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Drought_VCI_from_EVI_with_cloudmask.Rmd
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Drought_VCI_from_EVI_with_cloudmask.Rmd
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<<<<<<< HEAD
---
title: ""
author: "UN-SPIDER"
date: "October 15, 2018"
output:
html_notebook: default
html_document: default
---
##Recommended Practice: Drought monitoring using the Vegetation Condition Index (VCI) - R script
#### ToDo (data preparation):
1. Download MOD13Q1 data at https://lpdaacsvc.cr.usgs.gov/appeears/
2. Create two folders, where you will store all data, one for the EVI files and one for the Pixel Reliability files, e.g. C:/Data_EVI and C:/Data_Pixel_Reliability.
3. In both folders for each day of the year (DOY) create one subfolder. The name of each folder must start with "DOY_", e.g. DOY_033.
4. Rename the files by addding a prefix following the pattern DOY_YYYY_, e.g. 033_2001 or 001_2005.
Total Commander is a useful tool to rename multiple files (Download Link: http://www.ghisler.com/index.htm).
Renaming the files is important to automatize the filenames and the titles of the resulting maps.
6. Sort the EVI and Pixel Reliability data according to the DOY in the respective folders
7. Create another subfolder within the EVI data folder called "shape". Store the shapefile with the country border here.
8. Adjust the capture of the resulting jpg map to fit your area of interest in line 103.
****
Installing relevant packages
****
```{r eval=FALSE}
install.packages("raster") # you only have to do this once
install.packages("rgdal") # you only have to do this once
install.packages("sp") # you only have to do this once
```
Calling packages for use
```{r eval=FALSE}
library(raster)
library(rgdal)
library(sp)
```
Load borders (Download country borders as shapefiles: http://www.gadm.org/download)
**ToDo: insert link to the shapefile with the country borders**
```{r eval=FALSE}
border <- shapefile("C:/User/MOD13Q1/Data/shape/gadm36_GTM_0.shp")
```
**ToDo: enter link to the folder where you have stored the MODIS EVI data**
```{r eval=FALSE}
path <- "C:/User/MOD13Q1/Data/Data_EVI"
dlist <- dir(path,pattern="DOY")
```
**ToDo: enter link to the folder where you have stored the MODIS Pixel Reliability data**
```{r eval=FALSE}
path_c <- "C:/User/MOD13Q1/Data/Data_Pixel_Reliability"
dlist_c <- dir(path_c,pattern="DOY")
```
Showing an example of the downloaded EVI data
<center>
</center>
Showing an example of the corresponding Pixel Reliability
</center>
The pixel reliability rank includes the following:
Rank Key|Summary Quality Assurance
--------|--------
-1 |Fill/No Data
***0*** |***Good data***
***1*** |***Marginal Data***
2 |Snow/Ice
3 |Cloudy
**ToDo: enter the links to the folder where you want to store the resulting .jpg-images and .tif-files.**
```{r eval=FALSE}
path_jpg <- "C:/User/MOD13Q1/Data/VCI_Maps_Guatemala_jpg"
path_tif <- "C:/User/MOD13Q1/Data/VCI_Maps_Guatemala_jpg"
```
Creating a progress bar in the Console, wich ends at the end of the loop. The progress bar looks like this:
"=======""
```{r eval=FALSE}
pb <- txtProgressBar (min=0, max=length(dlist), style=1)
setTxtProgressBar (pb, 0)
```
****
> Main code for processing the EVI data, masking out clouds, calculating the VCI and writing the results
****
```{r eval=FALSE}
for (i in 1:length(dlist)) { # start of the outer for-loop
fold <- paste(path,dlist[i],sep="/") # the respective DOY folder of the Data_EVI folder
fls <- dir(fold,pattern=".tif") # all files that are available in the respective EVI DOY folder
