[GRASS-SVN] r57062 - grass-addons/grass6/raster/r.broscoe

svn_grass at osgeo.org svn_grass at osgeo.org
Wed Jul 10 07:06:21 PDT 2013 

Author: annalisapg
Date: 2013-07-10 07:06:21 -0700 (Wed, 10 Jul 2013)
New Revision: 57062

Modified:
   grass-addons/grass6/raster/r.broscoe/description.html
Log:
main help upgrade with the new APTDTM statistical test

Modified: grass-addons/grass6/raster/r.broscoe/description.html
===================================================================
--- grass-addons/grass6/raster/r.broscoe/description.html	2013-07-10 14:05:28 UTC (rev 57061)
+++ grass-addons/grass6/raster/r.broscoe/description.html	2013-07-10 14:06:21 UTC (rev 57062)
@@ -1,7 +1,7 @@
 <h2>DESCRIPTION</h2>
 
-<em>r.broscoe.sh</em> Calculates waerden test and t test statistics for some values of threshold area on a single basin, according to A.J.Broscoe theory (1959).<br />
-The program uses some <em><a href="http://www.r-project.org/">R</a></em> commands for statistical analisys and graphic rapresentation. In particoular the R package <em>"agricolae"</em> is required.<br />
+<em>r.broscoe.sh</em> Calculates ADTDTM test and t test statistics for some values of threshold area on a single basin, according to A.J.Broscoe theory (1959).<br />
+The program uses some <em><a href="http://www.r-project.org/">R</a></em> commands for statistical analisys and graphic rapresentation.<br />
 The A.J.Broscoe theory is well known as the theory of the "Mean Stream Drop" and it says that, for the extraction by DEM of a stream network, exists a threshold value wich makes <em>drop</em> constant, and this is the <em>right</em> one extraction threshold. <br />
 By definig the <em>drop</em> (H) as:<br />
 <br />
@@ -18,45 +18,35 @@
 <em>H<sub>w</sub> = H<sub>w+1</sub> = H<sub>w+2</sub> = ...</em><br />
 <br />
 where H<sub>w</sub> is the <em>mean</em> of the drops related to the streams in the same Strahler order (w).<br />
-The area can be found by making some attempts for different area thresholds, doing some statistical tests (Van der Waerden test and linear regression), and choosing the <em>right</em> threshold from the output of the tests.<br />
+The area can be found by making some attempts for different area thresholds, doing some statistical tests (the suggested ADTDTM test and t-test), and choosing the <em>right</em> threshold from the output of the tests.<br />
 <br />
-<em>r.broscoe.sh</em> takes in input the DEM, the threshold values on wich calculate statistics, the outlet coords of the basin you want to study; it returns a table (text file) with the output of the Van der Waerden test and linear regression (t test) for each threshold value.<br />
-For the Van der Waerden test the parameter <em>Pvalue</em> is taken. It has to be greater than the possible, it represents the possibility of success of the test (the <em>Mean Stream Drop</em> is the same for all Strahler orders).<br />
-For the linear regression the parameters <em>t, Pr, R_squared_adj</em> are taken. <em>t</em> is the t statistic value, <em>Pr</em> is the possibility of success of the t test, <em>R_squared_adj</em> measures the dispersion of data around the mean value (for each order) for given degrees of freedom.<br />
-Three graphics called "linear_regression", "waerden_test" and "all_tests" are also generated as PDF in the home folder.<br />
-<br />
-Preferably let's take the threshold value wich gives <em>Pvalue</em> (or <em>Pr</em>) greater than 0.95, but is not granted that you can reach that result because it depends of the well-graduation (by Horton-Strahler) of the basin, its geomorphological maturity, so it is not rare that you have to take threshold where <em>Pvalue</em> is simlpy the greatest.<br />
-At the end of the calculation, at first <em>Pvalue</em> is examinated, then, only if Van der Waerden test gives no good results (low <em>Pvalue</em>), the linear regression output (<em>Pr</em>) is examinated; in fact the Van der Waerden test is preferred to linear regression because it allows you to consider the real dispersion of data around the mean: this makes you able to know the real significance of the probability (e.g. the significance is low for few data in the sample) considering an unique parameter.<br />
-<br />
+<em>r.broscoe.sh</em> takes in input the DEM, the threshold values on wich calculate statistics, the outlet coords of the basin you want to study; it returns a table (text file) with the output of the t-test (according to the Tarboton approach) and the ADTDTM test for each threshold value.<br />
+For the ADTDTM test the parameter <em>Pvalue</em> is taken. The right threshold should be the first value just greather than the choosen statistical significance. Here a statistical significance valure of 0.05 is proposed.<br />
+
+An output graphs is generated as PDF in the working folder.<br />
+
 <h2>EXAMPLE</h2>
 
-An example on Menotre stream (Umbria, Italy):<br />
-The syntax:
+An example on Chiascio stream (Umbria, Italy):<br />
+The command syntax:
 
 <div class="code"><pre>
-  r.broscoe.sh dem=dtm20_regione at AB 'thresholds=400 600 800 1000 1200 1400 1800 2000' xcoor=2291350.34 ycoor=4765192.22 lt=4 result=menotre_txt
+  r.broscoe.sh dem=dem_abt 'thresholds=2000 3000 4000 5000' xcoor=2320378.547 ycoor=4779694.770 lt=3 result=broscoe_chiascio
 </pre></div>
 
