forked from FarmMaps/FarmMapsApiClient
update DataDownloadApplication
parent
f8a412be64
commit
334d890f13
|
|
@ -19,7 +19,7 @@ namespace FarmmapsApiSamples
|
|||
public const string VRAHAULMKILLING_TASK = "vnd.farmmaps.task.vrahaulmkilling";
|
||||
public const string VRAPLANTING_TASK = "vnd.farmmaps.task.vrapoten";
|
||||
public const string VRAZONERING_TASK = "vnd.farmmaps.task.vrazonering";
|
||||
public const string SATELLITE_TASK = "vnd.farmmaps.task.satellite";
|
||||
public const string SATELLITE_TASK = "vnd.farmmaps.task.sentinelhub"; //"vnd.farmmaps.task.satellite";
|
||||
public const string VANDERSAT_TASK = "vnd.farmmaps.task.vandersat";
|
||||
public const string TASKMAP_TASK = "vnd.farmmaps.task.taskmap";
|
||||
public const string WORKFLOW_TASK = "vnd.farmmaps.task.workflow";
|
||||
|
|
|
|||
|
|
@ -1,178 +0,0 @@
|
|||
# ShowGeotiff.r
|
||||
# I downloaded and calculated the stats for the polygon defined in C:\git\FarmMapsApiClient_WURtest\FarmmapsDataDownload\DataDownloadInput.json
|
||||
# in which I set "SatelliteBand": "wdvi" and in which in the console I requested the image for date '2020-09-22'
|
||||
# FarmmapsBulkSatDownload generates many files. Here is what I tried when inputing the same field for a number of years using BulkSatDownloadInput.json
|
||||
# see list below
|
||||
|
||||
library(raster)
|
||||
library(sf)
|
||||
library(rgdal)
|
||||
setwd("C:/workdir/groenmonitor/DataDownload/")
|
||||
|
||||
# FarmmapsDataDownload and BulkSatDownload can be used to download zip files with inside two files:
|
||||
fileGeotiff <- "wenr.tif"
|
||||
fileJpg <- "thumbnail.jpg"
|
||||
# Here is what I tried when inputing the same field for a number of years:
|
||||
# fileGeotiff <- "wheat_fld5641_20210224.tif" # 2 files for year 2021. This file is a nice example having in upperleft corner no data
|
||||
# fileGeotiff <- "wheat_fld5641_20210331.tif" # 2 files for year 2021
|
||||
# fileGeotiff <- "wheat_fld5641_20200321.tif" # 14 files for year 2020, earliest
|
||||
# fileGeotiff <- "wheat_fld5641_20200922.tif" # 14 files for year 2020, latest
|
||||
# fileGeotiff <- "wheat_fld5641_20190121.tif" # 9 files for year 2019, earliest
|
||||
# fileGeotiff <- "wheat_fld5641_20191117.tif" # 9 files for year 2019, latest
|
||||
# 1 file for year 2018, with error message 'End of Central Directory record could not be found' and invalid wheat_fld5641_20180630.zip
|
||||
# fileGeotiff <- "wheat_fld5641_20170526.tif" # 1 file for year 2017
|
||||
# Zero files for 2016
|
||||
lenfilename <- nchar(fileGeotiff)
|
||||
year <- substr(fileGeotiff,lenfilename-11,lenfilename-8)
|
||||
imgdate <- substr(fileGeotiff,lenfilename-11,lenfilename-4)
|
||||
|
||||
# The thumbnail has the polygon clipped out, has 1 layer, no crs and the mean value is not the mean wdvi we are looking for
|
||||
r.thumbnail <- raster(fileJpg)
|
||||
plot(r.thumbnail)
|
||||
crs(r.thumbnail)
|
||||
#CRS arguments: NA
|
||||
cellStats(r.thumbnail,'mean') #87.5128 # nonsense
|
||||
|
||||
stk.wenr <- stack(x=fileGeotiff)
|
||||
# plot(stk.wenr) shows 5 plots (5 bands)
|
||||
# I think these are:
|
||||
# 1. ndvi (since it runs from 0 to 1)
|
||||
# 2. wdvi (since it runs from 0 to 0.5)
|
||||
# 3-5: RGB (since they run from 0 to 255)
|
||||
plot(stk.wenr)
|
||||
# CRS arguments:
|
||||
# +proj=sterea +lat_0=52.1561605555556 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs
|
||||
# Or use st_crs(stk.wenr) to get more info, but no EPSG code in there.
|
||||
# Likely it is epsg:28992 (Amersfoort)
|
||||
crs(stk.wenr)
|
||||
stk.wenr <- projectRaster(stk.wenr, crs = CRS('+init=EPSG:28992'))
|
||||
crs(stk.wenr)
|
||||
# Looks the same but strangely, if we don't do projectRaster(stk.wenr, crs = CRS('+init=EPSG:28992')), we find below the bottom left corner of the polygon missing
|
||||
|
||||
r.wenr.rd.wdvi <- subset(stk.wenr,2)
|
||||
dev.off()
|
||||
plot(r.wenr.rd.wdvi,main=paste("wdvi",imgdate),xlab="RDX",ylab="RDY")
