diff --git a/code/04_buildHeader.Rmd b/code/04_buildHeader.Rmd
index f65feafb8a7786f1774f8fd954dc13ecc4c956ea..9e7e932a75b15944bc910182b2c8f073c0238b78 100644
--- a/code/04_buildHeader.Rmd
+++ b/code/04_buildHeader.Rmd
@@ -26,8 +26,11 @@ This report documents the construction of the header file for sPlot 3.0. It is b
```{r results="hide", message=F, warning=F}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
+library(viridis)
library(readr)
library(xlsx)
+library(knitr)
+library(kableExtra)
## Spatial packages
library(rgdal)
@@ -35,6 +38,10 @@ library(sp)
library(rgeos)
library(raster)
library(rworldmap)
+library(elevatr)
+library(sf)
+library(rnaturalearth)
+library(dggridR)
#save temporary files
write("TMPDIR = /data/sPlot/users/Francesco/_tmp", file=file.path(Sys.getenv('TMPDIR'), '.Renviron'))
@@ -42,6 +49,7 @@ write("R_USER = /data/sPlot/users/Francesco/_tmp", file=file.path(Sys.getenv('R_
rasterOptions(tmpdir="/data/sPlot/users/Francesco/_tmp")
```
+
```{bash, eval=F}
# escape all double quotation marks. Run in Linux terminal
#sed 's/"/\\"/g' sPlot_3_0_2_header.csv > sPlot_3_0_2_header_test.csv
@@ -53,7 +61,7 @@ rasterOptions(tmpdir="/data/sPlot/users/Francesco/_tmp")
```
-
+## 1 Import data
Import header data
```{r import}
header0 <- readr::read_delim("../sPlot_data_export/sPlot_3_0_2_header_test.csv", locale = locale(encoding = 'UTF-8'),
@@ -111,7 +119,8 @@ header0 <- readr::read_delim("../sPlot_data_export/sPlot_3_0_2_header_test.csv",
)) %>%
rename(Sparse.vegetation=`Sparse vegetation`,
ESY=EUNIS) %>%
- dplyr::select(-COMMUNITY, -ALLIAN_REV, -REV_AUTHOR, -SUBSTRATE) #too sparse information to be useful
+ dplyr::select(-COMMUNITY, -ALLIAN_REV, -REV_AUTHOR, -SUBSTRATE) %>% #too sparse information to be useful
+ dplyr::select(-PlotID) #identical to PlotObservationID
```
The following column names occurred in the header of sPlot v2.1 and are currently missing from the header of v3.0
@@ -130,7 +139,7 @@ The following column names occurred in the header of sPlot v2.1 and are currentl
Columns #1, #2, #3, #10, #11 will be dropped. The others will be derived below.
-## 1 Solve spatial problems
+### 1.1 Solve spatial problems
There are 2020 plots in the Nile dataset without spatial coordinates. Assign manually with wide (90km) location uncertainty.
```{r}
header <- header0 %>%
@@ -151,19 +160,19 @@ toswap <- c(which(header$Dataset=="Japan" & header$Latitude>90),
header[toswap, c("Latitude", "Longitude")] <- header[toswap, c("Longitude", "Latitude")]
```
-There are `r nrow(header %>% filter(is.na(`Location uncertainty (m)`)))`. As a first approximation, we assign the median of the respective dataset, as a negative value to indicate this is an estimation, rather than a measure.
+There are `r nrow(header %>% filter(is.na("Location uncertainty (m)")))`. As a first approximation, we assign the median of the respective dataset, as a negative value to indicate this is an estimation, rather than a measure.
```{r}
header <- header %>%
left_join(header %>%
group_by(Dataset) %>%
summarize(loc.uncer.median=median(`Location uncertainty (m)`, na.rm=T)),
by="Dataset") %>%
- mutate(`Location uncertainty (m)`=ifelse( is.na(`Location uncertainty (m)`),
+ mutate(`Location uncertainty (m)`=ifelse( is.na(`Location uncertainty (m)` & !is.na(Latitude)),
-abs(loc.uncer.median),
`Location uncertainty (m)`)) %>%
dplyr::select(-loc.uncer.median)
```
-There are still `r nrow(header %>% filter(is.na(`Location uncertainty (m)`)))` plots with no estimation of location uncertainty.
+There are still `r nrow(header %>% filter(is.na("Location uncertainty (m)")))` plots with no estimation of location uncertainty.
\newline
Assign plot size to plots in the Patagonia dataset (input of Ana Cingolani)
```{r}
@@ -173,12 +182,11 @@ header <- header %>%
```
## 2 Formations
-Fill out the columns `Forest:Sparse.vegetation` with 0\\NAs, where necessary. Create columns `is.forest` and `is.non.forest` using script developed for sPlot 2.1
+Fill out the columns `Forest:Sparse.vegetation` with NAs, where necessary. Create columns `is.forest` and `is.non.forest` using script developed for sPlot 2.1
~~
I am not assigning plots to Faber-Langedon formation at this stage, as this is only possible for European plots having an ESY classification.
