Tools for foresters • foRestools

FoRestools

The goal of foRestools is to providing toolkits and methods to support foresters in data processing and forest environment management.

Installation

You can install the development version of foRestools from GitHub with:

# install.packages("devtools")
devtools::install_github("j-miszczyszyn/foRestools")

Calculating new coordinates

The function calculate_new_position calculates the new coordinates, taking into account the initial longitude and latitude, distance in meters and direction in degrees.

library(foRestools)

# Starting coordinates
start_lon <- 19.9449799  # longitude for Krakow, Poland
start_lat <- 50.0646501  # latitude for Krakow, Poland

# Distance in meters and direction in degrees
distance <- 1000  # 1 km
bearing <- 45  # 45 degrees (northeast)

# Calculate new coordinates
new_position <- calculate_new_position(start_lon, start_lat, distance, bearing)

# Print new coordinates
print(paste("New longitude:", new_position$longitude))
print(paste("New latitude:", new_position$latitude))

Clipping LiDAR Point Cloud to AOI

The function cliping_cloud_to_aoi reads a LAS file, assigns a new coordinate reference system (CRS) to both the point cloud and the Area of Interest (AOI), clips the point cloud to the AOI, and optionally saves the clipped point cloud to a new LAS file.


library(foRestools)

# Assuming 'path_to_las', 'aoi_sf', 'output_folder', and 'crs_definition' are predefined:
path_to_las <- "path/to/your/input.las"
aoi_sf <- sf::st_read("path/to/your/aoi.shp")
output_folder <- "path/to/output/folder"
crs_definition <- "+proj=utm +zone=33 +datum=WGS84 +units=m +no_defs"

# Clipping the point cloud to the AOI
clipped_las <- cliping_cloud_to_aoi(path_to_las, aoi_sf, output_folder, crs_definition, TRUE)

# Displaying the clipped point cloud
print(clipped_las)

Creating Canopy Height Model (CHM) from LAS File

The function create_CHM_from_LAS processes a LAS file to generate a Canopy Height Model (CHM) using the specified resolution and pixel-to-region (p2r) parameters. The resulting CHM can optionally be saved as a TIFF file in the specified output folder.


library(foRestools)

# Assuming 'las_file_path', 'resolution', 'p2r_parameter', and 'output_folder_path' are predefined:
las_file_path <- "path/to/your/las_file.las"
resolution <- 0.5  # size of each pixel in the output raster
p2r_parameter <- 2  # pixel-to-region parameter for rasterizing the point cloud
output_folder_path <- "path/to/output/folder"

# Creating the CHM from the LAS file
create_CHM_from_LAS(las_file_path, resolution, p2r_parameter, TRUE, output_folder_path)

Downloading DEM for a Large Area from RGUGIK

The function dem_download_big_area_rgugik downloads Digital Elevation Model (DEM) data for a large area specified by a shapefile. The area is divided into a grid of smaller sections to manage the request size. The DEM data for each section is requested from the RGUGIK service and combined into a single dataframe.


library(foRestools)

# Assuming 'shapefile_path' and 'grid_cells' are predefined:
shapefile_path <- "path/to/your/shapefile.shp"
grid_cells <- 10  # number of grid cells to divide the area of interest

# Downloading DEM data for the large area
result <- dem_download_big_area_rgugik(shapefile_path, grid_cells)

# Printing the result
print(result)

Detecting Tree Tops in a Canopy Height Model (CHM)

The function detect_tops_in_CHM identifies tree tops in a Canopy Height Model (CHM) file using the local maxima filter (LMF) algorithm provided by the lidR package. The identified tree tops can optionally be saved as a shapefile.


library(foRestools)

# Assuming 'CHM_file_path', 'window_size', 'min_height', and 'output_folder_path' are predefined:
CHM_file_path <- "path/to/your/CHM.tif"
window_size <- 5  # size of the window used to find local maxima
min_height <- 10  # minimum tree height to be considered
output_folder_path <- "path/to/output/folder"

# Detecting tree tops in the CHM
detect_tops_in_CHM(CHM_file_path, window_size, min_height, output_folder_path, TRUE)

Normalizing Height of LiDAR Point Cloud

The function normalize_height_in_clouds reads a LAS file, normalizes the heights of the point cloud using a TIN surface, and optionally saves the normalized point cloud to a new LAS file. Only the points classified as ground (class = 2) are used to compute the TIN surface.


library(foRestools)

# Assuming 'path_to_las' and 'output_folder' are predefined:
path_to_las <- "path/to/your/input.las"
output_folder <- "path/to/output/folder"

# Normalizing the height of the point cloud
normalized_las <- normalize_height_in_clouds(path_to_las, output_folder, TRUE)

# Displaying the normalized point cloud
print(normalized_las)

Downloading Orthophoto for a Large Area from RGUGIK

The function ortho_download_big_area_rgugik downloads orthophoto data for a large area specified by a shapefile. The area is divided into a grid of smaller sections to manage the request size. The orthophoto data for each section is requested from the RGUGIK service and combined into a single dataframe.


library(foRestools)

# Assuming 'shapefile_path' and 'grid_cells' are predefined:
shapefile_path <- "path/to/your/shapefile.shp"
grid_cells <- 10  # number of grid cells to divide the area of interest

# Downloading orthophoto data for the large area
result <- ortho_download_big_area_rgugik(shapefile_path, grid_cells)

# Printing the result
print(result)

Thinning Point Cloud by Keeping Specific Classes

The function thin_cloud reads a LAS file, filters it to retain only specified classes of points (e.g., vegetation), and optionally saves the thinned point cloud to a new LAS file. The function specifically keeps points classified as 4 (medium vegetation) and 5 (high vegetation) by default.


library(foRestools)

# Assuming 'path_to_las' and 'output_folder' are predefined:
path_to_las <- "path/to/your/point_cloud.las"
output_folder <- "path/to/output/folder"

Processing CHM Files in Folder and Optionally Saving Tree Tops to Shapefile

The function tops_in_folder_to_shp processes all Canopy Height Model (CHM) TIFF files in a specified folder, detects tree tops using the detect_tops_in_CHM function, and optionally saves the results as a shapefile. Each tree top detection is assigned an ID number and the results are combined into a spatial dataframe.


library(foRestools)

# Assuming 'folder_path', 'window_size', 'min_height', 'crs_definition', and 'output_folder_path' are predefined:
folder_path <- "path/to/your/folder"
window_size <- 5  # size of the window used to find local maxima
min_height <- 2  # minimum tree height to be considered
crs_definition <- 4326  # coordinate reference system definition
output_folder_path <- "path/to/output/folder"

# Processing CHM files in the folder and detecting tree tops
tops <- tops_in_folder_to_shp(folder_path, window_size, min_height, crs_definition, output_folder_path, TRUE, TRUE)

# Displaying the detected tree tops
print(tops)