CCcode_multiple.md

title	author	date	output
MapBiomas Classification - Multiple	James D.A. Millington	July 2018	html_document keep_md code_folding true hide

This script (will eventually) analyses all years for multiple classifications

rm(list=ls())
library(tidyverse)
library(raster)
library(readxl)      #for reading Excel sheets
library(scales)      #useful for ggplotting
library(knitr)
library(rasterVis)   #more useful raster plotting
library(cowplot)     #useful for ggplotting

#raster to xyz  (with help from https://stackoverflow.com/a/19847419)
#sepcify input raster, whether nodata cells should be output, whether a unique cell ID should be added
#return is a matrix. note format is row (Y) then col (X)
extractXYZ <- function(raster, nodata = FALSE, addCellID = TRUE){
  
  vals <- raster::extract(raster, 1:ncell(raster))   #specify raster otherwise dplyr used
  xys <- rowColFromCell(raster,1:ncell(raster))
  combine <- cbind(xys,vals)
  
  if(addCellID){
    combine <- cbind(1:length(combine[,1]), combine)
  }
  
  if(!nodata){
    combine <- combine[!rowSums(!is.finite(combine)),]  #from https://stackoverflow.com/a/15773560
  }
  
  return(combine)
}


getLCs <- function(data)
{
  #calculates proportion of each LC in the muni (ignoring NAs, help from https://stackoverflow.com/a/44290753)
  data %>%
    group_by(muniID) %>%
    dplyr::summarise(LC1 = round(sum(map == 1, na.rm = T) / sum(!is.na(map)), 3),
                     LC2 = round(sum(map == 2, na.rm = T) / sum(!is.na(map)), 3),
                     LC3 = round(sum(map == 3, na.rm = T) / sum(!is.na(map)), 3),
                     LC4 = round(sum(map == 4, na.rm = T) / sum(!is.na(map)), 3),
                     LC5 = round(sum(map == 5, na.rm = T) / sum(!is.na(map)), 3),
                     NonNAs = sum(!is.na(map)),
                     NAs = sum(is.na(map))
    ) -> LCs

  return(LCs)
}

unzip(zipfile="MapBiomas_23_ASCII_unclassified_allYears.zip")  # unzip all files 

mb_data <- read_csv("LandCover Data - MapBiomas - Collection 2.3 - 2018.01.04 Municipios.csv")  

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   Municípios = col_character(),
##   Estados = col_character(),
##   `Classe Nivel 1` = col_character(),
##   `Classe Nivel 2` = col_character(),
##   `Classe Nivel 3` = col_character()
## )

## See spec(...) for full column specifications.

unzip(zipfile="sim10_BRmunis_latlon_5km_2018-04-27.zip",files="sim10_BRmunis_latlon_5km_2018-04-27.asc",exdir="ASCII")  # unzip file 
munis.r <- raster("ASCII/sim10_BRmunis_latlon_5km_2018-04-27.asc")  #do this with zip file

#extract cell values to table format
munis.t <- extractXYZ(munis.r, addCellID = F)
munis.t <- as.data.frame(munis.t)
munis.t <- plyr::rename(munis.t, c("vals" = "muniID"))

Specify classifications and years to examine. Classifications should be the names of Sheets in the Classifications Excel file. Years should be between 2000 and 2015

#classifications to loop through 
cls <- c("PastureA", "PastureB", "PastureC")

yrls <- seq(2000,2015,1)

Loop over classifications and years

#lists to hold data tables 
CData_ls <- vector('list', length(cls))
CDataW_ls <- vector('list', length(cls))
SDataW_ls <- vector('list', length(cls))
Stotals_ls <- vector('list', length(cls))
SDataW_Adj_ls <- vector('list', length(cls))
mapStack_ls <- vector('list', length(cls))

names(CData_ls) <- cls
names(CDataW_ls) <- cls
names(SDataW_ls) <- cls
names(Stotals_ls) <- cls
names(SDataW_Adj_ls) <- cls
names(mapStack_ls) <- cls

#loop over classifications
for(i in seq_along(cls)){
  
  classification <- read_excel("MapBiomas_CRAFTY_classifications.xlsx", sheet = cls[i], range="B2:C21", col_names=F) 
  
  #reset mapStack for this Classification
  mapStack <- stack()

  #loop over years  
  for(j in seq_along(yrls)){

  #print(paste0("Classification: ",cls[i],", Year: ",yrls[j]))
  
  map <- raster(paste0("ASCII/brazillc_",yrls[j],"_5km_int.txt"))
  map <- reclassify(map, rcl=as.matrix(classification))
  
  
  #add categories for later plotting etc. (see https://stackoverflow.com/a/37214431)
  map <- ratify(map)     #tell R that the map raster is categorical 
  rat <- levels(map)[[1]]    #apply the levels (i.e. categories) 
 
  #not all classes may be present after classification, so conditionally construct labels
  labs <- c()
  if(1 %in% levels(map)[[1]]$ID) { labs <- c(labs, "Nature") }
  if(2 %in% levels(map)[[1]]$ID) { labs <- c(labs, "OtherAgri") }
  if(3 %in% levels(map)[[1]]$ID) { labs <- c(labs, "Agriculture") }
  if(4 %in% levels(map)[[1]]$ID) { labs <- c(labs, "Other") }
  if(5 %in% levels(map)[[1]]$ID) { labs <- c(labs, "Pasture") }
    
  rat$landcover <- labs  
  levels(map) <- rat 
  
  #add to mapStack for later plotting
  mapStack <- stack(map, mapStack)

  #extract cell values to table format
  map.t <- extractXYZ(map, addCellID = F)
  map.t <- as.data.frame(map.t)
  map.t <- plyr::rename(map.t, c("vals" = "map"))

  #so need to join 
  map_munis <- left_join(as.data.frame(munis.t), as.data.frame(map.t), by = c("row" = "row", "col" = "col"))

  #now summarise by muniID
  lcs_map_munis <- getLCs(map_munis)

