[GRASS-SVN] r72412 - grass-addons/grass7/imagery/i.nightlights.intercalibration
svn_grass at osgeo.org
svn_grass at osgeo.org
Tue Mar 20 06:16:27 PDT 2018
Author: neteler
Date: 2018-03-20 06:16:27 -0700 (Tue, 20 Mar 2018)
New Revision: 72412
Modified:
grass-addons/grass7/imagery/i.nightlights.intercalibration/i.nightlights.intercalibration.html
Log:
i.nightlights.intercalibration addon manual: HTML fixes, closer to https://trac.osgeo.org/grass/wiki/Submitting
Modified: grass-addons/grass7/imagery/i.nightlights.intercalibration/i.nightlights.intercalibration.html
===================================================================
--- grass-addons/grass7/imagery/i.nightlights.intercalibration/i.nightlights.intercalibration.html 2018-03-20 12:57:53 UTC (rev 72411)
+++ grass-addons/grass7/imagery/i.nightlights.intercalibration/i.nightlights.intercalibration.html 2018-03-20 13:16:27 UTC (rev 72412)
@@ -7,11 +7,11 @@
<p>
<strong>Note</strong>, the module is still under testing. Eventually minor,
-but important, changes might be applied to the intercalibration process.</p>
+but important, changes might be applied to the intercalibration process.
<h3>Overview</h3>
-<pre><code>
+<div class="code"><pre>
+----------------------------------------------------------------------+
| |
| +-----------------+ |
@@ -37,79 +37,94 @@
| +------------------------------------------------------------------+ |
| |
+----------------------------------------------------------------------+
-</code></pre>
+</pre></div>
<h3>Details</h3>
-<p>From a review paper:</p>
+
+<p>From a review paper:
+
<blockquote>
-<p>"Several methods were proposed to overcome the lack of inter-satellite calibration. These include the invariant region and the quadratic regression method proposed by Elvidge et al. [23], the second-order regression and optimal threshold method proposed by Liu et al. [24], and a power-law regression method proposed by Wu et al. [25]. Although studies based on these calibration methods showed performance improvement after the rectification [24,25], the assumption that the nighttime light remains stableover time in a particular area requires a careful choice of the invariant region manually." [Huang 2014]</p>
+<p>"Several methods were proposed to overcome the lack of inter-satellite calibration. These include the invariant region and the quadratic regression method proposed by Elvidge et al. [23], the second-order regression and optimal threshold method proposed by Liu et al. [24], and a power-law regression method proposed by Wu et al. [25]. Although studies based on these calibration methods showed performance improvement after the rectification [24,25], the assumption that the nighttime light remains stableover time in a particular area requires a careful choice of the invariant region manually." [Huang 2014]
</blockquote>
-<p>References above are: [23]: [Elvidge 2009] [24]: [Liu 2012], [25]: [Wu 2013]</p>
-<h2 id="notes">NOTES</h2>
-<p>?</p>
-<h2 id="examples">EXAMPLES</h2>
-<p>Given all maps are imported in GRASS' data base, which are:</p>
-<div class="code">
-<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>
-<div class="code">
-<p>i.nightlights.intercalibration image=<code>g.list rast pattern="F*" sep=,</code> model=wu2013 output=l</p>
-</div>
-<h2 id="remarks">Remarks</h2>
-<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>
-</div>
-<h3 id="example-figures">Example figures</h3>
-<div class="figure">
-<p>To add...</p>
-</div>
-<h2 id="todo">TODO</h2>
-<p>in general:</p>
+
+<p>References above are: [23]: [Elvidge 2009] [24]: [Liu 2012], [25]: [Wu 2013]
+
+<h2>EXAMPLES</h2>
+<p>Given all maps are imported in GRASS' data base, which are:
+<div class="code"><pre>
+g.list rast pattern="F*" sep=comma
+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
+</pre></div>
+
+<p>the default inter-calibration, based on [Elvidge 2014], can be performed as:
+<div class="code"><pre>
+i.nightlights.intercalibration image=`g.list rast pattern="F*" sep=comma` suffix=calib_elv
+</pre></div>
+<p>
+<p>An improved inter-calibration model is based on [Wu 2013], can be performed as:
+<div class="code"><pre>
+i.nightlights.intercalibration image=`g.list rast pattern="F*stable_lights*" sep=comma` model=wu2013 suffix=calib_wu
+</pre></div>
+
+<h2>Remarks</h2>
+<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:
+<div class="code"><pre>
+i.nightlights.intercalibration image=`g.list rast pattern="F??????" sep=comma` model=liu2012 --v
+... ValueError: The selected model does not know about this combination of Satellite + Year!
+</pre></div>
+
+<h3>Example figures</h3>
+
+<p>To be added...
