useCaseCHM.R

# Crown Segmentation -------------------
#
# The reliable and reproducible segmentation of tree entities also called tree
# crowns is the basic need for all individual based determination of structure,
# competition ecological traits and so on. While this kind of segmentation is
# even hard in real live there are tons of approaches to perform it on LiDAR
# data. It is crucial to understand that only high end terrestrial 3D scanning
# together with terrestrial LiDAR will provide reliable data for doing so.
# Nevertheless let's try. We have to keep in mind that UAV data is not *really*
# 3D because you will find only a small vertical variation in the height of
# retrieved point in a column. Due to this we are not able to use the well known
# LiDAR Segmentation approaches on 3D data. We have to rely on a canopy height
# model (CHM)
#
# Canopy Height Models --------------------
#
# Canopy Height Models (CHM) are usually derived from airborne LiDAR data. Since
# they are rasterized, they can be comprehended as an aggregated simplification
# of the 3D point cloud model.They are typically representing the **tree**
# canopy. A reliable CHM has removed all other above-ground features such as
# buildings etc.
#
# Even using LiDAR data there are some massive limitations with the most CHMs.
# Same and even worse with UAV 3D point clouds. Nevertheless CHMs are very
# useful for all kind of forest and ecological management, for conservation
# biology and a lot of other topics.
#
# The aim of this short tutorial is to show some possibilities to do so. To
# derive Orthoimages or point clouds one can use between different tools the
# tutorial data is produced using [Agisoft Photoscan](http://www.agisoft.com)
# which is a great tool for deriving point clouds and all kind of surface
# models. In the end of the processing chain you will have an orthorectified
# image and a dense point cloud. We will use some UAV data from the 
# [Marburg Open Forest](https://www.sensing-biodiversity.org) project.

devtools::install_github("gisma/link2GI", ref = "master")
devtools::install_github("gisma/uavRst", ref = "master")
require(uavRst)
require(raster)
require(link2GI)

# proj subfolders
projRootDir<-getwd()
#setwd(paste0(projRootDir,"run"))

paths<-link2GI::initProj(projRootDir = projRootDir,
                         projFolders = c("data/","data/ref/","output/","run/","las/"),
                         global = TRUE,
                         path_prefix = "path_")

# get some colors
pal = mapview::mapviewPalette("mapviewTopoColors")

# get the data
url <- "https://github.com/gisma/gismaData/raw/master/uavRst/data/lidar.las"
res <- curl::curl_download(url, paste0(path_data,"lasdata.las"))
# make the folders and linkages
giLinks<-uavRst::get_gi()

# create 2D point cloud DTM
dtm <- uavRst::pc2D_dtm(laspcFile = paste0(path_data,"lasdata.las"),
                gisdbasePath = projRootDir,
                tension = 20 ,
                sampleGridSize = 25,
                targetGridSize = 0.5,
                giLinks = giLinks)
dsm <- uavRst::pc2D_dsm(laspcFile = paste0(path_data,"lasdata.las"),
                gisdbasePath = projRootDir,
                sampleMethod = "max",
                targetGridSize = 0.5, 
                giLinks = giLinks)
mapview::mapview(dsm-dtm,col.regions=pal)