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The paper addresses the issue of micro-climatic variations due to changing land-cover in the study area. Kuwait has undergone a unique environmental catastrophe during 1991 Gulf war in the form of oil lakes and spills on land, which has left permanent scars on the desert surface. The region is experiencing large scale urban expansion with the growing population needs and political stability. New townships planned with expansion of existing ones, rapid development in petroleum sector, all these developments are resulting in Urban Heat Island (UHI) effect both over the city and the hydrocarbon contaminated sites. In this study the Landsat image of 1989, 1991 and 2000 are used to compute surface temperature and land-cover classification to understand the relationship between micro-climate and land-cover. The results have shown increased temperatures over both the built-up areas and hydrocarbon contaminated surface. The long term temperature trend in the area shows an incremental trend since 1980s, which increases sharply in post 1990s, probably due to cumulative effect of hydrocarbon pollution, urban-industrial development and increased carbon emission. The study utilized the novel approach of land-cover classification using the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Bareness Index (NDBare). These indices are related to the Land Surface Temperature (LST) computed using satellite datasets. Results show a negative correlation of NDVI, NDWI, and NDBare with surface temperature. NDBI is positively correlated with temperature. Although there are isolated instances where bare surfaces show increased temperature at places and these temperatures are comparable to built-up area, the most pertinent reason for this temperature hike is obliteration of emissive property of desert soil due to hydrocarbon leftovers and mixing of hydrocarbon leftovers with soil for bioremediation. These remediated soils are used in plantation and re-vegetation giving higher thermal signatures, mainly because of scanty vegetation cover.
hydrocarbon pollution, NDBare, NDBI, NDVI, NDWI
[1] Carlson, T.N., Dodd, J.K., Benjamin, S.G. & Copper, J.N., Remote estimation of surface energy balance, moisture availability and thermal inertia. Journal Applied Meteorology, 20, pp. 300–306, 1981. doi:10.1175/15200450(1981)020<0067:SEOTSE>2.0.CO;2
[2] Balling, R.C. & Brazell, S.W., High resolution surface temperature patterns in a complex urban terrain. Photogrammetric Engineering and Remote Sensing, 54, pp. 1289–1293, 1988.
[3] Dousset, B., AVHRR-derived cloudiness and surface temperature patterns over the Los Angeles area and their relationships to land use. Digest – International Geoscience and Remote Sensing Symposium (IGARSS), 4, pp. 2132–2137, 1989.
[4] Dousset, B., Surface temperature statistics over Los Angeles: the influence of land use. Digest – International Geoscience and Remote Sensing Symposium (IGARSS), 2, pp. 367–371, 1991.
[5] Roth, M., Oke, T.R. & Emery, W.J., Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology. International Journal of Remote Sensing, 10(11), pp. 1699–1720, 1989. doi:10.1080/01431168908904002
[6] Quattrochi, D.A. & Ridd, M.K., Measurement and analysis of thermal energy responses from discrete urban surfaces using remote sensing data. International Journal of Remote Sensing, 15(10), pp. 1991–2022, 1994. doi:10.1080/01431169408954224
[7] Owen, T.W., Carlson, T.N. & Gillies, R.R., An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization. International Journal of Remote Sensing, 19(9), pp. 1663–1681, 1998. doi:10.1080/014311698215171
[8] Voogt, J.A. & Oke, T.R., Effects of urban surface geometry on remotely-sensed surface temperature. International Journal of Remote Sensing, 19(5), pp. 895–920, 1998. doi:10.1080/014311698215784
[9] Voogt, J.A. & Oke, T.R., Thermal remote sensing of urban areas. Remote Sensing of Environment (Special Issue on Urban Areas), 86, pp. 370–384, 2003.
[10] Carlson, T.N. & Arthur, S.T., The impact of land use-land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Global and Planetary Change, 25, pp. 49–65, 2000. doi:10.1016/S0921-8181(00)00021-7
[11] Jung, M., Henkel, K., Herold, M. & Churkina, G., Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sensing of Environment, 101, pp. 534–553, 2006. doi:10.1016/j.rse.2006.01.020
[12] Vicente-Serrano, S.M., Cuadrat-Prats, J.M. & Romo, A., Aridity influence on vegetation patterns in the middle Ebro Valley (Spain): evaluation by means of AVHRR images and climate interpolation techniques. Journal of Arid Environments, 66, pp. 353–375, 2006. doi:10.1016/j.jaridenv.2005.10.021
[13] Nieuwolt, S., The urban microclimate of Singapore. The Journal of Tropical Geography, 22, pp. 30–37, 1966.
