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    <div class="moz-cite-prefix">On 23/06/2022 21:51, carlos h via
      Qgis-user wrote:<br>
    </div>
    <blockquote type="cite"
cite="mid:CAKrv22w0XDuVQdoyMuwvcqiZsVUBPnmpCNgB9A3r3Kcbsybk8A@mail.gmail.com">
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        Hello, I have an NDVI image that has a pixel of 4 centimeters,
        that is, 16 square centimeters.<br>
        <br>
        I have 2 doubts:<br>
        <br>
        1. What method can I use to generalize the NDVI image to 1
        square meter in pixel size?<br>
      </div>
    </blockquote>
    <p>One of the tools that you can use within QGIS to resample to a
      coarser resolution is gdal_translate.</p>
    <p>From the documentation you can set the target resolution:<br>
    </p>
    <dl class="std option">
      <dt class="sig sig-object std" id="cmdoption-gdal_translate-tr"><font
          face="monospace"><span class="sig-name descname"><span
              class="pre">-tr</span></span><span class="sig-prename
            descclassname"> <span class="pre"><xres></span> <span
              class="pre"><yres></span></span><a
            class="headerlink"
href="https://gdal.org/programs/gdal_translate.html#cmdoption-gdal_translate-tr"
            title="Permalink to this definition"></a></font></dt>
      <dd>
        <p><font face="monospace">set target resolution. The values must
            be expressed in georeferenced units.
            Both must be positive values. This is mutually exclusive
            with
            <a class="reference internal"
href="https://gdal.org/programs/gdal_translate.html#cmdoption-gdal_translate-outsize"><code
                class="xref std std-option docutils literal notranslate"><span
                  class="pre">-outsize</span></code></a> and <a
              class="reference internal"
href="https://gdal.org/programs/gdal_translate.html#cmdoption-gdal_translate-a_ullr"><code
                class="xref std std-option docutils literal notranslate"><span
                  class="pre">-a_ullr</span></code></a>.</font></p>
      </dd>
    </dl>
    <p>and then choose among these resampling methods:</p>
    <dl class="std option">
      <dt class="sig sig-object std" id="cmdoption-gdal_translate-r">
        <span id="cmdoption-gdal-translate-r"></span><font
          face="monospace"><span class="sig-name descname"><span
              class="pre">-r</span></span><span class="sig-prename
            descclassname"> <span class="pre">{nearest</span> <span
              class="pre">(default),bilinear,cubic,cubicspline,lanczos,average,rms,mode}</span></span><a
            class="headerlink"
href="https://gdal.org/programs/gdal_translate.html#cmdoption-gdal_translate-r"
            title="Permalink to this definition"></a></font></dt>
      <dd>
        <p><font face="monospace">Select a resampling algorithm.</font></p>
        <font face="monospace">
        </font>
        <p><code class="docutils literal notranslate"><span class="pre">nearest</span></code><font
            face="monospace"> applies a nearest neighbour (simple
            sampling) resampler</font></p>
        <font face="monospace">
        </font>
        <p><code class="docutils literal notranslate"><span class="pre">average</span></code><font
            face="monospace"> computes the average of all non-NODATA
            contributing pixels. Starting with GDAL 3.1, this is a
            weighted average taking into account properly the weight of
            source pixels not contributing fully to the target pixel.</font></p>
        <font face="monospace">
        </font>
        <p><code class="docutils literal notranslate"><span class="pre">rms</span></code><font
            face="monospace"> computes the root mean squared / quadratic
            mean of all non-NODATA contributing pixels (GDAL >= 3.3)</font></p>
        <font face="monospace">
        </font>
        <p><code class="docutils literal notranslate"><span class="pre">bilinear</span></code><font
            face="monospace"> applies a bilinear convolution kernel.</font></p>
        <font face="monospace">
        </font>
        <p><code class="docutils literal notranslate"><span class="pre">cubic</span></code><font
            face="monospace"> applies a cubic convolution kernel.</font></p>
        <font face="monospace">
        </font>
        <p><code class="docutils literal notranslate"><span class="pre">cubicspline</span></code><font
            face="monospace"> applies a B-Spline convolution kernel.</font></p>
        <font face="monospace">
        </font>
        <p><code class="docutils literal notranslate"><span class="pre">lanczos</span></code><font
            face="monospace"> applies a Lanczos windowed sinc
            convolution kernel.</font></p>
        <font face="monospace">
        </font>
        <p><code class="docutils literal notranslate"><span class="pre">mode</span></code><font
            face="monospace"> selects the value which appears most often
            of all the sampled points.</font></p>
      </dd>
    </dl>
    <p><br>
    </p>
    <p>See: <a class="moz-txt-link-freetext" href="https://gdal.org/programs/gdal_translate.html">https://gdal.org/programs/gdal_translate.html</a> for the full
      set of options.</p>
    <p><br>
    </p>
    <blockquote type="cite"
cite="mid:CAKrv22w0XDuVQdoyMuwvcqiZsVUBPnmpCNgB9A3r3Kcbsybk8A@mail.gmail.com">
      <div dir="ltr">2. When generalizing the NDVI image to 1 square
        meter per pixel, what value does that new pixel take? the mean
        value of all pixels in 1 square meter? the maximum value of the
        pixels? etc, I ask this because if the original NDVI image has a
        pixel of 4 centimeters (16 cm2), in 1 square meter there are
        more or less 625 pixels, then what pixel value does it take if
        it is generalized to 1 square meter? the average? the maximum
        value?<br>
        <br>
        <br>
        Thank you very much<br>
        <br>
        Carlos<br>
        <br>
        <br>
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</pre>
    </blockquote>
    <pre class="moz-signature" cols="72">-- 
Micha Silver
Ben Gurion Univ.
Sde Boker, Remote Sensing Lab
cell: +972-523-665918</pre>
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