[GRASS-SVN] r71582 - grass-addons/grass7/imagery/i.nightlights.intercalibration
svn_grass at osgeo.org
svn_grass at osgeo.org
Mon Oct 23 15:41:03 PDT 2017
Author: nikosa
Date: 2017-10-23 15:41:03 -0700 (Mon, 23 Oct 2017)
New Revision: 71582
Modified:
grass-addons/grass7/imagery/i.nightlights.intercalibration/i.nightlights.intercalibration.html
grass-addons/grass7/imagery/i.nightlights.intercalibration/i.nightlights.intercalibration.py
Log:
i.nightlights.intercalibration: updated ascii overview
Modified: grass-addons/grass7/imagery/i.nightlights.intercalibration/i.nightlights.intercalibration.html
===================================================================
--- grass-addons/grass7/imagery/i.nightlights.intercalibration/i.nightlights.intercalibration.html 2017-10-23 20:35:22 UTC (rev 71581)
+++ grass-addons/grass7/imagery/i.nightlights.intercalibration/i.nightlights.intercalibration.html 2017-10-23 22:41:03 UTC (rev 71582)
@@ -1,3 +1,13 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+ <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+ <meta http-equiv="Content-Style-Type" content="text/css" />
+ <meta name="generator" content="pandoc" />
+ <title></title>
+ <style type="text/css">code{white-space: pre;}</style>
+</head>
+<body>
<h2 id="description">DESCRIPTION</h2>
<p><em>i.nightlights.intercalibration</em> is a GRASS-GIS module performing inter-satellite calibration on DMSP-OLS nighttime lights time series. Based on "well known" emprirical regression models, it calibrates average visible band Digital Number values.</p>
<p><strong>Note</strong>, the module is still under testing. Eventually minor, but important, changes might be applied to the intercalibration process.</p>
@@ -2,27 +12,27 @@
<h3 id="overview">Overview</h3>
-<pre><code> +----------------------------------------------------------------------+
- | |
- | +-----------------+ |
- | DN +--> |Calibration Model| +--> Calibrated DN |
- | +---^-------------+ ^ |
- | | | |
- | | +--Evaluation+Methods-------------------+ |
- | | | | |
- | | | ? | |
- | | | | |
- | | +---------------------------------------+ |
- | | |
- | +--Regression+Models-----------------------------------------------+ |
- | | | |
- | | Elvidge, 2009/2014: DNc = C0 + C1×DN + C2×DNv2 | |
- | | | |
- | | Liu, 2012: based on Elvidge's model + optimal threshold method | |
- | | | |
- | | Wu, 2014: DNc + 1 = a×(DN + 1)^b | |
- | | | |
- | | Others? | |
- | | | |
- | +------------------------------------------------------------------+ |
- | |
- +----------------------------------------------------------------------+</code></pre>
+<pre><code>+----------------------------------------------------------------------+
+| |
+| +-----------------+ |
+| DNi +--> |Calibration Model| +--> Calibrated DN |
+| +---^-------------+ ^ |
+| | | |
+| | +--Evaluation+Methods-------------------+ |
+| | | | |
+| | | ? | |
+| | | | |
+| | +---------------------------------------+ |
+| | |
+| +--Regression+Models-----------------------------------------------+ |
+| | | |
+| | Elvidge, 2009/2014: DNc = C0 + C1xDN + C2xDNv2 | |
+| | | |
+| | Liu,2012: based on Elvidge's model + optimal threshold method | |
+| | | |
+| | Wu, 2014: DNc + 1 = ax(DN + 1)^b | |
+| | | |
+| | Others? | |
+| | | |
+| +------------------------------------------------------------------+ |
+| |
++----------------------------------------------------------------------+</code></pre>
<h3 id="details">Details</h3>
@@ -41,12 +51,12 @@
<p>g.list rast pattern=F* sep=,</p>
<p>F101992,F101993,F101994,F121994,F121995,F121996,F121997,F121998,F121999,F141997, F141998,F141999,F142000,F142001,F142002,F142003,F152000,F152001,F152002,F152003, F152004,F152005,F152006,F152007,F162004,F162005,F162006,F162007,F162008, F162009,F182010,F182011,F182012</p>
</div>
-<p>the "default" inter-calibration, based on [Elvidge 2014] can be performed as:</p>
+<p>the default inter-calibration, based on [Elvidge 2014], can be performed as:</p>
<div class="code">
<p>i.nightlights.intercalibration.py image=<code>g.list rast pattern=F* sep=,</code> model=wu2013 output=l --o</p>
</div>
<h2 id="remarks">Remarks</h2>
-<p>The calibration models do <em>not</em> include regression coefficients for all of the yearly products. In such a case, the module will fail and inform with an error message like:</p>
+<p>The calibration models do not include regression coefficients for all of the yearly products. In which case, the module will fail and inform with an error message like:</p>
<div class="code">
<p>i.nightlights.intercalibration image=<code>g.list rast pattern=F?????? sep=,</code> model=liu2012 --v</p>
<p>... ValueError: The selected model does not know about this combination of Satellite + Year!</p>
@@ -99,7 +109,7 @@
<ul>
<li>Intercalibration of DMSP-OLS night-time light data by the invariant region method [Wu 2013]</li>
</ul>
-<h2 id="see-also">SEE ALSO</h2>
-<p>?</p>
<h2 id="authors">AUTHORS</h2>
<p>Nikos Alexandris</p>
+</body>
+</html>
Modified: grass-addons/grass7/imagery/i.nightlights.intercalibration/i.nightlights.intercalibration.py
===================================================================
--- grass-addons/grass7/imagery/i.nightlights.intercalibration/i.nightlights.intercalibration.py 2017-10-23 20:35:22 UTC (rev 71581)
+++ grass-addons/grass7/imagery/i.nightlights.intercalibration/i.nightlights.intercalibration.py 2017-10-23 22:41:03 UTC (rev 71582)
@@ -113,13 +113,13 @@
"""
#%Module
-#% description: Performs inter-satellite calibration on DMSP-OLS Nighttime Lights Time Series.
-#% keyword: imagery
-#% keyword: inter-satellite
-#% keyword: calibration
-#% keyword: nighttime lights
-#% keyword: time series
-#% keyword: DMSP-OLS
+#% description: Performs inter-satellite calibration on DMSP-OLS Nighttime Lights Time Series
+#% keywords: imagery
+#% keywords: inter-satellite
+#% keywords: calibration
+#% keywords: nighttime lights
+#% keywords: time series
+#% keywords: DMSP-OLS
#%End
#%flag
@@ -266,7 +266,7 @@
def main():
"""
- Main program: get nameds for input, output suffix, options and flags
+ Main program: get names for input, output suffix, options and flags
"""
input_list = options['image'].split(',')
outputsuffix = options['suffix']
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