flsp <-paste(fold,fls,sep="/") # all files that are available in the respective EVI DOY folder with complete path name
evistack <- stack(flsp) # creates a layer stack of all files within the EVI DOY folder
eviresize<- crop(evistack,border) # resizes the EVI layer stack to the rectangular extent of the border shapefile
evimask<-mask(eviresize,border) # masks the EVI layer stack using the border shapefile
evi<-evimask*0.0001 # rescaling of MODIS EVI data
evi[evi==-0.3]<-NA # EVI fill value(-0,3) in NA
evi[evi<(0)]<-NA # as we are only interested in vegetation valid EVI range is 0 to 1 and all EVI values smaller than 0 set to NA
fold_c <- paste(path_c,dlist_c[i],sep="/") # the respective DOY folder of the Data_Pixel_Reliability folder
fls_c <- dir(fold_c,pattern=".tif") # all files that are available in the respective Pixel Reliability DOY folder
flsp_c <-paste(fold_c,fls_c,sep="/") # all files that are available in the respective Pixel Reliability DOY folder with complete path name
cloudstack <- stack(flsp_c) # creates a layer stack of all files within the Pixel Relaibility DOY folder
cloudresize<- crop(cloudstack,border) # resizes the Pixel Reliability layer stack to the rectangular extent of the border shapefile
cloudmask<-mask(cloudresize,border) # masks the Pixel Reliability layer stack using the border shapefile
cloudmask[cloudmask==(3)]<-NA # Pixel Reliability rank 3 pixels (cloudy) set to NA
cloudmask[cloudmask==(2)]<-NA # Pixel Reliability rank 2 pixels (snow&ice) set to NA
cloudmask[cloudmask==(0)]<-1 # Pixel Reliability rank 0 pixels (good quality) set to 1
cloudmask[cloudmask>(3)]<-NA # as valid Pixel Reliability range is -1 to 3, all Pixel Reliability values >3 set to NA
# (as -1 rank pixels show value 255)
evi_c=evi*cloudmask # multiplying the EVI layer stack by the Pixel Reliability layer stack
# to get one single layer stack with applied cloud mask
# extracting max and min value for each pixel
evimax <- stackApply (evi_c, rep (1, nlayers (evi)), max, na.rm=T) #calculating the max value for the layer stack for each individual pixel
evimin <- stackApply (evi_c, rep (1, nlayers (evi)), min, na.rm=T) #calculating the min value for the layer stack for each individual pixel
# if na.rm is TRUE NA values are ignored, otherwise an NA value in any of the arguments will cause a value of NA to be returned,
# https://stat.ethz.ch/R-manual/R-devel/library/base/html/Extremes.html
VCI_all <- ((evi_c-evimin)/(evimax-evimin))*100 #calculating VCI
# define breaks and color scheme
my_palette <- colorRampPalette(c('#8B0000', '#FF4500', '#FFFF00', '#9ACD32', '#008000'))
fold_jpg <- paste(path_jpg) # the respective folder where you want to store the resulting .jpg-images.
fold_tif <- paste(path_tif) # the respective folder where you want to store the resulting .tif-files.
for (k in 1:nlayers(VCI_all)) { # start of the inner for-loop
year <- substr(fls[k],5,8) # extracting the fifth to eigths letter of the filename, which is the year (cf. data preparation above)
doy <- substr(fls[k],1,3) # extracting the first to third letter of the filename, which is the DOY (cf. data preparation above)
# writeRaster(evi[[k]], filename=paste(fold,"/",doy,"_",year,sep=""), format="ENVI", datatype='FLT4S', overwrite=TRUE)
# in case you would like to have Envi files (Attention: note the datatype)
jpeg(filename=paste(fold_jpg,"/",doy,"_",year,".jpg",sep=""), quality = 100)
# writes the jpg maps and names the files autmatically according to the pattern DOY_YYYY
plot(VCI_all[[k]],
zlim=c(0,100),
col=my_palette(101), # sets the colors as defined above
main=paste("VCI"," (EVI)"," sample ",doy," ",year,sep="")) # automizes the title of the plot.
