I'm in a new field of research and I'm having troubles following a procedure with QGIS. I'm extremely new to all this since I'm a chemist. The aim is to calculate the total green cover from a photo. Since this data isn't available through satellite, I have to use normal .jpg photography. I start with raster > calculator and add in the open box this equation: BAND2(green) * 2 – BAND1(Red) – BAND3(Blue) This comes out: Now in the search box on the right, I'm told to search for "RECLASSIFY VALUES(SIMPLE)" but this is all that appears. This is the reference photo I've been given of the tool that should appear: And this is the version of QGIS I'm using on this version of Windows. I've installed SAGA with QGIS in this version

2017年にMi Band2を買った。 ランニングの時に使ったが、画面を見ても反応が遅い、明るいと見えない。アプリの出来も悪く、通知なども限定的だった 最近、2024年製のスマートバンドを買った。7000円ぐらいのもの センサー類は充実。反応は早く腕時計のように使える、炎天下でもバッチリ見えて…

I'm new in python and I would like to know how I can read and plot MULTIPLE TIFF files in Colaboratory with (maybe) a for loop. Here is what I did for a single TIFF image: from google.colab import drive drive = "/content/drive/MyDrive/exercise" dataset = gdal.Open(r'/content/drive/MyDrive/exercise/image_20180127.tif') print(dataset.RasterCount) band1 = dataset.GetRasterBand(1) #Red Channel band2 = dataset.GetRasterBand(2) #Green Channel band2 = dataset.GetRasterBand(3) #Blue Channel #Read the band as Numpy arrays b1 = band1.ReadAsArray() b2 = band1.ReadAsArray() b3 = band1.ReadAsArray() #Plot the arrays using imshow() img = np.dstack((b1,b2,b3)) f = plt.figure() plt.show() I would like to perform the same actions for multiple TIFFs (I have 39) with a for loop.

I have created an array with the results of a Pearson correlation between image bands. I want to convert that array into a properly structured Table. Currently, I am able to export it, but the output table looks like this: All the lists in the array appear in the same cell. Instead, I would like to have each list in a row, and each element in the list in a column. Even better if I could add the name of the columns (band1, band2, R, p-value) My code: https://code.earthengine.google.com/?scriptPath=users%2Fericacinerea%2Fgee_training%3Aband_correlation and one extract of it: //check the correlation between bands in a stack //var list_of_results = ee.List([]); var myArray = []; // recommended way //Get the names of the input bands var bands = ["b1","b2","b3","b4"]; for (var i=0; i

I'm looking for python code which predicts the LULC of my data(other raster files apart from refernce data band1, band2 and soon) using svm classifier with rbf kernel based on reference data (truth data shapefile). Values underneath band columns are pixel values of different raster files corresponding to that particular point geometry( band1, band2... are my different raster files, Class 1 indicate land use and class 2 is vice-versa). ref_data =gpd.read_file(MyGeometries.shp) ref_data.head() lon lat band1 band2 band3 band4 class geometry 0 72.144033 15.837863 1.069935e+09 -14.396156 -20.063754 -20.359174 2 POINT (72.14403 15.83786) 1 72.144165 15.837762 1.072870e+09 -14.444141 -20.558783 -20.649671 1 POINT (72.14416 15.83776) 2 72.144086 15.837899 1.069935e+09 -14.396156 -20.063754 -20.359174 1 POINT (72.14409 15.83790) 3 72.143932 15.837984 1.069935e+09 -14.396156 -20.063754 -20.359174 2 POINT (72.14393 15.83798) 4 72.144117 15.837962 1.070610e+09 -14.383173 -20.112496 -20.536721 1 POINT (72.14412 15.83790)

I'm new with GDAL and I'm trying to mosaic different bands from Sentinel 2 in order, what I want to get is a raster file with right order band2, 3, 4, 5, 6, 7, 8, 8A, 11, 12. The picture below is the band list in my server. I tried to use gdal.BuildVRT function but finally the raster I get is a disorder file, do anybody know how to set the order when I apply with the raster mosaic? Or do you have some better ways to do this? from osgeo import gdal import os import numpy as np os.chdir("/sentinel/2021/") print(os.listdir()) ['2021_band2.tif', '2021_band8.tif', 'band.vrt', '2021_band12.tif', '2021_band11.tif', '2021_band6.tif', '2021_band7.tif', '2021_band5.tif', '2021_band4.tif', '2021_band3.tif', '2021_band8A.tif'] tifs = os.listdir() tmp = [2,3,4,5,6,7,8,9,0,1] tifs = np.array(sorted(tifs)) tifs = tifs[tmp] print(tifs) ['2021_band2.tif' '2021_band3.tif' '2021_band4.tif' '2021_band5.tif' '2021_band6.tif' '2021_band7.tif' '2021_band8.tif' '2021_band8A.tif' '2021_band11.tif' '2021_band12.tif'] alltif = tifs.tolist() print(alltif) ['2021_band2.tif', '2021_band3.tif', '2021_band4.tif', '2021_band5.tif', '2021_band6.tif', '2021_band7.tif', '2021_band8.tif', '2021_band8A.tif', '2021_band11.tif', '2021_band12.tif'] files_to_mosaic = alltif vrt = gdal.BuildVRT('2021stack.vrt', alltif) gdal.Translate( '/sentinel/2021/2021mosaic.tif',vrt) vrt = None

