VIIRS Nighttime Lights Creating the Next Generation of Global Remotely-Sensed Nighttime Lights...

7
VIIRS Nighttime Lights Creating the Next Generation of Global Remotely-Sensed Nighttime Lights Products From VIIRS Day/Night Band (DNB) Data Chris Elvidge Earth Observation Group (EOG) NOAA National Centers for Environmental Information (NCEI), USA [email protected] Kimberly Baugh, Mikhail Zhizhin, Feng-Chi Hsu, Tilottama Ghosh CIRES - University of Colorado, USA

description

VIIRS Day-Night Band vs DMSP-OLS Quantization: DNB is 14 bit versus 6 bit for OLS. Dynamic Range: Due to limited dynamic range, OLS data saturate on bright lights in operational data collections. Lower Detection Limits: DNB can detect dimmer lighting than OLS. Quantitative: DNB is calibrated, the OLS visible band has no in-flight calibration. Multispectral: VIIRS has additional spectral bands to discriminate combustion sources from lights and to characterize the optical thickness of clouds.

Transcript of VIIRS Nighttime Lights Creating the Next Generation of Global Remotely-Sensed Nighttime Lights...

Page 1: VIIRS Nighttime Lights Creating the Next Generation of Global Remotely-Sensed Nighttime Lights Products From VIIRS Day/Night Band (DNB) Data Chris Elvidge.

VIIRS Nighttime LightsCreating the Next Generation of Global

Remotely-Sensed Nighttime Lights Products From VIIRS Day/Night Band

(DNB) DataChris Elvidge

Earth Observation Group (EOG)NOAA National Centers for Environmental Information (NCEI), USA

[email protected]

Kimberly Baugh, Mikhail Zhizhin, Feng-Chi Hsu, Tilottama GhoshCIRES - University of Colorado, USA

Page 2: VIIRS Nighttime Lights Creating the Next Generation of Global Remotely-Sensed Nighttime Lights Products From VIIRS Day/Night Band (DNB) Data Chris Elvidge.

Nighttime Lights Composites(Historical OLS Products)

The EOG Group at NCEI has a long history of making global annual nighttime lights composite products using DMSP-OLS data.

http://www.ngdc.noaa.gov/eog/dmsp/downloadV4composites.html

Page 3: VIIRS Nighttime Lights Creating the Next Generation of Global Remotely-Sensed Nighttime Lights Products From VIIRS Day/Night Band (DNB) Data Chris Elvidge.

VIIRS Day-Night Band vs DMSP-OLS• Quantization: DNB is 14 bit versus 6 bit for OLS.• Dynamic Range: Due to limited dynamic range, OLS

data saturate on bright lights in operational data collections.• Lower Detection Limits: DNB can detect dimmer

lighting than OLS.• Quantitative: DNB is calibrated, the OLS visible band

has no in-flight calibration.• Multispectral: VIIRS has additional spectral bands to

discriminate combustion sources from lights and to characterize the optical thickness of clouds.

Page 4: VIIRS Nighttime Lights Creating the Next Generation of Global Remotely-Sensed Nighttime Lights Products From VIIRS Day/Night Band (DNB) Data Chris Elvidge.

VIIRS Nighttime Lights Composite – 2015/01

Still has aurora, fires and background noise

Page 5: VIIRS Nighttime Lights Creating the Next Generation of Global Remotely-Sensed Nighttime Lights Products From VIIRS Day/Night Band (DNB) Data Chris Elvidge.

VIIRS Nighttime Lights Composite

October 2014

Hong Kong

Page 6: VIIRS Nighttime Lights Creating the Next Generation of Global Remotely-Sensed Nighttime Lights Products From VIIRS Day/Night Band (DNB) Data Chris Elvidge.

Current Status • A time series of 18 monthly DNB cloud-free composites are available at:

http://www.ngdc.noaa.gov/eog/viirs/download_monthly.html• Core algorithms have been developed for filtering of:• Lightning• Fires• Blurry lights • Background noise

• 18,000+ gas flares have been identified using IR channel data (Nightfire).• In the coming months we will work on producing a clean nighttime

lights product for 2015

Page 7: VIIRS Nighttime Lights Creating the Next Generation of Global Remotely-Sensed Nighttime Lights Products From VIIRS Day/Night Band (DNB) Data Chris Elvidge.

7

Applications for VIIRS Nighttime Lights

• Spatial definition of human settlements and areas with built infrastructure• Measuring growth rates in built infrastructure• Spatial extent of electrification• Power grid stability analyses• Power outage detection• Estimating the density of constructed surfaces• Modeling habitat fragmentation• Light pollution studies• Spatial modeling of economic indices

• Gridded GDP• Poverty mapping

• Urban metabolism analyses• Spatial modeling of fossil fuel carbon emissions, water consumption, waste water

production, …

All of these applications work best with a cleaned NTL product, with fires, flares & noise removed