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Merge pull request #32 from JGCRI/carbon_update
Merge protected area and carbon updates into master
2 parents 2114dbb + 75c00db commit ed72e27

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‎.gitignore‎

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# exclude .idea dir
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.idea
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# exclude xcode project file/directory
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moirai.xcodeproj/
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# exclude the unzipped sage175crop files
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indata/HarvestedAreaYield175Crops_NetCDF/*.png
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indata/HarvestedAreaYield175Crops_NetCDF/*.nc
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# exclude output dirs
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outputs/
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examples/outputs/
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*.tif
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diagnostics/.RData
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diagnostics/.Rhistory
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*.xml
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*.tif
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*.nc4
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# exclude some other stuff
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ancillary/old/
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diagnostics/basins235_example_outputs_spatial_output_files/
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example_outputs/basins235/*
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example_outputs/basins235/aglu-data/
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*.stackdump

‎README.md‎

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‎ancillary/.RData‎

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‎ancillary/.Rhistory‎

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library(dplyr)
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library(data.table)
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library(testthat)
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#Add links to datasets
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#1. The original dataset
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Original_LDS_Data<-read.csv("D:/MOIRAI/moirai/outputs/basins235/Land_type_area_ha.csv",skip = 5)
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#2. Updated dataset
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Updated_LDS_Data<-read.csv("D:/MOIRAI_3.1_Final/moirai/ancillary/Land_type_area_ha _Updated_withIUCN_Categories.csv",skip = 5)
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#3. Old land types
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MOIRAI_Types_old<-read.csv("D:/MOIRAI/moirai/outputs/basins235/MOIRAI_land_types.csv",skip=4)
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#4. New land types
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MOIRAI_Types_new<-read.csv("D:/MOIRAI_3.1_Final/moirai/outputs/basins235_lulcc/MOIRAI_land_types.csv",skip = 4)
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#Add fast group_by function
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fast_group_by<- function(df,by,colname="value",func= "sum"){
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#Convert relevant column to numeric
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df[,colname]<- as.numeric(df[,colname])
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#Store as data.table
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df <- as.data.table(df)
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#Complete operations
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df<- df[, (colname) := (get(func)(get(colname))), by]
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#Save back to tibble
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df<- as_tibble(df)
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return(df)
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}
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#First compare by Country_Year
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Original_LDS_Data %>%
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fast_group_by(by=c("iso","year"),colname = "value",func="sum") %>%
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select(iso,year,value) %>%
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distinct()->ISO_Year_Data_Old
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Updated_LDS_Data %>%
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fast_group_by(by=c("iso","year"),colname = "value",func="sum") %>%
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select(iso,year,value) %>%
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rename(Updated_Value=value) %>%
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distinct()->ISO_Year_Data_New
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test_that("Compare difference by year and value",{
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expect_equal(ISO_Year_Data_Old$value,ISO_Year_Data_New$Updated_Value,tolerance=0.01,info=paste("ISO year differences are too high."))
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})
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#Compare by iso,glu,year
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Original_LDS_Data %>%
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fast_group_by(by=c("iso","glu_code","year"),colname = "value",func="sum") %>%
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select(iso,glu_code,year,value) %>%
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distinct()->ISO_GLU_Year_Data_Old
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Updated_LDS_Data %>%
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fast_group_by(by=c("iso","glu_code","year"),colname = "value",func="sum") %>%
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select(iso,glu_code,year,value) %>%
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rename(Updated_Value=value) %>%
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distinct()->ISO_GLU_Year_Data_New
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test_that("Compare difference by year and value",{
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expect_equal(ISO_GLU_Year_Data_Old$value,ISO_GLU_Year_Data_New$Updated_Value,tolerance=0.01,info=paste("ISO year differences are too high."))
