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monarchs_sampling_approach.Rmd
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---
title: "Monarchs Sampling Design - Canada"
author: "Guillaume Larocque"
date: "25/01/2022"
output: html_document
---
```{r setup, eval=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## CREATING THE BASE LAYERS FROM CEC LAND COVER MAP
#library(rgrass7)
#initGRASS('/usr/lib/grass74/',gisDbase = '/home/glaroc/Monarques/GRASS',location = 'CEC',mapset = 'PERMANENT',override = TRUE)
```{r, eval=TRUE}
library(rgrass7)
library(sp)
library(stringr)
library(spatialEco)
library(rgdal)
initGRASS('/usr/lib/grass74/',gisDbase = '/home/ubuntu/data/GRASS',location = 'monarques',mapset = 'PERMANENT',override = TRUE)
buffer=0
nrand=0.05
use_sp()
```
```{r, eval=FALSE}
##DEFINE THE LIMITS OF THE RANGE BASED ON MONARCHS AND MILKWEED DISTRIBUTION
execGRASS('v.in.ogr',input="/home/ubuntu/data/Monarques/Danaus_asclepias_intersected_range_only_cec.shp",output='range', flags=c('overwrite'))
#execGRASS('v.buffer',input="range", distance=buffer, output="range_buffer", flags=c('overwrite'))
execGRASS('v.in.ogr',input='/home/ubuntu/data/Monarques/canada_master_sample_poly_cec.shp', output='gtrs_10', flags=c('overwrite'))
execGRASS('v.in.ogr',input='/home/ubuntu/data/Monarques/canada_mastersample_cec.shp', output='gtrs_pts_10', flags=c('overwrite'))
execGRASS('v.clip',input='gtrs_10',clip='range',output='gtrs_range', flags=c('overwrite'))
```
```{r, eval=FALSE}
##CREATION OF THE BASE LAND COVER LAYER WITH ROADS, RAILS AND TRANSMISSION LINES
execGRASS('r.in.gdal',input = '/home/ubuntu/data/Monarques/CAN_NALCMS_2015_LC_30m_LAEA_mmu5pix_.tif',output = 'CEC', memory=100000, flags=c('overwrite'))
execGRASS('g.region',raster='CEC')
execGRASS('v.in.ogr',input='/home/ubuntu/data/Monarques/canada_hwy.shp', output= 'Roads', flags=c('overwrite'))
execGRASS('v.in.ogr',input='/home/ubuntu/data/Monarques/canada_rails_cec.shp', output= 'Rails', flags=c('overwrite'))
execGRASS('v.in.ogr',input='/home/ubuntu/data/Monarques/transmission_lines_OSM_CEC.gpkg', output= 'Transmission', flags=c('overwrite'))
execGRASS('v.in.ogr',input='/home/ubuntu/data/Monarques/protected_areas_canada_qc.shp', output= 'Protected', snap=1e-09, flags=c('overwrite'))
execGRASS('v.to.rast',input= 'Roads',output = 'Roads', use='val',value=20, memory=150000, flags=c('overwrite'))
execGRASS('v.to.rast',input= 'Rails',output = 'Rails', use='val',value=21, memory=150000, flags=c('overwrite'))
execGRASS('v.to.rast',input= 'Transmission',output = 'Transmission', use='val',value=22, memory=150000, flags=c('overwrite'))
execGRASS('v.to.rast',input= 'Protected',output = 'Protected', use='val',value=100, memory=150000, flags=c('overwrite'))
execGRASS('r.patch',input='Roads,Rails,Transmission,CEC',output='CEC2', flags=c('overwrite'))
execGRASS('r.null',null=0,map='CEC2')
execGRASS('r.null',null=0,map='Protected')
