Category Archives: Remote Sensing

Sensors | Free Full-Text | Spectral Identification of Lighting Type and Character

Sensors 2010, 10(4), 3961-3988; doi:10.3390/s100403961

Article

Spectral Identification of Lighting Type and Character

Christopher D. Elvidge 1,* , David M. Keith 2, Benjamin T. Tuttle 3,4 and Kimberly E. Baugh 3

1 Earth Observation Group, Solar and Terrestrial Division, NOAA National Geophysical Data Center , 325 Broadway, Boulder, CO 80305, USA

2 Marshall Design Inc., Boulder, CO, USA

3 Cooperative Institute for Research in Environmental Science, University of Colorado, Boulder, Colorado 80303, USA

4 Department of Geography, University of Denver, Denver, CO, USA

* Author to whom correspondence should be addressed.

Received: 22 February 2010; in revised form: 12 March 2010 / Accepted: 6 April 2010 / Published: 20 April 2010

(This article belongs to the Section Physical Sensors)

Download PDF Full-Text [1626 KB, uploaded 20 April 2010 13:41 CEST]

Abstract: We investigated the optimal spectral bands for the identification of lighting types and the estimation of four major indices used to measure the efficiency or character of lighting. To accomplish these objectives we collected high-resolution emission spectra (350 to 2,500 nm) for forty-three different lamps, encompassing nine of the major types of lamps used worldwide. The narrow band emission spectra were used to simulate radiances in eight spectral bands including the human eye photoreceptor bands (photopic, scotopic, and “meltopic”) plus five spectral bands in the visible and near-infrared modeled on bands flown on the Landsat Thematic Mapper (TM). The high-resolution continuous spectra are superior to the broad band combinations for the identification of lighting type and are the standard for calculation of Luminous Efficacy of Radiation (LER), Correlated Color Temperature (CCT) and Color Rendering Index (CRI). Given the high cost that would be associated with building and flying a hyperspectral sensor with detection limits low enough to observe nighttime lights we conclude that it would be more feasible to fly an instrument with a limited number of broad spectral bands in the visible to near infrared. The best set of broad spectral bands among those tested is blue, green, red and NIR bands modeled on the band set flown on the Landsat Thematic Mapper. This set provides low errors on the identification of lighting types and reasonable estimates of LER and CCT when compared to the other broad band set tested. None of the broad band sets tested could make reasonable estimates of Luminous Efficacy (LE) or CRI. The photopic band proved useful for the estimation of LER. However, the three photoreceptor bands performed poorly in the identification of lighting types when compared to the bands modeled on the Landsat Thematic Mapper. Our conclusion is that it is feasible to identify lighting type and make reasonable estimates of LER and CCT using four or more spectral bands with minimal spectral overlap spanning the 0.4 to 1.0 um region.

Keywords: lighting types; lighting efficiency; photopic band; nighttime lights; Nightsat; LED

via Sensors | Free Full-Text | Spectral Identification of Lighting Type and Character.

Remote Sensing

Using Remote Sensing Data

Remote Sensing data is information about the spectral reflectance or emittance of an object. Not unlike our human visual perception it is recorded by a sensor onboard a satellite or aircraft. The spectral information provided differs depending on the sensor and is characterised by its resolution.

A good interactive tool set which helps you to get into remote sensing can be found at BiodiversityInformatics.

 

Different types of Resolution

In remote sensing different types of resolution exist, which are relevant for Biodiversity and Conservation application:

spatial resolution (interactive example)

temporal resolution

spectral resolution (interactive example)

thematic resolution

Moreover all data sets differ in their coverage or extent of area which is covered by one scene (interactive example). Downloadable images of remote sensing data sets are called scenes and split based on different systems (e.g. the path and row system for Landsat WRS2 or the h and v tiles system by MODIS). If you mosaic these scenes you cover a larger area.

via Remote Sensing.

Light Pollution project

some links

Nightsat

http://ngdc.noaa.gov/eog/night_sat/nightsat.html

movie of cities wow!

http://ngdc.noaa.gov/eog/night_sat/download_iss_movies.html

http://www.skykeepers.org/ordsregs/califord.html

http://cleardarksky.com/lp/SanFranCAlp.html?Mn=telescope%20accessory

Light pollution atlas:

http://djlorenz.github.io/astronomy/lp2006/

Global Radiance Calibrated Nighttime Lights

http://ngdc.noaa.gov/eog/dmsp/download_radcal.html

The index for measuring

http://en.wikipedia.org/wiki/Bortle_Dark-Sky_Scale

Dark Sky association

http://www.darksky.org/

California contacts

IDA California
Jack Sales
5978 Woodbriar Way
Citrus Heights, California 95621
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Telephone: 916-726-7405916-726-7405
Email: jesales(at)surewest(dot)net
Website: www.skykeepers.org

 
IDA San Bernardino County, California – High Deserts Region
Tom O’Key
P.O. Box 2192
Joshua Tree, California 92252
USA
Email: scdvainfo(at)gmail(dot)com
Web site: http://www.scdva.org/
 
Telephone:714-325-7202714-325-7202

IDA San Diego County, California
Lisa Bruhn
1222 Mariposa Rd.
Carlsbad, California 92011
USA
Email: sandiegoida(at)yahoo(dot)com
Telephone: 760-583-7081760-583-7081

IDA Santa Barbara County, California
Nancy Emerson
2106 Creekside Drive
Solvang, California 93463
USA
Telephone/fax: 805-693-1386
Email: fnemerson(at)comcast(dot)net
URL: http://www.we-watch.org/focus/save-our-stars/