Pepijn van Oort
fd44f99c98
And updated ShowGeotiff.r using new 'r' 'terra' packages
And added comment for the DataDownloadApplication and for the BulkSatDownloadApplication:
//To run this app, first go to farmmaps datastore at https://farmmaps.eu/en/editor/plan (or on test)
//goto 'Apps and Data', goto 'Data', buy (or get for free?): 'SATELLITE'
82 lines
3.8 KiB
R
82 lines
3.8 KiB
R
# 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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# downloaded and calculated the stats for the polygon defined in C:\git\FarmMapsApiClient\FarmmapsDataDownload\DataDownloadInput.json
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# and in which in the console I requested the image for date '2022-08-25'
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library(terra)
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setwd("C:/git/FarmMapsApiClient/FarmmapsDataDownload/bin/Debug/netcoreapp3.1/Downloads")
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# FarmmapsDataDownload
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fileGeotiff <- "sentinelhub_test_BvdTFieldlabG92_20220825.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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r.sentinelhub <- rast(x=fileGeotiff)
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# plot(r.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(r.sentinelhub) <- c("ndvi","wdvired","ci-red","red","green","blue")
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# crs(r.sentinelhub)
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# CRS arguments: +proj=longlat +datum=WGS84 +no_defs
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r.sentinelhub.rd <- project(r.sentinelhub,'+init=EPSG:28992')
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# crs(r.sentinelhub.rd)
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r.sentinelhub.rd.wdvi <- subset(r.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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global(r.sentinelhub.rd.wdvi, fun="mean")
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# Convert the .rd.wdvi raster to WGS84
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r.sentinelhub.wgs84.wdvi <- project(r.sentinelhub.rd.wdvi, '+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 <- vect(pol$wkt,crs='+init=EPSG:4326')
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pol.rd <- project(pol.wgs84, "+init=epsg:28992")
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# Have a look at both polygons
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# wg84
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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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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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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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global(r.sentinelhub.wgs84.wdvi,'mean') # [1] 0.1733634 # Stats from rectangle, WGS84
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global(r.sentinelhub.rd.wdvi,'mean') # [1] 0.173531 # Stats from rectangle, RD. Almost but not exactly same as above
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global(r.sentinelhub.wgs84.wdvi.pol,'mean',na.rm=TRUE) # [1] 0.1740224 # Stats from raster clipped by polygon, WGS84
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global(r.sentinelhub.rd.wdvi.pol,'mean',na.rm=TRUE) # [1] 0.1738386 # Stats from raster clipped by polygon, RD. Almost but not exactly same as above
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# file satelliteStats_test_BvdTFieldlabG92_20220825.csv
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# "wdvi" "mean": 0.173905644768987
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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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global(r.sentinelhub.wgs84.wdvi.pol,'median', na.rm=TRUE) # Error in fun(values(x[[i]]), ...) : could not find function "fun"
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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] 0.1459627
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median(r.sentinelhub.wgs84.wdvi.vals,na.rm=TRUE) # [1] 0.1459627
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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] 0.1453323 |