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################## Validation and analyses of results #######################################
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############################ Covariate production for a given tile/region ##########################################
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#This script examines inputs and outputs from the interpolation step.
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#Part 1: Script produces plots for every selected date
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#Part 2: Examine
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#AUTHOR: Benoit Parmentier
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#DATE: 02/22/2013
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#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#???--
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##Comments and TODO:
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#Separate inteprolation results analyses from covariates analyses
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##################################################################################################
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###Loading R library and packages
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library(RPostgreSQL)
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library(sp) # Spatial pacakge with class definition by Bivand et al.
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library(spdep) # Spatial pacakge with methods and spatial stat. by Bivand et al.
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library(rgdal) # GDAL wrapper for R, spatial utilities
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library(raster)
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library(gtools)
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library(rasterVis)
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library(graphics)
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library(grid)
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library(lattice)
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### Parameters and arguments
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##Paths to inputs and output
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in_path <- "/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/input_data/"
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out_path<- "/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/output_data/"
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infile3<-"covariates__venezuela_region__VE_01292013.tif" #this is an output from covariate script
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setwd(in_path)
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### Functions used in the script
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load_obj <- function(f)
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{
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env <- new.env()
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nm <- load(f, env)[1]
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env[[nm]]
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}
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### PLOTTING RESULTS FROM VENEZUELA INTERPOLATION FOR ANALYSIS
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#Select relevant dates and load R objects created during the interpolation step
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date_selected<-c("20100103")
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gam_fus_mod<-load_obj("gam_fus_mod_365d_GAM_fus5_all_lstd_02202013.RData")
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validation_obj<-load_obj("gam_fus_validation_mod_365d_GAM_fus5_all_lstd_02202013.RData")
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clim_obj<-load_obj("gamclim_fus_mod_365d_GAM_fus5_all_lstd_02202013.RData")
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#determine index position matching date selected
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i_dates<-vector("list",length(date_selected))
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for (i in 1:length(gam_fus_mod)){
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for (j in 1:length(date_selected)){
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if(gam_fus_mod[[i]]$sampling_dat$date==date_selected[j]){
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i_dates[[j]]<-i
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}
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}
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}
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#Examine the first select date add loop or function later
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j=1
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date<-strptime(date_selected[j], "%Y%m%d") # interpolation date being processed
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month<-strftime(date, "%m") # current month of the date being processed
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#Get raster stack of interpolated surfaces
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index<-i_dates[[j]]
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pred_temp<-as.character(gam_fus_mod[[index]]$dailyTmax) #list of files
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rast_pred_temp<-stack(pred_temp) #stack of temperature predictions from models
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#Get validation metrics, daily spdf training and testing stations, monthly spdf station input
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sampling_dat<-gam_fus_mod[[index]]$sampling_dat
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metrics_v<-validation_obj[[index]]$metrics_v
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metrics_s<-validation_obj[[index]]$metrics_s
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data_v<-validation_obj[[index]]$data_v
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data_s<-validation_obj[[index]]$data_s
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data_month<-clim_obj[[index]]$data_month
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#Adding layer LST to the raster stack of covariates
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#The names of covariates can be changed...
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rnames <-c("x","y","lon","lat","N","E","N_w","E_w","elev","slope","aspect","CANHEIGHT","DISTOC")
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lc_names<-c("LC1","LC2","LC3","LC4","LC5","LC6","LC7","LC8","LC9","LC10","LC11","LC12")
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lst_names<-c("mm_01","mm_02","mm_03","mm_04","mm_05","mm_06","mm_07","mm_08","mm_09","mm_10","mm_11","mm_12",
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"nobs_01","nobs_02","nobs_03","nobs_04","nobs_05","nobs_06","nobs_07","nobs_08",
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"nobs_09","nobs_10","nobs_11","nobs_12")
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covar_names<-c(rnames,lc_names,lst_names)
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s_raster<-stack(infile3) #read in the data stack
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names(s_raster)<-covar_names #Assigning names to the raster layers: making sure it is included in the extraction
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LST_month<-paste("mm_",month,sep="") # name of LST month to be matched
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pos<-match("LST",layerNames(s_raster)) #Find the position of the layer with name "LST", if not present pos=NA
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s_raster<-dropLayer(s_raster,pos) # If it exists drop layer
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pos<-match(LST_month,layerNames(s_raster)) #Find column with the current month for instance mm12
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r1<-raster(s_raster,layer=pos) #Select layer from stack
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layerNames(r1)<-"LST"
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#Get mask image!!