|
||||
cellStats(r.wenr.rd.wdvi,'mean') #0.1350561
|
||||
|
||||
# Furthermore we can see
|
||||
# shows coordinates in RD
|
||||
# returns a rectangle, thus the shape of the polygon submitted is not clipped.
|
||||
# The polygon was provided in WGS84. Let's draw it on top.
|
||||
# First convert the raster to WGS84
|
||||
r.wenr.wgs84.wdvi <- projectRaster(r.wenr.rd.wdvi, crs = CRS('+init=EPSG:4326'))
|
||||
|
||||
# Draw a polygon on top of the raster
|
||||
# Example polygon p1
|
||||
# p1 <- data.frame(id = 1, wkt = 'POLYGON((4.963 52.801, 4.966 52.801, 4.966 52.803, 4.963 52.803, 4.963 52.801))')
|
||||
# p1 <- st_as_sf(p1, wkt = 'wkt', crs = targetcrs)
|
||||
# plot(p1,add=TRUE, col="transparent",border="black")
|
||||
# Draw the polygon on top of the raster
|
||||
# Polygon p2 from C:\git\FarmMapsApiClient_WURtest\FarmmapsDataDownload\DataDownloadInput.json
|
||||
p2 <- data.frame(id = 1, wkt = gsub("\n","",'POLYGON((
|
||||
4.960707146896585 52.800583669708487,
|
||||
4.960645975538824 52.800470217610922,
|
||||
4.962140695752897 52.799177147194797,
|
||||
4.967523821195745 52.801502400041208,
|
||||
4.966336768950911 52.802543735879809,
|
||||
4.961711880764330 52.801009996856429,
|
||||
4.960707146896585 52.800583669708487))'))
|
||||
# Polygon p2 from C:\git\FarmMapsApiClient_WURtest\FarmmapsBulkSatDownload\BulkSatDownloadInput.json
|
||||
p2 <- data.frame(id = 1, wkt = gsub("\n","",'POLYGON((
|
||||
3.37837807779104 51.3231095796538,
|
||||
3.38065689232502 51.3212527499355,
|
||||
3.38022924592256 51.3210683536359,
|
||||
3.37980548452565 51.3208801127141,
|
||||
3.37959556105776 51.3207540143696,
|
||||
3.3793691292654 51.3205959677371,
|
||||
3.37822219207335 51.3215667913007,
|
||||
3.37816999925795 51.3216109809456,
|
||||
3.37646704574705 51.3208025481261,
|
||||
3.37646695791282 51.3208025061493,
|
||||
3.37608401443192 51.3206231652693,
|
||||
3.37607169507628 51.3206173959751,
|
||||
3.37606021048754 51.320612017601,
|
||||
3.37582728410659 51.3205029306946,
|
||||
3.37580409779263 51.3206502985963,
|
||||
3.37575872019649 51.3207993094705,
|
||||
3.37575476634361 51.3208122883487,
|
||||
3.37571181656268 51.3208797459348,
|
||||
3.3756624532907 51.3209415238446,
|
||||
3.37557609963811 51.3210110142077,
|
||||
3.37541089899821 51.3211055871218,
|
||||
3.37477516102591 51.3214102985009,
|
||||
3.37473173914127 51.3214311108204,
|
||||
3.37455904622072 51.3215138815012,
|
||||
3.37415098054777 51.3217199232877,
|
||||
3.37313700916272 51.3222422862785,
|
||||
3.37748824689601 51.3242852920348,
|
||||
3.37749760805371 51.3242713084009,
|
||||
3.37811903757028 51.3233437635596,
|
||||
3.37818758851947 51.3232647797363,
|
||||
3.37823803668144 51.3232236798646,
|
||||
3.37837807779104 51.3231095796538))'))
|
||||
p2.wgs84 <- st_as_sf(p2, wkt = 'wkt', crs = CRS('+init=EPSG:4326'))
|
||||
# Or other way round, in RD Amersfoort. That looks ok
|
||||
p2.rd <- st_transform(p2.wgs84, "+init=epsg:28992")
|
||||
|
||||
# Have a look at both
|
||||
# wg84
|
||||
dev.off()
|
||||
plot(r.wenr.wgs84.wdvi,main=paste("wdvi",imgdate),xlab="LON",ylab="LAT")
|
||||
plot(p2.wgs84,add=TRUE, col="transparent",border="red")
|
||||
# RD
|
||||
dev.off()
|
||||
plot(r.wenr.rd.wdvi,main=paste("wdvi",imgdate),xlab="RDX",ylab="RDY")
|
||||
plot(p2.rd,add=TRUE, col="transparent",border="red")
|
||||
|
||||
#Let's clip the polygon
|
||||
r.wenr.rd.wdvi.pol <- mask(r.wenr.rd.wdvi,p2.rd)
|
||||
r.wenr.wgs84.wdvi.pol <- mask(r.wenr.wgs84.wdvi,p2.wgs84)
|
||||
dev.off()
|
||||
plot(r.wenr.wgs84.wdvi.pol,main=paste("wdvi",imgdate),xlab="LON",ylab="LAT")
|
||||
plot(p2.wgs84,add=TRUE, col="transparent",border="red")
|
||||
#That's what we want! Now compare the stats
|
||||
cellStats(r.wenr.rd.wdvi,'mean') # [1] 0.1350561 # Stats from rectangle, RD
|
||||
cellStats(r.wenr.wgs84.wdvi,'mean') # [1] 0.1351411 # Stats from rectangle, WGS84
|
||||
cellStats(r.wenr.rd.wdvi.pol,'mean') # [1] 0.05723957 # Stats from raster clipped by polygon, RD
|
||||
cellStats(r.wenr.wgs84.wdvi.pol,'mean') # [1] 0.05723607 # Stats from raster clipped by polygon, WGS84
|
||||
# file SatelliteDataStatistics_test_satData_wdvi_2020-09-22.csv
|
||||
# "mean": 0.057430520945401985 # SatelliteDataStatistics_test_satData_wdvi_2020.csv returns stats for the clipped raster (.pol). 'mean' almost the same, maybe