execGRASS('r.mapcalc',expression='CEC_pa = CEC2+Protected', flags=c('overwrite'))
```
```{r, eval=FALSE}
execGRASS('r.out.gdal', input='CEC_pa',output='/home/ubuntu/data/Monarques/cec_pa_lines.tif', format='GTiff',createopt="COMPRESS=DEFLATE", flags=c('overwrite'))
##LAND COVER SUITABLE FOR MONARCHS
execGRASS('r.reclass', input = 'CEC_pa', output='CEC_suitable', rules='/home/ubuntu/data/Monarques/cec_reclass.txt', flags=c('overwrite'))
```
```{r, eval=FALSE}
##EXTRACT LAND COVERS FOR EACH GRTS ZONE
execGRASS('v.to.rast',input='gtrs_range', output='gtrs_range', use='attr',attribute_column='GRTS_ID',memory=150000, flags=c('overwrite'))
execGRASS('r.stats.zonal',base='gtrs_range', cover='CEC_suitable',method='sum',output='gtrs_suitable_zonal', flags=c('overwrite'))
execGRASS('r.out.gdal',input='gtrs_suitable_zonal',output='/home/ubuntu/data/Monarques/gtrs_suitable_zonal.tif', format='GTiff',createopt="COMPRESS=DEFLATE", flags=c('overwrite'))
execGRASS('r.to.vect',input='gtrs_suitable_zonal', output='gtrs_suitable_zonal',type='area',column='suitable', flags=c('overwrite'))
execGRASS('v.out.ogr',input='gtrs_suitable_zonal',output='/home/ubuntu/data/Monarques/gtrs_suitable_zonal.gpkg',type='area', flags=c('overwrite'))
execGRASS('v.db.addcolumn', map='gtrs_suitable_zonal',col="area double")
execGRASS('v.to.db',map='gtrs_suitable_zonal',option='area',columns='area')
```
### SELECTION OF GRTS SQUARES ZONES
```{r, eval=FALSE}
execGRASS('v.db.dropcolumn',map='gtrs_suitable_zonal',columns="rand")
execGRASS('v.db.dropcolumn',map='gtrs_suitable_zonal',columns="grts_id")
execGRASS('v.db.addcolumn',map='gtrs_suitable_zonal',columns="rand double")
execGRASS('db.execute', sql="UPDATE gtrs_suitable_zonal SET rand=Random()")
execGRASS('v.db.addcolumn',map='gtrs_suitable_zonal',columns="grts_id int")
execGRASS('v.what.vect',map='gtrs_suitable_zonal',query_map='gtrs_pts_10',query_column='grts_id',column='grts_id')
execGRASS('v.extract',input='gtrs_suitable_zonal',output='suitable_blocks',where='suitable>50000 AND area>99000000',flags=c('overwrite'))
```{r, eval=FALSE}
grts=readVECT('suitable_blocks')
set.seed(2022)
rnd=ceiling(nrow(grts)*nrand)
randval=grts$rand[sort(grts$rand,index.return=TRUE)$ix[rnd]]
where=paste0('grts_id=191585 OR grts_id=244428 OR (suitable>50000 AND rand<=',randval,')')
execGRASS('v.extract',input='suitable_blocks',output='selected_blocks',where=where,flags=c('overwrite'))
#execGRASS('v.extract',input='tmp',output='selected_blocks_10',random=rnd,flags=c('overwrite'))
#execGRASS('v.out.ogr',input='suitable_blocks', type='area',output='/home/ubuntu/data/Monarques/suitable_blocks_2023.gpkg', flags=c('overwrite'))
execGRASS('v.out.ogr',input='selected_blocks', type='area',output='/home/ubuntu/data/Monarques/selected_blocks_2023c.gpkg', flags=c('overwrite'))
```
# Old approach
#gtrs_suitable_zonal file is opened in QGIS and a random identifier is given to each polygon. A random 5% of grid blocks are choosen.