-
```{r}
eunis.key <- read.xlsx("../_input/EUNIS_WFT.xlsx", sheetIndex = "Sheet1", endRow = 246) %>%
dplyr::select(EUNIS_code, NATURALNESS:SPARSE_VEG) %>%
@@ -193,8 +201,6 @@ eunis.key <- read.xlsx("../_input/EUNIS_WFT.xlsx", sheetIndex = "Sheet1", endRow
header.backup <- header
-#test <- header.backup %>% slice(100000:110000) %>% dplyr::select(PlotObservationID, Dataset, ESY, Forest:Shrubland)
-
header <- header %>% # header.backup %>%
mutate(ESY=as.character(ESY)) %>%
#mutate(ESY=ifelse(ESY=="?", NA, ESY)) %>%
@@ -249,7 +255,7 @@ header <- header %>%
mutate(`Plants recorded`=factor(`Plants recorded`, exclude = "#N/A"))
```
Align labels consortium
-```{r}
+```{r message=F}
databases <- read_csv("/data/sPlot/users/Francesco/_sPlot_Management/Consortium/Databases.out.csv")
header <- header %>%
@@ -338,7 +344,7 @@ continent.out$continent[which(is.na(continent.out$continent))] <- as.character(c
save(continent.out, file = "../_derived/continent.out")
stopCluster(cl)
```
-Reload and manipulate continent. Correct header.sRoot
+Reload, manipulate continent and attach to header
```{r}
load("../_derived/continent.out")
header <- header %>%
@@ -354,97 +360,52 @@ header <- header %>%
```
## 4.2 Assign to sBiomes
-```{r, eval=F}
+Performed in EVE HPC cluster using function `A98_PredictorsExtract.R`. Divided in 99 chunks.
+```{r eval=F}
sBiomes <- readOGR("/data/sPlot/users/Francesco/Ancillary_Data/Biomes_sPlot/sBiomes.shp")
crs(sBiomes) <- crs(header.shp)
-library(parallel)
-library(doParallel)
-ncores=10
-cl <- makeForkCluster(ncores, outfile="" )
-registerDoParallel(cl)
-
-#divide in chunks #should take around 40'
-indices <- 1:length(header.shp)
-chunks <- split(indices, sort(indices%%99))
-chunkn <- length(chunks)
-sBiomes.out <- foreach(i=1:chunkn, .packages=c('raster'), .combine=rbind) %dopar% {
- sBiomes.tmp <- sp::over(x=header.shp[chunks[[i]],], y=sBiomes)
-}
-sum(is.na(sBiomes.out$Name))
-sBiomes.out.backup <- sBiomes.out
-stopCluster(cl)
-```
-There are `r sum(is.na(sBiomes.out$Name))` still to assign. Using the `dist2Line` is not feasible due to long computing times (3' for each plot!). Fall back on raster version of sBiomes (res 0.1°).
-
-```{r}
-load("../_derived/sBiome.out.RData")
-sBiomes01 <- raster("/data/sPlot/users/Francesco/Ancillary_Data/Biomes_sPlot/sBiome_raster01/sBiomes_raster01.tif")
-## Fix NAs
-toassign <- header.shp[which(is.na(sBiomes.out$Name)),] #116878 not assigned (!)
-crs(sBiomes01) <- crs(toassign)
-
-system.time(sBiomes.out2 <- extract(sBiomes01, toassign))
-
-
-toassign3 <- toassign[which(sBiomes.out2==0),] #91793 not assigned (!)
-robust.mode <- function(x){if(any(x!=0)) {
- a <- x[which(x!=0)] #exclude zero (i.e. NAs)
- return(as.numeric(names(sort(table(a), decreasing=T))[1]))} else
- return(NA)
- }
-
-ncores=10
-cl <- makeForkCluster(ncores, outfile="" )
-registerDoParallel(cl)
-sBiomes.out3 <- foreach(i=1:length(toassign3), .packages=c('raster'), .combine=rbind) %dopar% {
- sBiomes.out.tmp <- extract(sBiomes01, toassign3[i,], buffer=10000, fun=robust.mode)
-}
-
-stopCluster(cl)
-save(sBiomes.out, sBiomes.out2, sBiomes.out3, file = "../_derived/sBiome.out.RData")
-
-
-toassign4 <- toassign3[which(is.na(sBiomes.out3)),] ##37887 still to classify
-ncores=10
-cl <- makeForkCluster(ncores, outfile="" )
+cl <- makeForkCluster(5, outfile="")
registerDoParallel(cl)
-sBiomes.out4 <- foreach(i=1:length(toassign4), .packages=c('raster'), .combine=rbind) %dopar% {
- sBiomes.out.tmp <- extract(sBiomes01, toassign4[i,], buffer=50000, fun=robust.mode)
-}
-
-stopCluster(cl)
-save(sBiomes.out, sBiomes.out2, sBiomes.out3, sBiomes.out4, file = "../_derived/sBiome.out.RData")
+clusterEvalQ(cl, {
+ library(rgdal)
+ library(raster)
+ library(sp)
+ library(elevatr)
+ library(dplyr)
+ })
-toassign5 <- toassign4[which(is.na(sBiomes.out4)),] #4158 still to assign
-nearestBiome <- foreach(i=1:1000, .packages=c('raster'), .combine=rbind) %dopar% {
- nearestBiome.tmp <- geosphere::dist2Line(toassign[i,], sBiomes)
+sBiomes.out <- foreach(i=1:99, .combine=rbind) %dopar% {
+ source("A98_PredictorsExtract.R")
+ PredExtr(header.shp, myfunction=NA, output="../_derived/sBiomes/",
+ toextract=sBiomes, typp="shp", ncores=1, chunkn=99, chunk.i=i)
}
-sBiomes.out[which(is.na(sBiomes.out$Name))] <- as.character(sBiomes@data[nearestBiome[,"ID"],])
-sum(is.na(sBiomes.out$Name))
-save(sBiomes.out, file = "../_derived/sBiome.out.RData")
-
stopCluster(cl)
-
```
-
Reimport output and join to header
-```{r}
-
+```{r, message=F, warning=F}
+sBiome.files <- list.files("../_derived/sBiomes", pattern="sBiomes-[0-9]+.csv", full.names=T)
+sBiomes.out <- do.call(rbind, lapply(sBiome.files, function(x) {read_csv(x)}))
header <- header %>%
- bind_rows(sBiomes.out %>%
- dplyr::select(Name, BiomeID) %>%
- dplyr::rename(sBioem=Name, s))
-
+ left_join(sBiomes.out %>%
+ rename(PlotObservationID=X1) %>%
+ dplyr::select(PlotObservationID, Name, BiomeID) %>%
+ dplyr::rename(sBiome=Name, sBiomeID=BiomeID),
+ by="PlotObservationID")
```
+There are `r sum(is.na(sBiomes.out$Name))` unassigned plots.