# ToDo: Adjust the file naming according to the data you are processing!
# E.g. if you base your VCI on EVI data, write (EVI)
dev.off()
writeRaster(VCI_all[[k]], filename=paste(fold_tif,"/",doy,"_",year,".tif",sep=""), format="GTiff", overwrite=TRUE)
# writes the geotiff and automizes the file naming according to the pattern DOY_YYYY
} # end of the inner for-loop
setTxtProgressBar (pb, i)
} # end of the outer for-loop
=======
---
title: ""
author: "UN-SPIDER"
date: "October 15, 2018"
output:
html_notebook: default
html_document: default
---
##Recommended Practice: Drought monitoring using the Vegetation Condition Index (VCI) - R script
#### ToDo (data preparation):
1. Download MOD13Q1 data at https://lpdaacsvc.cr.usgs.gov/appeears/
2. Create two folders, where you will store all data, one for the EVI files and one for the Pixel Reliability files, e.g. C:/Data_EVI and C:/Data_Pixel_Reliability.
3. In both folders for each day of the year (DOY) create one subfolder. The name of each folder must start with "DOY_", e.g. DOY_033.
4. Rename the files by addding a prefix following the pattern DOY_YYYY_, e.g. 033_2001 or 001_2005.
Total Commander is a useful tool to rename multiple files (Download Link: http://www.ghisler.com/index.htm).
Renaming the files is important to automatize the filenames and the titles of the resulting maps.
6. Sort the EVI and Pixel Reliability data according to the DOY in the respective folders
7. Create another subfolder within the EVI data folder called "shape". Store the shapefile with the country border here.
8. Adjust the capture of the resulting jpg map to fit your area of interest in line 103.
****
Installing relevant packages
****
```{r eval=FALSE}
install.packages("raster") # you only have to do this once
install.packages("rgdal") # you only have to do this once
install.packages("sp") # you only have to do this once
```
Calling packages for use
```{r eval=FALSE}
library(raster)
library(rgdal)
library(sp)
```
Load borders (Download country borders as shapefiles: http://www.gadm.org/download)
**ToDo: insert link to the shapefile with the country borders**
```{r eval=FALSE}
border <- shapefile("C:/User/MOD13Q1/Data/shape/gadm36_GTM_0.shp")
```
**ToDo: enter link to the folder where you have stored the MODIS EVI data**
```{r eval=FALSE}
path <- "C:/User/MOD13Q1/Data/Data_EVI"
dlist <- dir(path,pattern="DOY")
```
**ToDo: enter link to the folder where you have stored the MODIS Pixel Reliability data**
```{r eval=FALSE}
path_c <- "C:/User/MOD13Q1/Data/Data_Pixel_Reliability"
dlist_c <- dir(path_c,pattern="DOY")
```
Showing an example of the downloaded EVI data
<center>
</center>
Showing an example of the corresponding Pixel Reliability
</center>
The pixel reliability rank includes the following:
Rank Key|Summary Quality Assurance
--------|--------
-1 |Fill/No Data
***0*** |***Good data***
***1*** |***Marginal Data***
2 |Snow/Ice
3 |Cloudy
**ToDo: enter the links to the folder where you want to store the resulting .jpg-images and .tif-files.**
```{r eval=FALSE}
path_jpg <- "C:/User/MOD13Q1/Data/VCI_Maps_Guatemala_jpg"
path_tif <- "C:/User/MOD13Q1/Data/VCI_Maps_Guatemala_jpg"
```
Creating a progress bar in the Console, wich ends at the end of the loop. The progress bar looks like this:
"=======""
```{r eval=FALSE}
pb <- txtProgressBar (min=0, max=length(dlist), style=1)
setTxtProgressBar (pb, 0)
```
****
> Main code for processing the EVI data, masking out clouds, calculating the VCI and writing the results
****
```{r eval=FALSE}
for (i in 1:length(dlist)) { # start of the outer for-loop
fold <- paste(path,dlist[i],sep="/") # the respective DOY folder of the Data_EVI folder
fls <- dir(fold,pattern=".tif") # all files that are available in the respective EVI DOY folder
flsp <-paste(fold,fls,sep="/") # all files that are available in the respective EVI DOY folder with complete path name
evistack <- stack(flsp) # creates a layer stack of all files within the EVI DOY folder