|
||||
# cellStats cannot return median, just a few stats.
|
||||
cellStats(r.wenr.wgs84.wdvi.pol,'median') # Error in .local(x, stat, ...) : invalid 'stat'. Should be sum, min, max, sd, mean, or 'countNA'
|
||||
r.wenr.wgs84.wdvi.vals <- values(r.wenr.wgs84.wdvi)
|
||||
median(r.wenr.wgs84.wdvi.vals) # [1] NA
|
||||
median(r.wenr.wgs84.wdvi.vals,na.rm=TRUE) # [1] 0.076
|
||||
r.wenr.wgs84.wdvi.pol.vals <- values(r.wenr.wgs84.wdvi.pol)
|
||||
median(r.wenr.wgs84.wdvi.pol.vals) # [1] NA
|
||||
median(r.wenr.wgs84.wdvi.pol.vals,na.rm=TRUE) # [1] 0.048
|
||||
# "median": 0.04800000041723251 # SatelliteDataStatistics_test_satData_wdvi_2020.csv returns stats for the clipped raster (.pol).
|
||||
# An image may contain NA values. Check:
|
||||
cellStats(r.wenr.wgs84.wdvi,'countNA') # [1] 22956
|
||||
ncell(r.wenr.wgs84.wdvi) # [1] 221696
|
||||
cellStats(r.wenr.wgs84.wdvi,'countNA') / ncell(r.wenr.wgs84.wdvi) # [1] 0.1035472 # 10% no data? doesn't show in the plot?
|
||||
cellStats(r.wenr.wgs84.wdvi.pol,'countNA') # [1] 147387
|
||||
summary(r.wenr.wgs84.wdvi.pol.vals) # shows the same: NA's: 147387
|
||||
ncell(r.wenr.wgs84.wdvi.pol) # [1] 221696
|
||||
cellStats(r.wenr.wgs84.wdvi.pol,'countNA') / ncell(r.wenr.wgs84.wdvi.pol) # [1] 0.6648158 # 66% no data? doesn't show in the plot?
|
||||
|
||||
# The project FarmmapsNbs can generate a wenr.tif file, application.tif, uptake.tif (in rtest1.uptake.zip)and shape.shp (in rtest1.taskmap.zip)
|
||||
r.application <- raster("C:/git/FarmMapsApiClient_WURtest/FarmmapsNbs/bin/Debug/netcoreapp3.1/Downloads/application.tif")
|
||||
dev.off()
|
||||
plot(r.application)
|
||||
plot(p2.rd,add=TRUE, col="transparent",border="red")
|
||||
# The application.tif file is a rectangle (polygon not yet clipped), in projection Amersfoort RD New (EPSG:28992)
|
||||
|
||||
r.uptake <- raster("C:/git/FarmMapsApiClient_WURtest/FarmmapsNbs/bin/Debug/netcoreapp3.1/Downloads/uptake.tif")
|
||||
dev.off()
|
||||
plot(r.uptake)
|
||||
plot(p2.rd,add=TRUE, col="transparent",border="red")
|
||||
# The uptake.tif file is a rectangle (polygon not yet clipped), in projection Amersfoort RD New (EPSG:28992)
|
||||
|
||||
shp.wgs84 <- readOGR(dsn="C:/git/FarmMapsApiClient_WURtest/FarmmapsNbs/bin/Debug/netcoreapp3.1/Downloads", layer="shape")
|
||||
crs(shp.wgs84)
|
||||
# CRS arguments: +proj=longlat +datum=WGS84 +no_defs
|
||||
dev.off()
|
||||
plot(r.wenr.wgs84.wdvi,main="wdvi",xlab="LON",ylab="LAT")
|
||||
plot(shp.wgs84,add=TRUE, col="transparent",border="black")
|
||||
plot(p2.wgs84,add=TRUE, col="transparent",border="red")
|
||||
# The shape file is in WGS84
|
||||
|
|
@ -38,7 +38,7 @@ namespace FarmmapsDataDownload
|
|||
|
||||
public async Task RunAsync()
|
||||
{
|
||||
var fieldsInputJson = File.ReadAllText("DataDownloadInput.json");
|
||||
string fieldsInputJson = File.ReadAllText("DataDownloadInput.json");
|
||||
|
||||
List<DataDownloadInput> fieldsInputs = JsonConvert.DeserializeObject<List<DataDownloadInput>>(fieldsInputJson);
|
||||
|
||||
|
|
@ -61,6 +61,28 @@ namespace FarmmapsDataDownload
|
|||
|
||||
private async Task Process(List<UserRoot> roots, DataDownloadInput input)
|
||||
{
|
||||
//PO20220311: first time a call is made to download satellite images or statistics, an empty list is returned
|
||||
//If we wait a bit longer, e.g. 10 secs, then e.g. a list of 3 images may be returned
|
||||
//If we wait still longer, maybe 4 images.
