<div dir="ltr">Hi Ruben,<div><br></div><div>Thanks so much for your reply. I'm not doing time-series analysis, but instead looking at animal's fine-grained habitat use using movement data. I'd like to avoid interpolation, because my landscape is a mosaic of different habitats and transitions between habitats can be sharp, so I'm worried that interpolation is not appropriate. I'm just hoping that I can get enough clear pixels and then I'll remove any telemetry locations where there is no NDVI data. Due to the high cloud cover in my study area, right now I'm settling for a monthly (for each year) composite. I'm really only interested in the summer months (~May to September). I plan to use this monthly time-series variable as an input into a resource selection function.</div><div><br></div><div>I'm really interested to know if there are any best practices around using the qa bits to detect clouds (or if there are better ways of masking clouds). I looked around your rsMove code (very impressive!), but could not find if you are doing any cloud masking. For example, when you do interpolation, how do you decide that a particular pixel is not good and should instead be interpolated?</div><div><br></div><div>Thanks a lot for the help!</div><div><br></div><div>Ben</div></div><br><div class="gmail_quote"><div dir="ltr">On Tue, Dec 4, 2018 at 12:47 PM Ruben Soares Lapa Remelgado <<a href="mailto:ruben.remelgado@uni-wuerzburg.de">ruben.remelgado@uni-wuerzburg.de</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div text="#000000" bgcolor="#FFFFFF">
<p>Hi Ben,<br>
<br>
Depends on what kind of analysis you are doing. If you are doing
some kind of time series analysis, I would suggest you interpolate
the missing values (taking in consideration the acquisition dates)
and then smooth the output using e.g. a running mean. Even if you
get a mask from USGS, that is still a generic estimation and will
contain artifacts. Might also be useful to filter out very small
values (a typical empirical threshold is -0.3) if you are not
interested in water bodies. Still, note that not every
cloud/shadow has a low NDVI.<br>
<br>
If you are interested, I implemented a function for RS data
interpolation. You can install rsMove and use the intime()
function. You should install it from my gitHub (see <a href="https://github.com/RRemelgado/rsMove" target="_blank">here</a>) because I
still haven't submitted an update to CRAN. intime() It's a c++
that performs a linear, time sensitive interpolation. This means
that for, each observation, it will check what are the closest
time steps in relation to the date(s) you want to have NDVI values
for (in the past and in the future) and used them to interpolate
the missing value, assuming that the time different (in days) is
smaller than a predefined temporal buffer. A temporal buffer is
required because, if the gaps are too big, you will likely
over-generalize the NDVI curve and thus miss a lot of important,
seasonal changes. The function is also called by imgInt() - which
applies it to raster objects - but I'm still working on it. So I
suggest you use getValues() to extract the data as a matrix before
applying the function. You can then set it back with setValues()
and the original raster stack. It will build a new stack with the
interpolated values.<br>
<br>
If you really want to build mosaics, I can propose you another
function from the same package called rsComposite(). It builds
composites for the nearest date (if you want something for a
specific year) or composites that are phenology dependent (if your
landscape doesn't change much over the years , it uses multi-year
data to build a composite around a specific day of the year).
After, you can maybe use a spatial smoothing (e.g. using the
median with the focal() function of the raster package) to deal
with outliers.<br>
<br>
Best,<br>
Ruben</p>
<div class="m_-8529814442834977966moz-cite-prefix">On 04/12/2018 18:19, Ben Carlson wrote:<br>
</div>
<blockquote type="cite">
<div dir="ltr">
<div dir="ltr">
<div dir="ltr">Hello,
<div><br>
</div>
<div>I'm working with landsat 8 collection 1 surface
collection data. I'd like to mask out clouds, with the
goal of generating NDVI so that I can examine how animal
movements respond to this variable.</div>
<div><br>
</div>
<div>In the product guide for L8 SR data (page 22), it is
recommended that "clear" conditions correspond to pixel_qa
values of 322, 386, 834, 898, and 1346.</div>
<div><br>
</div>
<div><a href="https://landsat.usgs.gov/sites/default/files/documents/lasrc_product_guide.pdf" target="_blank">https://landsat.usgs.gov/sites/default/files/documents/lasrc_product_guide.pdf</a><br>
</div>
<div><br>
</div>
<div>I've implemented this recommendation as a cloud mask
and based on visual inspection it looks fine. However, I'm
wondering if there are other, better recommendations based
on my intended use of the data.</div>
<div><br>
</div>
<div>Note: In case this affects the answer, I'm also
mosaicing multiple landsat scenes in order to fill in
blank areas due to clouds.</div>
<div><br>
</div>
<div>Thank you!</div>
<div><br>
</div>
<div>Ben</div>
<div><br>
</div>
<div><br>
</div>
</div>
</div>
</div>
<br>
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<pre class="m_-8529814442834977966moz-quote-pre">_______________________________________________
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</blockquote>
<pre class="m_-8529814442834977966moz-signature" cols="72">--
Ruben Remelgado, M.Sc.
Department of Remote Sensing
Institute of Geography and Geology
University of Wuerzburg
Oswald-Kuelpe-Weg 86
97074 Wuerzburg, Germany
Phone - +49 (0) 931 31-83562
Email - <a class="m_-8529814442834977966moz-txt-link-abbreviated" href="mailto:ruben.remelgado@uni-wuerzburg.de" target="_blank">ruben.remelgado@uni-wuerzburg.de</a>
Url - <a class="m_-8529814442834977966moz-txt-link-freetext" href="https://www.geographie.uni-wuerzburg.de/fernerkundung/personen/remelgado_ruben_msc/" target="_blank">https://www.geographie.uni-wuerzburg.de/fernerkundung/personen/remelgado_ruben_msc/</a></pre>
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</blockquote></div>