  #convert cell counts to areas (km2) and add state id
  map_areas_munis <- lcs_map_munis %>%
    mutate(LC1area = round(LC1 * NonNAs) * 25) %>%
    mutate(LC2area = round(LC2 * NonNAs) * 25) %>%
    mutate(LC3area = round(LC3 * NonNAs) * 25) %>%
    mutate(LC4area = round(LC4 * NonNAs) * 25) %>%
    mutate(LC5area = round(LC5 * NonNAs) * 25) %>%
    mutate(state = substr(muniID, 1, 2))

  #drop original cell-count columns (work with area km2 from now on)
  map_areas_munis <- map_areas_munis %>% dplyr::select(-LC1, -LC2, -LC3, -LC4, -LC5, -NonNAs, -NAs)

  #summarise muni areas to state level
  map_areas <- map_areas_munis %>%
    group_by(state) %>%
    dplyr::summarise_at(vars(LC1area:LC5area),sum, na.rm=T) %>%  #use _at so state is not summarised
    mutate_if(is.character, as.integer)

  #gather to long format for union below
  map_areas <- map_areas %>%
    gather(key = LCa, value = area, -state)
  
  #recode LCs for union below
  map_areas <- map_areas %>%
    mutate(LC = if_else(LCa == "LC1area", 1, 
      if_else(LCa == "LC2area", 2,
      if_else(LCa == "LC3area", 3,
      if_else(LCa == "LC4area", 4,
      if_else(LCa == "LC5area", 5, 0)
      )))))

  #add source variable for plotting below (re-order to match map table for union below)
  map_areas <- map_areas %>%
    dplyr::select(-LCa) %>%
    mutate(source = "Map") %>%
    dplyr::select(state, LC, source, area)

  ###Summarise MapBiomas data to the states we are simulating

  #filter to get only the states we want
  mb_areas <- mb_data %>%
    filter(Estados == "TOCANTINS" | 
        Estados == "BAHIA" |
        Estados == "MINAS GERAIS" |
        Estados == "SÃO PAULO" |
        Estados == "PARANÁ" |
        Estados == "SANTA CATARINA" |
        Estados == "RIO GRANDE DO SUL" |
        Estados == "MATO GROSSO DO SUL" |
        Estados == "MATO GROSSO" |
        Estados == "GOIÁS")
  
  #add state column containing state ids
  mb_areas <- mb_areas %>%
    mutate(state = if_else(Estados == "TOCANTINS", 17, 
      if_else(Estados == "BAHIA", 29,
      if_else(Estados == "MINAS GERAIS", 31,
      if_else(Estados == "SÃO PAULO", 35,
      if_else(Estados == "PARANÁ", 41,
      if_else(Estados == "SANTA CATARINA", 42,
      if_else(Estados == "RIO GRANDE DO SUL", 43, 
      if_else(Estados == "MATO GROSSO DO SUL", 50, 
      if_else(Estados == "MATO GROSSO", 51,
      if_else(Estados == "GOIÁS", 52, 0
      ))))))))))
    )
  
      
  #select only columns we want    
  mb_areas <- mb_areas %>%
    dplyr::select(state, paste0(yrls[j]), `Classe Nivel 3`) %>% 
    dplyr::rename(area = paste0(yrls[j]))
  
  #because there is no consistency between land cover labels
  legenda = c("Forest Formations"=1, "Natural Forest Formations"=2, "Dense Forest"=3, "Savanna Formations"=4, "Mangroves"=5,"Forest Plantations"=9, "Non-Forest Natural Formations"=10, "Non Forest Wetlands"=11, "Grasslands"=12, "Other Non Forest Natural Formations"=13, "Farming"=14, "Pasture"=15, "Agriculture"=18, "Agriculture or Pasture"=21, "Non-Vegetated Areas"=22, "Dunes and Beaches"=23, "Urban Infrastructure"=24,"Other Non-Vegetated Area"=25,"Water Bodies"=26, "Non-Observed"=27)
  
  #recode to values (which match the map)
  mb_areas <- mb_areas %>%
    mutate(LC = recode(`Classe Nivel 3`, !!!legenda))
  
  #use the classification values from above to relassify land covers
  mb_areas$LC <- plyr::mapvalues(mb_areas$LC, from=as.numeric(classification$X__1), to=as.numeric(classification$X__2))
  
  #calculate total LC area by state
  mb_areas <- mb_areas %>%
    group_by(state, LC) %>%
    dplyr::summarise_at(vars(area),sum, na.rm=T) 
  
  #round to integer
  mb_areas <- mb_areas %>%
    mutate(area_km2 = round(area,0))
  
  #add source variable for plotting below (re-order to match map table for union below)
  mb_areas <- mb_areas %>%
    dplyr::select(-area) %>%
    mutate(source = "MB") %>%
    rename(area = area_km2) %>%
    dplyr::select(state, LC, source, area)
    

  CData_yr <- union_all(map_areas, mb_areas)

  #relabel states to characters
  CData_yr <- CData_yr %>%
    mutate(state = if_else(state == 17, "TO", 
      if_else(state == 29, "BA",
      if_else(state == 31, "MG",
      if_else(state == 35, "SP",
      if_else(state == 41, "PR",
      if_else(state == 42, "SC",
      if_else(state == 43, "RS", 
      if_else(state == 50, "MS",
      if_else(state == 51, "MT",
      if_else(state == 52, "GO", "NA"
      ))))))))))
    )
  
  #relabel LCs to characters
  CData_yr <- CData_yr %>%
    mutate(LC = if_else(LC == 1, "Nature", 
      if_else(LC == 2, "OtherAgri",
      if_else(LC == 3, "Agri",
      if_else(LC == 4, "Other",
      if_else(LC == 5, "Pasture", "NA"
      )))))
    )
  
  #add year column
  CData_yr <- CData_yr %>%
    mutate(year = yrls[j])
  
  #union CData for years here.
  #if first iteration of classification loop (re)create the tibble
  if(j == 1){
      CData <- CData_yr
  }
    