|
||||
//The solution implemented below is to fire calls as long as the number of images returned keeps increasing
|
||||
//While in between each call, sleep for sleepSecs
|
||||
//Continue this until the number no longer increases or the maximum number of calls has been reached
|
||||
//Out of politeness, don't be too impatient. Don't set sleepSecs to 5 or 10 or 30 secs. Just accept this may take a while, have a coffee, we suggest sleepSecs = 60;
|
||||
int sleepSecs = 60;
|
||||
int callCntMax = 4;
|
||||
//For example we may set: "sleepSecs = 10;" and "callCntMax = 24;" and following result:
|
||||
//Call no: 1. Giving FarmMaps 10 seconds to get SatelliteItems...
|
||||
//Call no: 1: Received 2 images
|
||||
//Call no: 2. Giving FarmMaps 10 seconds to get SatelliteItems...
|
||||
//Call no: 2: Received 7 images
|
||||
//Call no: 3. Giving FarmMaps 10 seconds to get SatelliteItems...
|
||||
//Call no: 3: Received 7 images
|
||||
//And the firing of calls would stop because the number of images returned is no longer increasing
|
||||
//In the worst case, this could would lead to a total sleeping period of "sleepSecsSum = sleepSecs * callCntMax" seconds. After that we give up
|
||||
|
||||
//This is an ugly fix. Neater would if FarmMaps would just take a bit longer and then do always deliver all satellite images on first call.
|
||||
//Once this has been fixed on the side of FarmMaps we can set callCntMax = 0 and the code below will work smoothly without any sleeping
|
||||
|
||||
string downloadFolder = input.DownloadFolder;
|
||||
if (string.IsNullOrEmpty(downloadFolder)) {
|
||||
downloadFolder = "Downloads";
|
||||
|
|
@ -74,7 +96,8 @@ namespace FarmmapsDataDownload
|
|||
var fieldName = input.fieldName;
|
||||
bool storeSatelliteStatistics = input.StoreSatelliteStatisticsSingleImage;
|
||||
bool storeSatelliteStatisticsCropYear = input.StoreSatelliteStatisticsCropYear;
|
||||
List<string> SatelliteBands = new List<string>(1) { input.SatelliteBand };
|
||||
//List<string> SatelliteBands = new List<string>(1) { input.SatelliteBand };
|
||||
List<string> satelliteBands = input.SatelliteBands;
|
||||
string headerLineStats = $"FieldName,satelliteDate,satelliteBand,max,min,mean,mode,median,stddev,minPlus,curtosis,maxMinus,skewness,variance,populationCount,variationCoefficient,confidenceIntervalLow, confidenceIntervalHigh,confidenceIntervalErrorMargin" + Environment.NewLine;
|
||||
|
||||
|
||||
|
|
@ -166,12 +189,58 @@ namespace FarmmapsDataDownload
|
|||
SaveSettings(settingsfile);
|
||||
}
|
||||
|
||||
|
||||
// Select all satellite items
|
||||
//Call first time
|
||||
int callCnt = 1;
|
||||
int sleepSecsSum = 0;
|
||||
//if callCntMax == 0 then don't sleep
|
||||
//if callCntMax = 1 then sleep first 1x
|
||||
if (callCntMax > 0)
|
||||
{
|
||||
_logger.LogInformation($"Call no: {callCnt}. Giving FarmMaps {sleepSecs} seconds to get SatelliteItems...");
|
||||
System.Threading.Thread.Sleep(1000 * sleepSecs);
|
||||
sleepSecsSum = sleepSecsSum + sleepSecs;
|
||||
}
|
||||
List<Item> satelliteItemsCropYear = await _generalService.FindSatelliteItems(cropfieldItem, _settings.SatelliteTaskCode);
|
||||
int satelliteItemsCropYearCntPrev = satelliteItemsCropYear.Count;
|
||||
_logger.LogInformation($"Call no: {callCnt}. Received {satelliteItemsCropYearCntPrev} images");
|
||||
callCnt++;
|
||||
int satelliteItemsCropYearCnt = satelliteItemsCropYearCntPrev;
|
||||
//if callCntMax > 1 then sleep untill (1) no more increase in number of images received OR (2) maximum number of calls reached
|
||||
if (callCntMax > 1)
|
||||
{
|
||||
//Call second time
|
||||
_logger.LogInformation($"Call no: {callCnt}. Giving FarmMaps another {sleepSecs} seconds to get SatelliteItems...");
|
||||
System.Threading.Thread.Sleep(1000 * sleepSecs);
|
||||
satelliteItemsCropYear = await _generalService.FindSatelliteItems(cropfieldItem, _settings.SatelliteTaskCode);