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date_proc<-strptime(sampling_dat$date, "%Y%m%d") # interpolation date being processed
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mo<-as.integer(strftime(date_proc, "%m")) # current month of the date being processed
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day<-as.integer(strftime(date_proc, "%d"))
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year<-as.integer(strftime(date_proc, "%Y"))
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datelabel=format(ISOdate(year,mo,day),"%b %d, %Y")
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## Figure 1: LST_TMax_scatterplot
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rmse<-metrics_v$rmse[nrow(metrics_v)]
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rmse_f<-metrics_s$rmse[nrow(metrics_s)]
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png(paste("LST_TMax_scatterplot_",sampling_dat$date,"_",sampling_dat$prop,"_",sampling_dat$run_samp,
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out_prefix,".png", sep=""))
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plot(data_month$TMax,data_month$LST,xlab="Station mo Tmax",ylab="LST mo Tmax")
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title(paste("LST vs TMax for",datelabel,sep=" "))
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abline(0,1)
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nb_point<-paste("n=",length(data_month$TMax),sep="")
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mean_bias<-paste("Mean LST bias= ",format(mean(data_month$LSTD_bias,na.rm=TRUE),digits=3),sep="")
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#Add the number of data points on the plot
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legend("topleft",legend=c(mean_bias,nb_point),bty="n")
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dev.off()
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## Figure 2: Daily_tmax_monthly_TMax_scatterplot
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png(paste("Daily_tmax_monthly_TMax_scatterplot_",sampling_dat$date,"_",sampling_dat$prop,"_",sampling_dat$run_samp,
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out_prefix,".png", sep=""))
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plot(dailyTmax~TMax,data=data_s,xlab="Mo Tmax",ylab=paste("Daily for",datelabel),main="across stations in VE")
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nb_point<-paste("ns=",length(data_s$TMax),sep="")
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nb_point2<-paste("ns_obs=",length(data_s$TMax)-sum(is.na(data_s[[y_var_name]])),sep="")
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nb_point3<-paste("n_month=",length(data_month$TMax),sep="")
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#Add the number of data points on the plot
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legend("topleft",legend=c(nb_point,nb_point2,nb_point3),bty="n",cex=0.8)
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dev.off()
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## Figure 3: Predicted_tmax_versus_observed_scatterplot
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#This is for mod_kr!! add other models later...
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png(paste("Predicted_tmax_versus_observed_scatterplot_",sampling_dat$date,"_",sampling_dat$prop,"_",sampling_dat$run_samp,
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out_prefix,".png", sep=""))
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plot(data_s$mod_kr~data_s[[y_var_name]],xlab=paste("Actual daily for",datelabel),ylab="Pred daily")
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#plot(data_v$mod_kr~data_v[[y_var_name]],xlab=paste("Actual daily for",datelabel),ylab="Pred daily")
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abline(0,1)
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title(paste("Predicted_tmax_versus_observed_scatterplot for",datelabel,sep=" "))
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nb_point1<-paste("ns_obs=",length(data_s$TMax)-sum(is.na(data_s[[y_var_name]])),sep="")
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rmse_str1<-paste("RMSE= ",format(rmse,digits=3),sep="")
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rmse_str2<-paste("RMSE_f= ",format(rmse_f,digits=3),sep="")
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#Add the number of data points on the plot
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legend("topleft",legend=c(nb_point1,rmse_str1,rmse_str2),bty="n",cex=0.8)
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dev.off()
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## Figure 4: delta surface and bias
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#Plot bias,delta and prediction?
