Revision f2b2e7d5
Added by Adam M. Wilson over 12 years ago
| climate/extra/Interpolation_flowchart/flowchart.svg | ||
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sodipodi:role="line" |
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sodipodi:linespacing="125%"><tspan |
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| 554 |
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| 556 |
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| 558 |
xml:space="preserve" |
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style="font-size:40px;font-style:normal;font-weight:normal;text-align:center;line-height:125%;letter-spacing:0px;word-spacing:0px;text-anchor:middle;fill:#000000;fill-opacity:1;stroke:none;font-family:Sans" |
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x="121.97128" |
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id="text2987-0-4-8-3-3-4" |
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| 563 |
sodipodi:linespacing="125%"><tspan |
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| 564 |
style="font-size:12px;text-align:center;text-anchor:middle;fill:#4d4d4d" |
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sodipodi:role="line" |
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| 566 |
x="121.97128" |
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sodipodi:role="line" |
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| 571 |
x="121.97128" |
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| 572 |
y="687.45776" |
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| 573 |
id="tspan4096">Generate daily</tspan><tspan |
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| 574 |
style="font-size:12px;text-align:center;text-anchor:middle;fill:#4d4d4d" |
|
| 575 |
sodipodi:role="line" |
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| 576 |
x="121.97128" |
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y="702.45776" |
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| 578 |
id="tspan4017">product</tspan></text> |
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| 579 |
<text |
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| 580 |
xml:space="preserve" |
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| 581 |
style="font-size:40px;font-style:normal;font-weight:normal;text-align:center;line-height:125%;letter-spacing:0px;word-spacing:0px;text-anchor:middle;fill:#000000;fill-opacity:1;stroke:none;font-family:Sans" |
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| 582 |
x="226.96114" |
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| 583 |
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| 584 |
id="text2987-0-4-8-1" |
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| 585 |
sodipodi:linespacing="125%"><tspan |
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| 586 |
style="font-size:12px;text-align:center;text-anchor:middle" |
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| 587 |
sodipodi:role="line" |
|
| 588 |
x="226.96114" |
|
| 589 |
y="765.32288" |
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| 590 |
id="tspan3912-2">Calculate monthly</tspan><tspan |
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| 591 |
style="font-size:12px;text-align:center;text-anchor:middle" |
|
| 592 |
sodipodi:role="line" |
|
| 593 |
x="226.96114" |
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| 594 |
y="780.32288" |
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| 595 |
id="tspan4055">climatologies</tspan></text> |
|
| 596 |
<text |
|
| 597 |
xml:space="preserve" |
|
| 598 |
style="font-size:40px;font-style:normal;font-weight:normal;text-align:center;line-height:125%;letter-spacing:0px;word-spacing:0px;text-anchor:middle;fill:#000000;fill-opacity:1;stroke:none;font-family:Sans" |
|
| 599 |
x="387.41956" |
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| 600 |
y="832.5813" |
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id="text2987-0-4-8" |
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| 602 |
sodipodi:linespacing="125%"><tspan |
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| 603 |
style="font-size:12px;text-align:center;text-anchor:middle" |
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| 604 |
sodipodi:role="line" |
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| 605 |
x="387.41956" |
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| 606 |
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| 607 |
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|
| 608 |
style="font-size:12px;text-align:center;text-anchor:middle" |
|
| 609 |
sodipodi:role="line" |
|
| 610 |
x="389.32971" |
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| 611 |
y="847.5813" |
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| 612 |
id="tspan3910">local projection </tspan><tspan |
|
| 613 |
