The Effectiveness of Using MODIS Products for Monitoring Climate Change Risks over the Nile Delta, Egypt
International Journal of Environmental Monitoring and Analysis
Volume 3, Issue 6, December 2015, Pages: 382-396
Received: Sep. 26, 2015; Accepted: Oct. 24, 2015; Published: Dec. 7, 2015
Views 3907      Downloads 82
Author
Hossam Ismael, Geography and GIS Department, Faculty of Arts., Assiut University, New Valley Branch, Egypt
Article Tools
Follow on us
Abstract
Climate change is the one of greatest challenges that faces the human being nowadays as the Earth’s climate is getting warmer. The National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) have indicated that the temperature average of the Earth’s surface has increased about 1.2 to 1.4 C since 1900. Other climatic aspects are exposed to change as well such as patterns of precipitation and storms. The most common reason that leads to climate change is very likely human activities (e.g. fuel combustion and pollution). The Study area is the most affected region in the world by climate change impacts according to the fourth report of the Intergovernmental Panel on Climate Change 4th Report of IPCC, 2007. This report presents a scenario of destruction of the settlement centers in Nile Delta, Port Said in the east and Alexandria in the west (10 million people are at risk), besides, losing more than 86 square kilometers of the northern lakes, about 200,000 acres of the most valuable agricultural land as a result of high temperature and the consequent rise in average sea level. In Egypt, air pollutants (e.g. SO2 and CO2) gave rise to high concentrations of air pollutants especially in Nile delta, due to bio mass fire which is called 'Black Cloud' phenomenon. The main aim of this study was to present the effectiveness of using both the MODIS atmosphere data produced by the Terra mission and to describe differences with comparable products to be produced by Aqua. To achieve this aim the study used the HYDRA visualization software with the characteristics of the MODIS climatic data. Results obtained from MODIS data are validated by using the previously mentioned data sets to reveal the nature and the characteristics of the climate change. Fire, dust Detection with MODIS, AIRS, and AOD analysis clearly indicates large amounts of aerosols that form the black cloud events over various locations within the Nile delta region. Also the results agreed with the observed values in the study area, and highly required for many applications related to integrated remote sensing techniques with actual field measurements and data Meteorological Authority in different periods to reduce the risk of climate.
Keywords
HYDRA Visualization, Heat Island Impacts, MODIS Images, Terra and Aqua
To cite this article
Hossam Ismael, The Effectiveness of Using MODIS Products for Monitoring Climate Change Risks over the Nile Delta, Egypt, International Journal of Environmental Monitoring and Analysis. Vol. 3, No. 6, 2015, pp. 382-396. doi: 10.11648/j.ijema.20150306.12
Copyright
Copyright © 2015 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
Abu-Allaban, M.; Lowenthal, D.H.; Gertler, A.W. & Labib, M. (2009). Sources of volatile organic compounds in Cairo’s ambient air. Environmental Monitoring and Assessment, Vol.157, No.1-4, (October 2009), pp. 179–189, ISSN 0167-6369.
[2]
Alfaro, S.C. & Abdel Wahab, M.M. (2006). Extreme variability of aerosol optical properties: The Cairo aerosol characterization experiment case study. Proceedings of the NATO Advanced Research Workshop on Remote Sensing of the Atmosphere for Environmental Security, pp. 285–299, ISBN: 978-1-4020-5089-3, Rabat, Morocco, 16-19 November, 2005.
[3]
Astitha, M.; Kallos, G. & Katsafados, P. (2008). Air pollution modeling in the Mediterranean Region: Analysis and forecasting of episodes. Atmospheric Research, Vol.89, No.4, (September 2008), pp. 358-364, ISSN 0169-8095
[4]
B. H. Tang, Z.-L. Li, and Y. Y. Bi, “Estimation of land surface directional emissivity in midinfrared channel around 4.0 μm from MODIS data,” Opt. Express 17(5), 3173–3182 (2009), http://dx.doi.org/10.1364/OE.17.003173.
[5]
Becker. F, and Z.-L. Li, “Towards a local split window method over land surfaces,” Int. J. Remote Sens. 11(3), 369–393 (1990), http://dx.doi.org/10.1080/01431169008955028.