+
## 4.3 Extract elevation
-Split data into tiles of 1 x 1 degrees, and create sp::SpatialPointsDataFrame files. Only for plots having a location uncertainty < 50 km. (Include also plots without location uncertainty, arbitrarily set to 100 m)
+
+Extract elevation for each plot. Loops over tiles of 1 x 1°, projects to mercator, and extract elevation for plot coordinates, as well as 2.5, 50, and 97.5 quantiles for a buffer area having a radius equal to the location uncertainty of each plot (but only if location uncertainty < 50 km). DEM derive from package [elevatr](https://cran.r-project.org/web/packages/elevatr/vignettes/introduction_to_elevatr.html#get_raster_elevation_data), which uses the [Terrain Tiles on Amazon Web Services](https://registry.opendata.aws/terrain-tiles/). Resolutions of DEM rasters vary by region. I set a zoom factor z=10, which corresponds to ~ 75-150 m. Sources are: SRTM, data.gov.at in Austria, NRCAN in Canada, SRTM, NED/3DEP 1/3 arcsec, data.gov.uk in United Kingdom, INEGI in Mexico, ArcticDEM in latitudes above 60°, LINZ in New Zealand, Kartverket in Norway, as described [here](https://github.com/tilezen/joerd/blob/master/docs/data-sources.md).
+\newline
+Split data into tiles of 1 x 1 degrees, and create `sp::SpatialPointsDataFrame` files. Only for plots having a location uncertainty < 50 km.
```{r create tiles}
header.tiles <- header %>%
dplyr::select(PlotObservationID, Dataset, Longitude, Latitude, `Location uncertainty (m)`) %>%
@@ -456,20 +417,8 @@ header.tiles <- header %>%
mutate(tilenam=factor(paste(Longitude_tile, Latitude_tile)))
```
-There are `r nrow(header.tiles)` plots out of `r nrow(header)` plots with Location uncertainty <= 50km (or absent).
-
-
-
-Extract elevation for each plot. Loops over tiles of 1 x 1°, projects to mercator, and extract elevation for plot coordinates, as well as 2.5, 50, and 97.5 quantiles for a buffer area having a radius equal to the location uncertainty of each plot (but only if location uncertainty < 50 km). DEM derive from package [elevatr](https://cran.r-project.org/web/packages/elevatr/vignettes/introduction_to_elevatr.html#get_raster_elevation_data), which uses the [Terrain Tiles on Amazon Web Services](https://registry.opendata.aws/terrain-tiles/). Resolutions of DEM rasters vary by region. I set a zoom factor z=10, which corresponds to ~ 75-150 m. Sources are: SRTM, data.gov.at in Austria, NRCAN in Canada, SRTM, NED/3DEP 1/3 arcsec, data.gov.uk in United Kingdom, INEGI in Mexico, ArcticDEM in latitudes above 60°, LINZ in New Zealand, Kartverket in Norway, as described [here](https://github.com/tilezen/joerd/blob/master/docs/data-sources.md)
-
-```{r message==F}
-library(elevatr)
-
-continent.high.merc <- spTransform(continent.high, CRS( "+init=epsg:3857 +proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m
-+nadgrids=@null +no_defs"))
-
-require(parallel)
-require(doParallel)
+There are `r nrow(header.tiles)` plots out of `r nrow(header)` plots with Location uncertainty <= 50km (or absent). The total number of tiles is `r nlevels(header.tiles$tilenam)`.
