In ldbk/EMISR: EMIS and GMIS data extraction and analyses

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).

Choose your 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)

Environmental parameters extraction and analysis

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:

• graphically, some figures are elaborated (plot of the whole series, average, climatology and boxplot of the climatology). The function 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]]

• summary statistics for the parameter are computed thanks to the 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.

ldbk/EMISR documentation built on May 20, 2019, 11:28 p.m.