115 lines
5.3 KiB
R
115 lines
5.3 KiB
R
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# ShowGeotiff.r
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# Have a look at a downloaded satellite image and check if stats are correctly calculated
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# I downloaded and calculated the stats for the polygon defined in C:\git\FarmMapsApiClient_WURtest\FarmmapsDataDownload\DataDownloadInput.json
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# in which I set "SatelliteBand": "wdvi" and in which in the console I requested the image for date '2022-03-08'
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library(raster)
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library(sf)
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library(rgdal)
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setwd("C:/git/FarmMapsApiClient_WURtest/FarmmapsDataDownload/bin/Debug/netcoreapp3.1/Downloads")
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# FarmmapsDataDownload
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fileGeotiff <- "sentinelhub_test_BvdTFieldlabG92_20220308.tif"
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lenfilename <- nchar(fileGeotiff)
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year <- substr(fileGeotiff,lenfilename-11,lenfilename-8)
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imgdate <- substr(fileGeotiff,lenfilename-11,lenfilename-4)
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stk.sentinelhub <- stack(x=fileGeotiff)
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# plot(stk.sentinelhub) shows 6 plots (6 bands)
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# 1. ndvi
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# 2. wdvi Note wdvi-red
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# 3. ci-red
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# 4. natural: red
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# 5. natural: green
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# 6. natural: blue
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names(stk.sentinelhub) <- c("ndvi","wdvired","ci-red","red","green","blue")
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plot(stk.sentinelhub)
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crs(stk.sentinelhub)
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# CRS arguments: +proj=longlat +datum=WGS84 +no_defs
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stk.sentinelhub.rd <- projectRaster(stk.sentinelhub, crs = CRS('+init=EPSG:28992'))
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crs(stk.sentinelhub)
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r.sentinelhub.rd.wdvi <- subset(stk.sentinelhub.rd,2)
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dev.off()
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plot(r.sentinelhub.rd.wdvi,main=paste("wdvi",imgdate),xlab="RDX",ylab="RDY")
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cellStats(r.sentinelhub.rd.wdvi,'mean') # 0.2252725
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# Convert the .rd.wdvi raster to WGS84
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r.sentinelhub.wgs84.wdvi <- projectRaster(r.sentinelhub.rd.wdvi, crs = CRS('+init=EPSG:4326'))
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# Draw a polygon on top of the raster
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# Polygon pol from C:\git\FarmMapsApiClient_WURtest\FarmmapsDataDownload\DataDownloadInput.json
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pol <- data.frame(id = 1, wkt = gsub("\n","",'POLYGON((
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5.563472073408009 52.547554398144172,
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5.567425915520115 52.547725375100377,
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5.567917474269188 52.540608459298582,
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5.563878143678981 52.54048022658143,
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5.563472073408009 52.547554398144172
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))'))
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pol.wgs84 <- st_as_sf(pol, wkt = 'wkt', crs = CRS('+init=EPSG:4326'))
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pol.rd <- st_transform(pol.wgs84, "+init=epsg:28992")
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#Calculate approximate middle of polygon
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res <- as.data.frame(do.call("rbind", lapply(st_geometry(pol.wgs84), st_bbox)))
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res$latmid <- (res$ymax+res$ymin)/2.0
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res$lonmid <- (res$xmax+res$xmin)/2.0
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res
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# xmin ymin xmax ymax latmid lonmid
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# 1 5.563472 52.54048 5.567917 52.54773 52.5441 5.565695
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# Have a look at both polygons
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# wg84
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dev.off()
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plot(r.sentinelhub.wgs84.wdvi,main=paste("wdvi",imgdate),xlab="LON",ylab="LAT")
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plot(pol.wgs84,add=TRUE, col="transparent",border="red")
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# RD
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dev.off()
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plot(r.sentinelhub.rd.wdvi,main=paste("wdvi",imgdate),xlab="RDX",ylab="RDY")
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plot(pol.rd,add=TRUE, col="transparent",border="red")
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# Clip the polygon from the full rectangle figure
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r.sentinelhub.rd.wdvi.pol <- mask(r.sentinelhub.rd.wdvi,pol.rd)
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r.sentinelhub.wgs84.wdvi.pol <- mask(r.sentinelhub.wgs84.wdvi,pol.wgs84)
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dev.off()
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plot(r.sentinelhub.wgs84.wdvi.pol,main=paste("wdvi",imgdate),xlab="LON",ylab="LAT")
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plot(pol.wgs84,add=TRUE, col="transparent",border="red")
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#That's what we want!