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})
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#Compare Hyde type
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Original_LDS_Data %>%
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left_join(MOIRAI_Types_old %>% select(Category,LT_HYDE) %>% rename(land_type=Category),by=c("land_type")) %>%
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fast_group_by(by=c("iso","glu_code","LT_HYDE","year"),colname = "value",func="sum") %>%
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select(iso,glu_code,year,LT_HYDE,value) %>%
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distinct() %>%
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filter(value>2)->ISO_GLU_LT_Year_Data_Old
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Updated_LDS_Data %>%
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left_join(MOIRAI_Types_new %>% select(Category,LT_HYDE) %>% rename(land_type=Category),by=c("land_type")) %>%
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fast_group_by(by=c("iso","glu_code","LT_HYDE","year"),colname = "value",func="sum") %>%
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select(iso,glu_code,year,LT_HYDE,value) %>%
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rename(Updated_Value=value) %>%
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distinct() %>%
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filter(Updated_Value>2)->ISO_GLU_LT_Year_Data_New
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ISO_GLU_LT_Year_Data_Old %>% left_join(ISO_GLU_LT_Year_Data_New,by=c("iso","glu_code","LT_HYDE","year")) %>%
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mutate(Difference=Updated_Value-value) %>%
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mutate(Difference_Percent=(Difference/value)*100) %>%
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filter(value>50)->tmp
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#Compare Hyde type
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Original_LDS_Data %>%
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left_join(MOIRAI_Types_old %>% select(Category,LT_HYDE,LT_SAGE) %>% rename(land_type=Category),by=c("land_type")) %>%
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fast_group_by(by=c("iso","glu_code","LT_HYDE","year","LT_SAGE"),colname = "value",func="sum") %>%
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select(iso,glu_code,year,LT_HYDE,value,LT_SAGE) %>%
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distinct() %>%
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filter(value>2)->ISO_GLU_LT_Year_Data_Old
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Updated_LDS_Data %>%
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left_join(MOIRAI_Types_new %>% select(Category,LT_HYDE,LT_SAGE) %>% rename(land_type=Category),by=c("land_type")) %>%
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fast_group_by(by=c("iso","glu_code","LT_HYDE","year","LT_SAGE"),colname = "value",func="sum") %>%
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select(iso,glu_code,year,LT_HYDE,value,LT_SAGE) %>%
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rename(Updated_Value=value) %>%
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distinct() %>%
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filter(Updated_Value>2)->ISO_GLU_LT_Year_Data_New
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ISO_GLU_LT_Year_Data_Old %>% left_join(ISO_GLU_LT_Year_Data_New,by=c("iso","glu_code","LT_HYDE","year","LT_SAGE")) %>%
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mutate(Difference=Updated_Value-value) %>%
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mutate(Difference_Percent=(Difference/value)*100) %>%
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filter(value>5)->tmp
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library(ggplot2)
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g<-ggplot(data=tmp,aes(x=tmp$value,y=tmp$Updated_Value))+
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geom_point()
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g
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View(tmp)
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g<-ggplot(data=tmp,aes(x=value,y=Updated_Value))+
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geom_point()+
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ggtitle("Comparing LDS outputs between old and new across all years")
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g<-ggplot(data=tmp,aes(x=tmp$value,y=tmp$Updated_Value))+
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g
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g<-ggplot(data=tmp,aes(x=value,y=Updated_Value))+
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geom_point()+
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ggtitle("Comparing LDS outputs between old and new across all years")
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g
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View(tmp)
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ISO_GLU_LT_Year_Data_Old %>% left_join(ISO_GLU_LT_Year_Data_New,by=c("iso","glu_code","LT_HYDE","year","LT_SAGE")) %>%
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mutate(Difference=abs(Updated_Value-value)) %>%
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mutate(Difference_Percent=(Difference/value)*100) %>%
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filter(value>5)->tmp
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tmp %>% filter(Difference>5)->Data_for_comparison
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View(tmp)
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ISO_GLU_LT_Year_Data_Old %>% left_join(ISO_GLU_LT_Year_Data_New,by=c("iso","glu_code","LT_HYDE","year","LT_SAGE")) %>%
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mutate(Difference=abs(Updated_Value-value)) %>%
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mutate(Difference_Percent=(Difference/value)*100) %>%
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filter(value>5)->tmp
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tmp %>% filter(Difference>5)->Data_for_comparison
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View(Data_for_comparison)
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View(Data_for_comparison)
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View(Data_for_comparison)
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View(Data_for_comparison)
