[GRASS-SVN] r59754 - grass-promo/grassposter/2014_EGU_WD_Landscape

svn_grass at osgeo.org svn_grass at osgeo.org
Wed Apr 16 20:18:11 PDT 2014


Author: ychemin
Date: 2014-04-16 20:18:11 -0700 (Wed, 16 Apr 2014)
New Revision: 59754

Modified:
   grass-promo/grassposter/2014_EGU_WD_Landscape/poster.tex
Log:
update poster

Modified: grass-promo/grassposter/2014_EGU_WD_Landscape/poster.tex
===================================================================
--- grass-promo/grassposter/2014_EGU_WD_Landscape/poster.tex	2014-04-16 22:53:42 UTC (rev 59753)
+++ grass-promo/grassposter/2014_EGU_WD_Landscape/poster.tex	2014-04-17 03:18:11 UTC (rev 59754)
@@ -59,7 +59,7 @@
 	 \raisebox{-0.9\totalheight}{\includegraphics[width=0.45\textwidth]{./images/Yann_flowchart}}
 	&
 	The transpiration data is created from energy balance modelling [3] modules (i.eb.*, i.evapo.*) within GRASS GIS version 7, by partitioning the net radiation (r.sun) into soil heat flux (i.eb.soilheatflux), sensible heat flux (i.eb.h\_*) and the residual being the energy needed to evaporate water (i.eb.evapfr, i.eb.eta). This information is then fractionated into biotic (transpiration) and abiotic (evaporation) parts using vegetation fraction.\newline\linebreak
-The accumulated transpiration (t.rast.aggregate) is subjected to Landscape analysis (r.li.*) for search of patchiness and diversity indices [4]. Further analysis of transpiration is also done with the object oriented classifier (i.segment) for spatiotemporal objects within the transpiration original dataset, the accumulated one and the Landscape indices, respectively.\newline
+The accumulated transpiration (t.rast.aggregate) is subjected to Landscape analysis (r.li.*, inspired from [4]) for search of patchiness and diversity indices (not shown). Further analysis of transpiration is also done with the object oriented classifier (i.segment) for spatiotemporal objects within the transpiration original dataset, the accumulated one and the Landscape indices, respectively.\newline
  	\end{tabular}\newline
 \end{center}
 \begin{center}
@@ -140,15 +140,7 @@
  	\end{tabular}\newline
 \end{center}
 The methodology involves the following steps: 1) a time series of EVI composites is subjected to PCA; 2) the Principal Component images explaining most (ca. 95\%) of spatio-temporal variation in the time series, are used as input layers or ‘bands’ in the iterative self-organising cluster routine; 3) for each spatial cluster in the image output a Boolean mask is created and 4) overlaid (multiplied) with the original time series, which results in 5) a VI time series for each cluster in the output. Analysis of the temporal signature for each cluster reveals seasonal or long-term trends in VI that occur within the system.
-%A summary of the code needed to reach this point of analysis is here.\newline
-%{\footnotesize \fontfamily{pcr}\selectfont i.group \textcolor{blue}{group}=pca\_group \textcolor{blue}{input}=\$(g.mlist \textcolor{blue}{type}=rast \textcolor{blue}{pattern}=*h28v07*EVI)\newline
-%i.pca \textcolor{blue}{input}=pca\_group \textcolor{blue}{output\_prefix}=pca\_ \textcolor{blue}{percent}=99 --o\newline}
 
-%Minted version Not working (+ compile needs -shell-escape)
-%\begin{minted}[frame=single,linenos,mathescape,fontsize=\small]{sh}
-%	i.group group=pca_group input=$(g.mlist type=rast pattern=*h28v07*EVI)
-%	i.pca input=pca_group output_prefix=pca_ percent=99 --o
-%\end{minted}
 }
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -170,7 +162,7 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \blocknode{Acknowledgements}{
 \smallskip
-The authors would like to acknowledge the WLE Seed funding for innovative research.\newline
+The authors would like to acknowledge the WLE  (\url{wle.cgiar.org}) Seed funding for innovative research.\newline
 }
 
 
@@ -193,7 +185,7 @@
 }
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-\blocknode{Recession cropping \& natural vegetation}{
+\blocknode{From recession cropping (UL) to natural vegetation (LR)}{
 \begin{center}
 	\begin{tabular}{cc}
  	\begin{tabular}{c}
@@ -210,8 +202,27 @@
 
 }
 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\blocknode{GRASS GIS script for MODIS PCA \& segmentation of PCA}{
+\smallskip
+{\footnotesize \fontfamily{pcr}\selectfont 
+\textbf{\#Select MODIS EVI archive\newline} 
+i.group \textcolor{blue}{group}=pca\_group \textcolor{blue}{input}=\$(g.mlist \textcolor{blue}{type}=rast \textcolor{blue}{pattern}=*h28v07*EVI)\newline
+\textbf{\#Run the PCA on the EVI archive\newline}
+i.pca \textcolor{blue}{input}=pca\_group \textcolor{blue}{output\_prefix}=pca \textcolor{blue}{percent}=99 --o\newline
+\textbf{\#As an example, you can select the 1$^{st}$ to the 9$^{th}$ PCA members\newline}
+i.group \textcolor{blue}{group}=ta\_group \textcolor{blue}{input}=\$(g.mlist \textcolor{blue}{type}=rast \textcolor{blue}{pattern}=pca.[123456789] sep=,) \newline
+\textbf{\#and run an object-based classification analysis on them\newline}
+i.segment \textcolor{blue}{group}=ta\_group \textcolor{blue}{output}=seg\_ta \textcolor{blue}{threshold}=0.9 \textcolor{blue}{memory}=5000 \textcolor{blue}{iterations}=50 --o \& \newline}
 
+%Minted version Not working (+ compile needs -shell-escape)
+%\begin{minted}[frame=single,linenos,mathescape,fontsize=\small]{sh}
+%	i.group group=pca_group input=$(g.mlist type=rast pattern=*h28v07*EVI)
+%	i.pca input=pca_group output_prefix=pca_ percent=99 --o
+%\end{minted}
+}
 
+
 \startfourthcolumn
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



More information about the grass-commit mailing list