+```{r eval=F}
cl <- makeForkCluster(5, outfile="")
registerDoParallel(cl)
@@ -481,75 +430,15 @@ clusterEvalQ(cl, {
library(dplyr)
})
-#create list of tiles for which dem could not be extracted
-myfiles <- list.files("../_derived/elevatr/", pattern = "[A-Za-z]*_[0-9]+\\.RData$")
-done <- NULL
-done <- as.numeric(unlist(regmatches(myfiles, gregexpr("[[:digit:]]+", myfiles))))
-todo <- 1:nlevels(header.tiles$tilenam)
-todo <- todo[-which(todo %in% done)]
-#todo <- sample(todo, replace=F)
-#foreach(i = 1:nlevels(header.sp$tilenam)) %do% {
-#foreach(i = todo) %dopar% {
-for(i in todo){
- #create sp and project data
- if(nrow(header.tiles %>%
- filter(tilenam %in% levels(header.tiles$tilenam)[i])) ==0 ) next()
- sp.tile <- SpatialPointsDataFrame(coords=header.tiles %>%
- filter(tilenam %in% levels(header.tiles$tilenam)[i]) %>%
- dplyr::select(Longitude, Latitude),
- data=header.tiles %>%
- filter(tilenam %in% levels(header.tiles$tilenam)[i]) %>%
- dplyr::select(-Longitude, -Latitude),
- proj4string = CRS("+init=epsg:4326"))
- sp.tile <- spTransform(sp.tile, CRSobj = CRS("+init=epsg:3857 +proj=merc +a=6378137 +b=6378137 +lat_ts=0.0
- +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null
- +no_defs ")) #project to mercator
-
- #retrieve dem raster
- tryCatch(raster.tile <- get_elev_raster(sp.tile, z=9, expand=max(sp.tile$`Location uncertainty (m)`), clip="bbox"),
- error = function(e){e}
- )
- if(!exists("raster.tile")) {
- raster.tile <- NA
- save(raster.tile, file = paste("../_derived/elevatr/elevation_tile_", i, "failed.RData", sep=""))
- message(paste("tile", i, "doesn't work!, skip to next"))
- rm(raster.tile)
- next
- }
- # clip dem tile with continent shape
- raster.tile <- mask(raster.tile, continent.high.merc)
-
- #extract and summarize elevation data
- elev.tile <- raster::extract(raster.tile, sp.tile, small=T)
- elev.tile.buffer <- raster::extract(raster.tile, sp.tile,
- buffer=sp.tile@data$`Location uncertainty (m)`,
- small=T)
- tmp <- round(mapply( quantile,
- x=elev.tile.buffer,
- #center=elev.tile,
- probs=rep(c(0.025, 0.5, 0.975), each=length(elev.tile)),
- #loc.uncert=sp.tile$`Location uncertainty (m)`,
- na.rm=T))
- elev.q95 <- setNames(data.frame(matrix(tmp, ncol = 3, nrow = length(elev.tile.buffer))),
- c("Elevation_q2.5", "Elevation_median", "Elevation_q97.5"))
- output.tile <- data.frame(PlotObservationID=sp.tile$PlotObservationID,
- elevation=round(elev.tile),
- elev.q95,
- DEM.res=res(raster.tile)[1])
-
- #save output
- save(output.tile, file = paste("../_derived/elevatr/elevation_tile_", i, ".RData", sep=""))
- print(i)
-}
+# Divided in 99 chunks
+elevation.out <- foreach(i=1:99, .combine=rbind) %dopar% {
+ source("A97_ElevationExtract.R")
+ ElevationExtract(header.shp, output, ncores=1, chunk.i=1)}
stopCluster(cl)
-
```
-
-###### TO CHECK BELOW HERE
-
For those tiles that failed, extract elevation of remaining plots one by one
-```{r}
+```{r, eval=F}
#create list of tiles for which dem could not be extracted
myfiles <- list.files("../_derived/elevatr/")
failed <- list.files("../_derived/elevatr/", pattern = "[A-Za-z]*_[0-9]+failed\\.RData$")
@@ -567,20 +456,36 @@ sp.tile0 <- spTransform(sp.tile0, CRSobj = CRS("+init=epsg:3857 +proj=merc +a=63
+lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null
+no_defs ")) #project to mercator
output.tile <- data.frame(NULL)
+
+cl <- makeForkCluster(5, outfile="")
+registerDoParallel(cl)
+
+clusterEvalQ(cl, {
+ library(rgdal)
+ library(raster)
+ library(sp)
+ library(elevatr)
+ library(dplyr)
+ })
+
#Loop over all plots
-for(i in 1:10){#nrow(sp.tile0)){
+#for(i in nrow(sp.tile0)){
+elevation.failed <- foreach(i=1:nrow(sp.tile0), .packages=c('raster'), .combine=rbind) %dopar% {
sp.tile <- sp.tile0[i,]
tryCatch(raster.tile <- get_elev_raster(sp.tile, z=10,
expand=max(sp.tile$`Location uncertainty (m)`)),
- error = function(e){#bind_rows(output.tile,
- # data.frame(PlotObservationID=sp.tile$PlotObservationID,
- # elevation=NA,
- # Elevation_q2.5=NA,
- # Elevation_median=NA,
- # Elevation_q97.5=NA,
- # DEM.res=NA))
+ error = function(e){
print(paste("could not retrieve DEM for", sp.tile$PlotObservationID))}
)
+ if(!exists("raster.tile")) {
+ output.tile <- data.frame(PlotObservationID==sp.tile$PlotObservationID,
+ elevation=NA,
+ Elevation_q2.5=NA,
+ Elevation_median=NA,
+ Elevation_q97.5=NA,
+ DEM.res=NA)
+ return(output.tile)
+ } else {
# clip dem tile with continent shape
raster.tile <- mask(raster.tile, continent.high.merc)
@@ -595,63 +500,100 @@ for(i in 1:10){#nrow(sp.tile0)){
probs=rep(c(0.025, 0.5, 0.975), each=length(elev.tile)),
#loc.uncert=sp.tile$`Location uncertainty (m)`,
na.rm=T)))
- output.tile <- bind_rows(output.tile,
- data.frame(PlotObservationID=sp.tile$PlotObservationID,
+ output.tile <- data.frame(PlotObservationID=sp.tile$PlotObservationID,
elevation=round(elev.tile),
elev.q95,
DEM.res=res(raster.tile)[1]) %>%
- rename(Elevation_q2.5=X2.5., Elevation_median=X50., Elevation_q97.5=X97.5.))
