Hi Martin, On 6/5/07, Martin Wegmann <[EMAIL PROTECTED]> wrote:
- point of global maximum (already in r.series -> max_raster) - point of global minimum (already in r.series -> min_raster) - turning point (Wendepunkt) - point of max./min. slope (e.g. growing season, senescence) - information about unimodal/bimodal etc. phenology (no idea how to add this)
I work with similar time series data (it looks as if you're interested in monitoring phenology, probably based on some veg. index or some other satellite derived biophysical variable). The problem with these series (and I guess with many other data of this kind) is that the data is very noisy (changes in solar illumination, sensor geometry, atmospheric effects...). If you filter out the noise, you could do away with temporal resolution. Over homogenous regions, you can filter spatially, thus reducing your spatial resolution. Either way, resolution degradation. You can also fit curves to your time series (double logistic functions and so on), and use the fit parameters to infer onset of senescence, budburst dates, etc. What I am trying to get at is that this very application and data source dependent. It would be very hard to code something which is generally useful. What I do is to export the time series into Python, and process them further there. The results so far are very good for crop phenology monitoring using MODIS data. The scipy python module has a lot of very useful functionality to do any of the stuff I mentioned above. My two cents :) _______________________________________________ grassuser mailing list [email protected] http://grass.itc.it/mailman/listinfo/grassuser