style="font-size:12px;text-align:center;text-anchor:middle" |
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| 614 |
sodipodi:role="line" |
|
| 615 |
x="387.41956" |
|
| 616 |
y="862.5813" |
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| 617 |
id="tspan3912">for interpolation</tspan></text> |
|
| 618 |
<text |
|
| 619 |
xml:space="preserve" |
|
| 620 |
style="font-size:40px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Sans" |
|
| 621 |
x="387.04453" |
|
| 622 |
y="909.78186" |
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| 623 |
id="text2987-4-53-9-5-9" |
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| 624 |
sodipodi:linespacing="125%"><tspan |
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| 625 |
style="font-size:12px;text-align:center;text-anchor:middle" |
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| 626 |
sodipodi:role="line" |
|
| 627 |
x="387.04453" |
|
| 628 |
y="909.78186" |
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| 629 |
id="tspan4348">Daily interpolation</tspan><tspan |
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| 630 |
style="font-size:12px;text-align:center;text-anchor:middle" |
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| 631 |
sodipodi:role="line" |
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| 632 |
x="387.04453" |
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| 633 |
y="924.78186" |
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| 634 |
id="tspan4346">(Kriging, GAM, GWR)</tspan></text> |
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| 635 |
<text |
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| 636 |
xml:space="preserve" |
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| 637 |
style="font-size:40px;font-style:normal;font-weight:normal;line-height:125%;letter-spacing:0px;word-spacing:0px;fill:#000000;fill-opacity:1;stroke:none;font-family:Sans" |
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| 638 |
x="387.04456" |
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| 639 |
y="981.50269" |
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| 640 |
id="text2987-4-53-9-5-9-1" |
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| 641 |
sodipodi:linespacing="125%"><tspan |
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| 642 |
style="font-size:12px;text-align:center;text-anchor:middle" |
|
| 643 |
sodipodi:role="line" |
|
| 644 |
x="387.04456" |
|
| 645 |
y="981.50269" |
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| 646 |
id="tspan4348-8">Daily Predictions</tspan><tspan |
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| 647 |
style="font-size:12px;text-align:center;text-anchor:middle" |
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| 648 |
sodipodi:role="line" |
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| 649 |
x="387.04456" |
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| 650 |
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| climate/extra/landcover_lst.R | ||
|---|---|---|
| 1 |
### Script to explore the relationships between LST and land cover in Oregon |
|
| 2 |
### The goal is to inform the interpolation of LST using land cover |
|
| 3 |
|
|
| 4 |
|
|
| climate/procedures/MOD06_L2_data_summary.r | ||
|---|---|---|
| 39 | 39 |
########################## |
| 40 | 40 |
#### explore the data |
| 41 | 41 |
|
| 42 |
## load data |
|
| 43 | 42 |
months=seq(as.Date("2000-01-15"),as.Date("2000-12-15"),by="month")
|
| 43 |
|
|
| 44 |
|
|
| 45 |
## load data |
|
| 44 | 46 |
cerfiles=list.files(summarydatadir,pattern="CER_mean_.*tif$",full=T); cerfiles |
| 45 | 47 |
cer=brick(stack(cerfiles)) |
| 46 | 48 |
setZ(cer,months,name="time") |
| ... | ... | |
| 71 | 73 |
cldm=mean(cld,na.rm=T) |
| 72 | 74 |
### TODO: change to bilinear if reprojecting! |
| 73 | 75 |
|
| 76 |
cer20files=list.files(summarydatadir,pattern="CER_P20um_.*tif$",full=T); cer20files |
|
| 77 |
cer20=brick(stack(cer20files)) |
|
| 78 |
setZ(cer20,months,name="time") |
|
| 79 |
cer20@z=list(months) |
|
| 80 |
cer20@zname="time" |
|
| 81 |
layerNames(cer20) <- as.character(format(months,"%b")) |
|
| 82 |
#cot=projectRaster(from=cot,crs="+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0",method="ngb") |
|
| 83 |
cotm=mean(cot,na.rm=T) |
|
| 84 |
### TODO: change to bilinear! |
|
| 85 |
|
|
| 86 |
|
|
| 74 | 87 |
### load PRISM data for comparison |
| 75 | 88 |
prism=brick("data/prism/prism_climate.nc",varname="ppt")
|
| 76 | 89 |
## project to sinusoidal |
| ... | ... | |
| 83 | 96 |
layerNames(prism) <- as.character(format(months,"%b")) |
| 84 | 97 |
|
| 85 | 98 |
#### build a pixel by variable matrix |
| 86 |
vars=c("cer","cld","cot","prism")
|
|
| 99 |
vars=c("cer","cer20","cld","cot","prism")
|
|
| 87 | 100 |
bd=melt(as.matrix(vars[1])) |
| 88 | 101 |
colnames(bd)=c("cell","month",vars[1])
|
| 89 | 102 |
