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Universidad Nacional de Rosario – Facultad de Ciencias Exactas, Ingeniería y Agrimensura – Centro de Sensores Remotos – CONICET – CONAE – NASA Universidad.

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Presentation on theme: "Universidad Nacional de Rosario – Facultad de Ciencias Exactas, Ingeniería y Agrimensura – Centro de Sensores Remotos – CONICET – CONAE – NASA Universidad."— Presentation transcript:

1 Universidad Nacional de Rosario – Facultad de Ciencias Exactas, Ingeniería y Agrimensura – Centro de Sensores Remotos – CONICET – CONAE – NASA Universidad Nacional de Rosario Centro de Sensores Remotos Researchers:Arq. Carlos CotlierDr. Ernesto Seselovsky Agrim. Benito ViciosoDra. Cristina Pacino Ing. Cecilia CorneroIng. Maria José Milicich Tec. Diego A. G. López Monitoring Urban Night-Time Lights Related to Economic Activity (Gross Domestic Product) using the Observatory SAC-D/Aquarius Satellite data. Facultad de Ciencias Exactas, Ingeniería y Agrimensura 2011 Satellite Direct Readout Conference

2 Satellite and Instruments SAC-D HSC ARGENTINA SAC-C HSTC ARGENTINA NOAA-DMSP-OLS Defense Meteorological Satellite Program – Operational Linescan System. USA Spatial Res.300 m ADC Res. 8 bits SNR 50 Swath Width700 Km Spatial Res.200 m ADC Res. 10 bits SNR 200 Swath Width830 Km Spatial Res.0.55 Km ADC Res. 6 bits SNR Swath Width3000 Km

3 Relationship between Night-time Lights. *Night-time lights remote sensing data can be used to analyze Gross Domestic Product (GDP) by monitoring night-time radiance (urban energy dome) and linear energy detection over roads. *By means of the analysis of multitemporal data, can be correlated the economic activities by means of the relation between energy consumption and the national economy, regional or county level. *Argentina had different periods where economy has been up and down, is our intention to correlate archive data from SAC- C and NOAA DSMP and try to add new information from SAC-D HSC sensor, especially from years where economy was in low performance and to compare this urban night-time energy data with periods where the economy of Argentina where going up and GDP was growing up in unusual levels.

4 Proposed Cities for Urban and Sub-Urban Monitoring A.Buenos Aires – La Plata. B.Rosario. C.Córdoba. D.Santa Fe – Paraná. E.Resistencia – Corrientes. F.San Luis Cities A B C D E F

5 Methodology I 1.We will have to mask the images (SAC-D, SAC-C, DMSP data) in order following the criteria for get the best night-time data for analysis (Elvidge et al, 2007): a.No presence of sunlight. b.No presence of moonlight c.No presence of solar glare d.Cloud free based on thermal detection of clouds and lightning. 2.To counteract illumination glare that exceeds urban city limits a “threshold calculation” must be done (Imhoff, 1997) and will be applied to clean the images data. 3.With the processed data and several data like social, economic and urban variables, the following methodology will be addressed in order to correlate them and to obtain correlation coefficients.

6 3D Representation of the Night-Time Lights Dome Energy Distribution Pattern of an Urban Area resembles a dome, x,y plane represents the illuminated area and z axis the energy utilization x y z Rainbow PaletteB/W Palette

7 Methodology II Social and urban variables: a.Generate the 3D model surface with the z axis (energy values) to obtain the energetic dome and Calculate the volume of the dome using remote sensing and GIS software. b.Compare the illuminated area with urban shapes by superimposing both layers, in order to determine the relationship between the illuminated with georeferenced urbanized area maps of the city. c.Scattergrams to show relationship between: dome volume and population density of the urbanized area, dome volume and energy consumption (in kw/h), consumption (in kw/h) and population of the urbanized area, illuminated area and gross domestic product will be constructed and find an equation that best fit the mentioned relationship, performed with math software like R or Mathlab (Welch, 1980).

8 Methodology III Social and urban variables: d.For each of the four relationships will be obtained the correlation coefficient R. e.As results, the correlation coefficient R will allow to determinate if the variables researched are related with the economical statistical data and the remote sensing nightlights processed data. f.Color composite multitemporal study images will allow to detect changes in nighttime lights in the city urban core. g.To follow temporal roads illumination evolution in the Province of San Luis with SAC-D, SAC-C and DMSP image data creating RGB color composite images. If possible anothers areas from Argentina will be analyzed.

9 Methodology IV Economic variables: a.Determine relationships between economic activity values and the radiance measured for determined geographical (urban- industrial) areas and the economic variables. For this, will be used regression models that can be linear or logarithmic. Logaritmic tend to be more precise, especially when trying to make comparisons, both temporal and geographical space. b.Gross Domestic Product (GDP) and other "proxy" variables will be used, by approximation to substitutes GDP. The “proxy” variable can be classified such as when there is a high correlation between it (proxy) and the GDP variable.

10 Methodology V Economic variables: c.From the temporal point of view, to analyse two different periods: 1.The period , where we will try to make a comparative historical analysis. 2.From 2010 onwards. d.From the spatial point of view, first we will analyze macro relationships that could be established using the national and provincial values. In these cases the IVA (gross income taxes) and the energy consumption variable can be sensible as “proxy" variables for this type of relationship.

11 Rosario Night-time Lights SAC-C HSTC – 27 May 2003 – 1:30 am UT National Route 9 Industrial Fringe (Rosario - San Lorenzo) Pto. San Martín San Lorenzo City Fray Luis Beltrán Granadero Baigorria Capitán Bermudez Cities V. G. Galvez

12 Night-time Lights SAC-C HSTC – 27 May 2003 – 1:30 am UT Easter border due Parana River Lights due Rosario-Victoria Bridge Lights patterns due illuminated avenues and populated neighborhoods

13 San Luis National Route 7 (The National Route 7 in San Luis’s territory is an illuminated highway) Night-time Lights SAC-C HSTC – 27 May 2003 – 1:30 am UT Villa Mercedes Provincial Route 9 (Highway) The beginning and end of the illuminated road and highways are the San Luis State borders.

14 San Luis National Route 7 (The National Route 7 in San Luis’s territory is an illuminated highway) Villa Mercedes Provincial Route 9 (Highway) The beginning and end of the illuminated road and highways are the San Luis State borders. SAC-C HSTC RGB Composition: 27/05/03-15/04/02-24/08/01

15 Santa Fe Paraná Rafaela Night-time Lights SAC-C HSTC – 27 May 2003 – 1:30 am UT

16 Ciudad Autónoma de Buenos Aires (Buenos Aires City) La Plata Panamericana Highway Night-time Lights SAC-C HSTC – 27 May 2003 – 1:30 am UT Zárate Campana

17 Valdez Peninsula Argentina Coastline Night-time Lights SAC-C HSTC – 13 Jun 2004 – 1:30 am UT Puerto Deseado Trelew Rawson

18 Fishing Boats predig squid Argentina Coastline Night-time Lights SAC-C HSTC – 25 Dec 2008 – 1:30 am UT Puerto Deseado City Note the intensity of the fishing boats compared with Puerto Deseado City – Argentina (population 12000) Continental Argentina - Patagonia Atlantic Ocean

19 Inhabitants per Hectare Relationship between Night-time Lights and Population Density of the city of Rosario

20 The End The End


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