Hi Larry
Thank you for this useful information. I was not quite sure how useful a
PCA analysis actually is to the remote sensing community, so good to hear
your thoughts. I am only an occasional user. It certainly makes sense that
if one was looking for a specific signature, or set range of sensors, then
this would* not* be a good approach to take. I guess it all depends on the
kinds of questions one hopes to answer. After running my Landsat 8 image
through a PCA, I admit I was most impressed with the visual output of the
first 3 axes. In my analysis I was looking at a proposed RAMSAR site and
the wetlands were really quite striking, more so than just looking at
individual bands, or combinations thereof. But I may have been lucky in
this particular instance as I am sure that the PCA output is very dependent
on the input imagery and area of interest in the analysis.
For the interest of others ... in species distribution modelling, running a
PCA is a great way of ensuring that the input layers are not correlated
(they should never be). The unfortunate result is that it then becomes
difficult to relate observed environmental drivers to the the actual
output, so it is near-impossible to state that, for example, minimum
temperature during the coldest month was an important variable driving a
particular species model. One can then only say that PCA Axis 5 was
informing the output of the analysis ... But if you want to maintain a
robust selection of environmental variables, then correct geoprocessing
tool to use in this case is the* Band Collection Statistics* tool, making
sure to tick the Compute covariance and correlation matrix option. This
would identify the extent to which various environmental layers are
correlated to one another and negate the need to use PCA. Bearing in mind
what Larry wrote, one may also loose some of the finer nuances within a
particular environmental variable layer if one runs a PCA - so I guess
best to use original layers.
Thanks for the feedback Larry, much appreciated.