+ rename(Elevation_q2.5=X2.5., Elevation_median=X50., Elevation_q97.5=X97.5.)
+ return(output.tile)
+ }
}
-save(output.tile, file = paste("../_derived/elevatr/elevation_tile_", 0, ".RData", sep=""))
+stopCluster(cl)
+save(elevation.failed, file = "../_derived/elevatr/elevation_missing.RData")
```
-
Compose tiles into a single output, and export
-```{r }
-myfiles <- list.files(path)
-myfiles <- myfiles[grep(pattern="*.RData$", myfiles)]
+```{r eval=F}
+myfiles <- list.files("../_derived/elevatr/", pattern = "elevation_tile_[0-9]+\\.RData$", full.names = T)
+
#create empty data.frame
-elevation.out <- data.frame(PlotObservationID=header$PlotObservationID,
- elevation=NA,
- Elevation_q2.5=NA,
- Elevation_median=NA,
- Elevation_q97.5=NA,
- DEM.res=NA)
+elevation.out <- matrix(NA, nrow=nrow(header.tiles), ncol=6)
+elevation.out <- as.data.frame(elevation.out)
+colnames(elevation.out) <- c("PlotObservationID", "elevation", "Elevation_q2.5", "Elevation_median", "Elevation_q97.5","DEM.res")
+elevation.out$PlotObservationID <- header.tiles$PlotObservationID
+
+tmp <- NULL
for(i in 1:length(myfiles)){
- load(paste(path, myfiles[i], sep=""))
+ load(myfiles[i])
#attach results to empty data.frame
- mymatch <- match(output.tile$PlotObservationID, elevation.out$PlotObservationID)
- elevation.out[mymatch,] <- output.tile
- if(i %in% seq(1,length(myfiles), by=25)){print(i)}
+ tmp <- bind_rows(tmp, output.tile)
+ if(i %in% seq(5000, length(myfiles), by=5000)){
+ mymatch <- base::match(x=tmp$PlotObservationID, table=elevation.out$PlotObservationID)
+ mymatch <- mymatch[!is.na(mymatch)]
+ elevation.out[mymatch,] <- tmp
+ tmp <- NULL
+ print(paste("Attached first", i, "files"))
+ }
+ if(i %in% seq(1,length(myfiles), by=250)){print(i)}
}
-write_csv(elevation.out, path ="../_derived/elevatr/Elevation_out_v301.csv")
+load(file = "../_derived/elevatr/elevation_missing.RData")
+
+mymatch <- base::match(x=elevation.failed$PlotObservationID, table=elevation.out$PlotObservationID)
+mymatch <- mymatch[!is.na(mymatch)]
+elevation.out[mymatch,] <- elevation.failed
+write_csv(elevation.out, path ="../_derived/elevatr/elevation.out.csv")
```
-Reimport output and check
+
+
+Reimport output, attach to header and check
```{r message=F}
-elevation.out <- read_csv("../_derived/elevatr/Elevation_out.csv")
+elevation.out <- read_csv("../_derived/elevatr/elevation.out.csv")
knitr::kable(head(elevation.out,10),
- caption="Example of elevation output") %>%
+ caption="Example of elevation output (only first 10 shown)") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
full_width = F, position = "center")
-
summary(elevation.out)
```
-There are `r sum(elevation.out$elevation < -100, na.rm=T)` plots with elevation < -100 !
+There are `r sum(is.na(elevation.out$elevation))` plots without elevation info, corresponding to `r round(sum(is.na(elevation.out$elevation))/nrow(header)*100,1)`% of total.
+There are `r sum(elevation.out$elevation < -1, na.rm=T)` plots with elevation below sea level.
