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

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1 Monitoring Urban Night-Time Lights Related to Economic Activity (Gross Domestic Product), Urban Heat Island (UHI) and Fires detection in the Paraná Flooding Valley - Argentina using the Observatory SAC- D/Aquarius Satellite data. 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 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 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.

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

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

5 Methodology I Criteria to select the best night-time data for analysis. 1. No presence of sunlight. 2. No presence of moonlight. 3. No presence of solar glare. 4. Cloud free based on thermal detection of clouds and lightning. a. We know that the revisit hour from SAC-D, (very early in the morning), can be a strong drawback especially in summer and spring seasons, we will try to use SAC-D data from winter and late autumn seasons. b. Archive SAC-C data will be used to obtain historical nighttime data. c. Archive DMSP Data will be required to NOAA to obtain summer and spring data.

6 Methodology II 1. Color composite multitemporal images will allow detecting changes in nighttime lights in the urban core. 2. Multitemporal archive night-time images from SAC-C HSTC and DMSP-OLS instrument data will be used as historical support for this research. 3. With the data obtained, a 3D representation of the energy dome will be done to compare against the urban cities structure. 4. Compare urban population and building density with nighttime lights images from satellite sensors.

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

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

9 Rafaela Santa Fe Paraná Night-time Lights SAC-C HSTC 27 May :30 am UT

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

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

12 3D Representation of the Night-Time Lights Dome Rainbow Palette z y x B/W Palette Energy Distribution Pattern of an Urban Area resembles a dome, x,y plane represents the illuminated area and z axis the energy utilization

13 Urban Heat Island (UHI) The term heat island (HI), it is used to name the phenomena that appear in cities as a thermal gradient from different urban areas. Remote sensing techniques are a powerful tool for the analysis of urban heat islands (UHI) since provides a view of big urban areas, the urban fringe and the neighboring region. The correlation between the urban frame and urban pattern with the UHI will be analyzed. Because SAC-D thermal data will be available between 3 µm to 12.3 µm UHI could be well analyzed.

14 Urban Heat Island (UHI) 1. Land Surface Temperature (LST) is very important for environmental researches, aid to discover local climate and city development. Average temperature of urban area is usually higher than rural or suburban area in summer. 2. There are temperature differences between suburban and downtown areas, but also from one district to another in a same city, which are called Urban Heat Island (UHI) and they changes as the city grows, therefore is important to research temporal and regional changes of land surface temperature in relationship with land use land cover evolution. 3. The city s core temperature is generally higher than the surrounding areas, cover vegetation and water areas temperatures are obviously lower than road, building and ground with concrete layers.

15 Methodology 1. Comparing city plans with housing type characteristic against LST (Land Surface Temperature) images to correlate thermal response with the urban patterns of several cities of Argentina. 2. Data must be Cloud free over the study area, so mask cloud must be created. 3. Make Atmospheric Corrections to the data captured. 4. Research on multitemporal and seasonal image time series must be done, in order to discover how, when and where the urban heat island are.

16 Land Surface Temperature with NOAA Images of Rosario City and Great Rosario LST created with Split Window Algorithm Dartcom Software with NOAA-AVHRR bands 4 and 5 Rosario 22.5ºC 30.5ºC 38.5ºC

17 Fire/Heat Focus Detection Burns of pastures and bushes in the islands belonging to the Flood Valley of the Paraná River with the purpose of obtaining soft pastures for the cattle raising, it has been intensified in the last years, causing the partial destruction of a wetlands with unique wild characteristic in the world and should be monitored against it indiscriminate burns. Satellite Images are useful and a suitable tool for the control and systematic monitoring of these type activities and the control of possible damages to the environment. The proposed research is to calculate the land surface temperature (LST) with the data collected by the NIRST sensor and validates the results with MODIS LST models.

18 Example Images about Fire/Heat Focus Detection Rosario Rosario 1 1 A B C D Cities A -V. G. Galvez B -Ramallo C -Baradero D -Zárate Buenos Aires City Feng Yun1D, 26/03/2004, 321 RGB 2 Buenos Aires City NOAA 12, 25/08/2004, 321 RGB 2 Rosario Rosario 1 1 A B C D Cities A -V. G. Galvez B -Ramallo C -Baradero D -Zárate Buenos Aires City Feng Yun1D, 26/03/2004, 3T21 RGB 2 Buenos Aires City NOAA 12, 25/08/2004, 3T21 RGB 2

19 Satellite Constellation Bands Instruments Comparative Diagram In relation with future bands of SAC-D

20 Methodology 1. The NIRST sensor installed in the SAC-D satellite have two adjacent thermal infrared channels, LWIR1 ( µm) and LWIR2 ( µm) data, that allows to calculate land surface temperature using the General Split Window algorithm developed by Wan and Dozier (1996)

21 Methodology T s = Superficial Temperature a 0 to a 6 = are the unknown coefficients which will be calculated T i and T j = are the Top of Atmosphere brightness temperatures measured in channels i (11.0 µm)(tir) and j (12.0 µm)(tir). ε i and ε j = are, respectively, the land surface emissivities in channels i and j ε = ε i ε j, the emissivity difference between the two adjacent channels ε = (ε i + ε j ) / 2 is the averaged emissivity

22 Methodology 1. Image time series must be used in order to calculate the coefficients for the General Split Window for being able to locate the fire heat focus. 2. Data must be Cloud free over the study area, so mask cloud must be created and calibration to the data captured. 3. With NIRST sensor data capture we are going to monitor high temperature events like biomass combustion with the aim of determine its extension and temperature. 4. The Fire/Heat Focus results can be validated with aid of MODIS and NOAA images.

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