  #else join data to tibble (by creating another tibble, then join (ensure rows are not lost)
  else {
      CData <- union_all(CData, CData_yr)
  }
  }
  
  CData_ls[[i]] <- CData
  
  names(mapStack) <- yrls
  mapStack_ls[[i]] <- mapStack
}

##Maps

#use loop instead of lapply to construct approproate palette
for(i in seq_along(mapStack_ls)){

  clabs <- c()
  if(1 %in% levels(mapStack_ls[[i]])[[1]]$ID) { clabs <- c(clabs, 'forestgreen') }
  if(2 %in% levels(mapStack_ls[[i]])[[1]]$ID) { clabs <- c(clabs, 'wheat1') }
  if(3 %in% levels(mapStack_ls[[i]])[[1]]$ID) { clabs <- c(clabs, 'orange2') }
  if(4 %in% levels(mapStack_ls[[i]])[[1]]$ID) { clabs <- c(clabs, 'gray') }
  if(5 %in% levels(mapStack_ls[[i]])[[1]]$ID) { clabs <- c(clabs, 'darkolivegreen') }
  
  print(cls[i])
  print(rasterVis::levelplot(mapStack_ls[[i]], pretty=T,att = 'landcover', col.regions=clabs), main=paste0(cls[i]))
  
}

## [1] "PastureA"

## [1] "PastureB"

## [1] "PastureC"

##All states for all years by classification

for(i in seq_along(cls)){

  ##All States
  SDataW <- CData_ls[[i]] %>%
    mutate(sourceState = paste(source, state, sep=" ")) %>%
    dplyr::select(-source, -state) %>%
    spread(key = c(sourceState), value = area)
  
  #following with help from https://stackoverflow.com/a/42015507
  SDataW <- SDataW %>%
    mutate(MapTotal = rowSums(.[grep("Map", names(.))], na.rm =TRUE)) %>%
    mutate(MBTotal = rowSums(.[grep("MB", names(.))], na.rm =TRUE)) %>%
    dplyr::select(LC, year, MapTotal, MBTotal) %>%
    mutate(AbsDiffc = MBTotal - MapTotal) %>%
    mutate(PropDiffc = round(AbsDiffc / MBTotal,3))
  
  Stotals <- SDataW %>%
    dplyr::select(-AbsDiffc, -PropDiffc) %>%
    gather(key = source, value = area, -LC, -year)
  
  SDataW_ls[[i]] <- SDataW
  Stotals_ls[[i]] <- Stotals
}

for(i in seq_along(cls)){
  
  clabs <- c("Agri" = 'orange2', "Nature" ='forestgreen', "Other"='gray', "OtherAgri"='wheat1', "Pasture"='darkolivegreen')
  
  p1 <- ggplot(Stotals_ls[[i]], aes(x=source, y=area, fill=LC)) + 
    geom_bar(stat="identity", colour="white", position = "fill") +
    scale_fill_manual(values=clabs) +
    facet_grid(.~year) +
    scale_y_continuous(labels = percent_format()) +
    xlab("Data Source")+ylab("Percentage") +
      theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    ggtitle(cls[i])
  
  p2 <- ggplot(Stotals_ls[[i]], aes(x=source, y=area, fill=LC)) + 
    scale_fill_manual(values=clabs) +
    geom_bar(stat="identity", colour="white") +
    scale_y_continuous(labels = comma_format()) +
    labs(x=expression(Data~Source),y=expression(Area~km^{2})) +
    facet_grid(.~year) +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    ggtitle(cls[i])
  
  print(p1)
  print(p2)
}

for(i in seq_along(cls)){
  
  clabs <- c("Agri" = 'orange2', "Nature" ='forestgreen', "Other"='gray', "OtherAgri"='wheat1', "Pasture"='darkolivegreen')
  
  p1 <- ggplot(SDataW_ls[[i]], aes(x=LC, y=AbsDiffc, fill=LC)) + 
    geom_bar(stat="identity", colour="white", position = "dodge") +
    scale_fill_manual(values=clabs) +
    scale_y_continuous(labels = comma_format()) +
    facet_grid(.~year) +
    xlab("")+ylab("Abs Diff") +
    theme(axis.text.x = element_blank()) +
    ggtitle(cls[i])
  
  p2 <- ggplot(SDataW_ls[[i]], aes(x=LC, y=PropDiffc, fill=LC)) + 
    geom_bar(stat="identity", colour="white", position = "dodge") +
    scale_fill_manual(values=clabs) +
    facet_grid(.~year) +
    xlab("")+ylab("Prop Diff") +
    theme(axis.text.x = element_blank()) +
    ggtitle(cls[i])
  
  print(p1)
  print(p2)
  
  MBTotalArea = sum(SDataW_ls[[i]]$MBTotal) 
  MapTotalArea = sum(SDataW_ls[[i]]$MapTotal) 
  ADiffc = MBTotalArea - MapTotalArea
  PDiffc = ADiffc / MBTotalArea
  
  SDataW_ls[[i]] <- bind_rows(SDataW_ls[[i]], list(LC = 'Total', MapTotal = MapTotalArea, MBTotal = MBTotalArea, AbsDiffc = ADiffc, PropDiffc = round(PDiffc,3)))

  
}

for(i in seq_along(cls)){
  print(kable(SDataW_ls[[i]], caption=paste0(cls[i]) ))
}

