knitr::opts_chunk$set(collapse=TRUE,comment="#>",fig.width=6,fig.height=6) library(EMISR)
The vignette illustrates the use of the EMISR package to analyse data for a marine protected area (MPA).
The SpatialPolygonsDataFrames mpa1
and mpa2
contain the marine protected areas of the world.
To select one MPA:
wdpaid<-555540558 mpa<-mpa2[mpa2@data$wdpaid==wdpaid,] proj4string(mpa)<-CRS("+proj=longlat +datum=WGS84")
Then plot it:
#build a terrestrial polygon around the mpa mappoly<-map("worldHires",fill=T,plot=FALSE,xlim=c(extent(mpa)@xmin-1,extent(mpa)@xmax+1),ylim=c(extent(mpa)@ymin-1,extent(mpa)@ymax+1)) IDs <- sapply(strsplit(mappoly$names, ":"), function(x) x[1]) coast<- map2SpatialPolygons(mappoly, IDs=IDs, proj4string=CRS("+proj=longlat +datum=WGS84")) #plot at global scale map("worldHires",col="light grey",fill=T) points(coordinates(mpa),cex=2,col="blue",pch="+") title(paste("MPA",mpa@data$wdpaid,"(",mpa@data$name,")","in",mpa@data$country),cex=.5) #local plot plot(mpa,xlim=c(extent(mpa)@xmin-1,extent(mpa)@xmax+1),ylim=c(extent(mpa)@ymin-1,extent(mpa)@ymax+1),axes=T,col="red") map("worldHires",add=T,col="light grey",fill=T) plot(mpa,add=T,col="blue") title(paste("MPA",mpa@data$wdpaid,"(",mpa@data$name,")","in",mpa@data$country),cex=.5)
The function mpaextract
download the requested parameter on EMIS (european waters, high spatial resolution) or GMIS
(global scale, but only available at 4 and 9 km).
#extraction of the MODIS sea surface temperature at 2 km between 2009 and 2012 on the Pantelleria marine protected area (Italy) pantelleria_sst<-mpaextract("EMIS_T_SST","2km","2009-01","2012-12",555540558) #extraction of the MODIS sea surface temperature at 2 km between 2009 and 2012 on the Pantelleria marine protected area (Italy) pantelleria_chl<-mpaextract("EMIS_A_CHLA","2km","2009-01","2012-12",555540558)
Then the extracted parameters can be processed in two ways:
mpaprocessplt
does this graphical processing. plt<-mpaprocessplot(imgs=pantelleria_sst,mpa=pantelleria_mpa,name="EMIS_T_SST",unite="oC",logscale=FALSE) #map of the whole series plt[[1]]+latticeExtra::layer(sp.polygons(coast,fill="grey",col="grey"))+latticeExtra::layer(sp.polygons(pantelleria_mpa)) #map of the average SST plt[[2]]+latticeExtra::layer(sp.polygons(coast,fill="grey",col="grey"))+latticeExtra::layer(sp.polygons(pantelleria_mpa)) #map of the climatology plt[[3]]+latticeExtra::layer(sp.polygons(coast,fill="grey",col="grey"))+latticeExtra::layer(sp.polygons(pantelleria_mpa)) #boxplot of the climatology plt[[4]]
mpaprocessstat
function. See the help of this
function for the details of the statistical analysis done. A graphic with the time series decomposition of the
parameter values averaged on the MPA area is given.datstat<-mpaprocessstat(imgs=pantelleria_sst,mpa=pantelleria_mpa,name="EMIS_T_SST",unite="oC") #the statistics datstat[[1]] #time series decomposition datstat[[2]]
-the function mpaprocess
calls successively the functions mpaextract
,mpaprocessplt
and mpaprocessstat
and gives
all the outputs (plot and statistics) in a list.
pltstat<-mpaprocess(name = "EMIS_A_CHLA", resolution = "4km", startdate = "2009-01", enddate = "2012-12", wdpaid = 555540558)
Results can be combine with more than one parameters repeating extraction and analysis changing the name of the parameters.
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