Updated coregistration

coregistration.Rmd

 ---
-title: "Workflow for plot coregistration with ALS data"
+title: "R workflow for field plot coregistration with ALS data"
 author: "Jean-Matthieu Monnet"
-date: "Jan 28, 2021"
+date: "`r Sys.Date()`"
 output:
   pdf_document: default
   html_document: default
-bibliography: workflow.treedetection.bib
+bibliography: "./bib/bibliography.bib"
 ---
 
 ```{r setup, include=FALSE}
-# library(knitr)
 knitr::opts_chunk$set(echo = TRUE)
 # Set so that long lines in R will be wrapped:
 knitr::opts_chunk$set(tidy.opts=list(width.cutoff=80),tidy=TRUE)
@@ -18,21 +17,9 @@ knitr::opts_chunk$set(fig.align = "center")
 
 
 ---
-This workflow compares a canopy height model (CHM) derived from airborne laser scanning (ALS) data and inventoried tree positions, and proposes a translation in plot position for better matching. It works with circular plots. The method is described in @Monnet2014. The workflow uses field and ALS data acquired in the forest of Lac des Rouges Truites. It is based on functions from packages `lidaRtRee` and `lidR`.
+This workflow compares a canopy height model (CHM) derived from airborne laser scanning (ALS) data with tree positions inventoried in the field, and then proposes a translation in plot position for better matching. The method is described in @Monnet2014. Here it is exemplified with circular plots, but it can be applied to any shape of field plots. The workflow is based on functions from packages `lidaRtRee` and `lidR`. Example data were acquired in the forest of Lac des Rouges Truites (Jura, France).
 
-Licence: CC BY  
-Source page: https://gitlab.irstea.fr/jean-matthieu.monnet/lidaRtRee/wikis/forest-plot-coregistration-workflow
-
-Changelog:
-
-* Jan, 2021: checked compatibility with lidR 3.1.0 and lidaRtRee 3.0.0
-+ Oct, 2020: checked compatibility with lidR 3.0.3
-+ Feb, 2019: checked compatibility with lidR 2.0.0 and lidaRtRee 2.0.0
-+ May, 2018: initial version
-
-```{r include = FALSE}
-library(lidR)
-```
+Licence: CC BY / [Source page](https://gitlab.irstea.fr/jean-matthieu.monnet/lidartree_tutorials/-/blob/master/coregistration.Rmd)
 
 
 ## Material
@@ -49,7 +36,7 @@ The study area is a part of the forest of Lac des Rouges Truites. 44 plots have
 
 ```{r plots, include = TRUE}
 # load plot coordinates (data.frame with lines corresponding to the las objects)
-load(file="./data.coregistration/plotsCoregistration.rda")
+load(file="./data/coregistration/plotsCoregistration.rda")
 head(p, n=3L)
 ```
 
@@ -68,7 +55,7 @@ On each plot, five trees which were considered  suitable for coregistration (ver
 
 ```{r trees, include = TRUE}
 # load inventoried trees (data.frame with plot id info )
-load(file="./data.coregistration/treesCoregistration.rda")
+load(file="./data/coregistration/treesCoregistration.rda")
 head(ap, n=3L)
 ```
 
@@ -76,11 +63,11 @@ head(ap, n=3L)
 
 Airborne laser scanning data on the study area is part of a campaign acquired in 2016 with an airborne RIEGL LMS Q680i sensor. Acquisition was funded by the Région Franche-Comté.
 
-ALS data over the plots is provided as a list of LAS objects in rda file. 
+ALS data over the plots is provided as a list of LAS objects in `rda` file. 
 
 ```{r las, include = TRUE}
 # load point cloud over reference plots (list of las objects)
-load(file="./data.coregistration/lasCoregistration.rda")
+load(file="./data/coregistration/lasCoregistration.rda")
 ```
 
 Display point cloud of plot 1.
@@ -102,7 +89,7 @@ las <- lidR::clip_circle(cata, p$XGPS, p$YGPS, p.radius + b.size + 5)
 # normalize heights if point cloud are not already normalized
 las <- lapply(las, function(x) {lidR::normalize_height(x, lidR::tin())})
 # save as rda file for later use
-save(las, file="./data.coregistration/lasCoregistration.rda")
+save(las, file="./data/coregistration/lasCoregistration.rda")
 ```
 
 ## Parameters
@@ -149,7 +136,7 @@ raster::plot(chmfilt, main="Filtered canopy height model")
 
 The trees corresponding to the plot are extracted, and a plot mask is computed from the plot center and radius.
 
-```{r mask, include = TRUE, out.width = '60%', fig.dim=c(5.5, 4.5), warning="FALSE" }
+```{r mask, include = TRUE, out.width = '50%', fig.dim=c(5.5, 4.5), warning=FALSE}
 # plot centre
 centre <- p[i,c("XGPS","YGPS")]
 # extract plot trees