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I frequently work with Landsat 4/5 imagery from 1994 and/or 1995 to evaluate land cover changes up to the present day, especially changes in natural forest cover.

Nowadays, I can easily use CBERS-4 imagery for the present year and for the region I work in (southern Brazil), which offers 2x2 m pixel resolution. However, the 1994–1995 imagery has a resolution of 30x30 m, making it very difficult to detect changes in natural vegetation cover.

I know that I can use the pansharpening tool when a panchromatic band with higher resolution is available than the RGB bands. But in this case, it's not useful because all of the Landsat imagery I have access to has the same 30 m resolution for all bands.

Is there any way I can enhance or upscale the Landsat imagery to make it easier to detect changes and produce more reliable information?

I’ve tried using the r.resamp.interp tool, but the exported raster appeared in grayscale.

Note: I use mainly QGIS (3.34.15)

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  • Not really. Landsat 4-5 TM didn't have a 15m pan band, that didn't come along until Landsat 7 ETM+. There's a paper reporting using deep learning models trained on Landsat 7 ETM+ to pseudo pan-sharpen Landsat TM data, but that's not something you could implement yourself with the tools available in QGIS.
    – user2856
    Commented Jul 7 at 20:36

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