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I'm in a new field of research and I'm having troubles following a procedure with QGIS. I'm extremely new to all this since I'm a chemist. The aim is to calculate the total green cover from a photo. Since this data isn't available through satellite, I have to use normal .jpg photography. I start with raster > calculator and add in the open box this equation: BAND2(green) * 2 – BAND1(Red) – BAND3(Blue) This comes out: Now in the search box on the right, I'm told to search for "RECLASSIFY VALUES(SIMPLE)" but this is all that appears. This is the reference photo I've been given of the tool that should appear: And this is the version of QGIS I'm using on this version of Windows. I've installed SAGA with QGIS in this version

I'm new in python and I would like to know how I can read and plot MULTIPLE TIFF files in Colaboratory with (maybe) a for loop. Here is what I did for a single TIFF image: from google.colab import drive drive = "/content/drive/MyDrive/exercise" dataset = gdal.Open(r'/content/drive/MyDrive/exercise/image_20180127.tif') print(dataset.RasterCount) band1 = dataset.GetRasterBand(1) #Red Channel band2 = dataset.GetRasterBand(2) #Green Channel band2 = dataset.GetRasterBand(3) #Blue Channel #Read the band as Numpy arrays b1 = band1.ReadAsArray() b2 = band1.ReadAsArray() b3 = band1.ReadAsArray() #Plot the arrays using imshow() img = np.dstack((b1,b2,b3)) f = plt.figure() plt.show() I would like to perform the same actions for multiple TIFFs (I have 39) with a for loop.

Warren Haynes Band2/27/2026Suwannee Amp Jam #2Spirit of Suwannee Music ParkLive Oak, FLSource: Ch 1-2: DPA 4015c (ORTF) > MixPre-6 (24/48) > SD...

Warren Haynes Band2/27/2026Suwannee Amp Jam #2Spirit of Suwannee Music ParkLive Oak, FLSource: Ch 1-2: DPA 4015c (ORTF) > MixPre-6 (24/48) > SD...

Roosevelt Collier Band2/27/2026Suwannee Amp Jam #2Spirit of Suwannee Music ParkLive Oak, FLSource: Ch 3-4: DPA 2012 (DIN) > Lunatec V2 > MixPre-6 (24/48)...

I have created an array with the results of a Pearson correlation between image bands. I want to convert that array into a properly structured Table. Currently, I am able to export it, but the output table looks like this: All the lists in the array appear in the same cell. Instead, I would like to have each list in a row, and each element in the list in a column. Even better if I could add the name of the columns (band1, band2, R, p-value) My code: https://code.earthengine.google.com/?scriptPath=users%2Fericacinerea%2Fgee_training%3Aband_correlation and one extract of it: //check the correlation between bands in a stack //var list_of_results = ee.List([]); var myArray = []; // recommended way //Get the names of the input bands var bands = ["b1","b2","b3","b4"]; for (var i=0; i

Warren Haynes Band2/27/2026Suwannee Amp Jam #2Spirit of Suwannee Music ParkLive Oak, FLSource: Ch 3-4: DPA 2012 (DIN) > Lunatec V2 > MixPre-6 (24/48)...

Warren Haynes Band2/27/2026Suwannee Amp Jam #2Spirit of Suwannee Music ParkLive Oak, FLSource: Ch 3-4: DPA 2012 (DIN) > Lunatec V2 > MixPre-6 (24/48)...

Roosevelt Collier Band2/27/2026Suwannee Amp Jam #2Spirit of Suwannee Music ParkLive Oak, FLSource: Ch 1-2: DPA 4015c (ORTF) > + Ch 3-4: DPA 2012 (DIN) >...

I'm looking for python code which predicts the LULC of my data(other raster files apart from refernce data band1, band2 and soon) using svm classifier with rbf kernel based on reference data (truth data shapefile). Values underneath band columns are pixel values of different raster files corresponding to that particular point geometry( band1, band2... are my different raster files, Class 1 indicate land use and class 2 is vice-versa). ref_data =gpd.read_file(MyGeometries.shp) ref_data.head() lon lat band1 band2 band3 band4 class geometry 0 72.144033 15.837863 1.069935e+09 -14.396156 -20.063754 -20.359174 2 POINT (72.14403 15.83786) 1 72.144165 15.837762 1.072870e+09 -14.444141 -20.558783 -20.649671 1 POINT (72.14416 15.83776) 2 72.144086 15.837899 1.069935e+09 -14.396156 -20.063754 -20.359174 1 POINT (72.14409 15.83790) 3 72.143932 15.837984 1.069935e+09 -14.396156 -20.063754 -20.359174 2 POINT (72.14393 15.83798) 4 72.144117 15.837962 1.070610e+09 -14.383173 -20.112496 -20.536721 1 POINT (72.14412 15.83790)