for(v in vars[-1]) {print(v); bd[,v]=melt(as.matrix(get(v)))$value}
|
| ... | ... | |
| 116 | 129 |
|
| 117 | 130 |
### extract MOD06 data for each station |
| 118 | 131 |
stcer=extract(cer,st2)#;colnames(stcer)=paste("cer_mean_",1:12,sep="")
|
| 132 |
stcer20=extract(cer20,st2)#;colnames(stcer)=paste("cer_mean_",1:12,sep="")
|
|
| 119 | 133 |
stcot=extract(cot,st2)#;colnames(stcot)=paste("cot_mean_",1:12,sep="")
|
| 120 | 134 |
stcld=extract(cld,st2)#;colnames(stcld)=paste("cld_mean_",1:12,sep="")
|
| 121 |
mod06=cbind.data.frame(id=st2$id,lat=st2$lat,lon=st2$lon,stcer,stcot,stcld) |
|
| 135 |
mod06=cbind.data.frame(id=st2$id,lat=st2$lat,lon=st2$lon,stcer,stcer20,stcot,stcld)
|
|
| 122 | 136 |
mod06l=melt(mod06,id.vars=c("id","lon","lat"))
|
| 123 | 137 |
mod06l[,c("variable","moment","month")]=do.call(rbind,strsplit(as.character(mod06l$variable),"_"))
|
| 124 | 138 |
mod06l=as.data.frame(cast(mod06l,id+lon+lat+month~variable+moment,value="value")) |
| ... | ... | |
| 158 | 172 |
|
| 159 | 173 |
## melt it |
| 160 | 174 |
mod06sl=melt(mod06s[,!grepl("lppt",colnames(mod06s))],id.vars=c("id","lon","lat","elev","month","ppt","glon","glon2","gelev","gbin"))
|
| 161 |
levels(mod06sl$variable)=c("Effective Radius (um)","Cloudy Days (%)","Optical Thickness (%)","Liquid Water Path")
|
|
| 175 |
levels(mod06sl$variable)=c("Effective Radius (um)","Very Cloudy Days (%)","Cloudy Days (%)","Optical Thickness (%)","Liquid Water Path")
|
|
| 162 | 176 |
|
| 163 | 177 |
################################################################### |
| 164 | 178 |
################################################################### |
| ... | ... | |
| 234 | 248 |
print(p) |
| 235 | 249 |
|
| 236 | 250 |
### monthly comparisons of variables |
| 237 |
mod06sl=melt(mod06s,measure.vars=c("value","COT_mean","CER_mean"))
|
|
| 238 |
bwplot(value~month|variable,data=mod06sl,cex=.5,pch=16,col="black",scales=list(y=list(relation="free")),layout=c(1,3)) |
|
| 239 |
splom(mod06s[grep("CER|COT|CLD",colnames(mod06s))],cex=.2,pch=16,main="Scatterplot matrix of MOD06 products")
|
|
| 251 |
#mod06sl=melt(mod06s,measure.vars=c("ppt","COT_mean","CER_mean","CER_P20um"))
|
|
| 252 |
#bwplot(value~month|variable,data=mod06sl,cex=.5,pch=16,col="black",scales=list(y=list(relation="free")),layout=c(1,3))
|
|
| 253 |
#splom(mod06s[grep("CER|COT|CLD|ppt",colnames(mod06s))],cex=.2,pch=16,main="Scatterplot matrix of MOD06 products")
|
|
| 240 | 254 |
|
| 241 | 255 |
### run some regressions |
| 242 | 256 |
#plot(log(ppt)~COT_mean,data=mod06s) |
| ... | ... | |
| 246 | 260 |
xyplot(ppt~value|variable,groups=glon,data=mod06sl, |
| 247 | 261 |
scales=list(y=list(log=T),x=list(relation="free",log=F)), |
| 248 | 262 |
par.settings = list(superpose.symbol = list(col=bgyr(5),pch=16,cex=.5)),auto.key=list(space="top",title="Station Longitude"), |
| 249 |
main="Comparison of MOD06_L2 and Precipitation Monthly Climatologies",ylab="Mean Monthly Station Precipitation (mm)",xlab="MOD06_L2 Product",layout=c(4,1))+
|
|
| 263 |
main="Comparison of MOD06_L2 and Precipitation Monthly Climatologies",ylab="Mean Monthly Station Precipitation (mm)",xlab="MOD06_L2 Product",layout=c(5,1))+
|
|
| 250 | 264 |
layer(panel.text(9,2.5,label="Coastal stations",srt=30,cex=1.3,col="blue"),columns=1)+ |
| 251 |
layer(panel.text(13,.9,label="Inland stations",srt=10,cex=1.3,col="red"),columns=1) |
|
| 265 |
layer(panel.text(13,.9,label="Inland stations",srt=10,cex=1.3,col="red"),columns=1)+ |
|
| 266 |
layer(panel.abline(lm(y~x),col="red"))+ |
|
| 267 |
layer(panel.text(0,0,paste("R2=",round(summary(lm(y~x))$r.squared,2)),pos=4,cex=.5,col="grey"))
|
|
| 268 |
|
|
| 252 | 269 |
|
| 253 | 270 |
## ppt~metric with longitude bins |
| 254 | 271 |
#CairoPNG("output/COT.png",width=10,height=5,units="in",dpi=300,pointsize=20)
|
| ... | ... | |
| 257 | 274 |
at=quantile(as.matrix(subset(m01,subset=3)),seq(0,1,len=100),na.rm=T) |
| 258 | 275 |
p1=levelplot(subset(m01,subset=3),xlab.top="Optical Thickness (%)",at=at,col.regions=bgyr(length(at)),margin=F, |
| 259 | 276 |
)+layer(sp.lines(roi_geo, lwd=1.2, col='black'))+layer(sp.points(st2, cex=.5,col='black')) |
| 277 |
at=quantile(as.matrix(subset(m01,subset=3)),seq(0,1,len=100),na.rm=T) |
|
| 278 |
|
|
| 260 | 279 |
at=quantile(as.matrix(subset(m01,subset=4)),seq(0,1,len=100),na.rm=T) |
| 261 | 280 |
p2=levelplot(subset(m01,subset=4),xlab.top="PRISM MAP",at=at,col.regions=bgyr(length(at)),margin=F, |
| 262 | 281 |
)+layer(sp.lines(roi_geo, lwd=1.2, col='black'))+layer(sp.points(st2, cex=.5, col='black')) |
| ... | ... | |
| 268 | 287 |
layer(panel.text(9,2.6,label="Coastal stations",srt=10,cex=1.3,col="blue"),columns=1)+ |
| 269 | 288 |
layer(panel.text(13,.95,label="Inland stations",srt=10,cex=1.3,col="red"),columns=1) |
| 270 | 289 |
|
| 271 |
save(p1,p2,p3,file="plotdata.Rdata") |
|
| 272 |
load("plotdata.Rdata")