|
||||
satelliteItemsCropYearCnt = satelliteItemsCropYear.Count;
|
||||
_logger.LogInformation($"Call no: {callCnt}. Received {satelliteItemsCropYearCnt} images");
|
||||
sleepSecsSum = sleepSecsSum + sleepSecs;
|
||||
//As long as there is progress, keep calling
|
||||
callCnt++;
|
||||
while (callCnt <= callCntMax && (satelliteItemsCropYearCnt == 0 || satelliteItemsCropYearCnt > satelliteItemsCropYearCntPrev))
|
||||
{
|
||||
_logger.LogInformation($"Surprise! The longer we wait, the more images we get. Sleep and call once more");
|
||||
satelliteItemsCropYearCntPrev = satelliteItemsCropYearCnt;
|
||||
_logger.LogInformation($"Call no: {callCnt} (max: {callCntMax}). Giving FarmMaps another {sleepSecs} seconds to get SatelliteItems...");
|
||||
System.Threading.Thread.Sleep(1000 * sleepSecs);
|
||||
satelliteItemsCropYear = await _generalService.FindSatelliteItems(cropfieldItem, _settings.SatelliteTaskCode);
|
||||
satelliteItemsCropYearCnt = satelliteItemsCropYear.Count;
|
||||
_logger.LogInformation($"Call no: {callCnt}. Received {satelliteItemsCropYearCnt} images");
|
||||
callCnt++;
|
||||
sleepSecsSum = sleepSecsSum + sleepSecs;
|
||||
}
|
||||
}
|
||||
|
||||
if (satelliteItemsCropYearCnt == 0)
|
||||
{
|
||||
_logger.LogWarning($"DataDownloadApplication.cs: after calling one or more times and " +
|
||||
$"sleeping in total {sleepSecsSum} seconds, still no images found. " +
|
||||
$"Please check your settings for parameters callCntMax and sleepSecs in DataDownloadApplication.cs or contact FarmMaps");
|
||||
}
|
||||
|
||||
satelliteItemsCropYear = satelliteItemsCropYear.OrderBy(x => x.DataDate).ToList();
|
||||
|
||||
if (input.StoreSatelliteStatisticsSingleImage == true) {
|
||||
if (input.StoreSatelliteStatisticsSingleImage == true && satelliteItemsCropYearCnt > 0) {
|
||||
_logger.LogInformation("Available satellite images:");
|
||||
var count = 0;
|
||||
TimeSpan.FromSeconds(0.5);
|
||||
|
|
@ -188,43 +257,49 @@ namespace FarmmapsDataDownload
|
|||
|
||||
var SatelliteDate = selectedSatelliteItem.DataDate.Value.ToString("yyyyMMdd");
|
||||
string fileName = string.Format($"satelliteGeotiff_{fieldName}_{SatelliteDate}"); // no need to add satelliteBand in the name because the tif contains all bands
|
||||
string fileNameZip = string.Format($"{fileName}.zip");
|
||||
string fileNameGeotiff = string.Format($"{fileName}.tif");
|
||||
await _farmmapsApiService.DownloadItemAsync(selectedSatelliteItem.Code, Path.Combine(downloadFolder, fileNameZip));
|
||||
string fileNameZip = Path.Combine(downloadFolder, string.Format($"{fileName}.zip"));
|
||||
await _farmmapsApiService.DownloadItemAsync(selectedSatelliteItem.Code, fileNameZip);
|
||||
|
||||
// Download a csv file with stats
|
||||
List<Item> selectedSatalliteItems = new List<Item>(1) { selectedSatelliteItem };
|
||||
List<Item> selectedSatelliteItems = new List<Item>(1) { selectedSatelliteItem };
|
||||
string fileNameStats = Path.Combine(downloadFolder, string.Format($"satelliteStats_{fieldName}_{SatelliteDate}.csv"));
|
||||
string downloadedStats = await _generalService.DownloadSatelliteStats(selectedSatalliteItems, fieldName, SatelliteBands, downloadFolder);
|
||||
_logger.LogInformation($"First call to get DownloadSatelliteStats for selected image...");
|
||||
string downloadedStats = await _generalService.DownloadSatelliteStats(selectedSatelliteItems, fieldName, satelliteBands, downloadFolder);
|
||||
|
||||
//rename the csv file with stats
|
||||
//if the targe file already exists, delete it
|
||||
File.Delete(fileNameStats);
|
||||
//rename
|
||||
File.Move(downloadedStats, fileNameStats);
|
||||
// wenr.tif. Contains 5 layers: (1) ndvi, (2) wdvi, (3) Red, (4) Green and (5) Blue
|
||||
// download the geotiffs. Returns a zip file with always these three files:
|
||||
// data.dat.aux.xml
|
||||
|
||||