Table: PastureA

LC year MapTotal MBTotal AbsDiffc PropDiffc

---

Agri 2000 142425 135122 -7303 -0.054 Agri 2001 214225 183971 -30254 -0.164 Agri 2002 228725 192352 -36373 -0.189 Agri 2003 240475 198790 -41685 -0.210 Agri 2004 250375 207424 -42951 -0.207 Agri 2005 267225 223400 -43825 -0.196 Agri 2006 280425 234583 -45842 -0.195 Agri 2007 288675 244208 -44467 -0.182 Agri 2008 288100 245586 -42514 -0.173 Agri 2009 293675 251299 -42376 -0.169 Agri 2010 298550 255195 -43355 -0.170 Agri 2011 294825 253439 -41386 -0.163 Agri 2012 312950 264888 -48062 -0.181 Agri 2013 363700 306525 -57175 -0.187 Agri 2014 369875 309374 -60501 -0.196 Agri 2015 413525 347256 -66269 -0.191 Nature 2000 1967225 1861438 -105787 -0.057 Nature 2001 1923950 1834532 -89418 -0.049 Nature 2002 1903225 1827182 -76043 -0.042 Nature 2003 1884975 1818191 -66784 -0.037 Nature 2004 1871500 1811702 -59798 -0.033 Nature 2005 1862775 1811515 -51260 -0.028 Nature 2006 1862050 1811314 -50736 -0.028 Nature 2007 1866200 1808660 -57540 -0.032 Nature 2008 1862125 1802154 -59971 -0.033 Nature 2009 1856025 1795074 -60951 -0.034 Nature 2010 1856050 1802116 -53934 -0.030 Nature 2011 1852450 1798535 -53915 -0.030 Nature 2012 1843625 1786902 -56723 -0.032 Nature 2013 1855775 1782221 -73554 -0.041 Nature 2014 1863300 1789857 -73443 -0.041 Nature 2015 1852825 1779907 -72918 -0.041 Other 2000 63450 76261 12811 0.168 Other 2001 62725 75831 13106 0.173 Other 2002 65725 78283 12558 0.160 Other 2003 65800 78442 12642 0.161 Other 2004 67700 79437 11737 0.148 Other 2005 68225 80339 12114 0.151 Other 2006 69000 81216 12216 0.150 Other 2007 69575 82223 12648 0.154 Other 2008 69700 82419 12719 0.154 Other 2009 70175 82563 12388 0.150 Other 2010 70825 84999 14174 0.167 Other 2011 72200 86881 14681 0.169 Other 2012 73900 87901 14001 0.159 Other 2013 74750 88267 13517 0.153 Other 2014 75425 88792 13367 0.151 Other 2015 74350 88256 13906 0.158 OtherAgri 2000 674350 816139 141789 0.174 OtherAgri 2001 648375 790970 142595 0.180 OtherAgri 2002 640275 773071 132796 0.172 OtherAgri 2003 626625 749172 122547 0.164 OtherAgri 2004 608675 729296 120621 0.165 OtherAgri 2005 587775 704182 116407 0.165 OtherAgri 2006 541325 672398 131073 0.195 OtherAgri 2007 555125 688580 133455 0.194 OtherAgri 2008 582225 710484 128259 0.181 OtherAgri 2009 615875 733324 117449 0.160 OtherAgri 2010 619800 731243 111443 0.152 OtherAgri 2011 648825 750781 101956 0.136 OtherAgri 2012 622325 735623 113298 0.154 OtherAgri 2013 507350 668280 160930 0.241 OtherAgri 2014 445650 624558 178908 0.286 OtherAgri 2015 400700 595762 195062 0.327 Pasture 2000 1227200 961270 -265930 -0.277 Pasture 2001 1225375 964929 -260446 -0.270 Pasture 2002 1236700 979343 -257357 -0.263 Pasture 2003 1256775 1005636 -251139 -0.250 Pasture 2004 1276375 1022373 -254002 -0.248 Pasture 2005 1288600 1030793 -257807 -0.250 Pasture 2006 1321875 1050725 -271150 -0.258 Pasture 2007 1295075 1026562 -268513 -0.262 Pasture 2008 1272500 1009591 -262909 -0.260 Pasture 2009 1238925 987970 -250955 -0.254 Pasture 2010 1229475 976680 -252795 -0.259 Pasture 2011 1206300 960593 -245707 -0.256 Pasture 2012 1221850 974917 -246933 -0.253 Pasture 2013 1273100 1004938 -268162 -0.267 Pasture 2014 1320475 1037648 -282827 -0.273 Pasture 2015 1333275 1039053 -294222 -0.283 Total NA 65194500 61603706 -3590794 -0.058

