Dear Group

I am working on three years MODIS time series data (16 day NDVI ) . We have
masked out all the landcover types other than agriculture from the
imageries using a latest landcover map. I want to do a time series trend
analysis to divide the agriculture area into different types (like
irrigated single crop, double crop, rain fed etc.)

I would like to create a workflow in R and has been trying out different
options. It would be great if any of you can give suggestions on any
particular packages, or methodology, samples to achieve this.

What I want to achieve are:

a) Group the similar trend ( from the raster brick) pixels - may be using
clustering. (Which cluster method can be used, and best way to adopt it to
the multi band modis imageries in R? Is there any other way than doing
simple clustering. I tried bfast and could get the trend for each pixel. Is
there any way to quantify this and group similar ones?

b) Matching and assigning classes: We have ground truth data and kind of
established library of ideal trends for each agriculture type based
on location. I want to generate trends for all the pixels and match each
pixel trend with the library and assign class based on the matched one. I
think it will be complicated, but is it worth a try and possible to do in R?

If yes, any hints or suggestions towards the possible methodology in R
would be great.

Regards

-- 
Sajid

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