|
|
| 290 |
p4=xyplot(ppt~value,groups=glon,data=mod06sl[mod06sl$variable=="Very Cloudy Days (%)",], |
|
| 291 |
scales=list(y=list(log=T),x=list(relation="free",log=F)), |
|
| 292 |
par.settings = list(superpose.symbol = list(col=bgyr(5),pch=16,cex=.3)),auto.key=list(space="right",title="Station \n Longitude"), |
|
| 293 |
ylab="Mean Monthly Station Precipitation (mm)",xlab="Proportion days with Cloud Effective Radius >20um from MOD06_L2 (%)",layout=c(1,1))+ |
|
| 294 |
layer(panel.text(9,2.6,label="Coastal stations",srt=10,cex=1.3,col="blue"),columns=1)+ |
|
| 295 |
layer(panel.text(13,.95,label="Inland stations",srt=10,cex=1.3,col="red"),columns=1) |
|
| 296 |
|
|
| 297 |
#save(p1,p2,p3,file="plotdata.Rdata") |
|
| 298 |
#load("plotdata.Rdata")
|
|
| 273 | 299 |
|
| 274 |
CairoPDF("output/MOD06_Summaryfig.pdf",width=11,height=8.5)
|
|
| 300 |
#CairoPDF("output/MOD06_Summaryfig.pdf",width=11,height=8.5)
|
|
| 275 | 301 |
print(p3,position=c(0,0,1,.5),save.object=F) |
| 302 |
#print(p4,position=c(0,0,1,.5),save.object=F) |
|
| 276 | 303 |
print(p1,split=c(1,1,2,2),new=F) |
| 277 | 304 |
print(p2,split=c(2,1,2,2),new=F) |
| 278 |
dev.off() |
|
| 279 |
system("convert output/MOD06_Summaryfig.pdf output/MOD06_Summaryfig.png")
|
|
| 305 |
#dev.off()
|
|
| 306 |
#system("convert output/MOD06_Summaryfig.pdf output/MOD06_Summaryfig.png")
|
|
| 280 | 307 |
#dev.off() |
| 281 | 308 |
|
| 282 | 309 |
## with elevation |
| climate/procedures/interpolation.r | ||
|---|---|---|
| 1 |
#! /bin/r |
|
| 2 |
|
|
| 3 |
|
|
| 4 |
#system("ssh -X turaco")
|
|
| 5 |
#export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:+$LD_LIBRARY_PATH:}/home/adamw/local/lib
|
|
| 6 |
#export KMP_DUPLICATE_LIB_OK=TRUE |
|
| 7 |
#R |
|
| 8 |
### Download and clean up GHCN data for CFR |
|
| 9 |
#install.packages("ncdf4",lib="/home/adamw/rlib",configure.args="--with-nc-config=/home/adamw/local/bin/nc-config")
|
|
| 10 |
|
|
| 11 |
library(sp);library(rgdal); |
|
| 12 |
library(reshape) |
|
| 13 |
library(ncdf4) |
|
| 14 |
library(geosphere) |
|
| 15 |
library(rgeos) |
|
| 16 |
library(multicore);library(RSQLite) |
|
| 17 |
library(spBayes) |
|
| 18 |
library(geoR) #for conventional kriging |
|
| 19 |
library(raster) |
|
| 20 |
## for review/analysis |
|
| 21 |
library(coda) |
|
| 22 |
library(lattice) |
|
| 23 |
library(MBA) |
|
| 24 |
|
|
| 25 |
### Load functions |
|
| 26 |
#source("code/interpolationfunc.r")
|
|
| 27 |
|
|
| 28 |
X11.options(type="Xlib") |
|
| 29 |
ncores=20 #number of threads to use |
|
| 30 |
|
|
| 31 |
setwd("/home/adamw/acrobates/projects/interp")
|
|
| 32 |
|
|
| 33 |
### read in full dataset |
|
| 34 |
load("data/ghcn/roi_ghcn.Rdata")
|
|
| 35 |
load("stroi.Rdata")
|
|
| 36 |
source("code/GHCN_functions.r")
|
|
| 37 |
|
|
| 38 |
## rescale units to C and mm |
|
| 39 |
d$value=d$value/10.0 |
|
| 40 |
|
|
| 41 |
## add flag for Benoit's training stations to identify training/validation |
|
| 42 |
stt=readOGR("data/ghcn/tempdata/ghcn_1507_s.shp","ghcn_1507_s")$STAT_ID
|
|
| 43 |
d$fit=d$id%in%stt |
|
| 44 |
|
|
| 45 |
### read in and subset to Benoit's dates |
|
| 46 |
dates=as.Date(as.character( |
|
| 47 |
read.table("data/ghcn/tempdata/dates_interpolation_03052012.txt")$V1),"%Y%m%d")
|
|
| 48 |
|
|
| 49 |
########################################################## |
|
| 50 |
#### load region of interest |
|
| 51 |
roi=readOGR("data/boundaries/statesp020.shp","statesp020")
|
|
| 52 |
proj4string(roi)=CRS("+proj=longlat +datum=WGS84")
|
|
| 53 |
roi=roi[roi$STATE=="Oregon",] |
|
| 54 |
roi=gUnionCascaded(roi) #dissolve any subparts of roi |
|
| 55 |
|
|
| 56 |
## distance from ROI to buffer for border stations |
|
| 57 |
dis=100 #km |
|
| 58 |
## buffer roi (transform to azimuthal equidistant with centroid of roi, add 'dis' buffer, then transform back) |
|
| 59 |
roic=centroid(roi) |
|
| 60 |
|
|
| 61 |
roib=spTransform( |
|
| 62 |
gBuffer(spTransform(roi, |
|
| 63 |
CRS(paste("+proj=aeqd +lat_0=",roic[2]," +lon_0=",
|
|
| 64 |
roic[1]," +ellps=WGS84 +datum=WGS84 +units=m +no_defs ",sep=""))), |
|
| 65 |
width=dis*1000), |
|
| 66 |
CRS("+proj=longlat +datum=WGS84"))
|
|
| 67 |
|
|
| 68 |
### generate knots |
|
| 69 |
knots=makegrid(roib,cellsize=0.5) |
|
| 70 |
coordinates(knots)=c("x1","x2")
|
|
| 71 |
proj4string(knots)=proj4string(roi) |
|
| 72 |
knots=knots[!is.na(overlay(knots,roi))] |
|
| 73 |
|
|
| 74 |
|
|
| 75 |
###########################################################3 |
|
| 76 |
#### interpolation function |
|
| 77 |
|
|
| 78 |
interp<-function(parms,niter=100,istart=60,ithin=1){
|
|
| 79 |
## load variables from parameter list |
|
| 80 |
idate=as.Date(parms$date) |
|
| 81 |
ivar=parms$variable |
|
| 82 |
type=parms$type |
|
| 83 |
model=parms$model |
|
| 84 |
|
|
| 85 |
## subset single date from data |
|
| 86 |
td=d[d$date==idate,] |
|
| 87 |