[14] Streutker, D.R., A remote sensing study of the urban heat island of Houston, Texas. International Journal of Remote Sensing, 23(13), pp. 2595–2608, 2002. doi:10.1080/01431160110115023
[15] Gallo, K.P. & Owen, T.W., Assessment of urban heat island: a multi-sensor perspective for the Dallas-Ft. Worth, USA region. Geocarto International, 13, pp. 35–41, 1998. doi:10.1080/10106049809354662
[16] Gallo, K.P. & Owen, T.W., Satellite-based adjustments for the urban heat island temperature bias. Journal of Applied Meteorology, 38, pp. 806–813, 1998. doi:10.1175/1520-0450(1999)038<0806:SBAFTU>2.0.CO;2
[17] Streutker, D.R., Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sensing of Environment, 85, pp. 282–289, 2003. doi:10.1016/S0034-4257(03)00007-5
[18] ud Din, S., Al Dousari, A. & Literathy, P., Evidence of hydrocarbon contamination from the Burgan oil field, Kuwait – interpretations from thermal remote sensing data. Journal of Environmental Management, 86(4), pp. 605–615, 2008. doi:10.1016/j.jenvman.2006.12.028
[19] Chen, Y., Wang, J. & Li, X., A study on urban thermal field in summer based on satellite remote sensing. Remote Sensing for Land and Resources, 4, pp. 55–59, 2002.
[20] Weng, Q., A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in Zhujiang Delta, China. International Journal of Remote Sensing, 22(10), pp. 1999–2014, 2001.
[21] Chen, Z.M., Babiker, I.S., Chen, Z.X., Komaki, K., Mohamed. M.A.A. & Kato, K., Estimation of interannual variation in productivity of global vegetation using NDVI data. International Journal of Remote Sensing, 25(16), pp. 3139–3150, 2004. doi:10.1080/0143116032000160435
[22] Wang, J., Rich, P.M., Price, K.P. & Kettle, W.D., Relations between NDVI and tree productivity in the central Great Plains. International Journal of Remote Sensing, 25(16), pp. 3127–3138, 2004. doi:10.1080/0143116032000160499
[23] Chen, X.L., Zhao, H.M., Li, P.X. & Yin, Z.Y., Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104, pp. 133–146, 2006. doi:10.1016/j.rse.2005.11.016
[24] Rouse, J.W.J., Haas, R.H., Schell, R.A. & Deering, D.W., Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA SP-351, Washington, DC, December 10–14, 1973, pp. 309–317, 1974.
[25] Gao, B.C., NDWI – a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, pp. 257–266, 1996. doi:10.1016/ S0034-4257(96)00067-3
[26] Purevdorj, T.S., Tateishi, R., Ishiyama, T. & Honda, Y., Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing, 19(18), pp. 3519–3535, 1998. doi:10.1080/014311698213795
[27] Zarco-Tejada, P.J., Rueda, C.A. & Ustin, S.L., Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85, pp. 109–124, 2003. doi:10.1016/S0034-4257(02)00197-9
[28] Jackson, T.J., Chen, D., Cosh, M., Li, F., Anderson, M. & Walthall, C., Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans.
Remote Sensing of Environment, 92, pp. 475–482, 2004. doi:10.1016/j.rse.2003.10.021
[29] Maki, M., Ishiahra, M. & Tamura, M., Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data. Remote Sensing of Environment, 90, pp. 441–450, 2004. doi:10.1016/j.rse.2004.02.002
[30] Zha, Y., Gao, J. & Ni, S., Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), pp. 583–594, 2003. doi:10.1080/01431160304987
[31] Zhao, H.M. & Chen, X.L., Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. Geoscience and Remote Sensing Symposium, 3(25–29), pp. 1666–1668, 2005.
[32] Qin, Z., Karnieli, A. & Berliner, P., A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel–Egypt border region. International Journal of Remote Sensing, 22(18), pp. 3719–3746, 2001. doi:10.1080/01431160010006971
[33] Al Fahed, S., Al Hawaj, O. & Chakroun, W., Recent air temperature rise in Kuwait. Renewable Energy, 12(1), pp. 83–90, 1997. doi:10.1016/S0960-1481(97)00015-3