airborne laser scanning (LiDAR remote sensing) data: tree detection (method 1 in Eysn et al. (2015) ) 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) ); extraction of both physical (gaps, edges, trees) and statistical features from LiDAR data useful for e.g. habitat suitability modeling (Glad et al. (2020) ) and forest maturity mapping (Fuhr et al. (2022) ); model calibration with ground reference, and maps export.

Copy Link

Version

Install

install.packages('lidaRtRee')

Monthly Downloads

267

Version

4.0.5

License

GPL-3

Maintainer

Jean-Matthieu Monnet

Last Published

April 7th, 2023

Functions in lidaRtRee (4.0.5)

Raster format conversion

clouds_tree_metrics

Computes metrics on trees detected in list of point clouds.

Applies thresholds and mask to a raster object

Computes metrics on list of point clouds

boxcox_itr_bias_cor

Inverse Box-Cox transformation with bias correction

Disk-shaped matrix mask

add_vegetation_indices

Add vegetation indices on a IRC image

Cimg to SpatRaster conversion

Tree inventory and canopy height model coregistration

Raster corresponding to circle extent

Image pre-processing (non-linear filtering and Gaussian smoothing)

height_regression

Regression of detected heights VS reference heights

Gap detection in a Canopy Height Model

Plot of matched pairs of detected and reference trees

maxima_selection

Image maxima selection based on values and neighborhood of local maxima

Stacked histogram

Checks linear model assumptions of a multiple regression model

Histogram of detection

Create elliptical polygons from centres and extensions in four directions

Edge detection in gap image

raster_local_max

Statistics of raster local maximum

Table of species names, abreviations and display colors

std_tree_metrics

Computation of tree metrics

Box-Cox Transformation

Digital Surface Model

Canopy height model (Chablais 3 plot)

Computes metrics by aggregating a raster at lower resolution or summarizing attributes based on XY locations

SpatRaster to Cimg conversion

maxima_detection

Local maxima extraction on image

Raster mask by union of buffers around xy positions

raster_zonal_stats

Image statistic in segment

raster_chull_mask

Raster mask of convex hull

3D matching of detected tree top positions with reference positions

tree_segmentation

Preprocessing and segmentation of raster image for tree identification

Image segmentation by seed-based watershed algorithm

Modification of segments based on values

tree_inventory_chablais3

Tree inventory data in France (Chablais 3 plot, July 2010)

plot_tree_inventory

Displays a map of tree inventory data

Convert a list of points into spatial polygons object

tree_extraction

Tree extraction

terrain_points_metrics

Computation of terrain metrics

polar2Projected

Polar to cartesian coordinates conversion

las data in France (Chablais 3 plot)

Correlation between two rasters

Digital Terrain Model

rasters_moving_cor

Correlation between rasters for different XY translations

quatre_montagnes

Field plot inventory in the Quatre Montagnes area (France)

aa_las_chablais3

Load las_chablais3

aba_build_model

Calibrates and validates area-based models

aba_combine_strata

Combines a list of ABA models into a single ABA model object

Mapping of ABA prediction models

Inverse Box-Cox transformation

computes inference from area-based model and predicted values

Plots observed VS values predicted in leave one out cross validation of an aba_build_model

Function for area-based metrics computation