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tmp %>% filter(Difference>5) %>% filter(Difference_Percent>15)->Data_for_comparison
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View(Data_for_comparison)
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tmp %>% filter(Difference>5) %>% filter(Difference_Percent>12)->Data_for_comparison
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View(Data_for_comparison)
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tmp %>% filter(Difference>5) %>% filter(Difference_Percent>15)->Data_for_comparison
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test_that("Compare difference at lowest level",{
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expect(sum(nrow(Data_for_comparison))=0,info=paste("Differences at the lowest level are not reasonable"))
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})
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test_that("Compare difference at lowest level",{
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expect_equal(sum(nrow(Data_for_comparison)),0,info=paste("Differences at the lowest level are not reasonable"))
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})
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tmp %>% filter(year=1990) %>% filter(iso=="idn")->Indonesia_anomaly
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tmp %>% filter(year==1990) %>% filter(iso=="idn")->Indonesia_anomaly
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write.csv(Indonesia_anomaly,"Indonesia_anomaly.csv")
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#5. Old carbon file
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Old_Carbon_Data<- read.csv("D:/MOIRAI/moirai/outputs/basins235/Ref_veg_carbon_Mg_per_ha.csv")
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#6. New carbon file
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New_Carbon_Data<- read.csv("D:/MOIRAI_3.1_Final/moirai/outputs/basins235_lulcc/Ref_veg_carbon_Mg_per_ha.csv")
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colnames(Old_Carbon_Data)
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#5. Old carbon file
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Old_Carbon_Data<- read.csv("D:/MOIRAI/moirai/outputs/basins235/Ref_veg_carbon_Mg_per_ha.csv",skip = 5)
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#6. New carbon file
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New_Carbon_Data<- read.csv("D:/MOIRAI_3.1_Final/moirai/outputs/basins235_lulcc/Ref_veg_carbon_Mg_per_ha.csv",skip = 5)
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colnames(Old_Carbon_Data)
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colnames(Original_LDS_Data)
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#Part 2: Carbon accounting for soil_carbon, vegetation carbon
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Old_Carbon_Data %>%
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inner_join(Original_LDS_Data %>%
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filter(year=2015) %>%
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select(iso,glu_code,land_type,value) %>% rename(Land_value=value), by=c("iso","glu_code","land_type")) %>%
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mutate(Old_Carbon_Total = Land_value * value )
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#Part 2: Carbon accounting for soil_carbon, vegetation carbon
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Old_Carbon_Data %>%
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inner_join(Original_LDS_Data %>%
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filter(year==2015) %>%
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select(iso,glu_code,land_type,value) %>% rename(Land_value=value), by=c("iso","glu_code","land_type")) %>%
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mutate(Old_Carbon_Total = Land_value * value )
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#Part 2: Carbon accounting for soil_carbon, vegetation carbon
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Old_Carbon_Data %>%
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inner_join(Original_LDS_Data %>%
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filter(year==2015) %>%
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select(iso,glu_code,land_type,value) %>% rename(Land_value=value), by=c("iso","glu_code","land_type")) %>%
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mutate(Old_Carbon_Total = Land_value * value )->Old_Total_Carbon
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View(Old_Total_Carbon)
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New_Carbon_Data %>%
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inner_join(Updated_LDS_Data %>%
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filter(year==2015) %>%
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select(iso,glu_code,land_type,value) %>% rename(Land_value=value), by=c("iso","glu_code","land_type")) %>%
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mutate(New_Carbon_Total = Land_value * value )->New_Total_Carbon
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#Part 2: Carbon accounting for soil_carbon, vegetation carbon
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Old_Carbon_Data %>%
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inner_join(Original_LDS_Data %>%
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filter(year==2015) %>%
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select(iso,glu_code,land_type,value) %>% rename(Land_value=value), by=c("iso","glu_code","land_type")) %>%
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mutate(Old_Carbon_Total = Land_value * value ) %>%
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group_by(iso,glu_code,c_type) %>%
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mutate(Old_Carbon_Total=sum(Old_Carbon_Total))->Old_Total_Carbon
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New_Carbon_Data %>%
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inner_join(Updated_LDS_Data %>%
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filter(year==2015) %>%
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select(iso,glu_code,land_type,value) %>% rename(Land_value=value), by=c("iso","glu_code","land_type")) %>%
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mutate(New_Carbon_Total = Land_value * value ) %>%
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group_by(iso,glu_code,c_type) %>%
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mutate(New_Carbon_Total=sum(New_Carbon_Total))->New_Total_Carbon
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View(New_Total_Carbon)