### 30 M SELECTION
```{r, eval=FALSE}
execGRASS('r.reclass',input='CEC_pa',output='CEC_6cats',rules='/home/ubuntu/data/Monarques/Monarques-repo/cec_reclass_6cats.txt',c('overwrite'))
fids=execGRASS('v.db.select',map='selected_blocks',columns='grts_id',flags=c("c","v"))
fids=attr(fids,"resOut")
ii=0
for (i in fids) {
execGRASS('v.extract',input='selected_blocks',output='block_temp',where=paste0("grts_id=",i),flags = c('overwrite'))
execGRASS('g.region', vector='block_temp')
execGRASS('r.mask',vector='block_temp', flags = c("overwrite"))
execGRASS('r.to.vect',input='CEC_6cats', output='temp', type='point',flags = c('overwrite'))
for (j in (1:6)) {
execGRASS('v.extract',input='temp', output='temp2', where=paste0("value=",j),flags = c('overwrite'))
tt=execGRASS('v.info',map='temp2',flags=c('t'))
npoints=as.numeric(str_replace(attr(tt,"resOut")[2],'points=',''))
if(npoints>1){
pts <- readVECT('temp2',type='point')
nsamples <- min(npoints-1,20)
if(nsamples > 1){
if(npoints != 20){
pts2 <- subsample.distance(pts, size=nsamples, d=300)
}else{
pts2 <- pts
}
writeVECT(pts2, 'temp3',v.in.ogr_flags=c('o','overwrite','t'))
if(i==fids[1] && j==1){
execGRASS('v.patch',input='temp3', output='all30',flags = c('overwrite'))
}else{
execGRASS('v.patch',input='temp3', output='all30',flags = c('a','overwrite'))
}
}
}
}
ii=ii+1
print(ii/length(fids))
}
```
```{r, eval=FALSE}
#This doesn't quite work. Used approach below instead.
execGRASS('g.region',raster='CEC_pa')
#execGRASS('r.mask', flags = c("r","quiet"))
#execGRASS('v.db.droptable',map='all30',flags=c('f'))
#execGRASS('v.db.addtable',map='all30',columns="cover_type int")
#execGRASS('v.what.rast',map='all30',raster='CEC_pa',column='cover_type')
#execGRASS('v.db.addcolumn',map='all30',columns="grts_id int")
#execGRASS('v.what.vect',map='all30',query_map='gtrs_10',query_column='grts_id',column='grts_id')
execGRASS('v.out.ogr', input='all30', type='point', output='/home/ubuntu/data/Monarques/all30_2023c.gpkg',flags=c('overwrite'))
```
### GENERATION OF FINAL MAP
Point sampling tool is used in QGIS to combine the CEC_pa land cover and the GRTS block ids to the attribute table. The cec_landcover_sectors_realids.csv columns are also joined. Note that to assign the province, the Join attributes by Nearest tool in Processing is used since some points fall off the provinces shapefile.
For Lng in field calculator: x(transform($geometry, layer_property(@layer, 'crs'),'EPSG:4326'))
Then, to generate point ID
```{r, eval=TRUE}
execGRASS('v.in.ogr',input='/home/ubuntu/data/Monarques/Monarques-repo/data/all30_2023c_with_all_cats.gpkg', output= 'all30_with_cats', flags=c('overwrite','o'))
execGRASS('v.out.ogr', input='all30_with_cats', type='point', output='/home/ubuntu/data/Monarques/Monarques-repo/data/all30_with_cats.csv',format='CSV',flags=c('overwrite'))
all30<-read.csv('/home/ubuntu/data/Monarques/Monarques-repo/data/all30_with_cats.csv')
for (i in unique(all30$grts_id)) {
all30[all30$grts_id==i,'point_id'] <- seq(1,sum(all30$grts_id==i))
}
all30$point_id_full=paste0('CA-',str_pad(all30$grts_id,4,side='left',pad=0),'-',all30$point_id)
write.csv(all30,'/home/ubuntu/data/Monarques/Monarques-repo/data/all30_2023c_with_ids.csv')
```