td=data.frame(cast(td,id+fit~variable,value="value")) |
|
| 88 |
td$elev=stroi$elev[match(td$id,stroi$id)] |
|
| 89 |
td[c("lon","lat")]=stroi@data[match(td$id,stroi$id),c("lon","lat")]
|
|
| 90 |
td$intercept=1 |
|
| 91 |
|
|
| 92 |
## drop any points without coordinates |
|
| 93 |
td=td[!is.na(td$lat)&!is.na(td$lon),] |
|
| 94 |
|
|
| 95 |
########################### |
|
| 96 |
## apply climate correction |
|
| 97 |
if(type=="anom"){
|
|
| 98 |
cppt=brick("data/prism/prism_climate.nc",varname="ppt")
|
|
| 99 |
cppt=subset(cppt,subset=as.numeric(format(idate,"%m"))) |
|
| 100 |
cppt[cppt==-99.99]=NA |
|
| 101 |
td$cppt=as.numeric(extract(cppt,td[,c("lon","lat")],method="bilinear"))
|
|
| 102 |
## Tmax |
|
| 103 |
ctmax=brick("data/prism/prism_climate.nc",varname="tmax")
|
|
| 104 |
ctmax=subset(ctmax,subset=as.numeric(format(idate,"%m"))) |
|
| 105 |
ctmax[ctmax==-99.99]=NA |
|
| 106 |
td$ctmax=as.numeric(extract(ctmax,td[,c("lon","lat")],method="bilinear"))
|
|
| 107 |
## Tmin |
|
| 108 |
ctmin=brick("data/prism/prism_climate.nc",varname="tmin")
|
|
| 109 |
ctmin=subset(ctmin,subset=as.numeric(format(idate,"%m"))) |
|
| 110 |
ctmin[ctmin==-99.99]=NA |
|
| 111 |
td$ctmin=as.numeric(extract(ctmin,td[,c("lon","lat")],method="bilinear"))
|
|
| 112 |
## Calculate anomalies |
|
| 113 |
td$atmax=td$tmax-td$ctmax |
|
| 114 |
td$atmin=td$tmin-td$ctmin |
|
| 115 |
## Scale for precipitation (add 1 to climate to avoid dividing by 0, will be subtracted off later) |
|
| 116 |
## Add 1 to final value to avoid transformation troubles (geoR will only transform if >0 |
|
| 117 |
td$appt=(td$ppt/(ifelse(td$cppt<0,0,td$cppt)+1))+1 |
|
| 118 |
} |
|
| 119 |
|
|
| 120 |
### Add region flag to data |
|
| 121 |
td$region=ifelse(!is.na(overlay(SpatialPoints(td[,c("lon","lat")]),roi)),"roi","roib")
|
|
| 122 |
td=td[!is.na(overlay(SpatialPoints(td[,c("lon","lat")]),roib)),]
|
|
| 123 |
|
|
| 124 |
#### Extract data for validation |
|
| 125 |
fd=td[td$fit&td$region=="roi",] #fitting dataset |
|
| 126 |
pd=td[!td$fit&td$region=="roi",] #training dataset |
|
| 127 |
od=td[!td$fit&td$region!="roi",] #stations outside roi |
|
| 128 |
|
|
| 129 |
|
|
| 130 |
### drop missing values |
|
| 131 |
fd=fd[!is.na(fd[,as.character(ivar)]),] #&!is.na(fd$tmin) |
|
| 132 |
|
|
| 133 |
### define the formula |
|
| 134 |
if(type=="raw"&ivar=="tmax") f1=formula(tmax~intercept) |
|
| 135 |
if(type=="anom"&ivar=="tmax"&model=="intercept") f1=formula(atmax~intercept) |
|
| 136 |
if(type=="anom"&ivar=="tmax"&model=="full") f1=formula(atmax~lon+lat+elev) |
|
| 137 |
|
|
| 138 |
### Run the model |
|
| 139 |
t1=Sys.time() |
|
| 140 |
m1=spLM(f1,data=fd,coords=as.matrix(fd[,c("lon","lat")]),
|
|
| 141 |
starting=list("phi"=2.5,"sigma.sq"=4, "tau.sq"=1),
|
|
| 142 |
sp.tuning=list("phi"=0.8, "sigma.sq"=0.1, "tau.sq"=0.2),
|
|
| 143 |
priors=list("phi.Unif"=c(0.1, 4), "sigma.sq.IG"=c(2, 1),
|
|
| 144 |
"tau.sq.IG"=c(2, 1)), |
|
| 145 |
cov.model="exponential", |
|
| 146 |
knots=coordinates(knots), |
|
| 147 |
n.samples=niter, verbose=TRUE, n.report=1000) |
|
| 148 |
t2=Sys.time() |
|
| 149 |
|
|
| 150 |
|
|
| 151 |
#m1s=mcmc(m1$p.samples) |
|
| 152 |
#summary(m1s) |
|
| 153 |
#xyplot(m1s,scales=list(y=list(rot=0)),main="Posterior Samples of Model Parameters") |
|
| 154 |
|
|
| 155 |
### Run the model |
|
| 156 |
#q=3 |
|
| 157 |
#nltr=q*(q-1)/2+q |
|
| 158 |
|
|
| 159 |
#m1=spMvLM(list(atmin~1,atmax~1),data=fd,coords=as.matrix(fd[,c("lon","lat")]),
|
|
| 160 |
# starting=list("phi"=0.6,"sigma.sq"=1, "tau.sq"=1),
|
|
| 161 |
# sp.tuning=list("phi"=0.01, "sigma.sq"=0.05, "tau.sq"=0.05),
|
|
| 162 |
# priors=list("phi.Unif"=c(0.1, 3), "sigma.sq.IG"=c(2, 1),
|
|
| 163 |
# "tau.sq.IG"=c(2, 1)), |
|
| 164 |
# cov.model="exponential", |
|
| 165 |
# knots=coordinates(knots), |
|
| 166 |
# n.samples=100, verbose=TRUE, n.report=100) |
|
| 167 |
|
|
| 168 |
#save to play with later |
|
| 169 |
#save(m1,file="m1.Rdata") |
|
| 170 |
|
|
| 171 |
|
|
| 172 |
#load("m1.Rdata")
|
|
| 173 |
|
|
| 174 |
############################## |
|
| 175 |
#### make predictions |
|
| 176 |
|
|
| 177 |
### Prediction grid |
|
| 178 |
# pgrid=data.frame(coordinates(ctmax)) |
|
| 179 |
# ## crop to bbox |
|
| 180 |
# pgrid=pgrid[pgrid$x>=bbox(roi)[1,1]&pgrid$x<=bbox(roi)[1,2] |
|
| 181 |
# &pgrid$y<=bbox(roi)[2,2]&pgrid$y>=bbox(roi)[2,1],] |
|
| 182 |
# ## Crop to ROI polygon? |
|
| 183 |
# #pgrid=pgrid[!is.na(overlay(SpatialPoints(pgrid),roi)),] |
|
| 184 |
# rownames(pgrid)=1:nrow(pgrid) |
|
| 185 |
# ncluster=200 |
|
| 186 |
# pgrid$cluster=cut(as.numeric(rownames(pgrid)),ncluster,labels=1:ncluster) |
|
| 187 |
# ## predict for the full grid |
|
| 188 |
# m1pg=lapply(1:ncluster,function(i){
|
|
| 189 |
# print(paste("Starting cluster:",i))
|
|
| 190 |
# m1pg=spPredict(m1,pred.coords=pgrid[pgrid$cluster==i,c("x","y")],
|
|
| 191 |