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#Part 2: Carbon accounting for soil_carbon, vegetation carbon
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Old_Carbon_Data %>%
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inner_join(Original_LDS_Data %>%
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filter(year==2015) %>%
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select(iso,glu_code,land_type,value) %>% rename(Land_value=value), by=c("iso","glu_code","land_type")) %>%
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mutate(Old_Carbon_Total = Land_value * value ) %>%
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group_by(iso,glu_code,c_type) %>%
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mutate(Old_Carbon_Total=sum(Old_Carbon_Total)) %>%
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select(iso,glu_code,c_type,Old_Carbon_Total) %>%
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distinct()->Old_Total_Carbon
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New_Carbon_Data %>%
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inner_join(Updated_LDS_Data %>%
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filter(year==2015) %>%
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select(iso,glu_code,land_type,value) %>% rename(Land_value=value), by=c("iso","glu_code","land_type")) %>%
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mutate(New_Carbon_Total = Land_value * value ) %>%
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group_by(iso,glu_code,c_type) %>%
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mutate(New_Carbon_Total=sum(New_Carbon_Total)) %>%
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select(iso,glu_code,c_type,New_Carbon_Total) %>%
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distinct()->New_Total_Carbon
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#Part 2: Carbon accounting for soil_carbon, vegetation carbon
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Old_Carbon_Data %>%
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inner_join(Original_LDS_Data %>%
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filter(year==2015) %>%
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select(iso,glu_code,land_type,value) %>% rename(Land_value=value), by=c("iso","glu_code","land_type")) %>%
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mutate(Old_Carbon_Total = Land_value * value ) %>%
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group_by(iso,glu_code,c_type) %>%
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mutate(Old_Carbon_Total=sum(Old_Carbon_Total)) %>%
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select(iso,glu_code,c_type,Old_Carbon_Total) %>%
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distinct()->Old_Total_Carbon
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New_Carbon_Data %>%
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inner_join(Updated_LDS_Data %>%
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filter(year==2015) %>%
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select(iso,glu_code,land_type,value) %>% rename(Land_value=value), by=c("iso","glu_code","land_type")) %>%
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mutate(New_Carbon_Total = Land_value * value ) %>%
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group_by(iso,glu_code,c_type) %>%
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mutate(New_Carbon_Total=sum(New_Carbon_Total)) %>%
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select(iso,glu_code,c_type,New_Carbon_Total) %>%
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distinct()->New_Total_Carbon
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New_Total_Carbon %>%
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inner_join(Old_Total_Carbon,by=c("iso","glu_code","c_type")) %>%
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mutate(Difference=abs(New_Carbon_Total-Old_Carbon_Total)) %>%
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mutate(Percent_Difference=(Difference/Old_Total_Carbon)*100)->Carbon_Comparison
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New_Total_Carbon %>%
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inner_join(Old_Total_Carbon,by=c("iso","glu_code","c_type"))
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New_Total_Carbon %>%
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inner_join(Old_Total_Carbon,by=c("iso","glu_code","c_type"))->t
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New_Total_Carbon %>%
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inner_join(Old_Total_Carbon,by=c("iso","glu_code","c_type")) %>%
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mutate(Difference=abs(New_Carbon_Total-Old_Carbon_Total))->t
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New_Total_Carbon %>%
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inner_join(Old_Total_Carbon,by=c("iso","glu_code","c_type")) %>%
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mutate(Difference=abs(New_Carbon_Total-Old_Carbon_Total)) %>%
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mutate(Percent_Difference=(Difference/Old_Carbon_Total)*100)->Carbon_Comparison
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g<-ggplot(data=Carbon_Comparison,aes(x=Old_Carbon_Total,y=New_Carbon_Total))+
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geom_point()+
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ggtitle("Comparing carbon outputs between old and new across all years")+
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labs(subtitle = "This is a comparison at the lowest level i.e Year+ISO+GLU+SAGE_type+Hyde_type")+
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facet_wrap(~c_type)
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g
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View(Carbon_Comparison)
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median(Carbon_Comparison$New_Carbon_Total)
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New_Total_Carbon %>%
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inner_join(Old_Total_Carbon,by=c("iso","glu_code","c_type")) %>%
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mutate(New_Carbon_Total=if_else(is.na(New_Carbon_Total,0,New_Carbon_Total))) %>%
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mutate(Difference=abs(New_Carbon_Total-Old_Carbon_Total)) %>%
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mutate(Percent_Difference=(Difference/Old_Carbon_Total)*100)->Carbon_Comparison
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New_Total_Carbon %>%