+
+```{r message=F}
+elevation.out <- read_csv("../_derived/elevatr/elevation.out.csv")
+knitr::kable(header %>%
+ dplyr::select(PlotObservationID, Dataset, `GIVD ID`) %>%
+ left_join(elevation.out,
+ by="PlotObservationID") %>%
+ filter(elevation< -1) %>%
+ group_by( `GIVD ID`, Dataset) %>%
+ summarize(num.plot=n()) %>%
+ ungroup() %>%
+ arrange(desc(num.plot)) %>%
+ slice(1:10),
+ caption="Dataset with highest number of plots below sea level") %>%
+ kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
+ full_width = F, position = "center")
+
+```
+
+```{r}
+summary(elevation.out %>%
+ dplyr::select(-PlotObservationID))
+
+```
Create Scatterplot between measured elevation in the field, and elevation derived from DEM
```{r scatterplot, cache=T}
#join measured and derived elevation
-mydata <- header %>%
- dplyr::select(PlotObservationID, `Altitude (m)`) %>%
- filter(!is.na(`Altitude (m)`)) %>%
- rename(elevation_measured=`Altitude (m)`) %>%
+mydata <- header %>%
left_join(elevation.out %>%
- dplyr::select(PlotObservationID, elevation) %>%
- rename(elevation_dem=elevation),
- by="PlotObservationID")
+ rename(elevation_dem=Elevation_median), by="PlotObservationID") %>%
+ dplyr::select(PlotObservationID, `Altitude (m)`, elevation_dem) %>%
+ filter(!is.na(`Altitude (m)`) & !is.na(elevation_dem)) %>%
+ rename(elevation_measured=`Altitude (m)`)
+
ggplot(data=mydata) +
geom_point(aes(x=elevation_measured, y=elevation_dem), alpha=1/10, cex=0.8) +
theme_bw() +
@@ -661,22 +603,9 @@ ggplot(data=mydata) +
```
-
-
-
-
-
-
-
# 5 Map of plots
-```{r}
-library(sf)
-library(rnaturalearth)
-library(viridis)
-library(dggridR)
-#### BRT predicting
-#### alternative plotting
+```{r, message=F, warning=F}
countries <- ne_countries(returnclass = "sf") %>%
st_transform(crs = "+proj=eck4") %>%
st_geometry()
@@ -689,10 +618,6 @@ bb <- ne_download(type = "wgs84_bounding_box", category = "physical",
st_transform(crs = "+proj=eck4") %>%
st_geometry()
-label_parse <- function(breaks) {
- parse(text = breaks)
-}
-
## basic graph of the world in Eckert projection
w3a <- ggplot() +
geom_sf(data = bb, col = "grey20", fill = "white") +
@@ -706,65 +631,270 @@ w3a <- ggplot() +
legend.background = element_rect(size=0.1, linetype="solid", colour = 1),
legend.key.height = unit(1.1, "cm"),
legend.key.width = unit(1.1, "cm")) +
- scale_fill_viridis(label=label_parse)
+ scale_fill_viridis()
+```
-## Plotting of plot density in hexagons
+Graph of plot density (hexagons)
+```{r, fig.align="center", fig.width=8, fig.height=4, cache=T}
header2 <- header %>%
filter(!is.na(Longitude) | !is.na(Latitude)) %>%
dplyr::select(PlotObservationID, Latitude, Longitude, `GIVD ID`) %>%
filter(!(abs(Longitude) >171 & abs(Latitude>70)))
-dggs <- dgconstruct(spacing=300, metric=T, resround='down')
- #Get the corresponding grid cells for each earthquake epicenter (lat-long pair)
- header2$cell <- dgGEO_to_SEQNUM(dggs, header2$Longitude, header2$Latitude)$seqnum
-
- #Calculate number of plots for each cell
- header.out <- header2 %>%
- group_by(cell) %>%
- summarise(value.out=log(n(), 10))
- #Get the grid cell boundaries for cells
- grid <- dgcellstogrid(dggs, header.out$cell, frame=F) %>%
- st_as_sf() %>%
- mutate(cell = header.out$cell) %>%
- mutate(value.out=header.out$value.out) %>%
- st_transform("+proj=eck4") %>%
- st_wrap_dateline(options = c("WRAPDATELINE=YES"))
-
- ## plotting
- legpos <- c(0.160, .24)
-
- (w3 <- w3a +
- geom_sf(data=grid, aes(fill=value.out),lwd=0, alpha=0.9) +
- geom_sf(data = countries, col = "grey10", fill=NA, lwd = 0.3) +
- scale_fill_viridis(
- name="# plots", breaks=0:5, labels = c("1", "10", "100",
- "1,000", "10,000", "100,000"), option="viridis" ) +
- #labs(fill="# plots") +
- theme(legend.position = legpos +c(-0.06, 0.25))
- )
+dggs <- dgconstruct(spacing=300, metric=T, resround='down')
+#Get the corresponding grid cells for each earthquake epicenter (lat-long pair)
+header2$cell <- dgGEO_to_SEQNUM(dggs, header2$Longitude, header2$Latitude)$seqnum
- ggsave(filename="../_pics/PlotDensityLog10_vir.png", width = 15, height = 7, units="in", dpi=300, plot=w3)
+#Calculate number of plots for each cell
+header.out <- header2 %>%
+ group_by(cell) %>%
+ summarise(value.out=log(n(), 10))
-
-## Graph of plot location by Dataset
+#Get the grid cell boundaries for cells
+grid <- dgcellstogrid(dggs, header.out$cell, frame=F) %>%