[6]
Barnaba, F. & Gobbi, G.P. (2004). Aerosol seasonal variability over the Mediterranean region and relative impact of maritime, continental and Saharan dust particles over the basin from MODIS data in the year 2001. Atmospheric Chemistry and Physics Discussions, Vol.4, No.4, (August 2004), pp. 4285-4337, ISSN 16807367.
[7]
Christopher D. G et al, 2006. The impacts of climate change in coastal marine systems. Ecology Letters, (2006) 9: 228–241.
[8]
Dubovik, O., B. N. Holben, T. F. Eck, A. Smirnov, Y. J. Kaufman, M. D. King, D. Tanre´, and I. Slutsker (2002), Variability of absorption and optical properties of key aerosol types observed in worldwide locations,J. Atmos. Sci., 59, 590–608.
[9]
El-Askary, H.; Farouk, R.; Ichoku, C. & Kafatos, M. (2009). Transport of dust and anthropogenic aerosols across Alexandria, Egypt. Annales Geophysicae, Vol.27, No.7, (July 2009), pp. 2869-2879, ISSN 0992-7689
[10]
El-Askary H. & Kafatos, M. (2008). Dust Storm and Black Cloud Influence on Aerosol Optical Properties over Cairo and the Greater Delta Region, Egypt. International Journal of Remote Sensing, Vol.29, No.24, (December 2008), pp. 7199 – 7211, ISSN 0143-1161
[11]
El-Askary, H. (2006). Air pollution Impact on Aerosol Variability over mega cities using Remote Sensing Technology: Case study, Cairo, Egypt. Egyptian Journal of Remote Sensing & Space Science, Vol.9, (July 2006), pp. 31-40
[12]
El-Askary, H.; Sarkar, S.; Kafatos, M. & El-Ghazawi, T. (2003). A multisensor approach to dust storm monitoring over the Nile Delta. IEEE Transactions on Geoscience & Remote Sensing, Vol.41, No.10, (October 2003), pp. 2386 – 2391, ISSN 0196-2892.
[13]
El-Metwally, M.; Alfaro, S.C.; Abdel Wahab, M.M. & Chatenet, B. (2008). Aerosol characteristics over urban Cairo: Seasonal variations as retrieved from Sun photometer measurements. Journal Geophysical. Research, Vol.113, No.D14219, (July 2008), pp. 1-13 ISSN 0148-0227.
[14]
Jiménez-Muñoz. J, and J. A. Sobrino, “A generalized single-channel method for retrievingland surface temperature from remote sensing data,” J. Geophys. Res. 108(D22), 4688–4697 (2003), http://dx.doi.org/10.1029/2003JD003480.
[15]
Jiang. G -L. Li, and F. Nerry, “Land surface emissivity retrieval from combined mid-infrared and thermal infrared data of MSG-SEVIRI,” Remote Sens. Environ. 105(4), 326–340 (2006), http://dx.doi.org/10.1016/j.rse.2006.07.015.
[16]
Kaufman, Y. J., B. N. Holben, D. Tanre´, I. Slutsker, A. Smirnov, and T. F. Eck (2000), Will aerosol measurements from Terra and Aqua polar orbiting satellites represent the daily aerosol abundance and properties, Geophys. Res. Lett., 27, 3861– 3864.
[17]
Kaufman, Y. J., D. Tanre´, and O. Boucher (2002), A satellite view of aerosols in the climate system, Nature, 419, 215– 223.
[18]
King, M. D., Y. J. Kaufman, W. P. Menzel, and D. Tanre, 1992: Remote Sensing of Cloud, Aerosol and Water Vapor Properties from the Moderate Resolution Imaging Spectrometer (MODIS). IEEE Trans. and Geoscience and Remote Sensing, 30, 2-27.
[19]
King, M. D., W. P. Menzel, Y. J. Kaufman, D. Tanré, B. C. Gao, S. Platnick, S. A. Ackerman, L. Remer, R. Pincus, and P. A. Hubanks, 2003: Cloud and aerosol properties, precipitable water, and profiles of temperature and humidity from MODIS. IEEE Trans. Geosci. Remote Sens., 41, 442-458.
[20]
King, M. D., W. P. Menzel, Y. J. Kaufman, D. Tanré, B. C. Gao, S. Platnick, S. A. Ackerman, L. A. Remer, R. Pincus, and P. A. Hubanks, 2003: Cloud, Aerosol and Water Vapor Properties from MODIS., IEEE Trans. Geosci. Remote Sens., 41, pp. 442-458.