# pred.covars=cbind(intercept=rep(1,nrow(pgrid[pgrid$cluster==i,]))),start=istart,thin=ithin) |
|
| 192 |
# ## Generate summaries of y.pred |
|
| 193 |
# ty=t(apply(m1pg$y.pred,1,function(i) c(mean=mean(i),sd=sd(i),quantile(i,c(0.025,0.5,0.975))))) |
|
| 194 |
# colnames(ty)=paste("y.",c("mean","sd","Q2.5","Q50","Q97.5"),sep="")
|
|
| 195 |
# ## Generate summaries of w.pred |
|
| 196 |
# tw=t(apply(m1pg$w.pred,1,function(i) c(mean=mean(i),sd=sd(i),quantile(i,c(0.025,0.5,0.975))))) |
|
| 197 |
# colnames(tw)=paste("w.",c("mean","sd","Q2.5","Q50","Q97.5"),sep="")
|
|
| 198 |
# return(cbind(ty,tw)) |
|
| 199 |
# }) |
|
| 200 |
# m1pgs=do.call(rbind.data.frame,m1pg) |
|
| 201 |
|
|
| 202 |
|
|
| 203 |
########### Predict only to validation locations |
|
| 204 |
m1p=spPredict(m1,pred.coords=pd[,c("lon","lat")],pred.covars=model.matrix(lm(f1,data=pd)),start=istart,thin=ithin)#,
|
|
| 205 |
|
|
| 206 |
m1p.y=mcmc(t(m1p$y.pred),start=istart,thin=ithin) |
|
| 207 |
m1p.w=mcmc(t(m1$sp.effects.knots),start=istart,thin=ithin) |
|
| 208 |
m1p.ys=t(apply(m1p.y,2,quantile,c(0.025,0.5,0.975),na.rm=T)) |
|
| 209 |
m1p.ws=t(apply(m1p.w,2,quantile,c(0.025,0.5,0.975),na.rm=T)) |
|
| 210 |
|
|
| 211 |
### recover original scale if necessary |
|
| 212 |
resp=as.character(attr(terms.formula(f1),"variables"))[2] |
|
| 213 |
## if modeling anomalies, add the climate back on |
|
| 214 |
if(type=="anom") {
|
|
| 215 |
pd2=cbind.data.frame(pd,aQ2.5=m1p.ys[,"2.5%"],aQ50=m1p.ys[,"50%"],aQ97.5=m1p.ys[,"97.5%"]) |
|
| 216 |
# FIXME! ctmax below needs to be made general for other variables! |
|
| 217 |
pd2$Q2.5=pd2$ctmax+pd2$aQ2.5 |
|
| 218 |
pd2$Q50=pd2$ctmax+pd2$aQ50 |
|
| 219 |
pd2$Q97.5=pd2$ctmax+pd2$aQ97.5 |
|
| 220 |
} |
|
| 221 |
## if modeling raw values, don't add the climate back on |
|
| 222 |
if(type!="anom") {
|
|
| 223 |
pd2=cbind.data.frame(pd,Q2.5=m1p.ys[,"2.5%"],Q50=m1p.ys[,"50%"],Q97.5=m1p.ys[,"97.5%"]) |
|
| 224 |
} |
|
| 225 |
|
|
| 226 |
|
|
| 227 |
### save model output |
|
| 228 |
save(m1,m1p,pd2,file=paste("output/modeloutput_",
|
|
| 229 |
gsub("-","",idate),"_",ivar,"_",type,"_",model,".Rdata",sep=""))
|
|
| 230 |
|
|
| 231 |
|
|
| 232 |
|
|
| 233 |
#################################### |
|
| 234 |
#### Accuracy Assement |
|
| 235 |
m1.dic=spDiag(m1,start=istart,thin=ithin);m1.dic |
|
| 236 |
m1.accuracy=accuracy(pd2[,as.character(ivar)],pd2$Q50,ppt=ivar=="ppt")$summary |
|
| 237 |
|
|
| 238 |
#### Write summary file |
|
| 239 |
m1.summary=cbind.data.frame(date=idate,model=model,var=ivar,type=type, |
|
| 240 |
formula=paste(terms(f1),collapse=" "), |
|
| 241 |
t(m1.dic$DIC),t(m1.accuracy),n.iter=niter,runtime.hours=as.numeric(difftime(t1,t2,units="hours"))) |
|
| 242 |
write.csv(m1.summary,file=paste("output/modeloutput_summary_",gsub("-","",idate),"_",ivar,"_",type,".csv",sep=""))
|
|
| 243 |
|
|
| 244 |
################################################## |
|
| 245 |
### some summary plots |
|
| 246 |
pdf(width=11,height=8.5,file=paste("output/modeloutput_",gsub("-","",idate),"_",ivar,"_",type,".pdf",sep=""))
|
|
| 247 |
|
|
| 248 |
## Show knots and stations |
|
| 249 |
plot(roib);axis(1);axis(2) |
|
| 250 |
plot(roi,add=T) |
|
| 251 |
#points(knots,pch=3,cex=.5) |
|
| 252 |
points(unique(fd[,c('lon','lat')]),cex=.8,pch=16,col="red")
|
|
| 253 |
points(unique(pd[,c('lon','lat')]),cex=.5,pch=16,col="blue")
|
|
| 254 |
points(unique(od[,c('lon','lat')]),cex=.5,pch=16,col="black")
|
|
| 255 |
legend(-118,42,legend=c("Fitting","Validation","Border Stations"),pch=c(16,16,16),col=c("red","blue","black"),bg="white")
|
|
| 256 |
|
|
| 257 |
### Chain behavior |
|
| 258 |
m1s=mcmc(m1$p.samples) |
|
| 259 |
print(xyplot(m1s,scales=list(y=list(rot=0)),main="All Posterior Samples of Model Parameters")) |
|
| 260 |
m1s=mcmc(m1$p.samples,start=istart,thin=ithin) |
|
| 261 |
print(xyplot(m1s,scales=list(y=list(rot=0)),main="Post-burnin, thinned, Posterior Samples of Model Parameters")) |
|
| 262 |
|
|
| 263 |
|
|
| 264 |
### Pred vs. obs plots |
|
| 265 |
plot(pd2[,as.character(ivar)],pd2$Q50,pch=16,cex=.8,ylim=quantile(c(pd2$Q2.5,pd2$Q97.5),c(0.01,0.995)),xlim=quantile(c(pd2$Q2.5,pd2$Q97.5),c(0.01,0.995))) |
|
| 266 |
segments(pd2[,as.character(ivar)],pd2$Q2.5,pd2[,as.character(ivar)],pd2$Q97.5,col="grey") |
|
| 267 |
points(pd2[,as.character(ivar)],pd2$Q50,pch=16,cex=.8);abline(0,1,col="red") #xlim=c(0,10) |
|
| 268 |
|
|
| 269 |
### Maps |
|
| 270 |
par(mfrow=c(1,3)) |
|
| 271 |
## observed |
|
| 272 |
obs.surf <- mba.surf(cbind(fd[,c("lon","lat")],fd$tmax), no.X=100, no.Y=100, extend=TRUE)$xyz.est
|
|
| 273 |
image(obs.surf, xaxs = "r", yaxs = "r", main="Observed response",asp=1) |
|
| 274 |
points(fd[,c("lon","lat")])
|
|
| 275 |
plot(roi,add=T) |
|
| 276 |
#contour(obs.surf, add=T) |
|
| 277 |
plot(roi,add=T,border="darkgreen",lwd=3) |
|
| 278 |
## spatial effects |
|
| 279 |
w.pred.surf <- |
|
| 280 |
