[RS-ecology] Best practices for cloud-masking using L8 SR

[Ruben Soares Lapa Remelgado]

Hi Ben,

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.

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 here 
<https://github.com/RRemelgado/rsMove>) 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.

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.

Best,
Ruben

On 04/12/2018 18:19, Ben Carlson wrote:
> Hello,
>
> 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.
>
> 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.
>
> https://landsat.usgs.gov/sites/default/files/documents/lasrc_product_guide.pdf
>
> 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.
>
> Note: In case this affects the answer, I'm also mosaicing multiple 
> landsat scenes in order to fill in blank areas due to clouds.
>
> Thank you!
>
> Ben
>
>
>
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-- 
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 - ruben.remelgado at uni-wuerzburg.de
Url - https://www.geographie.uni-wuerzburg.de/fernerkundung/personen/remelgado_ruben_msc/

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