Table: PastureB

LC year MapTotal MBTotal AbsDiffc PropDiffc

---

Agri 2000 142425 135122 -7303 -0.054 Agri 2001 214225 183971 -30254 -0.164 Agri 2002 228725 192352 -36373 -0.189 Agri 2003 240475 198790 -41685 -0.210 Agri 2004 250375 207424 -42951 -0.207 Agri 2005 267225 223400 -43825 -0.196 Agri 2006 280425 234583 -45842 -0.195 Agri 2007 288675 244208 -44467 -0.182 Agri 2008 288100 245586 -42514 -0.173 Agri 2009 293675 251299 -42376 -0.169 Agri 2010 298550 255195 -43355 -0.170 Agri 2011 294825 253439 -41386 -0.163 Agri 2012 312950 264888 -48062 -0.181 Agri 2013 363700 306525 -57175 -0.187 Agri 2014 369875 309374 -60501 -0.196 Agri 2015 413525 347256 -66269 -0.191 Nature 2000 1592250 1508529 -83721 -0.055 Nature 2001 1541275 1475331 -65944 -0.045 Nature 2002 1524050 1469762 -54288 -0.037 Nature 2003 1507225 1457294 -49931 -0.034 Nature 2004 1498250 1452635 -45615 -0.031 Nature 2005 1483475 1447409 -36066 -0.025 Nature 2006 1479450 1445503 -33947 -0.023 Nature 2007 1486375 1444841 -41534 -0.029 Nature 2008 1481750 1438579 -43171 -0.030 Nature 2009 1479375 1433602 -45773 -0.032 Nature 2010 1482525 1442332 -40193 -0.028 Nature 2011 1482100 1441174 -40926 -0.028 Nature 2012 1478950 1430548 -48402 -0.034 Nature 2013 1493175 1428803 -64372 -0.045 Nature 2014 1503975 1440695 -63280 -0.044 Nature 2015 1498825 1431796 -67029 -0.047 Other 2000 63450 76261 12811 0.168 Other 2001 62725 75831 13106 0.173 Other 2002 65725 78283 12558 0.160 Other 2003 65800 78442 12642 0.161 Other 2004 67700 79437 11737 0.148 Other 2005 68225 80339 12114 0.151 Other 2006 69000 81216 12216 0.150 Other 2007 69575 82223 12648 0.154 Other 2008 69700 82419 12719 0.154 Other 2009 70175 82563 12388 0.150 Other 2010 70825 84999 14174 0.167 Other 2011 72200 86881 14681 0.169 Other 2012 73900 87901 14001 0.159 Other 2013 74750 88267 13517 0.153 Other 2014 75425 88792 13367 0.151 Other 2015 74350 88256 13906 0.158 OtherAgri 2000 674350 816139 141789 0.174 OtherAgri 2001 648375 790970 142595 0.180 OtherAgri 2002 640275 773071 132796 0.172 OtherAgri 2003 626625 749172 122547 0.164 OtherAgri 2004 608675 729296 120621 0.165 OtherAgri 2005 587775 704182 116407 0.165 OtherAgri 2006 541325 672398 131073 0.195 OtherAgri 2007 555125 688580 133455 0.194 OtherAgri 2008 582225 710484 128259 0.181 OtherAgri 2009 615875 733324 117449 0.160 OtherAgri 2010 619800 731243 111443 0.152 OtherAgri 2011 648825 750781 101956 0.136 OtherAgri 2012 622325 735623 113298 0.154 OtherAgri 2013 507350 668280 160930 0.241 OtherAgri 2014 445650 624558 178908 0.286 OtherAgri 2015 400700 595762 195062 0.327 Pasture 2000 1602175 1314181 -287994 -0.219 Pasture 2001 1608050 1324131 -283919 -0.214 Pasture 2002 1615800 1336762 -279038 -0.209 Pasture 2003 1634525 1366532 -267993 -0.196 Pasture 2004 1649650 1381439 -268211 -0.194 Pasture 2005 1667950 1394900 -273050 -0.196 Pasture 2006 1704450 1416536 -287914 -0.203 Pasture 2007 1674900 1390381 -284519 -0.205 Pasture 2008 1652875 1373166 -279709 -0.204 Pasture 2009 1615500 1349442 -266058 -0.197 Pasture 2010 1603000 1336462 -266538 -0.199 Pasture 2011 1576700 1317956 -258744 -0.196 Pasture 2012 1586550 1331272 -255278 -0.192 Pasture 2013 1635675 1358355 -277320 -0.204 Pasture 2014 1679725 1386808 -292917 -0.211 Pasture 2015 1687300 1387161 -300139 -0.216 Total NA 65194400 61603702 -3590698 -0.058

Table: PastureC

LC year MapTotal MBTotal AbsDiffc PropDiffc

---

Agri 2000 142425 135122 -7303 -0.054 Agri 2001 214225 183971 -30254 -0.164 Agri 2002 228725 192352 -36373 -0.189 Agri 2003 240475 198790 -41685 -0.210 Agri 2004 250375 207424 -42951 -0.207 Agri 2005 267225 223400 -43825 -0.196 Agri 2006 280425 234583 -45842 -0.195 Agri 2007 288675 244208 -44467 -0.182 Agri 2008 288100 245586 -42514 -0.173 Agri 2009 293675 251299 -42376 -0.169 Agri 2010 298550 255195 -43355 -0.170 Agri 2011 294825 253439 -41386 -0.163 Agri 2012 312950 264888 -48062 -0.181 Agri 2013 363700 306525 -57175 -0.187 Agri 2014 369875 309374 -60501 -0.196 Agri 2015 413525 347256 -66269 -0.191 Nature 2000 1592250 1508529 -83721 -0.055 Nature 2001 1541275 1475331 -65944 -0.045 Nature 2002 1524050 1469762 -54288 -0.037 Nature 2003 1507225 1457294 -49931 -0.034 Nature 2004 1498250 1452635 -45615 -0.031 Nature 2005 1483475 1447409 -36066 -0.025 Nature 2006 1479450 1445503 -33947 -0.023 Nature 2007 1486375 1444841 -41534 -0.029 Nature 2008 1481750 1438579 -43171 -0.030 Nature 2009 1479375 1433602 -45773 -0.032 Nature 2010 1482525 1442332 -40193 -0.028 Nature 2011 1482100 1441174 -40926 -0.028 Nature 2012 1478950 1430548 -48402 -0.034 Nature 2013 1493175 1428803 -64372 -0.045 Nature 2014 1503975 1440695 -63280 -0.044 Nature 2015 1498825 1431796 -67029 -0.047 Other 2000 63450 76261 12811 0.168 Other 2001 62725 75831 13106 0.173 Other 2002 65725 78283 12558 0.160 Other 2003 65800 78442 12642 0.161 Other 2004 67700 79437 11737 0.148 Other 2005 68225 80339 12114 0.151 Other 2006 69000 81216 12216 0.150 Other 2007 69575 82223 12648 0.154 Other 2008 69700 82419 12719 0.154 Other 2009 70175 82563 12388 0.150 Other 2010 70825 84999 14174 0.167 Other 2011 72200 86881 14681 0.169 Other 2012 73900 87901 14001 0.159 Other 2013 74750 88267 13517 0.153 Other 2014 75425 88792 13367 0.151 Other 2015 74350 88256 13906 0.158 OtherAgri 2000 0 0 0 NaN OtherAgri 2001 0 0 0 NaN OtherAgri 2002 0 0 0 NaN OtherAgri 2003 0 0 0 NaN OtherAgri 2004 0 0 0 NaN OtherAgri 2005 0 0 0 NaN OtherAgri 2006 0 0 0 NaN OtherAgri 2007 0 0 0 NaN OtherAgri 2008 0 0 0 NaN OtherAgri 2009 0 0 0 NaN OtherAgri 2010 0 0 0 NaN OtherAgri 2011 0 0 0 NaN OtherAgri 2012 0 0 0 NaN OtherAgri 2013 0 0 0 NaN OtherAgri 2014 0 0 0 NaN OtherAgri 2015 0 0 0 NaN Pasture 2000 2276525 2130319 -146206 -0.069 Pasture 2001 2256425 2115100 -141325 -0.067 Pasture 2002 2256075 2109838 -146237 -0.069 Pasture 2003 2261150 2115704 -145446 -0.069 Pasture 2004 2258250 2110734 -147516 -0.070 Pasture 2005 2255650 2099083 -156567 -0.075 Pasture 2006 2245775 2088933 -156842 -0.075 Pasture 2007 2230050 2078959 -151091 -0.073 Pasture 2008 2235100 2083648 -151452 -0.073 Pasture 2009 2231425 2082766 -148659 -0.071 Pasture 2010 2222825 2067703 -155122 -0.075 Pasture 2011 2225525 2068738 -156787 -0.076 Pasture 2012 2208875 2066893 -141982 -0.069 Pasture 2013 2143025 2026635 -116390 -0.057 Pasture 2014 2125425 2011368 -114057 -0.057 Pasture 2015 2088000 1982924 -105076 -0.053 Total NA 65194400 61603700 -3590700 -0.058

