Initial commit with cleaned history

2024-09-01 18:09:25 +02:00
commit 208004e9d0
273 changed files with 234697 additions and 0 deletions
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+library(raster)
+library(rgdal)
+setwd("~/OneDrive/Dokumente/Fernerkundung/Regio/11_Landwirtschaft")
+
+#### Build NDVI stack ####
+
+# read all files from the directory starting with "CU_LC08.001_SRB4" (OLI - red band)
+landsatred <- list.files(pattern="CU_LC08.001_SRB4", path="/Users/huaqo/OneDrive/Dokumente/Fernerkundung/Regio/11_Landwirtschaft/data", full.names=TRUE)
+
+# read all files from the directory starting with "CU_LC08.001_SRB5" (OLI - NIR band)
+landsatNIR <- list.files(pattern="CU_LC08.001_SRB5", path="/Users/huaqo/OneDrive/Dokumente/Fernerkundung/Regio/11_Landwirtschaft/data", full.names=TRUE)
+
+# Build stacks for red and NIR bands
+landsatred_stack <- stack(landsatred)
+landsatNIR_stack <- stack(landsatNIR)
+
+# calculate the NDVI
+NDVI_stack <- (landsatNIR_stack-landsatred_stack)/(landsatNIR_stack+landsatred_stack)
+
+#### Crop the dataset to the study area
+
+# read the shapefle
+myshp <- readOGR("study_area_sem10.shp")
+
+# Check crs of datasets
+crs(NDVI_stack)
+crs(myshp)
+
+# Reporject the shapefile
+myshp_proj <- spTransform(myshp, crs(NDVI_stack))
+
+# Subset the image data and save it
+NDVI_stack_subset <- crop(NDVI_stack, extent(myshp_proj), snap="out", filename="NDVI_timeseries_2019.tif", overwrite=TRUE)
+
+# split the file name with the delimiter "_", read the date and format the dates
+name_split  <- sapply(landsatred, function(i) unlist(strsplit(i,"_")))
+
+# identify number of substring containing the date (in my case 6)
+View(name_split)
+dates <- name_split [6,]
+dates <-substring(dates,4,10)
+dates <- as.Date(dates, "%Y%j")
+
+# rename the bands according to their date
+names(NDVI_stack_subset) <- dates
+
+ndvistack <- stack ("NDVI_timeseries_2019.tif")
+#or
+#ndvistack <- NDVI_stack_subset
+
+#### Create date vector (needed if NDVI time series is not created above) ####
+
+# read all files from the directory starting with "CU_LC08.001_SRB4" (OLI - red band)
+landsatred <- list.files(pattern="CU_LC08.001_SRB4", path="C:\\Users\\marion\\Documents\\2020_maerz\\RegionaleThemen_CA\\seminar10\\data", full.names=TRUE)
+
+# split the file name with the delimiter "_", read the date and format the dates
+name_split  <- sapply(landsatred, function(i) unlist(strsplit(i,"_")))
+
+# identify number of substring containing the date (in my case 6)
+View(name_split)
+dates <- name_split [6,]
+dates <-substring(dates,4,10)
+dates <- as.Date(dates, "%Y%j")
+
+# rename the bands according to their date
+names(ndvistack) <- dates
+
+plotRGB(ndvistack, 1,4,7, stretch="lin")
+
+
+cal_proj <- shapefile("training_pol_col.shp")
+
+View(cal_proj@data) ## attribute table
+
+crs(cal_proj)
+crs(ndvistack)
+
+
+cal <- spTransform(cal_proj, crs(ndvistack))
+
+
+cropcol <- rgb(cal$FIRST_RED, cal$FIRST_GREE, cal$FIRST_BLUE)
+
+plotRGB (ndvistack, 1, 3, 5, stretch="lin")
+plot (cal, add=T, col=cropcol, border ="black", lwd=3)
+
+calpix <- extract (ndvistack, cal, df=FALSE)
+
+names (calpix) <- cal@data[,1] 
+
+calpix2 <- extract (ndvistack, cal, df=TRUE) 
+
+View(calpix)
+View(calpix2)
+
+cl <- rep(cropcol, sapply(calpix, nrow))
+
+classcentroids <- t(sapply (calpix, colMeans)) 
+
+# plot empty plot of a defined size
+plot(0,
+     ylim = c(min(classcentroids), max(classcentroids)), 
+     xlim = c(min(dates), max(dates)), 
+     type = 'n', 
+     xlab = "time", 
+     ylab = "NDVI",
+     xaxt='n'
+)
+
+# label the x axis
+axis(1, dates, format(dates, "%b %d"), cex.axis = .7)
+
+
+# draw one line for each class
+for (i in 1:nrow(classcentroids)){
+    lines(dates, as.numeric(classcentroids[i,]), 
+          lwd = 4, 
+          col = cropcol[i]
+    )
+}
+
+# add a grid
+grid()
+
+# add a legend
+legend(as.character(cal@data[,1]),
+       x = "topleft",
+       col = cropcol,
+       lwd = 5,
+       bty = "n"
+)
+
+d <- c(1,3,5,7)
+dates4 <- dates[d]
+
+classcentroids_4bands <- aggregate(calpix2[, c(2,4,6,8)], list(calpix2$ID), mean)
+
+for (i in 1:4){
+    for (j in 1:4){
+        plot (calpix2[,i+1], calpix2[,j+1], col=cl, pch=19, cex=0.1, 
+              xlab=paste ("NDVI ",dates4[i]), ylab=paste ("NDVI ",dates4[j]), xlim= c(-0.3,1),
+              ylim= c(-0.3,1)) ## the '+1' is necessary to
+        ## skip the first column with the class codes
+        points (classcentroids[,i], classcentroids[,j], bg=cropcol, pch=21)
+        
+        legend("topleft",
+               legend = cal@data[,1],
+               fill = cropcol,
+               border = FALSE,
+               box.col="grey",
+               cex = 0.7) # t
+        readline ("Press ENTER for next plot")
+    }}
+
+source ("mindistclassifier.r")
+
+map <- mindistclassifier(class.codes=1:10, cal.ref=classcentroids,
+                         image.stack=ndvistack)
+
+plot(map)
+
+plot(map, col = cropcol, legend =FALSE
+)
+
+legend("topright",
+       legend = cal@data[,1],
+       fill = cropcol,
+       border = FALSE,
+       box.col="grey") # turn off legend border)
+
+map@legend@colortable <- c ("#000000", cropcol, rep ("#000000", 254))
+writeRaster (map, "mindist_map.tif", format="GTiff", overwrite=TRUE)
+
+val <- shapefile ("validation_points_v2.shp")
+val_proj <- spTransform(val, crs(map))
+
+head(val_proj)
+
+croptypes <- cal@data[,1]
+
+prediction <- extract (map, val_proj) 
+prediction <- croptypes[prediction]
+
+cfm <- table (prediction, val@data[,2])
+cfm
+oac <- sum (diag (cfm)) / sum (cfm)
+print(paste("OAC: ", oac))
+users <- diag (cfm) / apply (cfm, 1, sum)
+users
+producers <- diag (cfm) / apply (cfm, 2, sum)
+producers