mba.surf(cbind(coordinates(knots), m1p.ws[,3]), no.X=100, no.Y=100, extend=TRUE)$xyz.est |
|
| 281 |
image(w.pred.surf, xaxs = "r", yaxs = "r", main="Spatial Effects",asp=1) |
|
| 282 |
#points(pd[,c("lon","lat")], pch=1, cex=1)
|
|
| 283 |
points(knots, pch=3, cex=1) |
|
| 284 |
#contour(w.pred.surf, add=T) |
|
| 285 |
plot(roi,add=T,border="darkgreen",lwd=3) |
|
| 286 |
legend(1.5,2.5, legend=c("Obs.", "Knots", "Pred."),
|
|
| 287 |
pch=c(1,3,19), bg="white") |
|
| 288 |
## fitted |
|
| 289 |
y.pred.surf <- |
|
| 290 |
mba.surf(cbind(pd2[,c("lon","lat")], pd2$Q50), no.X=100, no.Y=100, extend=TRUE)$xyz.est
|
|
| 291 |
image(y.pred.surf, xaxs = "r", yaxs = "r", main="Predicted response",asp=1) |
|
| 292 |
points(pd[,c("lon","lat")], pch=1, cex=1)
|
|
| 293 |
#points(knots, pch=3, cex=1) |
|
| 294 |
#contour(y.pred.surf, add=T) |
|
| 295 |
plot(roi,add=T,border="darkgreen",lwd=3) |
|
| 296 |
legend(1.5,2.5, legend=c("Obs.", "Knots", "Pred."),
|
|
| 297 |
pch=c(1,3,19), bg="white") |
|
| 298 |
|
|
| 299 |
dev.off() |
|
| 300 |
|
|
| 301 |
### Return objects |
|
| 302 |
return(m1.summary) |
|
| 303 |
} |
|
| 304 |
|
|
| 305 |
|
|
| 306 |
|
|
| 307 |
########################################################### |
|
| 308 |
### define models to run |
|
| 309 |
ms=expand.grid(variable="tmax",type=c("anom","raw"),date=dates,model=c("intercept","full"))
|
|
| 310 |
## drop some |
|
| 311 |
ms[!ms$type=="raw"&ms$model=="intercept",] |
|
| 312 |
|
|
| 313 |
|
|
| 314 |
msl=apply(ms,1,as.list) |
|
| 315 |
|
|
| 316 |
|
|
| 317 |
### test it out |
|
| 318 |
parms=msl[[9]] |
|
| 319 |
interp(parms,niter=5000,istart=2000,ithin=3) |
|
| 320 |
|
|
| 321 |
#mresults=lapply(msl[1:3],interp) |
|
| 322 |
|
|
| 323 |
### run it! |
|
| 324 |
mresults =mclapply(msl,interp,niter=1000,istart=500,ithin=1,mc.cores=1) |
|
| 325 |
|
|
| 326 |
### drop any errors |
|
| 327 |
mresults=mresults[!sapply(mresults,function(x) grepl("Error",x[[1]]))]
|
|
| 328 |
|
|
| 329 |
res=do.call(rbind.data.frame,mresults) |
|
| 330 |
|
|
| 331 |
save(mresults,res,file="output/mresults.Rdata") |
|
| 332 |
|
|
| 333 |
q("no")
|
|
| 334 |
|
|
| 335 |
|
|
| 336 |
|
|
| 337 |
|
|
| 338 |
|
|
| climate/research/LST_landcover_exploration.R | ||
|---|---|---|
| 1 |
library(sp) |
|
| 2 |
library(spgrass6) |
|
| 3 |
library(rgdal) |
|
| 4 |
library(reshape) |
|
| 5 |
library(ncdf4) |
|
| 6 |
library(geosphere) |
|
| 7 |
library(rgeos) |
|
| 8 |
library(multicore) |
|
| 9 |
library(raster) |
|
| 10 |
library(lattice) |
|
| 11 |
library(rgl) |
|
| 12 |
library(hdf5) |
|
| 13 |
library(rasterVis) |
|
| 14 |
library(heR.Misc) |
|
| 15 |
library(spBayes) |
|
| 16 |
|
|
| 17 |
|
|
| 18 |
X11.options(type="X11") |
|
| 19 |
|
|
| 20 |
ncores=20 #number of threads to use |
|
| 21 |
|
|
| 22 |
|
|
| 23 |
### copy lulc data to litoria |
|
| 24 |
setwd("data/lulc")
|
|
| 25 |
system("scp atlas:/home/parmentier/data_Oregon_stations/W_Layer* .")
|
|
| 26 |
|
|
| 27 |
|
|
| 28 |
setwd("/home/adamw/acrobates/projects/interp")
|
|
| 29 |
|
|
| 30 |
projs=CRS("+proj=lcc +lat_1=43 +lat_2=45.5 +lat_0=41.75 +lon_0=-120.5 +x_0=400000 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs")
|
|
| 31 |
months=format(ISOdate(2004,1:12,1),"%b") |
|
| 32 |
|
|
| 33 |
### load station data, subset to stations in region, and transform to sinusoidal |
|
| 34 |
load("data/ghcn/roi_ghcn.Rdata")
|
|
| 35 |
load("data/allstations.Rdata")
|
|
| 36 |
|
|
| 37 |
d2=d[d$variable=="tmax"&d$date>=as.Date("2000-01-01"),]
|
|
| 38 |
d2=d2[,-grep("variable",colnames(d2)),]
|
|
| 39 |
st2=st[st$id%in%d$id,] |
|
| 40 |
st2=spTransform(st2,projs) |
|
| 41 |
d2[,c("lon","lat")]=coordinates(st2)[match(d2$id,st2$id),]
|
|
| 42 |
d2$elev=st2$elev[match(d2$id,st2$id)] |
|
| 43 |
d2$month=format(d2$date,"%m") |
|
| 44 |
#d2$value=d2$value/10 #convert to mm |
|
| 45 |
|
|
| 46 |
|
|
| 47 |
## load topographical data |
|
| 48 |
topo=brick(as.list(list.files("data/topography",pattern="rst$",full=T)))
|
|
| 49 |
topo=calc(topo,function(x) ifelse(x<0,NA,x)) |
|
| 50 |
names(topo)=c("aspect","dem","slope")
|
|
| 51 |
colnames(topo@data@values)=c("aspect","dem","slope")
|
|
| 52 |
projection(topo)=projs |
|
| 53 |
NS=sin(subset(topo,subset="slope")*pi/180)*cos(subset(topo,subset="aspect")*pi/180);names(NS)="northsouth" |
|
| 54 |
EW=sin(subset(topo,subset="slope")*pi/180)*sin(subset(topo,subset="aspect")*pi/180);names(EW)="eastwest" |
|
| 55 |
slope=sin(subset(topo,subset="slope")*pi/180);names(slope)="slope" |
|
| 56 |
topo2=stack(subset(topo,"dem"),EW,NS,slope) |
|
| 57 |
|
|
| 58 |
## create binned elevation for stratified sampling |
|
| 59 |
demc=quantile(subset(topo,subset="dem")) |
|
| 60 |
demb=calc(subset(topo,subset="dem"),function(x) as.numeric(cut(x,breaks=demc))) |
|
| 61 |
names(demb)="demb" |
|
| 62 |
#topo=brick(list(topo,demb)) |
|
| 63 |
|
|
| 64 |
|
|
| 65 |
### load the lulc data as a brick |
|
