[GIS] How to calculate minimum cell value for a multi band raster

imagerymulti-bandpythonqgisraster

I'm a python-qgis beginner.

I use to work with multi-band rasters.
Each raster is made of images coming from satellite sensors acquired in different dates and stacked in order to build a multi temporal dataset.

Is it possible to produce a raster layer in which each pixel has the minimum value that this pixel reaches in all the bands?

I mean, it is possible to produce the "layer of temporal minimum" of the multi-band raster?

Best Answer

The tool you are looking for in QGIS is called r.series (GRASS GIS) using a minimum statistic. This is also available as a command line utility.

r.series - Makes each output cell value a function of the values assigned to the corresponding cells in the input raster map layers.

Processing Toolbox > GRASS GIS > Raster > r.series

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[GIS] Multi-Band (RGBA) into single band raster

It depends what do you want to do with your outout data. Is your data on 1 of the layers of the raster or you have to convert 3 or 4 bands into 1? Either way you can make use of raster calculator. If you want mean of 3 or 4 bands in your output raster just add them up and divide by number of lyers: (r@1 + r@2 + r@3)/3, else you just select band of interest and export it to new file. Thats in theory, as I did not test it on QGIS.

[GIS] Per-pixel (statistical) calculations on a raster stack using GDAL

Edit: adding some more stuff to make clearer, as per suggestion

#import the numpy and gdal libraries
import numpy as np
from osgeo import gdal

#an empty array/vector in which to store the different bands
layers = []

#open raster
ds = gdal.Open('raster.tif')

#loop thru bands of raster and append each band of data to 'layers'
#note that 'ReadAsArray()' returns a numpy array
for i in range(1, ds.RasterCount+1):
    layers.append(ds.GetRasterBand(i).ReadAsArray())

#dstack will take a number of n by m in tuple or list and stack them
#in the 3rd dimension so you end up with raster_stack being n by m by i, 
#where i is the number of bands
raster_stack = np.dstack(layers)

#call built in numpy functions std and mean, with a specified axis. if   
#no axis is set then it will return a number (scaler) but specifying
#axis=2 means it will calculate along the 'depth' axis, per pixel.
#with the return being n by m, the shape of each band.
std_raster = np.std(raster_stack, axis=2)
mean_raster = np.mean(raster_stack, axis=2)

Best Answer

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[GIS] Multi-Band (RGBA) into single band raster

[GIS] Per-pixel (statistical) calculations on a raster stack using GDAL

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