// name the tif file
|
||||
string fileNameTifzipped = Path.Combine(downloadFolder, string.Format($"sentinelhub_{SatelliteDate}.tif"));
|
||||
string fileNameGeotiff = Path.Combine(downloadFolder, string.Format($"sentinelhub_{fieldName}_{SatelliteDate}.tif"));
|
||||
// download the geotiffs. Returns a zip file with always these two files:
|
||||
// thumbnail.jpg
|
||||
// wenr.tif. Contains 5 layers: (1) ndvi, (2) wdvi, (3) Red, (4) Green and (5) Blue
|
||||
// sentinelhub_yyyyMMdd.tif. Contains 4 layers: (1) ndvi, (2) wdvi, (3) ci-red and (4) natural. Natural has 3 layers inside: redBand, blueBand and greenBand
|
||||
if (true)
|
||||
{
|
||||
// Extract the file "wenr.tif" from zip, rename it to fileNameGeotiff
|
||||
ZipFile.ExtractToDirectory(Path.Combine(downloadFolder, fileNameZip), downloadFolder, true);
|
||||
File.Delete(Path.Combine(downloadFolder, fileNameGeotiff)); // Delete the fileNameGeotiff file if exists
|
||||
File.Move(Path.Combine(downloadFolder, "wenr.tif"), Path.Combine(downloadFolder, fileNameGeotiff)); // Rename the oldFileName into newFileName
|
||||
// Extract the file fileNameTifzipped from zip, rename it to fileNameGeotiff
|
||||
ZipFile.ExtractToDirectory(fileNameZip, downloadFolder, true);
|
||||
//if the targe file already exists, delete it
|
||||
File.Delete(fileNameGeotiff);
|
||||
//rename
|
||||
File.Move(fileNameTifzipped, fileNameGeotiff);
|
||||
|
||||
// Cleanup
|
||||
string[] filesToDelete = new string[] { fileNameZip, "wenr.tif", "thumbnail.jpg", "data.dat.aux.xml" };
|
||||
foreach (string f in filesToDelete)
|
||||
{
|
||||
File.Delete(Path.Combine(downloadFolder, f));
|
||||
}
|
||||
File.Delete(fileNameZip);
|
||||
File.Delete(Path.Combine(downloadFolder, "thumbnail.jpg"));
|
||||
}
|
||||
_logger.LogInformation($"Downloaded files {fileNameGeotiff} and {fileNameStats} to {downloadFolder}");
|
||||
//_logger.LogInformation($"Downloaded files {fileNameGeotiff} and {fileNameStats} to {downloadFolder}");
|
||||
_logger.LogInformation($"Downloaded files to {downloadFolder}");
|
||||
|
||||
}
|
||||
if (input.StoreSatelliteStatisticsCropYear == true) {
|
||||
string fileNameStats = Path.Combine(downloadFolder, string.Format($"satelliteStats_{fieldName}_{cropYear}.csv"));
|
||||
File.Delete(fileNameStats);
|
||||
string downloadedStats = await _generalService.DownloadSatelliteStats(satelliteItemsCropYear, fieldName, SatelliteBands, downloadFolder);
|
||||
_logger.LogInformation($"First call to get DownloadSatelliteStats for whole cropYear...");
|
||||
string downloadedStats = await _generalService.DownloadSatelliteStats(satelliteItemsCropYear, fieldName, satelliteBands, downloadFolder);
|
||||
File.Move(downloadedStats, fileNameStats);
|
||||
_logger.LogInformation($"Downloaded file {fileNameStats} with stats for field '{fieldName}', cropyear {cropYear}");
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,34 +1,30 @@
|
|||
[
|
||||
{
|
||||
"UseCreatedCropfield": true,
|
||||
"outputFileName": "TestData",
|
||||
"fieldName": "TestField",
|
||||
"UseCreatedCropfield": false, // if false, program will make new CropfieldItemCode and SatelliteTaskCode; if true, the program will read CropfieldItemCode and SatelliteTaskCode from a file called "..\FarmmapsDataDownload\bin\Debug\netcoreapp3.1\Settings_{fieldName}.json", which will be faster
|
||||
"outputFileName": "test_BvdTFieldlabG92",
|
||||
"fieldName": "test_BvdTFieldlabG92",
|
||||
"DownloadFolder": "Downloads", //"C:\\workdir\\groenmonitor\\", // "Downloads", -> if you just put "Downloads" the program will download to somewhere in ..\FarmMapsApiClient_WURtest\FarmmapsDataDownload\bin\Debug\netcoreapp3.1\Downloads\
|
||||
"GetCropRecordings": true,
|
||||
"GetCropRecordings": false,
|
||||
"CrprecItem": "...", //item code of de crop recording parrent - can be found by opening the crop recording page of a field.
