i.nightlights.intercalibration

Note: This document is for an older version of GRASS GIS that will be discontinued soon. You should upgrade, and read the current manual page.

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

i.nightlights.intercalibration --help

i.nightlights.intercalibration [-ciegxznt] image=name[,name,...] suffix=suffix model=author [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

-c: Print out citation for selected calibration model
-i: Print out calibration equations
-e: Evaluation based on the Normalised Difference Index
-g: Print in shell script style (currently only NDI via -e)
-x: Match computational region to extent of input image
-z: Exclude zero values from the analysis (retain zero cells in output)
-n: Exclude zero values from the analysis (set zero cells to NULL in output)
-t: Do not try to transfer timestamps (for input without timestamp)
--overwrite: Allow output files to overwrite existing files
--help: Print usage summary
--verbose: Verbose module output
--quiet: Quiet module output
--ui: Force launching GUI dialog

Parameters:

image=name[,name,...] [required]: Clean average DMSP-OLS visible band digital number image(s)
suffix=suffix [required]: output file(s) suffix; Suffix for calibrated average digital number output image(s); Default: c
model=author [required]: Calibration model; Inter-satellite calibration model for average DMSP-OLS nighttime lights time series; Options: elvidge2009, elvidge2014, liu2012, wu2013; Default: elvidge2014

DESCRIPTION
- Overview
- Details
EXAMPLES
Remarks
- Example figures
TODO
REFERENCES
AUTHOR

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.

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 stable over 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]

EXAMPLES

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

g.list rast pattern="F*"
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

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

i.nightlights.intercalibration image=$(g.list rast pattern="F*" sep=comma) suffix=calib_elv

An improved inter-calibration model is based on [Wu 2013], can be performed as:

i.nightlights.intercalibration image=$(g.list rast pattern="F*stable_lights*" sep=comma) model=wu2013 suffix=calib_wu

Remarks

In case the calibration models do not include regression coefficients for all of the yearly products, the module will fail and inform with an error message like:

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!

Example figures

To be added...

TODO

in general:

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 = SUMi DNi * Ci where DNi is the grey value of i-level pixels and Ci is the number of i-level pixels

REFERENCES

Review paper

Huang, Q., Yang, X., Gao, B., Yang, Y., Zhao, Y., 2014. Application of DMSP/OLS Nighttime Light Images: A Meta-Analysis and a Systematic Literature Review. Remote Sensing 6, 6844-6866. https://doi.org/10.3390/rs6086844

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

Zhang, L., Qu, G., Wang, W., 2015. Estimating Land Development Time Lags in China Using DMSP/OLS Nighttime Light Image. Remote Sensing 7, 882-904. https://doi.org/10.3390/rs70100882
Elvidge, C.D., Hsu, F.-C., Baugh, K.E., Ghosh, T., 2014. National trends in satellite-observed lighting. Global urban monitoring and assessment through earth observation 23, 97-118.
Fan, J., Ma, T., Zhou, C., Zhou, Y., Xu, T., 2014. Comparative Estimation of Urban Development in China's Cities Using Socioeconomic and DMSP/OLS Night Light Data. Remote Sensing 6, 7840-7856. https://doi.org/10.3390/rs6087840
Shao, Z., Liu, C., 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. Remote Sensing 6, 9359-9378. https://doi.org/10.3390/rs6109359
Xu, T., Ma, T., Zhou, C., Zhou, Y., 2014. Characterizing Spatio-Temporal Dynamics of Urbanization in China Using Time Series of DMSP/OLS Night Light Data. Remote Sensing 6, 7708-7731. https://doi.org/10.3390/rs6087708
Small, C., Elvidge, C.D., 2013. Night on Earth: Mapping decadal changes of anthropogenic night light in Asia. International Journal of Applied Earth Observation and Geoinformation, Spatial Statistics for Mapping the Environment 22, 40-52. https://doi.org/10.1016/j.jag.2012.02.009

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

Liang, H., Tanikawa, H., Matsuno, Y., Dong, L., 2014. Modeling In-Use Steel Stock in China's Buildings and Civil Engineering Infrastructure Using Time-Series of DMSP/OLS Nighttime Lights. Remote Sensing 6, 4780-4800. https://doi.org/10.3390/rs6064780
Gao, B., Huang, Q., He, C., Ma, Q., 2015. Dynamics of Urbanization Levels in China from 1992 to 2012: Perspective from DMSP/OLS Nighttime Light Data. Remote Sensing 7, 1721-1735. https://doi.org/10.3390/rs70201721

Non-linear, power regression model

Wu, J., He, S., Peng, J., Li, W., Zhong, X., 2013. Intercalibration of DMSP-OLS night-time light data by the invariant region method. International Journal of Remote Sensing 34, 7356-7368. https://doi.org/10.1080/01431161.2013.820365

AUTHOR

Nikos Alexandris

SOURCE CODE

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

Latest change: Tuesday Mar 26 20:41:36 2024 in commit: 3b09b1d78f6e96ffebacac6e36f0afd91ad0c091