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inner_join(Old_Total_Carbon,by=c("iso","glu_code","c_type")) %>%
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mutate(New_Carbon_Total=if_else(is.na(New_Carbon_Total),0,New_Carbon_Total)) %>%
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mutate(Difference=abs(New_Carbon_Total-Old_Carbon_Total)) %>%
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mutate(Percent_Difference=(Difference/Old_Carbon_Total)*100)->Carbon_Comparison
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New_Total_Carbon %>%
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inner_join(Old_Total_Carbon,by=c("iso","glu_code","c_type")) %>%
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mutate(New_Carbon_Total=ifelse(is.na(New_Carbon_Total),0,New_Carbon_Total)) %>%
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mutate(Difference=abs(New_Carbon_Total-Old_Carbon_Total)) %>%
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mutate(Percent_Difference=(Difference/Old_Carbon_Total)*100)->Carbon_Comparison
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g<-ggplot(data=Carbon_Comparison,aes(x=Old_Carbon_Total,y=New_Carbon_Total))+
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geom_point()+
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ggtitle("Comparing carbon outputs between old and new across all years")+
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labs(subtitle = "This is a comparison at the lowest level i.e Year+ISO+GLU+SAGE_type+Hyde_type")+
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facet_wrap(~c_type)
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g
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View(Carbon_Comparison)
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median(Carbon_Comparison$New_Carbon_Total)
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Carbon_Comparison %>% filter(Difference>1000000)->t
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View(t)
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Carbon_Comparison %>% filter(Difference>4000000) %>%
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filter(Percent_Difference>15)->Carbon
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View(Carbon)
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Carbon_Comparison %>% filter(iso=="ecu") %>%filter(c_type=="soil_c")->Ecuador_example
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View(Ecuador_example)
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write.csv(Ecuador_example,"Ecuador_example.csv")
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ISO_GLU_LT_Year_Data_Old %>% left_join(ISO_GLU_LT_Year_Data_New,by=c("iso","glu_code","LT_HYDE","year","LT_SAGE")) %>%
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mutate(Difference=abs(Updated_Value-value)) %>%
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mutate(Difference_Percent=(Difference/value)*100) %>%
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filter(value>5)->tmp
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g<-ggplot(data=tmp,aes(x=value,y=Updated_Value))+
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geom_point()+
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ggtitle("Comparing LDS outputs between old and new across all years")+
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labs(subtitle = "This is a comparison at the lowest level i.e Year+ISO+GLU+SAGE_type+Hyde_type")
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g
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library(rgdal)
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library(raster)
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GDALinfo("D:/MOIRAI/moirai/EPA/AgLands_IUCN_All_IUCN.tif")
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gdalinfo<-GDALinfo("D:/MOIRAI/moirai/EPA/AgLands_IUCN_All_IUCN.tif")
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gdalinfo
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?gdal_translate
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??gdal_translate
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library(rgdal)
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library(raster)
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gdalinfo<-GDALinfo("D:/MOIRAI/moirai/EPA/AgLands_IUCN_All_IUCN.tif")
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gdal_translate()
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library(gdal)
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library(rgdal)
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??gdal_translate
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rgdal::gdalwarp
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gdal_warp
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install.packages("gdalUtils")
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library(gdalUtils)
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??gdal_translate
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gdal_translate(src_dataset = "D:/MOIRAI/moirai/EPA/AgLands_IUCN_All_IUCN.tif", dst_dataset = "tmp.bil",of="ENVI")
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gdalwarp(srcfile = "D:/MOIRAI/moirai/EPA/AgLands_IUCN_All_IUCN.tif",dstfile = "tmp.tif",srcnodata = 2147483647,dstnodata = 0)
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gdalinfo<-GDALinfo("D:/MOIRAI/moirai/EPA/All_IUCN.tif")
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gdalinfo<-GDALinfo("D:/MOIRAI/moirai/EPA/All_IUCN.tif")
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gdalinfo<-GDALinfo("D:/MOIRAI/moirai/EPA/AgLands_IUCN_All_IUCN.tif")
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gdalinfo
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gdalwarp(srcfile = "D:/MOIRAI/moirai/EPA/AgLands_IUCN_All_IUCN.tif",dstfile = "tmp.tif",srcnodata = 2147483647,dstnodata = 0)
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gdalinfo<-GDALinfo("D:/MOIRAI/moirai/EPA/AgLands_IUCN_All_IUCN.tif")
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gdalwarp(srcfile = "D:/MOIRAI/moirai/EPA/AgLands_IUCN_All_IUCN.tif",dstfile = "tmp.tif",srcnodata = 2147483647,dstnodata = 0)
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gdalinfo<-GDALinfo("D:/MOIRAI/moirai/EPA/AgLands_IUCN_All_IUCN.tif")
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gdalinfo<-GDALinfo("D:/MOIRAI/moirai/EPA/AgLands_IUCN_All_IUCN.tif")
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gdalinfo
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gdalwarp(srcfile = "D:/MOIRAI/moirai/EPA/AgLands_IUCN_All_IUCN.tif",dstfile = "tmp.tif",srcnodata = 2147483647,dstnodata = 0)

