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NAME

i.nightlights.intercalibration - Performs inter-satellite calibration on DMSP-OLS Nighttime Lights Time Series

KEYWORDS

imagery, inter-satellite, calibration, nighttime lights, time series, DMSP-OLS

SYNOPSIS

i.nightlights.intercalibration --help

DESCRIPTION

i.nightlights.intercalibration 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.

Note, the module is still under testing. Eventually minor, but important, changes might be applied to the intercalibration process.

Overview

+----------------------------------------------------------------------+
|                                                                      |
|          +-----------------+                                         |
| 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?                                                          | |
| |                                                                  | |
| +------------------------------------------------------------------+ |
|                                                                      |
+----------------------------------------------------------------------+

Details

From a review paper:

"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]

References above are: [23]: [Elvidge 2009] [24]: [Liu 2012], [25]: [Wu 2013]

NOTES

?

EXAMPLES

Given all maps are imported in GRASS' data base, which are:

the default inter-calibration, based on [Elvidge 2014], can be performed as:

Remarks

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:

Example figures

TODO

in general:

• Fix html manual!

• improve missing key handling and error reporting

• code deduplication

• test -- will it compile in other systems?

in i.nightlights.intercalibration.py:

• use *args or **kwargs where appropriate

in calibration_models.py:

• improve checks for missing combinations of Satellite + Year in models

• separate test_function from this "module"

another module?

• 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

REFERENCES

[Review paper(s)]

• Application of DMSP⁄OLS Nighttime Light Images A Meta-Analysis and a Systematic Literature Review [Huang 2014]

[Empirical second order regression model by Elvidge, 2009 | Y = C0 + C1*X + C2*X^2 ]

• Estimating Land Development Time Lags in China Using DMSP⁄OLS Nighttime Light Image [Zhang 2015]

• National Trends in Satellite-Observed Lighting 1992–2012 [Elvidge 2014]

• Comparative Estimation of Urban Development in China’s Cities Using socioeconomic and DMSP⁄OLS Night Light Data [Fan 2014]

• 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]

• Characterizing Spatio-Temporal Dynamics of Urbanization in China Using Time Series of DMSP⁄OLS Night Light Data [Xu 2014]

• Night on Earth Mapping decadal changes of anthropogenic night light in Asia [Small & Elvidge 2013]

[Second order regression model & optimal threshold method by Liu, 2012]

• Modeling In-Use Steel Stock in China's Buildings and Civil Engineering Infrastructure Using Time-Series of DMSP⁄OLS Nighttime Lights [Liang 2014]

• Dynamics of Urbanization Levels in China from 1992 to 2012 Perspective from DMSP⁄OLS Nighttime Light Data] [?]

[Non-linear, power regression model]

• Intercalibration of DMSP-OLS night-time light data by the invariant region method [Wu 2013]

AUTHORS

Nikos Alexandris

SOURCE CODE

Available at: i.nightlights.intercalibration source code (history)

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© 2003-2018 GRASS Development Team, GRASS GIS 7.4.1svn Reference Manual