+ st_as_sf() %>%
+ mutate(cell = header.out$cell) %>%
+ mutate(value.out=header.out$value.out) %>%
+ st_transform("+proj=eck4") %>%
+ st_wrap_dateline(options = c("WRAPDATELINE=YES"))
+
+## plotting
+legpos <- c(0.160, .24)
+(w3 <- w3a +
+ geom_sf(data=grid, aes(fill=value.out),lwd=0, alpha=0.9) +
+ geom_sf(data = countries, col = "grey10", fill=NA, lwd = 0.3) +
+ scale_fill_viridis(
+ name="# plots", breaks=0:5, labels = c("1", "10", "100",
+ "1,000", "10,000", "100,000"), option="viridis" ) +
+ #labs(fill="# plots") +
+ theme(legend.position = legpos +c(-0.06, 0.25))
+)
+ggsave(filename="../_pics/PlotDensityLog10_vir.png", width = 15, height = 7, units="in", dpi=300, plot=w3)
+```
+
+Graph of plot location by Dataset
+```{r, fig.align="center", fig.width=8, fig.height=4, cache=T}
header.sf <- header.shp %>%
st_as_sf() %>%
- #sample_frac(0.01) %>%
- st_transform(crs = "+proj=eck4") #%>%
- #st_geometry()
+ st_transform(crs = "+proj=eck4")
-set.seed(1984)
-w3 <- w3a +
+(w4 <- w3a +
geom_sf(data=header.sf %>%
mutate(GIVD.ID=fct_shuffle(GIVD.ID)), aes(col=factor(GIVD.ID)), pch=16, size=0.8, alpha=0.6) +
geom_sf(data = countries, col = "grey10", fill=NA, lwd = 0.3) +
- #scale_color_brewer(palette = "Dark2") +
- #labs(fill="# plots") +
- theme(legend.position = "none")
+ theme(legend.position = "none"))
+
+ggsave(filename="../_pics/PlotDistrib_Dark2_shuffle1984.png", width = 15, height = 7, units="in", dpi=300, plot=w4) ## takes ~40' to render
+```
+## 6 Fix output and export
+```{r}
+header.out <- header %>%
+ dplyr::select(
+ #Metadata
+ PlotObservationID, Dataset, "GIVD ID", "TV2 relevé number", "ORIG_NUM", "GUID",
+ #Geographic info
+ Longitude:"Location uncertainty (m)", Country, CONTINENT, sBiome, sBiomeID, Locality,
+ #Methodological info
+ "Relevé area (m²)", "Cover abundance scale", "Date of recording", "Plants recorded",
+ "Herbs identified (y/n)","Mosses identified (y/n)","Lichens identified (y/n)",
+ #Topographical
+ elevation_dem, "Altitude (m)", "Aspect (°)", "Slope (°)",
+ #Vegetation type
+ Forest:Naturalness, ESY,
+ #Vegetation structure
+ "Cover total (%)":"Maximum height cryptogams (mm)")
+save(header.out, file = "../_output/header_splot3.0")
+
+```
-ggsave(filename="../_pics/PlotDistrib_Dark2_shuffle1984.png", width = 15, height = 7, units="in", dpi=300, plot=w3) ## takes ~40' to render
+## Supplementary Material
+### ANNEX 1 - Ancillary function - `PredExtr`
+```{r}
+PredExtr <- function(x.shp, myfunction=NA, output,
+ toextract, typp=c("raster", "shp"), ncores, chunkn=1, chunk.i=NA){
+ print("Load Packages")
+ require(foreach)
+ require(parallel)
+ require(doParallel)
+ require(raster)
+ require(rgeos)
+ require(rgdal)
+
+ print(paste("Loading", typp, "data :", toextract))
+ print(paste("output will be:", output))
+ if(typp=="raster"){ mypredictor <- raster(toextract)} else
+ mypredictor <- readOGR(toextract)
+ header.shp <-readOGR(x.shp)
+ crs(mypredictor) <- crs(header.shp) #should be verified beforehand!
+
+ ## Divide in chunks if requested
+ if(chunkn>1 & !is.na(chunk.i)){
+ print(paste("divide in chunks and run on chunk n.", chunk.i))
+ indices <- 1:length(header.shp)
+ chunks <- split(indices, sort(indices%%chunkn))
+ header.shp <- header.shp[chunks[[chunk.i]],]
+ }
+
+ # define ancillary functions
+ robust.mean <- function(x){mean(x, rm.na=T)}
+ robust.mode <- function(x){if(any(x!=0)) {
+ a <- x[which(x!=0)] #exclude zero (i.e. NAs)
+ return(as.numeric(names(sort(table(a), decreasing=T))[1]))} else
+ return(NA) }
+
+ print("go parallel")
+ cl <- makeForkCluster(ncores, outfile="" )
+ registerDoParallel(cl)
+
+ print("start main foreach loop")
+ if(typp=="raster"){
+ out <- foreach(i=1:length(header.shp), .packages=c('raster'), .combine=rbind) %dopar% {
+ tmp <- raster::extract(mypredictor, header.shp[i,],
+ buffer=min(10000, max(250, header.shp@data$loc.uncert[i])), fun=myfunction)
+ }
+ write.csv(out, file = output)
+ } else {
+ out <- sp::over(x=header.shp, y=mypredictor)
+ toassign <- header.shp[which(is.na(out[,1])),]
+ print(paste(length(toassign), "plots not matched directely - seek for NN"))
+ if(length(toassign)>0){
+
+ nearest <- foreach(i=1:length(toassign), .packages=c('raster'), .combine=rbind) %dopar% {
+ print(i)
+ nearest.tmp <- tryCatch(geosphere::dist2Line(header.shp[i,], mypredictor),
+ error = function(e){data.frame(distance=NA, lon=NA, lat=NA,ID=NA)}
