In bcgov/forestDroughtTool: Predict Forest Drought Hazard In British Columbia

knitr::opts_chunk$set(echo = TRUE)

# load libraries
library(tidyverse)
library(sf)
library(raster)
library(mapview)
library(here)

Analysis of SBSwk1 data

Below, I show the location of the 30 points in the SBSwk1. As you pointed out, quite a few of them lie near the booundary of the BGC unit, which I don't think is ideal.

SBSwk1<-
  st_read(here("data-raw","dailyData_prelim","Extraction shapes","SBS wk 1_sampled.shp"))

pts<-
  st_read(here("data-raw","dailyData_prelim","Extraction shapes","SBS_wk1_pts.shp"))



SBSwk1 %>% 
ggplot()+
  geom_sf()+
  geom_sf(data=pts,aes(color="red"))+
  theme(legend.position = "none")

ppt_grid<-
  raster(here("data-raw","dailyData_prelim","Extraction shapes","PCIC_Precipitation_grids.tif")) %>% 
  projectRaster(crs=crs("+init=epsg:3005")) %>% 
  crop(SBSwk1) 


# Plot ppt grids
plot(ppt_grid)
plot(SBSwk1,add=T,col=alpha("white",alpha=0.1))

As a solution, we can use the raster::extract function to extract all of the pixels

grids<-
  raster::extract(ppt_grid,SBSwk1,weight=TRUE,normalizeWeights=FALSE) 

bcgov/forestDroughtTool documentation built on March 20, 2021, 4:15 p.m.