‎ancillary/Processed_Rasters_EPA_KBN.zip‎

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version https://git-lfs.github.com/spec/v1
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oid sha256:9d7150cc2dc12ea085aed4be8af343f13b5408b80f3abcc64f35ab39e4887312
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size 11692433

‎ancillary/bash_scripts/get_FAO_HWS_data.sh‎

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#!/bin/bash
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echo "Please make sure folder contains T_C.nc4 file"
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# Translate nc file to tiff
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gdal_translate T_C.nc4 FAO.tif -of GTiff
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# Units are in kg/m2. Convert these to MgC/ha
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gdal_calc.py -A FAO_HWS.tif --calc="A*10" --outfile=FAO_HWS.tif --overwrite
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# Get weighted average value
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gdalwarp -ts 4320 2160 -r average FAO_HWS.tif FAO_HWS_wavg.tif -ot Float32 -t_srs "+proj=longlat +ellps=WGS84" -te -180 -90 180 90 -dstnodata -9999
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#Median
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gdalwarp -ts 4320 2160 -r med FAO_HWS.tif FAO_HWS_median.tif -ot Float32 -t_srs "+proj=longlat +ellps=WGS84" -te -180 -90 180 90 -dstnodata -9999
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#Minimum
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gdalwarp -ts 4320 2160 -r min FAO_HWS.tif FAO_HWS_min.tif -ot Float32 -t_srs "+proj=longlat +ellps=WGS84" -te -180 -90 180 90 -dstnodata -9999
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#Maximum
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gdalwarp -ts 4320 2160 -r max FAO_HWS.tif FAO_HWS_max.tif -ot Float32 -t_srs "+proj=longlat +ellps=WGS84" -te -180 -90 180 90 -dstnodata -9999
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#Q1
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gdalwarp -ts 4320 2160 -r q1 FAO_HWS.tif FAO_HWS_q1.tif -ot Float32 -t_srs "+proj=longlat +ellps=WGS84" -te -180 -90 180 90 -dstnodata -9999
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#Q3
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gdalwarp -ts 4320 2160 -r q3 FAO_HWS.tif FAO_HWS_q3.tif -ot Float32 -t_srs "+proj=longlat +ellps=WGS84" -te -180 -90 180 90 -dstnodata -9999
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#Convert to binary files
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gdal_translate -of ENVI FAO_HWS_wavg.tif FAO_HWS_wavg.bil -a_nodata none
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gdal_translate -of ENVI FAO_HWS_median.tif FAO_HWS_median.bil -a_nodata none
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gdal_translate -of ENVI FAO_HWS_min.tif FAO_HWS_min.bil -a_nodata none
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gdal_translate -of ENVI FAO_HWS_max.tif FAO_HWS_max.bil -a_nodata none
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gdal_translate -of ENVI FAO_HWS_q1.tif FAO_HWS_q1.bil -a_nodata none
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gdal_translate -of ENVI FAO_HWS_q3.tif FAO_HWS_q3.bil -a_nodata none

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