##Adjusted

Adjust areas by systematic difference between MB and Map

for(i in seq_along(cls)){
  
  TotalArea <- SDataW_ls[[i]] %>% 
    dplyr::filter(LC != "Total") %>%
    group_by(year) %>%
    dplyr::summarise_at(vars(MapTotal, MBTotal),sum, na.rm=T) %>%
    mutate(prop = MBTotal / MapTotal)
  
  adj <- mean(TotalArea$prop)
  
  ##All States
  SDataW_Adj <- CData_ls[[i]] %>%
    mutate(sourceState = paste(source, state, sep=" ")) %>%
    dplyr::select(-source, -state) %>%
    spread(key = c(sourceState), value = area)
  
  #following with help from https://stackoverflow.com/a/42015507
  SDataW_Adj <- SDataW_Adj %>%
    mutate(MapTotal = round(adj * rowSums(.[grep("Map", names(.))], na.rm =TRUE),0)) %>%
    mutate(MBTotal = rowSums(.[grep("MB", names(.))], na.rm =TRUE)) %>%
    dplyr::select(LC, year, MapTotal, MBTotal) %>%
    mutate(AbsDiffc = MBTotal - MapTotal) %>%
    mutate(PropDiffc = round(AbsDiffc / MBTotal,3))
  
  Stotals_Adj <- SDataW_Adj %>%
    dplyr::select(-AbsDiffc, -PropDiffc) %>%
    gather(key = source, value = area, -LC, -year)
  
  p1 <- ggplot(Stotals_Adj, aes(x=source, y=area, fill=LC)) +
    scale_fill_manual(values=clabs) + 
    geom_bar(stat="identity", colour="white") +
    scale_y_continuous(labels = comma_format()) +
    facet_grid(.~year) +
    labs(x=expression(Data~Source),y=expression(Area~km^{2})) +
    theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
    ggtitle(cls[i])
  
  p2 <- ggplot(SDataW_Adj, aes(x=LC, y=AbsDiffc, fill=LC)) + 
    geom_bar(stat="identity", colour="white", position = "dodge") +
    scale_fill_manual(values=clabs) +
    facet_grid(.~year) +
    xlab("")+ylab("Abs Diff") +
    theme(axis.text.x = element_blank()) +
    ggtitle(cls[i])
  
  print(p1)
  print(p2)
  
  MBTotalArea_Adj = sum(SDataW_Adj$MBTotal) 
  MapTotalArea_Adj = sum(SDataW_Adj$MapTotal) 
  ADiffc_Adj = MBTotalArea_Adj - MapTotalArea_Adj
  PDiffc_Adj = ADiffc_Adj / MBTotalArea_Adj
  
  SDataW_Adj <- bind_rows(SDataW_Adj, list(LC = 'Total', MapTotal = round(MapTotalArea_Adj,0), MBTotal = MBTotalArea_Adj, AbsDiffc = round(ADiffc_Adj,3), PropDiffc = round(PDiffc_Adj,3)))

  SDataW_Adj_ls[[i]] <- SDataW_Adj
}

for(i in seq_along(cls)){
  print(kable(SDataW_Adj_ls[[i]], caption=paste0(cls[i])))
}