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#To do
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#Delta surface
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#png(paste("Delta_surface_LST_TMax_",sampling_dat$date[i],"_",sampling_dat$prop[i],
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# "_",sampling_dat$run_samp[i],out_prefix,".png", sep=""))
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#surface(fitdelta,col=rev(terrain.colors(100)),asp=1,main=paste("Interpolated delta for",datelabel,sep=" "))
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#dev.off()
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#
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#bias_d_rast<-raster("fusion_bias_LST_20100103_30_1_10d_GAM_fus5_all_lstd_02082013.rst")
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#plot(bias_d_rast)
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## Figure 5: prediction raster images
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png(paste("Raster_prediction_",sampling_dat$date,"_",sampling_dat$prop,"_",sampling_dat$run_samp,
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out_prefix,".png", sep=""))
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#paste(metrics_v$pred_mod,format(metrics_v$rmse,digits=3),sep=":")
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names(rast_pred_temp)<-paste(metrics_v$pred_mod,format(metrics_v$rmse,digits=3),sep=":")
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plot(rast_pred_temp)
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dev.off()
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## Figure 6: training and testing stations used
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plot(raster(rast_pred_temp,layer=5))
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plot(data_s,col="black",cex=1.2,pch=4,add=TRUE)
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plot(data_v,col="red",cex=1.2,pch=2,add=TRUE)
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## Figure 7: monthly stations used
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plot(raster(rast_pred_temp,layer=5))
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plot(data_month,col="black",cex=1.2,pch=4,add=TRUE)
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title("Monthly ghcn station in Venezuela for January")
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## Summarize information for the day:
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# ################
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# #PART 2: Region Covariate analyses ###
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# ################
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#
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# # This should be in a separate script to analyze covariates from region.
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#
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# #MAP1:Study area with LC mask and tiles/polygon outline
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#
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#
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# #MAP 2: plotting land cover in the study region:
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#
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# l1<-"LC1,Evergreen/deciduous needleleaf trees"
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# l2<-"LC2,Evergreen broadleaf trees"
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# l3<-"LC3,Deciduous broadleaf trees"
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# l4<-"LC4,Mixed/other trees"
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# l5<-"LC5,Shrubs"
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# l6<-"LC6,Herbaceous vegetation"
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# l7<-"LC7,Cultivated and managed vegetation"
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# l8<-"LC8,Regularly flooded shrub/herbaceous vegetation"
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# l9<-"LC9,Urban/built-up"
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# l10<-"LC10,Snow/ice"
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# l11<-"LC11,Barren lands/sparse vegetation"
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# l12<-"LC12,Open water"
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# lc_names_str<-c(l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12)
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#
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# names(lc_reg_s)<-lc_names_str
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#
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# png(paste("LST_TMax_scatterplot_",sampling_dat$date[i],"_",sampling_dat$prop[i],"_",sampling_dat$run_samp[i], out_prefix,".png", sep=""))
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# plot(modst$TMax,sta_tmax_from_lst,xlab="Station mo Tmax",ylab="LST mo Tmax",main=paste("LST vs TMax for",datelabel,sep=" "))
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# abline(0,1)
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# nb_point<-paste("n=",length(modst$TMax),sep="")
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# mean_bias<-paste("LST bigrasas= ",format(mean(modst$LSTD_bias,na.rm=TRUE),digits=3),sep="")
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# #Add the number of data points on the plot
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# legend("topleft",legend=c(mean_bias,nb_point),bty="n")
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# dev.off()
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#
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# #Map 3: Elevation and LST in January
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# tmp_s<-stack(LST,elev_1)
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# png(paste("LST_elev_",sampling_dat$date[i],"_",sampling_dat$prop[i],"_",sampling_dat$run_samp[i], out_prefix,".png", sep=""))
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# plot(tmp_s)
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#
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# #Map 4: LST climatology per month
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#