[21]
Marey, H. S., Gille, J. C., El-Askary, H. M., Shalaby, E. A., and El- Raey, M. E.: Study of the formation of the “black cloud” and its dynamics over Cairo, Egypt, using MODIS and MISR sensors, J. Geophys. Res., 115, D21206, doi:10.1029/2010JD014384, 2010.
[22]
Marey, H. S., El-Askary, H. M., Shalaby, E. A., and El- Raey, M. E: Aerosol climatology over Nile Delta based on MODIS, MISR and OMI satellite data, Atmos. Chem. Phys., 11, 10637–10648, 2011.
[23]
Menzel W. P., D. P. Wylie, and K. I. Strabala, 1992: Seasonal and diurnal changes in cirrus clouds as seen in four years of observations with the VAS, J. Appl. Meteor., 31, 370-385
[24]
Menzel W. P. and J. F. W. Purdom, 1994: Introducing GOES-I: The first of a new generation of Geostationary Operational environmental Satellites. Bull. Amer. Meteor. Soc., Vol. 75, No. 5, pp. 757-78.
[25]
Menzel W. P. et al, 2009: Laboratory Exercises for remote sensing applications with meteorological satellite, Space Science and Engineering Center, University of Wisconsin Madison, WI.
[26]
Moeller, C. C., H. E. Revercomb, S. A. Ackerman, W. P. Menzel, and R. O. Knuteson, 2003: Evaluation of MODIS thermal IR band L1B radiances during SAFARI 2000. J. Geophys. Res., 108, D13, 8494.
[27]
Mu Q, Heinsch FA, Zhao M, Running SW, Cleugh HA, Leuning R (2006b) Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens Environ 111(4):519–536.
[28]
Nerry. F: Petitcolin. F, and M. P. Stoll, “Bidirectional reflectivity in AVHRR channel 3: application to a region in North Africa,” Remote Sens. Environ. 66(3), 298–316 (1998), http://dx.doi.org/10.1016/S0034-4257(98)00066-2.
[29]
Platnick S, M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riédi, R. A. Frey, 2003: The MODIS cloud products: algorithms and examples from Terra. IEEE Trans. Geosci. Remote Sens., 41, pp. 459-473.
[30]
Qin. Z, A. Karnieli, and A. Berliner, “A mono-window algorithm for retrieving land surface temperature from landsat TM and its application to the Israel-Egypt border region,” Int. J. Remote Sens. 22(18), 3719–3746 (2001), http://dx.doi.org/10.1080/01431160010006971.
[31]
Remer, L. A., et al. (2005), The MODIS aerosol algorithm, products and validation, J. Atmos. Sci., in press.
[32]
Sobrino. J, et al., “Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR,” Int. J. Remote Sens. 17(11), 2089–2114 (1996), http://dx. doi.org/10.1080/01431169608948760.
[33]
Smirnov, A., B. N. Holben, T. F. Eck, O. Dubovik, and I. Slutsker (2000), Cloud screening and quality control algorithms for the AERONET data base, Remote Sens. Environ., 73, 337– 349.
[34]
Tang. T, et al., “Generalized split-window algorithm for estimate of land surface temperature from Chinese geostationary FengYun meteorological satellite (FY-2C) data,” Sensors 8(2), 933–951 (2008), http://dx.doi.org/10.3390/s8020933.
[35]
Zakey, A.S.; Abdel-Wahab, M.M.; Pettersson, J.B.C.; Gatari, M.J. & Hallquist, M. (2008). Seasonal and spatial variation of atmospheric particulate matter in a developing megacity, the Greater Cairo, Egypt. Atmَsfera Vol.21, No.2, (January 2008), pp. 171- 189, ISSN 0187-6236
[36]
Zhang. H, “A physically based algorithm for land surface emissivity retrieval from combined mid-infrared and thermal infrared data,” Sci. China Ser. E 43(Suppl 1), 22–33 (2000), http://dx.doi.org/10.1007/BF02916575.
[37]
Z.-L. Li et al., “Evaluation of six methods for extracting relative emissivity spectra from thermal infrared images,” Remote Sens. Environ. 69(3), 197–214 (1999), http://dx.doi.org/10.1016/S0034-4257 (99)00049-8.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186