|
||||
"GetShadowData": false,
|
||||
"GetSatelliteData": false,
|
||||
"SatelliteBand": "wdvi", // "natural", "ndvi" or "wdvi"
|
||||
"StoreSatelliteStatisticsSingleImage": false,
|
||||
"StoreSatelliteStatisticsCropYear": false,
|
||||
"GetSatelliteData": true,
|
||||
"SatelliteBands": [ "ndvi", "wdvi", "ci-red" ], // ["ndvi"] or ["wdvi"] or ["ci-red"] or multiple: [ "wdvi", "ndvi" ]
|
||||
"StoreSatelliteStatisticsSingleImage": true,
|
||||
"StoreSatelliteStatisticsCropYear": true,
|
||||
"GetVanDerSatData": false,
|
||||
"StoreVanDerSatStatistics": false,
|
||||
"CropYear": 2020,
|
||||
"CropYear": 2022,
|
||||
"geometryJson": {
|
||||
"type": "Polygon",
|
||||
"coordinates": [
|
||||
[
|
||||
[ 4.960707146896585, 52.800583669708487 ],
|
||||
[ 4.960645975538824, 52.800470217610922 ],
|
||||
[ 4.962140695752897, 52.799177147194797 ],
|
||||
[ 4.967523821195745, 52.801502400041208 ],
|
||||
[ 4.966336768950911, 52.802543735879809 ],
|
||||
[ 4.961711880764330, 52.801009996856429 ],
|
||||
[ 4.960707146896585, 52.800583669708487 ]
|
||||
[ 5.563472073408009, 52.547554398144172 ],
|
||||
[ 5.567425915520115, 52.547725375100377 ],
|
||||
[ 5.567917474269188, 52.540608459298582 ],
|
||||
[ 5.563878143678981, 52.54048022658143 ],
|
||||
[ 5.563472073408009, 52.547554398144172 ]
|
||||
]
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
]
|
||||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
<PropertyGroup>
|
||||
<OutputType>Exe</OutputType>
|
||||
<TargetFramework>netcoreapp3.0</TargetFramework>
|
||||
<TargetFramework>netcoreapp3.1</TargetFramework>
|
||||
</PropertyGroup>
|
||||
|
||||
<ItemGroup>
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
using System;
|
||||
using System.Collections.Generic;
|
||||
using Newtonsoft.Json.Linq;
|
||||
|
||||
namespace FarmmapsDataDownload.Models
|
||||
|
|
@ -16,7 +17,7 @@ namespace FarmmapsDataDownload.Models
|
|||
public string fieldName { get; set; }
|
||||
public bool GetSatelliteData { get; set; }
|
||||
public bool GetVanDerSatData { get; set; }
|
||||
public string SatelliteBand { get; set; }
|
||||
public List<string> SatelliteBands { get; set; }
|
||||
public bool StoreSatelliteStatisticsSingleImage { get; set; }
|
||||
public bool StoreSatelliteStatisticsCropYear { get; set; }
|
||||
public bool StoreVanDerSatStatistics { get; set; }
|
||||
|
|
|
|||
|
|
@ -0,0 +1,114 @@
|
|||
# ShowGeotiff.r
|
||||
# Have a look at a downloaded satellite image and check if stats are correctly calculated
|
||||
# I downloaded and calculated the stats for the polygon defined in C:\git\FarmMapsApiClient_WURtest\FarmmapsDataDownload\DataDownloadInput.json
|
||||
# in which I set "SatelliteBand": "wdvi" and in which in the console I requested the image for date '2022-03-08'
|
||||
|
||||
library(raster)
|
||||
library(sf)
|
||||
library(rgdal)
|
||||
setwd("C:/git/FarmMapsApiClient_WURtest/FarmmapsDataDownload/bin/Debug/netcoreapp3.1/Downloads")
|
||||
|
||||
# FarmmapsDataDownload
|
||||
fileGeotiff <- "sentinelhub_test_BvdTFieldlabG92_20220308.tif"
|
||||
lenfilename <- nchar(fileGeotiff)
|
||||
year <- substr(fileGeotiff,lenfilename-11,lenfilename-8)
|
||||
imgdate <- substr(fileGeotiff,lenfilename-11,lenfilename-4)
|
||||
|
||||
stk.sentinelhub <- stack(x=fileGeotiff)
|
||||
# plot(stk.sentinelhub) shows 6 plots (6 bands)
|
||||
# 1. ndvi
|
||||
# 2. wdvi Note wdvi-red
|
||||
# 3. ci-red
|
||||
# 4. natural: red
|
||||
# 5. natural: green
|
||||
# 6. natural: blue
|
||||
names(stk.sentinelhub) <- c("ndvi","wdvired","ci-red","red","green","blue")
|
||||
plot(stk.sentinelhub)
|
||||
crs(stk.sentinelhub)
|
||||
# CRS arguments: +proj=longlat +datum=WGS84 +no_defs
|
||||
stk.sentinelhub.rd <- projectRaster(stk.sentinelhub, crs = CRS('+init=EPSG:28992'))
|
||||
crs(stk.sentinelhub)
|
||||
|
||||
r.sentinelhub.rd.wdvi <- subset(stk.sentinelhub.rd,2)
|
||||
dev.off()
|
||||
plot(r.sentinelhub.rd.wdvi,main=paste("wdvi",imgdate),xlab="RDX",ylab="RDY")
|
||||
cellStats(r.sentinelhub.rd.wdvi,'mean') # 0.2252725
|
||||
|
||||
# Convert the .rd.wdvi raster to WGS84
|
||||
r.sentinelhub.wgs84.wdvi <- projectRaster(r.sentinelhub.rd.wdvi, crs = CRS('+init=EPSG:4326'))
|
||||
|
||||
# Draw a polygon on top of the raster
|
||||