Table: PastureA

LC year MapTotal MBTotal AbsDiffc PropDiffc

---

Agri 2000 134580 135122 542 0.004 Agri 2001 202426 183971 -18455 -0.100 Agri 2002 216127 192352 -23775 -0.124 Agri 2003 227230 198790 -28440 -0.143 Agri 2004 236585 207424 -29161 -0.141 Agri 2005 252507 223400 -29107 -0.130 Agri 2006 264980 234583 -30397 -0.130 Agri 2007 272775 244208 -28567 -0.117 Agri 2008 272232 245586 -26646 -0.108 Agri 2009 277500 251299 -26201 -0.104 Agri 2010 282106 255195 -26911 -0.105 Agri 2011 278587 253439 -25148 -0.099 Agri 2012 295713 264888 -30825 -0.116 Agri 2013 343668 306525 -37143 -0.121 Agri 2014 349503 309374 -40129 -0.130 Agri 2015 390749 347256 -43493 -0.125 Nature 2000 1858874 1861438 2564 0.001 Nature 2001 1817982 1834532 16550 0.009 Nature 2002 1798399 1827182 28783 0.016 Nature 2003 1781154 1818191 37037 0.020 Nature 2004 1768421 1811702 43281 0.024 Nature 2005 1760177 1811515 51338 0.028 Nature 2006 1759492 1811314 51822 0.029 Nature 2007 1763413 1808660 45247 0.025 Nature 2008 1759563 1802154 42591 0.024 Nature 2009 1753799 1795074 41275 0.023 Nature 2010 1753822 1802116 48294 0.027 Nature 2011 1750420 1798535 48115 0.027 Nature 2012 1742082 1786902 44820 0.025 Nature 2013 1753562 1782221 28659 0.016 Nature 2014 1760673 1789857 29184 0.016 Nature 2015 1750775 1779907 29132 0.016 Other 2000 59955 76261 16306 0.214 Other 2001 59270 75831 16561 0.218 Other 2002 62105 78283 16178 0.207 Other 2003 62176 78442 16266 0.207 Other 2004 63971 79437 15466 0.195 Other 2005 64467 80339 15872 0.198 Other 2006 65200 81216 16016 0.197 Other 2007 65743 82223 16480 0.200 Other 2008 65861 82419 16558 0.201 Other 2009 66310 82563 16253 0.197 Other 2010 66924 84999 18075 0.213 Other 2011 68223 86881 18658 0.215 Other 2012 69830 87901 18071 0.206 Other 2013 70633 88267 17634 0.200 Other 2014 71271 88792 17521 0.197 Other 2015 70255 88256 18001 0.204 OtherAgri 2000 637208 816139 178931 0.219 OtherAgri 2001 612664 790970 178306 0.225 OtherAgri 2002 605010 773071 168061 0.217 OtherAgri 2003 592112 749172 157060 0.210 OtherAgri 2004 575150 729296 154146 0.211 OtherAgri 2005 555401 704182 148781 0.211 OtherAgri 2006 511510 672398 160888 0.239 OtherAgri 2007 524550 688580 164030 0.238 OtherAgri 2008 550157 710484 160327 0.226 OtherAgri 2009 581954 733324 151370 0.206 OtherAgri 2010 585663 731243 145580 0.199 OtherAgri 2011 613089 750781 137692 0.183 OtherAgri 2012 588048 735623 147575 0.201 OtherAgri 2013 479406 668280 188874 0.283 OtherAgri 2014 421104 624558 203454 0.326 OtherAgri 2015 378630 595762 217132 0.364 Pasture 2000 1159608 961270 -198338 -0.206 Pasture 2001 1157884 964929 -192955 -0.200 Pasture 2002 1168585 979343 -189242 -0.193 Pasture 2003 1187554 1005636 -181918 -0.181 Pasture 2004 1206075 1022373 -183702 -0.180 Pasture 2005 1217626 1030793 -186833 -0.181 Pasture 2006 1249069 1050725 -198344 -0.189 Pasture 2007 1223745 1026562 -197183 -0.192 Pasture 2008 1202413 1009591 -192822 -0.191 Pasture 2009 1170687 987970 -182717 -0.185 Pasture 2010 1161758 976680 -185078 -0.189 Pasture 2011 1139859 960593 -179266 -0.187 Pasture 2012 1154553 974917 -179636 -0.184 Pasture 2013 1202980 1004938 -198042 -0.197 Pasture 2014 1247746 1037648 -210098 -0.202 Pasture 2015 1259841 1039053 -220788 -0.212 Total NA 61603709 61603706 -3 0.000

Table: PastureB

LC year MapTotal MBTotal AbsDiffc PropDiffc

---

Agri 2000 134581 135122 541 0.004 Agri 2001 202426 183971 -18455 -0.100 Agri 2002 216128 192352 -23776 -0.124 Agri 2003 227230 198790 -28440 -0.143 Agri 2004 236585 207424 -29161 -0.141 Agri 2005 252507 223400 -29107 -0.130 Agri 2006 264980 234583 -30397 -0.130 Agri 2007 272776 244208 -28568 -0.117 Agri 2008 272232 245586 -26646 -0.108 Agri 2009 277500 251299 -26201 -0.104 Agri 2010 282107 255195 -26912 -0.105 Agri 2011 278587 253439 -25148 -0.099 Agri 2012 295714 264888 -30826 -0.116 Agri 2013 343669 306525 -37144 -0.121 Agri 2014 349503 309374 -40129 -0.130 Agri 2015 390749 347256 -43493 -0.125 Nature 2000 1504554 1508529 3975 0.003 Nature 2001 1456387 1475331 18944 0.013 Nature 2002 1440110 1469762 29652 0.020 Nature 2003 1424212 1457294 33082 0.023 Nature 2004 1415731 1452635 36904 0.025 Nature 2005 1401770 1447409 45639 0.032 Nature 2006 1397967 1445503 47536 0.033 Nature 2007 1404510 1444841 40331 0.028 Nature 2008 1400140 1438579 38439 0.027 Nature 2009 1397896 1433602 35706 0.025 Nature 2010 1400872 1442332 41460 0.029 Nature 2011 1400471 1441174 40703 0.028 Nature 2012 1397494 1430548 33054 0.023 Nature 2013 1410936 1428803 17867 0.013 Nature 2014 1421141 1440695 19554 0.014 Nature 2015 1416275 1431796 15521 0.011 Other 2000 59955 76261 16306 0.214 Other 2001 59270 75831 16561 0.218 Other 2002 62105 78283 16178 0.207 Other 2003 62176 78442 16266 0.207 Other 2004 63971 79437 15466 0.195 Other 2005 64467 80339 15872 0.198 Other 2006 65200 81216 16016 0.197 Other 2007 65743 82223 16480 0.200 Other 2008 65861 82419 16558 0.201 Other 2009 66310 82563 16253 0.197 Other 2010 66924 84999 18075 0.213 Other 2011 68223 86881 18658 0.215 Other 2012 69830 87901 18071 0.206 Other 2013 70633 88267 17634 0.200 Other 2014 71271 88792 17521 0.197 Other 2015 70255 88256 18001 0.204 OtherAgri 2000 637209 816139 178930 0.219 OtherAgri 2001 612665 790970 178305 0.225 OtherAgri 2002 605011 773071 168060 0.217 OtherAgri 2003 592113 749172 157059 0.210 OtherAgri 2004 575151 729296 154145 0.211 OtherAgri 2005 555402 704182 148780 0.211 OtherAgri 2006 511511 672398 160887 0.239 OtherAgri 2007 524550 688580 164030 0.238 OtherAgri 2008 550158 710484 160326 0.226 OtherAgri 2009 581955 733324 151369 0.206 OtherAgri 2010 585663 731243 145580 0.199 OtherAgri 2011 613090 750781 137691 0.183 OtherAgri 2012 588049 735623 147574 0.201 OtherAgri 2013 479407 668280 188873 0.283 OtherAgri 2014 421105 624558 203453 0.326 OtherAgri 2015 378631 595762 217131 0.364 Pasture 2000 1513932 1314181 -199751 -0.152 Pasture 2001 1519484 1324131 -195353 -0.148 Pasture 2002 1526807 1336762 -190045 -0.142 Pasture 2003 1544501 1366532 -177969 -0.130 Pasture 2004 1558793 1381439 -177354 -0.128 Pasture 2005 1576085 1394900 -181185 -0.130 Pasture 2006 1610574 1416536 -194038 -0.137 Pasture 2007 1582652 1390381 -192271 -0.138 Pasture 2008 1561840 1373166 -188674 -0.137 Pasture 2009 1526523 1349442 -177081 -0.131 Pasture 2010 1514712 1336462 -178250 -0.133 Pasture 2011 1489860 1317956 -171904 -0.130 Pasture 2012 1499168 1331272 -167896 -0.126 Pasture 2013 1545587 1358355 -187232 -0.138 Pasture 2014 1587211 1386808 -200403 -0.145 Pasture 2015 1594369 1387161 -207208 -0.149 Total NA 61603702 61603702 0 0.000

