Nothing
## ----Loading packages, message=FALSE------------------------------------------
library(sf)
library(redlistr)
## ----Loading our example distributions----------------------------------------
mangrove.2000 <- raster(system.file("extdata", "example_distribution_2000.tif",
package = "redlistr"))
mangrove.2017 <- raster(system.file("extdata", "example_distribution_2017.tif",
package = "redlistr"))
## ----Importing shapefile, eval=FALSE------------------------------------------
# library(sf)
# my.shapefile <- st_read('./path/to/folder/', 'shapefile.shp')
# my.KML.file <- st_read('./path/to/folder/kmlfile.kml')
## ----Plotting the two rasters, fig.show='hold', fig.width=7, fig.height=7-----
plot(mangrove.2000, col = "grey30", legend = FALSE, main = "Mangrove Distribution")
plot(mangrove.2017, add = T, col = "darkorange", legend = FALSE)
## ----Checking CRS-------------------------------------------------------------
isLonLat(mangrove.2000)
isLonLat(mangrove.2000)
# If TRUE, they must be reprojected to a projected coordinate system
crs(mangrove.2000)@projargs == crs(mangrove.2017)@projargs
## ----Calculate area of rasters------------------------------------------------
a.2000 <- getArea(mangrove.2000)
a.2000
a.2017 <- getArea(mangrove.2017)
a.2017
## ----Binary object from multiclass, eval=FALSE--------------------------------
# # Create dummy raster for example
# r <- raster(nrows=10, ncols=10)
# r.multiple <- r
# values(r.multiple) <- rep(c(1:10), 10)
#
# # If the target ecosystem is represented by value = 5
# r.bin <- r.multiple == 5 # Has values of 1 and 0
# # Convert 0s to NAs
# values(r.bin)[values(r.bin) != 1] <- NA
## ----Using getAreaChange------------------------------------------------------
area.lost <- getAreaLoss(a.2000, a.2017)
# getAreaLoss(mangrove.2000, mangrove.2017) generates identical results
## ----Using getDeclineStats----------------------------------------------------
decline.stats <- getDeclineStats(a.2000, a.2017, 2000, 2017,
methods = c('ARD', 'PRD', 'ARC'))
decline.stats
## ----Estimating future area---------------------------------------------------
extrapolated.area <- futureAreaEstimate(a.2000, year.t1 = 2000,
ARD = decline.stats$ARD,
PRD = decline.stats$PRD,
ARC = decline.stats$ARC,
nYears = 50)
extrapolated.area
## ----Percent loss-------------------------------------------------------------
predicted.percent.loss <- (extrapolated.area$A.PRD.t3 - a.2000)/a.2000 * 100
predicted.percent.loss
## ----Make EOO, fig.width=7, fig.height=7--------------------------------------
EOO.polygon <- makeEOO(mangrove.2017)
plot(EOO.polygon)
plot(mangrove.2017, add = T, col = "green", legend = FALSE)
## ----Calculating EOO area-----------------------------------------------------
EOO.area <- getAreaEOO(EOO.polygon)
EOO.area
## ----Creating AOO grid, fig.width=7, fig.height=7-----------------------------
AOO.grid <- makeAOOGrid(mangrove.2017, grid.size = 10000,
min.percent.rule = F)
plot(AOO.grid)
plot(mangrove.2017, add = T, col = "green", legend = FALSE)
## ----Getting number of AOO grids----------------------------------------------
n.AOO <- length(AOO.grid)
# the getAOO function can also be used to directly get the AOO
# n.AOO <- getAOO(mangrove.2017, grid.size = 10000,
# min.percent.rule = T, percent = 0.1)
n.AOO
## ----gridUncertainty----------------------------------------------------------
gU.results <- gridUncertainty(mangrove.2017, 10000,
n.AOO.improvement = 5,
min.percent.rule = F)
# If it takes your computer very long to run this command, consider reducing
# n.AOO.improvement
gU.results$min.AOO.df
## ----Plotting out minimum AOO grid, fig.width=7, fig.height=7-----------------
plot(gU.results$min.AOO.grid$out.grid)
plot(mangrove.2017, add = T, col = "green", legend = FALSE)
## ----One percent grid, fig.width=7, fig.height=7------------------------------
AOO.grid.one.percent <- makeAOOGrid(mangrove.2017, grid.size = 10000,
min.percent.rule = T, percent = 1)
plot(AOO.grid.one.percent)
plot(mangrove.2017, add = T, col = "green", legend = FALSE)
## ----AOO Grid 0.1percent, fig.width=7, fig.height=7---------------------------
AOO.grid.min.percent <- makeAOOGrid(mangrove.2017, grid.size = 10000,
min.percent.rule = T, percent = 0.1)
par(mfrow = c(2,2))
plot(AOO.grid, main = 'AOO grid without one percent rule')
plot(mangrove.2017, add = T, col = "green", legend = FALSE)
plot(AOO.grid.one.percent, main = 'AOO grid with one percent rule')
plot(mangrove.2017, add = T, col = "green", legend = FALSE)
plot(AOO.grid.min.percent, main = 'AOO grid with one percent rule at 0.1%')
plot(mangrove.2017, add = T, col = "green", legend = FALSE)
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