+
+
+<h2>TODO</h2>
+<p>in general:
<ul>
-<li><p>Fix html manual!</p></li>
-<li><p>improve missing key handling and error reporting</p></li>
-<li><p>code deduplication</p></li>
-<li><p>test -- will it compile in other systems?</p></li>
+<li>Fix html manual!</li>
+<li>improve missing key handling and error reporting</li>
+<li>code deduplication</li>
+<li>test -- will it compile in other systems?</li>
</ul>
-<p>in <code>i.nightlights.intercalibration.py</code>:</p>
+<p>in <code>i.nightlights.intercalibration.py</code>:
<ul>
<li>use <code>*args</code> or <code>**kwargs</code> where appropriate</li>
</ul>
-<p>in <code>calibration_models.py</code>:</p>
+<p>in <code>calibration_models.py</code>:
<ul>
-<li><p>improve checks for missing combinations of Satellite + Year in models</p></li>
-<li><p>separate test_function from this "module"</p></li>
+<li>improve checks for missing combinations of Satellite + Year in models</li>
+<li>separate test_function from this "module"</li>
</ul>
-<p>another module?</p>
+<p>another module?
<ul>
-<li>Accuracy assessment of inter-calibrated nighttime lights time series [Wu 2013]: TLI= Σi DNi × Ci where DNi is the grey value of i-level pixels and Ci is the number of i-level pixels</li>
+<li>Accuracy assessment of inter-calibrated nighttime lights time series [Wu 2013]:
+ <tt>TLI = SUMi DNi × Ci</tt> where DNi is the grey value of i-level pixels and Ci is the number of i-level pixels</li>
</ul>
-<h2 id="references">REFERENCES</h2>
-<p>[Review paper(s)]</p>
+
+<h2>REFERENCES</h2>
+<p>[Review paper(s)]
<ul>
<li>Application of DMSP⁄OLS Nighttime Light Images A Meta-Analysis and a Systematic Literature Review [Huang 2014]</li>
</ul>
-<p>[Empirical second order regression model by Elvidge, 2009 | Y = C0 + C1*X + C2*X^2 ]</p>
+<p>[Empirical second order regression model by Elvidge, 2009 | Y = C0 + C1*X + C2*X^2 ]
<ul>
-<li><p>Estimating Land Development Time Lags in China Using DMSP⁄OLS Nighttime Light Image [Zhang 2015]</p></li>
-<li><p>National Trends in Satellite-Observed Lighting 1992–2012 [Elvidge 2014]</p></li>
-<li><p>Comparative Estimation of Urban Development in China’s Cities Using socioeconomic and DMSP⁄OLS Night Light Data [Fan 2014]</p></li>
-<li><p>The Integrated Use of DMSP-OLS Nighttime Light and MODIS Data for Monitoring Large-Scale Impervious Surface Dynamics A Case Study in the Yangtze River Delta [Shao & Liu 2014]</p></li>
-<li><p>Characterizing Spatio-Temporal Dynamics of Urbanization in China Using Time Series of DMSP⁄OLS Night Light Data [Xu 2014]</p></li>
-<li><p>Night on Earth Mapping decadal changes of anthropogenic night light in Asia [Small & Elvidge 2013]</p></li>
+<li>Estimating Land Development Time Lags in China Using DMSP⁄OLS Nighttime Light Image [Zhang 2015]</li>
+<li>National Trends in Satellite-Observed Lighting 1992–2012 [Elvidge 2014]</li>
+<li>Comparative Estimation of Urban Development in China’s Cities Using socioeconomic and DMSP⁄OLS Night Light Data [Fan 2014]</li>
+<li>The Integrated Use of DMSP-OLS Nighttime Light and MODIS Data for Monitoring Large-Scale Impervious Surface Dynamics A Case Study in the Yangtze River Delta [Shao & Liu 2014]</li>
+<li>Characterizing Spatio-Temporal Dynamics of Urbanization in China Using Time Series of DMSP⁄OLS Night Light Data [Xu 2014]</li>
+<li>Night on Earth Mapping decadal changes of anthropogenic night light in Asia [Small & Elvidge 2013]</li>
</ul>
-<p>[Second order regression model & optimal threshold method by Liu, 2012]</p>
+<p>[Second order regression model & optimal threshold method by Liu, 2012]
<ul>
-<li><p>Modeling In-Use Steel Stock in China's Buildings and Civil Engineering Infrastructure Using Time-Series of DMSP⁄OLS Nighttime Lights [Liang 2014]</p></li>
-<li><p>Dynamics of Urbanization Levels in China from 1992 to 2012 Perspective from DMSP⁄OLS Nighttime Light Data] [?]</p></li>
+<li>Modeling In-Use Steel Stock in China's Buildings and Civil Engineering Infrastructure Using Time-Series of DMSP⁄OLS Nighttime Lights [Liang 2014]</li>
+<li>Dynamics of Urbanization Levels in China from 1992 to 2012 Perspective from DMSP⁄OLS Nighttime Light Data] [?]</li>
</ul>
-<p>[Non-linear, power regression model]</p>
+<p>[Non-linear, power regression model]
<ul>
<li>Intercalibration of DMSP-OLS night-time light data by the invariant region method [Wu 2013]</li>
</ul>
-<h2 id="authors">AUTHORS</h2>
-<p>Nikos Alexandris</p>
+
+<h2>AUTHORS</h2>
+
+<p>Nikos Alexandris
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