Table: PastureC

LC year MapTotal MBTotal AbsDiffc PropDiffc

---

Agri 2000 134581 135122 541 0.004 Agri 2001 202426 183971 -18455 -0.100 Agri 2002 216128 192352 -23776 -0.124 Agri 2003 227230 198790 -28440 -0.143 Agri 2004 236585 207424 -29161 -0.141 Agri 2005 252507 223400 -29107 -0.130 Agri 2006 264980 234583 -30397 -0.130 Agri 2007 272776 244208 -28568 -0.117 Agri 2008 272232 245586 -26646 -0.108 Agri 2009 277500 251299 -26201 -0.104 Agri 2010 282107 255195 -26912 -0.105 Agri 2011 278587 253439 -25148 -0.099 Agri 2012 295714 264888 -30826 -0.116 Agri 2013 343669 306525 -37144 -0.121 Agri 2014 349503 309374 -40129 -0.130 Agri 2015 390749 347256 -43493 -0.125 Nature 2000 1504554 1508529 3975 0.003 Nature 2001 1456386 1475331 18945 0.013 Nature 2002 1440110 1469762 29652 0.020 Nature 2003 1424212 1457294 33082 0.023 Nature 2004 1415731 1452635 36904 0.025 Nature 2005 1401770 1447409 45639 0.032 Nature 2006 1397967 1445503 47536 0.033 Nature 2007 1404510 1444841 40331 0.028 Nature 2008 1400140 1438579 38439 0.027 Nature 2009 1397896 1433602 35706 0.025 Nature 2010 1400872 1442332 41460 0.029 Nature 2011 1400471 1441174 40703 0.028 Nature 2012 1397494 1430548 33054 0.023 Nature 2013 1410936 1428803 17867 0.013 Nature 2014 1421141 1440695 19554 0.014 Nature 2015 1416274 1431796 15522 0.011 Other 2000 59955 76261 16306 0.214 Other 2001 59270 75831 16561 0.218 Other 2002 62105 78283 16178 0.207 Other 2003 62176 78442 16266 0.207 Other 2004 63971 79437 15466 0.195 Other 2005 64467 80339 15872 0.198 Other 2006 65200 81216 16016 0.197 Other 2007 65743 82223 16480 0.200 Other 2008 65861 82419 16558 0.201 Other 2009 66310 82563 16253 0.197 Other 2010 66924 84999 18075 0.213 Other 2011 68223 86881 18658 0.215 Other 2012 69830 87901 18071 0.206 Other 2013 70633 88267 17634 0.200 Other 2014 71271 88792 17521 0.197 Other 2015 70255 88256 18001 0.204 OtherAgri 2000 0 0 0 NaN OtherAgri 2001 0 0 0 NaN OtherAgri 2002 0 0 0 NaN OtherAgri 2003 0 0 0 NaN OtherAgri 2004 0 0 0 NaN OtherAgri 2005 0 0 0 NaN OtherAgri 2006 0 0 0 NaN OtherAgri 2007 0 0 0 NaN OtherAgri 2008 0 0 0 NaN OtherAgri 2009 0 0 0 NaN OtherAgri 2010 0 0 0 NaN OtherAgri 2011 0 0 0 NaN OtherAgri 2012 0 0 0 NaN OtherAgri 2013 0 0 0 NaN OtherAgri 2014 0 0 0 NaN OtherAgri 2015 0 0 0 NaN Pasture 2000 2151141 2130319 -20822 -0.010 Pasture 2001 2132148 2115100 -17048 -0.008 Pasture 2002 2131818 2109838 -21980 -0.010 Pasture 2003 2136613 2115704 -20909 -0.010 Pasture 2004 2133873 2110734 -23139 -0.011 Pasture 2005 2131416 2099083 -32333 -0.015 Pasture 2006 2122085 2088933 -33152 -0.016 Pasture 2007 2107226 2078959 -28267 -0.014 Pasture 2008 2111998 2083648 -28350 -0.014 Pasture 2009 2108525 2082766 -25759 -0.012 Pasture 2010 2100399 2067703 -32696 -0.016 Pasture 2011 2102950 2068738 -34212 -0.017 Pasture 2012 2087217 2066893 -20324 -0.010 Pasture 2013 2024994 2026635 1641 0.001 Pasture 2014 2008363 2011368 3005 0.001 Pasture 2015 1973000 1982924 9924 0.005 Total NA 61603698 61603700 2 0.000

##Clean up

unlink("ASCII", recursive = T) #delete ASCII directory created above