eviresize<- crop(evistack,border) # resizes the EVI layer stack to the rectangular extent of the border shapefile
evimask<-mask(eviresize,border) # masks the EVI layer stack using the border shapefile
evi<-evimask*0.0001 # rescaling of MODIS EVI data
evi[evi==-0.3]<-NA # EVI fill value(-0,3) in NA
evi[evi<(0)]<-NA # as we are only interested in vegetation valid EVI range is 0 to 1 and all EVI values smaller than 0 set to NA
fold_c <- paste(path_c,dlist_c[i],sep="/") # the respective DOY folder of the Data_Pixel_Reliability folder
fls_c <- dir(fold_c,pattern=".tif") # all files that are available in the respective Pixel Reliability DOY folder
flsp_c <-paste(fold_c,fls_c,sep="/") # all files that are available in the respective Pixel Reliability DOY folder with complete path name
cloudstack <- stack(flsp_c) # creates a layer stack of all files within the Pixel Relaibility DOY folder
cloudresize<- crop(cloudstack,border) # resizes the Pixel Reliability layer stack to the rectangular extent of the border shapefile
cloudmask<-mask(cloudresize,border) # masks the Pixel Reliability layer stack using the border shapefile
cloudmask[cloudmask==(3)]<-NA # Pixel Reliability rank 3 pixels (cloudy) set to NA
cloudmask[cloudmask==(2)]<-NA # Pixel Reliability rank 2 pixels (snow&ice) set to NA
cloudmask[cloudmask==(0)]<-1 # Pixel Reliability rank 0 pixels (good quality) set to 1
cloudmask[cloudmask>(3)]<-NA # as valid Pixel Reliability range is -1 to 3, all Pixel Reliability values >3 set to NA
# (as -1 rank pixels show value 255)
evi_c=evi*cloudmask # multiplying the EVI layer stack by the Pixel Reliability layer stack
# to get one single layer stack with applied cloud mask
# extracting max and min value for each pixel
evimax <- stackApply (evi_c, rep (1, nlayers (evi)), max, na.rm=T) #calculating the max value for the layer stack for each individual pixel
evimin <- stackApply (evi_c, rep (1, nlayers (evi)), min, na.rm=T) #calculating the min value for the layer stack for each individual pixel
# if na.rm is TRUE NA values are ignored, otherwise an NA value in any of the arguments will cause a value of NA to be returned,
# https://stat.ethz.ch/R-manual/R-devel/library/base/html/Extremes.html
VCI_all <- ((evi_c-evimin)/(evimax-evimin))*100 #calculating VCI
# define breaks and color scheme
my_palette <- colorRampPalette(c('#8B0000', '#FF4500', '#FFFF00', '#9ACD32', '#008000'))
fold_jpg <- paste(path_jpg) # the respective folder where you want to store the resulting .jpg-images.
fold_tif <- paste(path_tif) # the respective folder where you want to store the resulting .tif-files.
for (k in 1:nlayers(VCI_all)) { # start of the inner for-loop
year <- substr(fls[k],5,8) # extracting the fifth to eigths letter of the filename, which is the year (cf. data preparation above)
doy <- substr(fls[k],1,3) # extracting the first to third letter of the filename, which is the DOY (cf. data preparation above)
# writeRaster(evi[[k]], filename=paste(fold,"/",doy,"_",year,sep=""), format="ENVI", datatype='FLT4S', overwrite=TRUE)
# in case you would like to have Envi files (Attention: note the datatype)
jpeg(filename=paste(fold_jpg,"/",doy,"_",year,".jpg",sep=""), quality = 100)
# writes the jpg maps and names the files autmatically according to the pattern DOY_YYYY
plot(VCI_all[[k]],
zlim=c(0,100),
col=my_palette(101), # sets the colors as defined above
main=paste("VCI"," (EVI)"," sample ",doy," ",year,sep="")) # automizes the title of the plot.
# ToDo: Adjust the file naming according to the data you are processing!
# E.g. if you base your VCI on EVI data, write (EVI)
dev.off()
writeRaster(VCI_all[[k]], filename=paste(fold_tif,"/",doy,"_",year,".tif",sep=""), format="GTiff", overwrite=TRUE)
# writes the geotiff and automizes the file naming according to the pattern DOY_YYYY
} # end of the inner for-loop
setTxtProgressBar (pb, i)
} # end of the outer for-loop
>>>>>>> 1903c5fe845011d166b80bef2c1c966c5cefc03c
```