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# Now compare the stats
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cellStats(r.sentinelhub.wgs84.wdvi,'mean') # [1] 0.2250987 # Stats from rectangle, WGS84
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cellStats(r.sentinelhub.rd.wdvi,'mean') # [1] 0.2252725 # Stats from rectangle, RD. Almost but not exactly same as above
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cellStats(r.sentinelhub.wgs84.wdvi.pol,'mean') # [1] 0.2275067 # Stats from raster clipped by polygon, WGS84
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cellStats(r.sentinelhub.rd.wdvi.pol,'mean') # [1] 0.2275073 # Stats from raster clipped by polygon, RD. Almost but not exactly same as above
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# file satelliteStats_test_BvdTFieldlabG92_20220308.csv
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# "wdvi" "mean": 0.22744397204465
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# Mean in csv corresponds with cellStats calculated from clipped tif!
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# So while the tif returned is a non-clipped image, the downloaded statistics are from the clipped image
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# Exactly as we wanted.
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cellStats(r.sentinelhub.wgs84.wdvi.pol,'median') # Error in .local(x, stat, ...) : invalid 'stat'. Should be sum, min, max, sd, mean, or 'countNA'
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r.sentinelhub.wgs84.wdvi.vals <- values(r.sentinelhub.wgs84.wdvi)
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median(r.sentinelhub.wgs84.wdvi.vals) # [1] NA
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median(r.sentinelhub.wgs84.wdvi.vals,na.rm=TRUE) # [1] 0.2318
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r.sentinelhub.wgs84.wdvi.pol.vals <- values(r.sentinelhub.wgs84.wdvi.pol)
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median(r.sentinelhub.wgs84.wdvi.pol.vals) # [1] NA
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median(r.sentinelhub.wgs84.wdvi.pol.vals,na.rm=TRUE) # [1] 0.2338
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# file satelliteStats_test_BvdTFieldlabG92_20220308.csv
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# "wdvi" "mean": 0.233799993991851
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# Median is same as for median(r.sentinelhub.wgs84.wdvi.pol.vals,na.rm=TRUE)
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# in csv corresponds with cellStats calculated from clipped tif!
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# So while the tif returned is a non-clipped image, the downloaded statistics are from the clipped image
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# Exactly as we wanted.
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cellStats(r.sentinelhub.wgs84.wdvi,'countNA') # [1] 27896
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ncell(r.sentinelhub.wgs84.wdvi) # [1] 272718
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cellStats(r.sentinelhub.wgs84.wdvi,'countNA') / ncell(r.sentinelhub.wgs84.wdvi) # [1] 0.1022888 # 10% no data? doesn't show in the plot?
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cellStats(r.sentinelhub.wgs84.wdvi.pol,'countNA') # [1] 57625
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summary(r.sentinelhub.wgs84.wdvi.pol.vals) # shows the same: NA's: 57625
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ncell(r.sentinelhub.wgs84.wdvi.pol) # [1] 272718
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populationCount = ncell(r.sentinelhub.wgs84.wdvi.pol) - cellStats(r.sentinelhub.wgs84.wdvi.pol,'countNA')
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populationCount # [1] 215093
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# file satelliteStats_test_BvdTFieldlabG92_20220308.csv
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# "wdvi" "populationCount": 214688
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# similar but not same
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