-The results:
+The results are an "output.csv":
 <div class="code"><pre>
-threshold	t	Pr	Radj	Pvalue
-400	0.5713518	0.568486	-0.003798402	0.6085511
-600	0.8791352	0.3810997	-0.001896266	0.2798474
-800	1.053110	0.2948033	0.001067895	0.29454
-1000	0.02578308	0.9794938	-0.01233737	0.8535388
-1200	0.3985548	0.69147		-0.01234108	0.6340721
-1400	-1.024254	0.3100425	0.0008457844	0.256408
-1800	-0.6368832	0.5274277	-0.01309044	0.5764749
-2000	-0.4003206	0.6908575	-0.01901582	0.814699
+threshold	n1	n2	Mean 1			Mean >1			diff			sd 1			sd >1			TrMean 1		Tr Mean >1		diff			Test t eq var		Perm Test eq var	Perm Test noeq var	Perm Test diff TrMean
+2000		127	47	59.3228346456693	64.1063829787234	-4.78354833305411	71.8789637294643	62.2363077459624	52.2869565217391	58.3953488372093	-6.10839231547018	0.687063371319418	0.663366336633663	0.712871287128713	0.534653465346535
+3000		87	32	67.9885057471264	63.78125		4.20725574712644	79.5933698087265	68.0449255651095	60.1139240506329	57.1666666666667	2.94725738396625	0.791240399171673	0.801980198019802	0.801980198019802	0.693069306930693
+4000		74	21	72.0945945945946	51.6190476190476	20.475546975547		100.947724016344	42.6104167903533	57.6764705882353	49.8421052631579	7.8343653250774		0.368259756306302	0.405940594059406	0.198019801980198	0.623762376237624
+5000		60	16	76.4833333333333	61.0625			15.4208333333333	108.929224676854	52.2193690118906	59.0555555555556	61.0625			-2.00694444444444	0.585538501927895	0.683168316831683	0.623762376237624	0.920792079207921
 </pre></div>
 <br />
-<img src="images/wt_rbroscoe.jpg"> <img src="images/lr_rbroscoe.jpg"> <img src="images/at_rbroscoe.jpg"> <br />
+..and an "output.pdf" file of the graphics where threshold values are natural (left) and logaritmic (right):
+<img src="outputN.png" width="450" height="350"> <img src="outputL.png" width="450" height="350"><br />
 <br />
-By the report and graphics, you can see that the Van der Werden test gives not-so-good results (<em>Pvalue_max</em>=0.85 for threshold=1000 cells) but, if you consider the linear regression output (<em>Pr</em>), you can see that for the same threshold value (1000 cells) <em>Pr</em> is 97%.<br />
-So the threshold=1000 cells is chosen. Moreover the program returns a set of vector map called <em>"orderd_thresholdvalue"</em> from wich you can extract the right one orderd-network (in this case the right one is <em>"orderd_1000"</em>), you can rename and use it as well as you want.<br />
-<br />
-<img src="images/menotre.jpg"><br />
-<br />
+
 <h2>NOTES</h2>
 The <em>lt</em> value requested in input is a parameter that prevents eventual errors in the DEM; it considers the presence of pits and represents the height difference <em>lesserthan</em> a drop is not considered as a drop but as a pit, and extracted from <em>Mean Stream Drop</em> analysis.<br />
 <br />
@@ -67,8 +57,7 @@
 <em><a href="r.strahler.sh.html">r.strahler.sh</a></em><br>
 
 <h2>REFERENCES</h2>
-NIST, (2006). <i>Van Der Waerden.</i><br />
-URL:  <em><a href="http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/vanderwa.htm">http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/vanderwa.htm</a></em><br />
+C. Cencetti et alii, (submitted to <b>Computers & Geosciences</b>). <i>A more efficient statistical test for drainage networks delineation using GIS and the theory of MEAN STREAM DROP.</i><br />
 <p>
 D. G. Tarboton and D. P. Ames, (2001). <i>Advances in the mapping of flow networks from digital elevation data.</i><b> World Water and Environment Resources Congress</b>, presentation (2001).<br />
 <p>
@@ -80,13 +69,11 @@
 J. C. Davis, (1990). <i>Statistics and Data Analysis in Geology</i>. John Wiley \& Sons editors (New York, NY, USA).<br />
 <p>
 A. J. Broscoe, (1959). <i>Quantitative analysis of longitudinal stream profiles of small watersheds</i>. Department of Geology, Columbia University, NY.<br />
-<p>
-F. De Mendiburu, (2006). <i>Statistical Procedures for Agricultural Research.</i><br />
-URL:  <em><a href="http://rss.acs.unt.edu/Rdoc/library/agricolae/html/agricolae.package.html">http://rss.acs.unt.edu/Rdoc/library/agricolae/html/agricolae.package.html</a></em><br />
 
+
 <h2>AUTHORS</h2>
 
-Ivan Marchesini and Annalisa Minelli, Univ. Perugia. <br>
+Pierluigi De Rosa, Ivan Marchesini and Annalisa Minelli, Univ. Perugia. <br>
 
 <p>
 <i>Last changed: $Date$</i>

More information about the grass-commit mailing list