'Re: [GRASS-user] Extracting vegetation phenology from Landsat-based time series'

[prev in list] [next in list] [prev in thread] [next in thread] 

List:       grass-user
Subject:    Re: [GRASS-user] Extracting vegetation phenology from Landsat-based time series
From:       Sajid Pareeth <spareeth () gmail ! com>
Date:       2016-10-31 11:07:33
Message-ID: CAO7vHBFjhN-hGeTcXQQXikGd=P40s73Kftz9SYxoEYYTJ2Wbsg () mail ! gmail ! com
[Download RAW message or body]

[Attachment #2 (multipart/alternative)]

Hi Markus

> 
> [ currently trying to get a grip on MODIS version 6 time series ]
> 
> In theory, extracting seasons such as cropping cycles is quite easy to
> implement: whenever a parameter in a time series is above/below a
> given threshold, start/stop the season. The question is how to store
> the results for multiple cropping cycles: a separate raster for each
> cycle and each start and stop date?
> 
> 
Yes, Output could be number of rasters equal to the maximum crop cycles
found in the scene. For those pixels with only one dominant cycle can be
represented with null() in the start and stop DOY maps of the next cycles.

I couldnt find any tool or study which captures multiple SOS and EOS in
case of crops. This would be really great to have. For example the
phenology parameters computed using R package greenbrown (
http://greenbrown.r-forge.r-project.org/phenology.php) considers only one
cycle (Not really for the crop phenology change). The method they use is
explained in page 5 of the associated paper:

Quoting the paper:

"In the third step, we used the smoothed and daily interpolated time series
to estimate start of growing season (SOS) and end of growing season (EOS)
by either using 50% thresholds on the seasonal greenness curve (approach
Trs) (White et al., 1997) or the derivative of the seasonal curve (approach
Deriv) (Tateishi & Ebata, 2004) (Data S1). Both approaches are based on the
definition of
*SOS and EOS as the mid-points of springgreenup and autumn senescence*,
respectively"

*Co-dominant water control on global inter-annual variability and trends in
land surface phenology and greenness*. Available from:
https://www.researchgate.net/publication/275050767_Co-dominant_water_control_on_global_inter-annual_variability_and_trends_in_land_surface_phenology_and_greenness
 [accessed Oct 31, 2016]."

[Attachment #5 (text/html)]

<div dir="ltr">Hi Markus<br><div><div class="gmail_extra"><div \
class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px \
0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div \
class="gmail-HOEnZb"><div class="gmail-h5"> <br>
</div></div>[ currently trying to get a grip on MODIS version 6 time series ]<br>
<br>
In theory, extracting seasons such as cropping cycles is quite easy to<br>
implement: whenever a parameter in a time series is above/below a<br>
given threshold, start/stop the season. The question is how to store<br>
the results for multiple cropping cycles: a separate raster for each<br>
cycle and each start and stop date?<br>
<br></blockquote></div><br></div><div class="gmail_extra">Yes, Output could be number \
of rasters equal to the maximum crop cycles found in the scene. For those pixels with \
only one dominant cycle can be represented with null() in the start and stop DOY maps \
of the next cycles.<br><br></div><div class="gmail_extra">I couldnt find any tool or \
study which captures multiple SOS and EOS in case of crops. This would be really \
great to have. For example the phenology parameters computed using R package \
greenbrown (<a href="http://greenbrown.r-forge.r-project.org/phenology.php" \
target="_blank">http://greenbrown.r-forge.r-<wbr>project.org/phenology.php</a>) \
considers only one cycle (Not really for the crop phenology change). The method they \
use is explained in page 5 of the associated paper:<br></div><div \
class="gmail_extra"><br></div><div class="gmail_extra">Quoting the \
paper:<br><br>&quot;In the third step, we used the smoothed and daily interpolated \
time series to estimate start of growing season (SOS) and end of growing season (EOS) \
by either using 50% thresholds on the seasonal greenness curve (approach Trs) (White \
et al., 1997) or the derivative of the seasonal curve (approach Deriv) (Tateishi \
&amp; Ebata, 2004) (Data S1). Both approaches are based on the definition of <b>SOS \
and EOS as the mid-points of spring<br>greenup and autumn senescence</b>, \
respectively&quot;<br><br><i>Co-dominant water control on global inter-annual \
variability and trends in land surface phenology and greenness</i>. Available from: \
<a href="https://www.researchgate.net/publication/275050767_Co-dominant_water_control_ \
on_global_inter-annual_variability_and_trends_in_land_surface_phenology_and_greenness" \
>https://www.researchgate.net/publication/275050767_Co-dominant_water_control_on_globa \
> l_inter-annual_variability_and_trends_in_land_surface_phenology_and_greenness</a> \
> [accessed Oct 31, 2016].&quot;<br></div></div></div>

[Attachment #6 (text/plain)]

_______________________________________________
grass-user mailing list
grass-user@lists.osgeo.org
http://lists.osgeo.org/mailman/listinfo/grass-user

[prev in list] [next in list] [prev in thread] [next in thread] 