Calibrations aspects in SAC-D, SAC-C and DMSP Urban Night-Time Lights.
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1 Calibrations aspects in SAC-D, SAC-C and DMSP Urban Night-Time Lights. Universidad Nacional de Rosario Facultad de Ciencias Exactas, Ingeniería y Agrimensura Centro de Sensores Remotos CONICET CONAE NASA Researchers: Arq. Carlos Cotlier Dr. Ernesto Seselovsky Agrim. Benito Vicioso Dra. Cristina Pacino Ing. Cecilia Cornero Ing. Maria José Milicich Tec. Diego A. G. López Universidad Nacional de Rosario Facultad de Ciencias Exactas, Ingeniería y Agrimensura Centro de Sensores Remotos
2 Satellite and Instruments SAC-C HSTC Satélite de Aplicaciones Científicas C High Sensitivity Technological Camera Spatial Res. 300 m ADC Res. 8 bits SNR 50 Swath Width 700 Km SAC-D HSC Satélite de Aplicaciones Científicas C High Sensitivity Camera Spatial Res. 200 m ADC Res. 10 bits SNR 200 Swath Width 830 Km DMSP-OLS Defense Meteorological Satellite Program Operational Linescan System. Spatial Res. ADC Res. SNR Swath Width 0.55 Km 6 bits 3000 Km
3 Night-Time Lights Calibration Aspects 1. DMSP/OLS Intercalibration of F14 to F18 satellite data. 2. SAC-C/HSTC and SAC-D/HSC multitemporal filter. 3. Sensors data calibration to radiance of SAC-C/HSTC, SAC-D/HSC, DMSP/OLS data.
4 DMSP/OLS Intercalibration I 1. F14 to F18 satellites data intercalibration: a. Why: be able to utilize together all DMSP-OLS data for comparison. b. For: create a whole data period for comparison. c. Use: correlate the large data set to economics and social variables to notice change evolution.
5 DMSP/OLS Intercalibration II Intercalibration is the procedure to ensure the same kind of response of the data sensing task. Because the DMSP/OLS has no on-board calibration instruments (over time degradation of sensor response is a possibility that can t be counteract because design aspects of the sensor) the individual mosaics have to be intercalibrated via an empirical procedure to obtain comparable results between all years.
6 DMSP/OLS Intercalibration III Year Satellite DMSP/OLS 2000 F14 F F14 F F14 F F14 F F15 F F15 F F15 F16 11 years period 2007 F15 F F F F18
7 DMSP/OLS Intercalibration IV Key aspects to go ahead regarding intercalibration 1. Differences in average DN between satellites 2. Reference Image: captured by satellite F12 in 1999 over Sicily (Elvidge, 2009) 3. The use of a Second order regression equation: DN Adjusted = C 0 + C1 DN + C2 DN 2 The reason for this algorithm is to have an adjusted digital number (DN) for all the data set to normalize them by the application of this formula. The coefficients are empirically derived by comparing images of Sicily with DMSP/OLS F Sicily Stables lights over the years. Elvidge et al, A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data.
8 DMSP/OLS Intercalibration V DN Data Period. Geographical Area: Rosario City, Santa Fe, Argentina. To utilize together all DMSP data for comparison creating a data period to correlate a similar economics and social variables period to notice evolution DMSP F14 DMSP F15 DNs Sum DNs Sum Year Year DNs Sum DMSP F Year 1. Differences in average DN between satellites 2. Reference Image: captured by satellite F14 in 2000 over Sicily 3. Second order regression equation: DN Adjusted = C C1 DN + C2 DN Elvidge et al, A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data.
9 Multitemporal Filter Application 2. SAC-C/HSTC and SAC-D/HSC multitemporal filter: a. Why: to clean the image noise and leave only urban lights. b. For: removal of noise and non-urban light sources or ephemeral light sources (lightning and fires). c. Use: be able to produce correlation between urban night-time lights data and economics/social variables. DMSP/OLS data obtained was already processed to identify and remove pixels containing clouds, aurora, fires, lightning and other transient sources of light leaving only stable lights.
10 SAC-C/HSTC Key Aspects I SAC-C/HSTC Image a Key aspects to correct on SAC-C/HSTC data: a. Illumination effect due solar angle and Earth curvature. b. Noise. c. Eventual lights (fires and lightnings). b Black/White LUT: 15/04/02
11 SAC-C/HSTC Key Aspects II SAC-C/HSTC Images Linear lights. No correlation with illuminated roads. IMAGE NOISE Black/White LUT: 15/04/02 Roads Map
12 SAC-C/HSTC Key Aspects III Needed Data to go ahead in the research a. Problem: Illumination effect due solar angle and Earth curvature. Needed From CONAE: Orbital data. b. Problem: Noise. Already provided by CONAE: Large dataset to build a multitemporal filter. c. Problem: Eventual lights (fires and lightnings). Already provided by CONAE: Large dataset detect the eventual lights.
13 SAC-D/HSC Key Aspects I SAC-D/HSC Image b Key aspects to correct on SAC-D/HSC data: a. Illumination effect due solar angle and Earth curvature. b. Noise patterns. c. Eventual lights (fires and lightnings). d. Georreference data. a Black/White LUT: 22/07/2011
14 SAC-D/HSC Key Aspects II Needed Data to go ahead in the research a. Problem: Illumination effect due solar angle and Earth curvature. Needed From CONAE: Orbital data. b. Problem: Noise patterns. Needed From CONAE: Large dataset to build a multitemporal filter. c. Problem: Eventual lights (fires and lightnings). Needed From CONAE: Large dataset detect the eventual lights. d. Problem: Georreference data. Needed From CONAE: Orbital data.
15 Examples of Cordoba City, Argentina SAC-D/HSC SAC-C/HSTC DMSP/OLS 250 m resolution 10 bits DN (0 to 1023) SNR m resolution 8 bits DN (0 to 255) SNR m resolution 6 bits DN (0 to 63) Improved pixel resolution and better response due higher radiometric resolution (10 bits) and signal noise ratio (> 200 SNR)
16 Example of Rosario City, Argentina A A C B Lights due Rosario-Victoria Bridge C B Lights patterns due illuminated avenues and populated neighborhoods Easter border due Paraná River Night-time Lights SAC-C HSTC 27 May :30 am UT
17 Radiance Calibration I To have the same kind of data of all the sensors. 3. Calibration of DN data to radiance of SAC- C/HSTC, SAC-D/HSC, DMSP/OLS: a. Why: to be able to compare all three sensor data sets. b. For: dispose of same kind of data for comparison. c. Use: be able to compare results of sensors data correlation to economics and social variables of the three satellites. Radiance is defined by: Where: L is the observed or measured radiance (Wm 2 sr 1 ), in the direction θ, d is the differential operator, Φ is the total radiant flux or power (W) emitted θ is the angle between the surface normal and the specified direction, A is the area of the surface (m 2 ), and Ω is the solid angle (sr, stereo radians) subtended by the observation or measurement. The approximation only holds for small A and Ω where cos θ is approximately constant.
18 1. For SAC-C/HSTC An algorithm needed to apply to DN data to be provided by CONAE. 2. For SAC-D/HSC: An algorithm needed to apply to DN data to be provided by CONAE. 3. For DMSP/OLS: Radiance Calibration II Needed Data to go ahead in the research: Algorithm to apply to DN data already developed by NOAA: Radiance = DN 3/2 x W cm -2 sr -1 µm -1 Elvidge et al, Radiance calibration of DMSP-OLS low-light imaging data of human settlements.
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