LiTreeAnyDensity

An R package for detecting and measuring tree attributes across 3D point clouds of any density

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Tutte le librerie

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suppressMessages(library(lidR)) suppressMessages(library(dplyr)) suppressMessages(library(raster)) suppressMessages(library(sp)) suppressMessages(library(terra)) suppressMessages(library(sf)) suppressMessages(library(dbscan)) suppressMessages(library(foreach)) suppressMessages(library(purrr)) suppressMessages(library(tidyverse)) suppressMessages(library(doParallel)) suppressMessages(library(parallel)) suppressMessages(library(microbenchmark))

Set number of cores to use

numCores <- detectCores() - 1 # Usa tutti i core disponibili meno uno cl <- makeCluster(numCores) registerDoParallel(cl)

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Source Codes

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source("C:/Users/calvi/OneDrive/Desktop/Iphone_metodology/Rcode/baseScripts.R")

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library

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setwd("C:/Users/calvi/OneDrive/Desktop/Iphone_metodology/Dati")

nome input

nome_sito = "iphone_M3_norm" nome_sito_slice = "iphone_M3_norm_slice"

nome_sito_output = "parte_CC"

Z_minimum = 1 Z_maximum = 1.5 voxel_size = 0.01

Parameters for DBSCAN and RANSAC

eps_value = 0.01 # DBSCAN eps minPts_value = 3 # DBSCAN minPts d1_m_minimum = 0.07 # RANSAC min diameter d1_m_maximum = 0.9 # RANSAC max diameter n_ransac_par = 20 # RANSAC iterations k_ransac_par = 10 # RANSAC k parameter t_ransac_par = 0.7 # RANSAC t parameter d_ransac_par = 10 # RANSAC d parameter crs_ransac_par = 32633 # CRS for RANSAC

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Import TLS data

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tls_slice <- readTLSLAS(paste0(nome_sito_slice, ".las")) tls <- readTLSLAS(paste0(nome_sito, ".las"))

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Slicing the point clouds in horizontal

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start.time_step1 <- Sys.time() result_step1 = Slice3DByHeight(tls_slice, Z_minimum, Z_maximum, voxel_size) end.time_step1 <- Sys.time() time.taken_step1 <- end.time_step1 - start.time_step1 time.taken_step1

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Matematical analysis in each Horizontal slice

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start.time_step2 <- Sys.time() result_step2 = Rasterize3DSlices(result_step1[[1]], voxel_size) end.time_step2 <- Sys.time() time.taken_step2 <- end.time_step2 - start.time_step2 time.taken_step2

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Selecting points falling in trunks cylinders

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start.time_step3 <- Sys.time() result_step3_improved = IdentifyPotentialTreeLocations(result_step1[[2]], result_step2[[1]], result_step2[[2]], result_step2[[3]], voxel_size) end.time_step3 <- Sys.time() time.taken_step3 <- end.time_step3 - start.time_step3 time.taken_step3

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Detecting and measuring circles of trunks and tree height

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start.time_step4 <- Sys.time() result_final_base <- DetectAndMeasureTrees(result_step3_improved, eps_value, minPts_value, d1_m_minimum, d1_m_maximum, paste(nome_sito_output), crs_ransac_par, n_ransac_par, k_ransac_par, t_ransac_par, d_ransac_par, 530) end.time_step4 <- Sys.time() time.taken_step4 <- end.time_step4 - start.time_step4 time.taken_step4

Plot for base data with customized circle colors and sizes

plot(result_final_base$centroid_x, result_final_base$centroid_y, main = "Base Data", xlab = "X Coordinate", ylab = "Y Coordinate", col = "red", pch = 16, cex = 0.8) # red color, filled circles, smaller size

stopCluster(cl) getwd()

# Example of printing times with units

cat("SLICING time secs:") time.taken_step1 cat("SLICING PROCESS time min:") time.taken_step2 cat("SLICING CLEANING time min:") time.taken_step3 cat("DETECTING/MEASURING TREES time min:") time.taken_step4

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THE END

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