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# names_tmp<-c("mm_01","mm_02","mm_03","mm_04","mm_05","mm_06","mm_07","mm_08","mm_09","mm_10","mm_11","mm_12")
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# LST_s<-subset(s_raster,names_tmp)
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# names_tmp<-c("nobs_01","nobs_02","nobs_03","nobs_04","nobs_05","nobs_06","nobs_07","nobs_08",
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# "nobs_09","nobs_10","nobs_11","nobs_12")
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# LST_nobs<-subset(s_raster,names_tmp)
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#
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# LST_nobs<-mask(LST_nobs,LC_mask,filename="test2.tif")
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# LST_s<-mask(LST_s,LC_mask,filename="test3.tif")
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# c("Jan","Feb")
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# plot(LST_s)
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# plot(LST_nobs)
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#
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# #Map 5: LST and TMax
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#
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# #note differnces in patternin agricultural areas and
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# min_values<-cellStats(LST_s,"min")
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# max_values<-cellStats(LST_s,"max")
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# mean_values<-cellStats(LST_s,"mean")
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# sd_values<-cellStats(LST_s,"sd")
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# #median_values<-cellStats(molst,"median") Does not extist
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# statistics_LST_s<-cbind(min_values,max_values,mean_values,sd_values) #This shows that some values are extremes...especially in October
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# LST_stat_data<-as.data.frame(statistics_LST_s)
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# names(LST_stat_data)<-c("min","max","mean","sd")
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# # Statistics for number of valid observation stack
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# min_values<-cellStats(nobslst,"min")
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# max_values<-cellStats(nobslst,"max")
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# mean_values<-cellStats(nobslst,"mean")
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# sd_values<-cellStats(nobslst,"sd")
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# statistics_LSTnobs_s<-cbind(min_values,max_values,mean_values,sd_values) #This shows that some values are extremes...especially in October
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# LSTnobs_stat_data<-as.data.frame(statistics_LSTnobs_s)
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#
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# X11(width=12,height=12)
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# #Plot statiscs (mean,min,max) for monthly LST images
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# plot(1:12,LST_stat_data$mean,type="b",ylim=c(-15,60),col="black",xlab="month",ylab="tmax (degree C)")
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# lines(1:12,LST_stat_data$min,type="b",col="blue")
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# lines(1:12,LST_stat_data$max,type="b",col="red")
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# text(1:12,LST_stat_data$mean,rownames(LST_stat_data),cex=1,pos=2)
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#
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# legend("topleft",legend=c("min","mean","max"), cex=1.5, col=c("blue","black","red"),
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# lty=1)
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# title(paste("LST statistics for Oregon", "2010",sep=" "))
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# savePlot("lst_statistics_OR.png",type="png")
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#
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# #Plot number of valid observations for LST
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# plot(1:12,LSTnobs_stat_data$mean,type="b",ylim=c(0,280),col="black",xlab="month",ylab="tmax (degree C)")
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# lines(1:12,LSTnobs_stat_data$min,type="b",col="blue")
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# lines(1:12,LSTnobs_stat_data$max,type="b",col="red")
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# text(1:12,LSTnobs_stat_data$mean,rownames(LSTnobs_stat_data),cex=1,pos=2)
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#
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# legend("topleft",legend=c("min","mean","max"), cex=1.5, col=c("blue","black","red"),
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# lty=1)
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# title(paste("LST number of valid observations for Oregon", "2010",sep=" "))
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# savePlot("lst_nobs_OR.png",type="png")
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#
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# plot(data_month$TMax,add=TRUE)
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#
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# ### Map 6: station in the region
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#
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# plot(tmax_predicted)
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# plot(data_s,col="black",cex=1.2,pch=4,add=TRUE)
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# plot(data_v,col="blue",cex=1.2,pch=2,add=TRUE)
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#
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# plot(tmax_predicted)
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# plot(data_month,col="black",cex=1.2,pch=4,add=TRUE)
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# title("Monthly ghcn station in Venezuela for 2000-2010")
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#
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#### End of script ####
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