| 66 |
lulc=brick(as.list(list.files("data/lulc",pattern="rst$",full=T)))
|
|
| 67 |
#projection(lulc)= |
|
| 68 |
#plot(lulc) |
|
| 69 |
|
|
| 70 |
## Enter lulc types (from Nakaegawa 2011) |
|
| 71 |
lulct=as.data.frame(matrix(c( |
|
| 72 |
"Forest",1, |
|
| 73 |
"Shrub",2, |
|
| 74 |
"Grass",3, |
|
| 75 |
"Crop",4, |
|
| 76 |
"Mosaic",5, |
|
| 77 |
"Urban",6, |
|
| 78 |
"Barren",7, |
|
| 79 |
"Snow",8, |
|
| 80 |
"Wetland",9, |
|
| 81 |
"Water",10),byrow=T,ncol=2,dimnames=list(1:10,c("class","id"))),stringsAsFactors=F)
|
|
| 82 |
colnames(lulc@data@values)=lulct$class[as.numeric(gsub("[a-z]|[A-Z]|[_]|83","",layerNames(lulc)))]
|
|
| 83 |
layerNames(lulc)=lulct$class[as.numeric(gsub("[a-z]|[A-Z]|[_]|83","",layerNames(lulc)))]
|
|
| 84 |
lulc=calc(lulc,function(x) ifelse(is.na(x),0,x)) |
|
| 85 |
projection(lulc)=projs |
|
| 86 |
|
|
| 87 |
### load the LST data |
|
| 88 |
lst=brick(as.list(list.files("data/lst",pattern="rescaled.rst$",full=T)[c(4:12,1:3)]))
|
|
| 89 |
lst=lst-273.15 |
|
| 90 |
colnames(lst@data@values)=format(as.Date(paste("2000-",as.numeric(gsub("[a-z]|[A-Z]|[_]|83","",layerNames(lst))),"-15",sep="")),"%b")
|
|
| 91 |
layerNames(lst)=format(as.Date(paste("2000-",as.numeric(gsub("[a-z]|[A-Z]|[_]|83","",layerNames(lst))),"-15",sep="")),"%b")
|
|
| 92 |
projection(lst)=projs |
|
| 93 |
|
|
| 94 |
|
|
| 95 |
###################################### |
|
| 96 |
## compare LULC with station data |
|
| 97 |
stlulc=extract(lulc,st2) #overlay stations and LULC values |
|
| 98 |
st2$lulc=do.call(c,lapply(apply(stlulc,1,function(x) which.max(x)),function(x) ifelse(is.null(names(x)),NA,names(x)))) |
|
| 99 |
|
|
| 100 |
|
|
| 101 |
### generate sample of points to speed processing |
|
| 102 |
n=10000/length(unique(demb)) |
|
| 103 |
n2=30 #number of knots |
|
| 104 |
s=sampleStratified(demb,size=n,sp=T) |
|
| 105 |
#s=spsample(as(topo,"SpatialGrid"),n=n,type="regular") |
|
| 106 |
s2=spsample(as(topo,"SpatialGrid"),n=n2,type="regular") |
|
| 107 |
|
|
| 108 |
|
|
| 109 |
s=SpatialPointsDataFrame(s,data=cbind.data.frame(extract(topo,s),extract(topo2,s),extract(lulc,s),extract(lst,s))) |
|
| 110 |
### drop areas with no LST data |
|
| 111 |
#s=s[!is.na(s$Aug),] |
|
| 112 |
s=s[apply(s@data,1,function(x) all(!is.na(x))),] |
|
| 113 |
|
|
| 114 |
### add majority rules lulc for exploration |
|
| 115 |
s$lulc=apply(s@data[,colnames(s@data)%in%lulct$class],1,function(x) (colnames(s@data)[colnames(s@data)%in%lulct$class])[which.max(x)]) |
|
| 116 |
|
|
| 117 |
|
|
| 118 |
### spatial regression to fit lulc coefficients |
|
| 119 |
|
|
| 120 |
niter=1250 |
|
| 121 |
mclapply(months,function(m){
|
|
| 122 |
print(paste("################################### Starting month ",m))
|
|
| 123 |
f1=formula(paste(m,"~Shrub+Grass+Crop+Mosaic+Urban+Barren+Snow+Wetland+dem+eastwest+northsouth",sep="")) |
|
| 124 |
m1=spLM(f1,data=s@data,coords=coordinates(s),knots=coordinates(s2), |
|
| 125 |
starting=list("phi"=0.6,"sigma.sq"=1, "tau.sq"=1),
|
|
| 126 |
sp.tuning=list("phi"=0.01, "sigma.sq"=0.05, "tau.sq"=0.05),
|
|
| 127 |
priors=list("phi.Unif"=c(0.3, 3), "sigma.sq.IG"=c(2, 1), "tau.sq.IG"=c(2, 1)),
|
|
| 128 |
cov.model="exponential", |
|
| 129 |
n.samples=niter, verbose=TRUE, n.report=100) |
|
| 130 |
assign(paste("mod_",m,sep=""),m1)
|
|
| 131 |
save(list=paste("mod_",m,sep=""),file=paste("output/mod_",m,".Rdata",sep=""))
|
|
| 132 |
}) |
|
| 133 |
|
|
| 134 |
|
|
| 135 |
m1.s=mcmc(t(m1$p.samples),start=round(niter/4),thin=1) |
|
| 136 |
bwplot(value~X2,melt(as.matrix(m1$p.samples)[,!colnames(m1$p.samples)%in%c("(Intercept)","sigma.sq","tau.sq","phi","northsouth","dem","eastwest")]))
|
|
| 137 |
densityplot(m1$p.samples) |
|
| 138 |
|
|
| 139 |
### load oregon boundary for comparison |
|
| 140 |
roi=spTransform(as(readOGR("data/regions/Test_sites/Oregon.shp","Oregon"),"SpatialLines"),projs)
|
|
| 141 |
|
|
| 142 |
|
|
| 143 |
bgyr=colorRampPalette(c("blue","green","yellow","red"))
|
|
| 144 |
pdf("output/lst_lulc.pdf",width=11,height=8.5)
|
|
| 145 |
|
|
| 146 |
### Summary plots of covariates |
|
| 147 |
## LULC |
|
| 148 |
at=seq(0.1,100,length=100) |
|
| 149 |
levelplot(lulc,at=at,col.regions=bgyr(length(at)), |
|
| 150 |
main="Land Cover Classes",sub="Sub-pixel %")+ |
|
| 151 |
layer(sp.lines(roi, lwd=1.2, col='black')) |
|
| 152 |
|
|
| 153 |
## TOPO |
|
| 154 |
at=unique(quantile(as.matrix(subset(topo2,c("eastwest","northsouth","slope"))),seq(0,1,length=100),na.rm=T))
|
|
| 155 |
levelplot(subset(topo2,c("eastwest","northsouth","slope")),at=at,col.regions=bgyr(length(at)),
|
|
| 156 |
main="Topographic Variables",sub="")+ |
|
| 157 |
layer(sp.lines(roi, lwd=1.2, col='black')) |
|
Also available in: Unified diff
Added initial version of LST_Landcover exploration (not finished). Also moved prior version of interpolation procedure (bayesian krig) into repository.