ProcessingEvaluationMonitoringCube.Rmd

---
title: "Processing and Evaluation of Monitoring Cube"
author: "Nick Jakuschona – n_jaku01@uni-muenster.de, Tom Niers – tom.niers@uni-muenster.de"
date: "24.3.2021"
output: html_document
---

```{r, out.width=600, echo=FALSE}
#Detect deforestation on a added month

# set variables
percentage= 0.1 # the quantile of reference data which should be used to detect deforestation
newMonth= "2019-09" # the month which should be proofed. Month must be deaseasonalized and saved. This can be ideally done by the PreprocessingMonitoringCube.Rmd file.

load("data/NDVIchanges.Rdata")

library("lubridate")
library("raster")

#calculate the NDVI decrease value at which we predict a deforestation
criticalValue=quantile(changes, percentage)

#Select NDVI values from 2 month ago, to detect changes
dat = as.Date(paste(newMonth,"-01",sep=""))
d1= dat%m-% months(2)
oldMonth = format(d1, "%Y-%m")

newFilename= paste0("data/MonitoringDeseasonalized/Des_", newMonth ,".tif")
rasterNew= raster(newFilename)
oldFilename= paste0("data/MonitoringDeseasonalized/Des_", oldMonth ,".tif")
rasterOld= raster(oldFilename)



# calculate the NDVI change
NDVIchange= rasterNew-rasterOld

#check where the NDVI decrease is over the critical value
overCritical= NDVIchange<=criticalValue


  
```

```{r, echo=FALSE}
#show NDVI map with detected deforestation

  #divide by zero to create NA
  criticalNA = overCritical/overCritical

plotFigure1= function(rasterNew, criticalNA){
  plot(rasterNew)
  plot(criticalNA, add=TRUE, legend=FALSE,col="blue")
}

plotFigure1(rasterNew, criticalNA)
  


```

```{r, echo=FALSE}
# Evaluation 


#date, where deforestation monitoring data is available
# http://terrabrasilis.dpi.inpe.br/geonetwork/srv/eng/catalog.search#/metadata/b75b83db-8026-43f9-9537-ee1dfa308158
dateToEvaluate= "2019-08"


  filenameDEF =paste0("data/deforestation/def",dateToEvaluate ,"RasterClipped.tif")
  
  #inspect NDVI rasters one month before and after the deforestation was monitored
  dat = as.Date(paste(dateToEvaluate,"-01",sep=""))
  d1= dat%m+% months(1)
  dat1String = format(d1, "%Y-%m")
  d2= dat%m-% months(1)
  dat2String = format(d2, "%Y-%m")
  print(dat2String)
  
  
  filenameNDVI1 = paste0("data/MonitoringDeseasonalized/Des_", dat1String ,".tif")
  filenameNDVI2 = paste0("data/MonitoringDeseasonalized/Des_", dat2String ,".tif")
  

  defRaster= raster(filenameDEF)

  NDVI1Raster=raster(filenameNDVI1)

  NDVI2Raster=raster(filenameNDVI2)

  
  ext= raster::extent(NDVI1Raster)
  defRaster = setExtent(defRaster, ext, keepres = TRUE)
  
  # calculate the NDVI change
  diff=  NDVI1Raster - NDVI2Raster
  
  
  #check where the NDVI decrease is over the critical value
  overCritical= diff<=criticalValue
  
```
  
  
  
```{r, echo=FALSE}
  #Proof our detection against reference data

  #change NA to 0
  defRaster[is.na(defRaster[])] <- 0 

  #calculate correct and incorrect detections 
  correctTrue= ((overCritical==1) + (defRaster==1)) == 2
  correctFalse= (overCritical==0) + (defRaster==0) ==2
  incorrectTrue= (overCritical==1) + (defRaster==0)==2
  incorrectFalse= (overCritical==0) + (defRaster==1)==2
  
  correctTrueCount = cellStats(correctTrue, "sum")
  correctTruePercentage = correctTrueCount / cellStats(defRaster==1, "sum")
  
  correctFalseCount = cellStats(correctFalse, "sum")
  correctFalsePercentage = correctFalseCount / cellStats(defRaster==0, "sum")
  
  incorrectTrueCount = cellStats(incorrectTrue, "sum")
  incorrectTruePercentage = cellStats(incorrectTrue, "sum") / cellStats(defRaster==0, "sum")
  
  incorrectFalseCount = cellStats(incorrectFalse, "sum")
  incorrectFalsePercentage = cellStats(incorrectFalse, "sum") / cellStats(defRaster==1, "sum")
  


```




```{r, echo=FALSE}
library(gridExtra)

x <- data.frame(row.names=c("Detected True", "Detected False"))
x[,1] <- c(paste0(floor(correctTruePercentage*10000)/100 ," %"), paste0(floor(incorrectFalsePercentage*10000)/100 ," %"))
x[,2] <- c(paste0(floor(incorrectTruePercentage*10000)/100 ," %"), paste0(floor(correctFalsePercentage*10000)/100 ," %"))
colnames(x) <- c("Actual True", "Actual False")

table <- tableGrob(x)

plotFigure2= function(table){
plot(table)
}

plotFigure2(table)

```