# Polygon pol from C:\git\FarmMapsApiClient_WURtest\FarmmapsDataDownload\DataDownloadInput.json
|
||||
pol <- data.frame(id = 1, wkt = gsub("\n","",'POLYGON((
|
||||
5.563472073408009 52.547554398144172,
|
||||
5.567425915520115 52.547725375100377,
|
||||
5.567917474269188 52.540608459298582,
|
||||
5.563878143678981 52.54048022658143,
|
||||
5.563472073408009 52.547554398144172
|
||||
))'))
|
||||
|
||||
|
||||
pol.wgs84 <- st_as_sf(pol, wkt = 'wkt', crs = CRS('+init=EPSG:4326'))
|
||||
pol.rd <- st_transform(pol.wgs84, "+init=epsg:28992")
|
||||
|
||||
#Calculate approximate middle of polygon
|
||||
res <- as.data.frame(do.call("rbind", lapply(st_geometry(pol.wgs84), st_bbox)))
|
||||
res$latmid <- (res$ymax+res$ymin)/2.0
|
||||
res$lonmid <- (res$xmax+res$xmin)/2.0
|
||||
res
|
||||
# xmin ymin xmax ymax latmid lonmid
|
||||
# 1 5.563472 52.54048 5.567917 52.54773 52.5441 5.565695
|
||||
|
||||
# Have a look at both polygons
|
||||
# wg84
|
||||
dev.off()
|
||||
plot(r.sentinelhub.wgs84.wdvi,main=paste("wdvi",imgdate),xlab="LON",ylab="LAT")
|
||||
plot(pol.wgs84,add=TRUE, col="transparent",border="red")
|
||||
# RD
|
||||
dev.off()
|
||||
plot(r.sentinelhub.rd.wdvi,main=paste("wdvi",imgdate),xlab="RDX",ylab="RDY")
|
||||
plot(pol.rd,add=TRUE, col="transparent",border="red")
|
||||
|
||||
# Clip the polygon from the full rectangle figure
|
||||
r.sentinelhub.rd.wdvi.pol <- mask(r.sentinelhub.rd.wdvi,pol.rd)
|
||||
r.sentinelhub.wgs84.wdvi.pol <- mask(r.sentinelhub.wgs84.wdvi,pol.wgs84)
|
||||
dev.off()
|
||||
plot(r.sentinelhub.wgs84.wdvi.pol,main=paste("wdvi",imgdate),xlab="LON",ylab="LAT")
|
||||
plot(pol.wgs84,add=TRUE, col="transparent",border="red")
|
||||
#That's what we want!
|
||||
|
||||
# Now compare the stats
|
||||
cellStats(r.sentinelhub.wgs84.wdvi,'mean') # [1] 0.2250987 # Stats from rectangle, WGS84
|
||||
cellStats(r.sentinelhub.rd.wdvi,'mean') # [1] 0.2252725 # Stats from rectangle, RD. Almost but not exactly same as above
|
||||
cellStats(r.sentinelhub.wgs84.wdvi.pol,'mean') # [1] 0.2275067 # Stats from raster clipped by polygon, WGS84
|
||||
cellStats(r.sentinelhub.rd.wdvi.pol,'mean') # [1] 0.2275073 # Stats from raster clipped by polygon, RD. Almost but not exactly same as above
|
||||
# file satelliteStats_test_BvdTFieldlabG92_20220308.csv
|
||||
# "wdvi" "mean": 0.22744397204465
|
||||
# Mean in csv corresponds with cellStats calculated from clipped tif!
|
||||
# So while the tif returned is a non-clipped image, the downloaded statistics are from the clipped image
|
||||
# Exactly as we wanted.
|
||||
cellStats(r.sentinelhub.wgs84.wdvi.pol,'median') # Error in .local(x, stat, ...) : invalid 'stat'. Should be sum, min, max, sd, mean, or 'countNA'
|
||||
r.sentinelhub.wgs84.wdvi.vals <- values(r.sentinelhub.wgs84.wdvi)
|
||||
median(r.sentinelhub.wgs84.wdvi.vals) # [1] NA
|
||||
median(r.sentinelhub.wgs84.wdvi.vals,na.rm=TRUE) # [1] 0.2318
|
||||
r.sentinelhub.wgs84.wdvi.pol.vals <- values(r.sentinelhub.wgs84.wdvi.pol)
|
||||
median(r.sentinelhub.wgs84.wdvi.pol.vals) # [1] NA
|
||||
median(r.sentinelhub.wgs84.wdvi.pol.vals,na.rm=TRUE) # [1] 0.2338
|
||||
# file satelliteStats_test_BvdTFieldlabG92_20220308.csv
|
||||
# "wdvi" "mean": 0.233799993991851
|
||||
# Median is same as for median(r.sentinelhub.wgs84.wdvi.pol.vals,na.rm=TRUE)
|
||||
# in csv corresponds with cellStats calculated from clipped tif!
|
||||
# So while the tif returned is a non-clipped image, the downloaded statistics are from the clipped image
|
||||
# Exactly as we wanted.
|
||||
cellStats(r.sentinelhub.wgs84.wdvi,'countNA') # [1] 27896
|
||||
ncell(r.sentinelhub.wgs84.wdvi) # [1] 272718
|
||||
cellStats(r.sentinelhub.wgs84.wdvi,'countNA') / ncell(r.sentinelhub.wgs84.wdvi) # [1] 0.1022888 # 10% no data? doesn't show in the plot?
|
||||
cellStats(r.sentinelhub.wgs84.wdvi.pol,'countNA') # [1] 57625
|
||||
summary(r.sentinelhub.wgs84.wdvi.pol.vals) # shows the same: NA's: 57625
|
||||
ncell(r.sentinelhub.wgs84.wdvi.pol) # [1] 272718
|
||||
populationCount = ncell(r.sentinelhub.wgs84.wdvi.pol) - cellStats(r.sentinelhub.wgs84.wdvi.pol,'countNA')
|
||||
populationCount # [1] 215093
|
||||
# file satelliteStats_test_BvdTFieldlabG92_20220308.csv
|
||||
# "wdvi" "populationCount": 214688
|
||||
# similar but not same
|
||||
|
||||
Loading…
Reference in New Issue