+ )
+ #nearest.tmp <- geosphere::dist2Line(toassign[i,], mypredictor)
+ return(nearest.tmp)
+ }
+ out[which(is.na(out[,1])),] <- mypredictor@data[nearest[,"ID"],]
+ }
+ write.csv(out, file=output)
+ }
+ stopCluster(cl)
+ }
+```
+### ANNEX 2 - Ancillary function - `ElevationExtract`
+```{r}
+
+ElevationExtract <- function(header, output, ncores, chunk.i){
+ print("load packages")
+ require(tidyverse)
+
+ require(rgdal)
+ require(sp)
+ require(rgeos)
+ require(raster)
+ require(rworldmap)
+ require(elevatr)
+
+ require(parallel)
+ require(doParallel)
+
+ print("Import header and divide in tiles")
+ header.shp <- readOGR(header)
+ header.tiles <- header.shp@data %>%
+ bind_cols(as.data.frame(header.shp@coords)) %>%
+ rename(PlotObservationID=PltObID, Longitude=coords.x1, Latitude=coords.x2) %>%
+ mutate(lc_ncrt=abs(lc_ncrt)) %>%
+ filter(lc_ncrt <= 50000) %>%
+ mutate_at(.vars=vars(Longitude, Latitude),
+ .funs=list(tile=~cut(., breaks = seq(-180,180, by=.2)))) %>%
+ mutate(tilenam=factor(paste(Longitude_tile, Latitude_tile)))
+
+
+ print("Get continent")
+ sPDF <- rworldmap::getMap(resolution="high")
+ continent.high <- sPDF[,"continent"]
+ crs(continent.high) <- CRS("+init=epsg:4326")
+ continent.high@data$continent <- fct_recode(continent.high@data$continent, "South America"="South America and the Caribbean")
+ continent.high.merc <- spTransform(continent.high, CRS( "+init=epsg:3857 +proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m
++nadgrids=@null +no_defs"))
+
+ print("go parallel")
+ cl <- makeForkCluster(ncores, outfile="")
+ registerDoParallel(ncores)
+
+ clusterEvalQ(cl, {
+ library(rgdal)
+ library(raster)
+ library(sp)
+ library(elevatr)
+ library(dplyr)
+ })
+
+ print("create list of tiles still to do")
+ myfiles <- list.files(path = output, pattern = "[A-Za-z]*_[0-9]+\\.RData$")
+ done <- NULL
+ done <- as.numeric(unlist(regmatches(myfiles, gregexpr("[[:digit:]]+", myfiles))))
+ todo <- 1:nlevels(header.tiles$tilenam)
+ if(length(done)>0) {todo <- todo[-which(todo %in% done)]}
+ todo <- sample(todo, replace=F)
+ print(paste(length(todo), "tiles to do"))
+
+ print("divide in chunks")
+ #divide in chunks #should take around 40'
+ indices <- 1:length(todo)
+ todo.chunks <- split(indices, sort(indices%%99))
+
+ print(paste("start main foreach loop on chunk n=", chunk.i))
+ foreach(i = todo.cunks[[chunk.i]]) %dopar% {
+ print(paste("doing", i))
+ #create sp and project data
+ if(nrow(header.tiles %>%
+ filter(tilenam %in% levels(header.tiles$tilenam)[i])) ==0 ) next()
+ sp.tile <- SpatialPointsDataFrame(coords=header.tiles %>%
+ filter(tilenam %in% levels(header.tiles$tilenam)[i]) %>%
+ dplyr::select(Longitude, Latitude),
+ data=header.tiles %>%
+ filter(tilenam %in% levels(header.tiles$tilenam)[i]) %>%
+ dplyr::select(-Longitude, -Latitude),
+ proj4string = CRS("+init=epsg:4326"))
+ sp.tile <- spTransform(sp.tile, CRSobj = CRS("+init=epsg:3857 +proj=merc +a=6378137 +b=6378137 +lat_ts=0.0
+ +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null
+ +no_defs ")) #project to mercator
+
+ #retrieve dem raster
+ tryCatch(raster.tile <- get_elev_raster(sp.tile, z=9, expand=max(sp.tile$lc_ncrt), clip="bbox"),
+ error = function(e){e}
+ )
+ if(!exists("raster.tile")) {
+ raster.tile <- NA
+ save(raster.tile, file = paste(output, "elevation_tile_", i, "failed.RData", sep=""))
+ message(paste("tile", i, "doesn't work!, skip to next"))
+ rm(raster.tile)
+ next
+ }
+ # clip dem tile with continent shape
+ raster.tile <- mask(raster.tile, continent.high.merc)
+
+ #extract and summarize elevation data
+ elev.tile <- raster::extract(raster.tile, sp.tile, small=T)
+ elev.tile.buffer <- raster::extract(raster.tile, sp.tile,
+ buffer=sp.tile@data$lc_ncrt,
+ small=T)
+ tmp <- round(mapply( quantile,
+ x=elev.tile.buffer,
+ #center=elev.tile,
+ probs=rep(c(0.025, 0.5, 0.975), each=length(elev.tile)),
+ #lc_ncrt=sp.tile$lc_ncrt,
+ na.rm=T))
+ elev.q95 <- setNames(data.frame(matrix(tmp, ncol = 3, nrow = length(elev.tile.buffer))),
+ c("Elevation_q2.5", "Elevation_median", "Elevation_q97.5"))
+ output.tile <- data.frame(PlotObservationID=sp.tile$PlotObservationID,
+ elevation=round(elev.tile),
+ elev.q95,
+ DEM.res=res(raster.tile)[1])
+
+ #save output
+ save(output.tile, file = paste(output, "elevation_tile_", i, ".RData", sep=""))
+ print(paste(i, "done"))
+ }
+ stopCluster(cl)
+ }
+
```