Spatial and Temporal Change Assessment in Land Surface Temperature of Lahore using GIS and Remote Sensing Techniques
Spatial and Temporal Change Assessment in Land Surface Temperature
Keywords:
LST, Temporal change, Heat Island, Urban warming, Landsat, Town, LahoreAbstract
This study is an attempt to evaluate Land Surface Temperature (LST) variations of Lahore, a metropolitan city of Pakistan. LST have wide-ranging application viz; global climate change, urban climate, evapotranspiration, hydrological cycle and environmental studies. Therefore, Spatio-temporal assessment of LST variation is becoming vital to recognize the contributing factors and corresponding magnitude of contribution to the variation using GIS and remote sensing techniques. This study employed the radiative transfer method in assessing Spatio-temporal LST change using multi-temporal imagery acquired by Landsat 5 TM and Landsat 8 TIRS satellite data, for the year 1990 and 2015, respectively. Thermal infrared images of Landsat satellite revealed its suitability in monitoring temporal change in LST. The results indicated that high mean LST was recognized in the areas of Shalamar town, Gulberg town, Data Ganj Baksh town and Ravi town. On the other hand, the low mean LST was observed in the areas of Aziz Bhatti town, Samanabad town, Wagha town and Iqbal town in 1990.The results further showed that the areas of Gulbarg town, Wagha town, Shalamar town, Ravi town, Nishter town and Iqbal town, had been warmer in the year 2015 than the year 1990. It was assessed that in the areas of Aziz Bhatti town, Nishtar town and Wagha town, there were no urbanization and urban development. Therefore the lowest LST was measured in the year 1990. However the expansion and urban development of Lahore in these areas increased surface radiant temperature and they reflect highest LST assessed in 2015. The present study explores the suitability of employing GIS and satellite remote sensing techniques in finding out the spatial and temporal temperature change to achieve accuracy in terms of urban planning, decision and policy making for sustainable urban environment of Lahore
References
Fu, P. & Q. Weng. A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote Sensing of Environment175:205-214 (2016).
Liu, X., B. H. Tang & Z. L. Li. Evaluation of Three Parametric Models for Estimating Directional Thermal Radiation from Simulation, Airborne, and Satellite Data. Remote Sensing 10(420): 1-18 (2018).
Li, Z. L., B. H.Tang, H.Wu, H. Ren, G. Yan, Z.Wan & J. A. Sobrino.Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment131: 14-37 (2013).
Chatterjee, R., N.Singh, S.Thapa, D. Sharma & D. Kumar. Retrieval of land surface temperature (LST) from Landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs. International Journal of Applied Earth Observation and Geoinformation58: 264-277 (2017).
Chen, Y., S. B. Duan, H. Ren, J. Labed & Z. L. Li.Algorithm development for land surface temperature retrieval: Application to Chinese Gaofen-5 Data. Remote Sensing 9: 161 (2017).
Manzo-Delgado, L., R. Aguirre-Gómez & R. Alvarez.Multitemporal analysis of land surface temperature using NOAA-AVHRR: preliminary relationships between climatic anomalies and forest fires. International Journal of Remote Sensing25: 4417-4424 (2004). 7. Jin, M., R. Dickinson & A. Vogelmann. A comparison of CCM2–BATS skin temperature and surface-air temperature with satellite and surface observations. Journal of Climate10: 1505-1524 (1997).
Xu, Y., Y. Shen & Z. Wu. Spatial and temporal variations of land surface temperature over the Tibetan Plateau based on harmonic analysis. Mountain Research and Development33: 85-95 (2013).
Kumar, K. S., P. U. Bhaskar & Padmakumari, K. Estimation of land surface temperature to study urban heat island effect using Landsat ETM+ image. International journal of Engineering Science and technology4: 771-778 (2012).
Gabler, R. E., J. F.Petersen, L. Trapasso & D. Sack. Physical geography. Nelson Education, (2008).
Voogt, J. A. & T. R. Oke. Thermal remote sensing of urban climates. Remote sensing of environment86: 370-384 (2003).
Howard, L. The Climate of London: deduced from Meteorological observations, made at different places in the neighborhood of the metropolis. Vol. 1 (W. Phillips, sold also by J. and A. Arch), (1818).
Harwood IV, J. W. Delineation and GIS Mapping of Urban Heat Islands Using Landsat TM Imagery, Kent State University, (2008). 14. James, M. & C. Mundia. Dynamism of land use changes on surface temperature in Kenya: a case study of Nairobi City. International Journal of Science and Research3: 38-41 (2014).
Kern, K. & G. Alber. Governing climate change in cities: modes of urban climate governance in multilevel systems. (2008).
Zhou, W., G. Huang & M. L. Cadenasso. Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landscape and urban planning102: 54-63 (2011).
Sun, Q., J. Tan & Y. Xu. An ERDAS image processing method for retrieving LST and describing urban heat evolution: a case study in the Pearl River Delta Region in South China. Environmental Earth Sciences59: 1047-1055 (2010).
Guillevic, P., R. Koster, M. Suarez, L. Bounoua, G. Collatz, S. Los & S. Mahanama.Influence of the Interannual variability of vegetation on the surface energy balance—A global sensitivity study. Journal of Hydrometeorology 3: 617-629 (2002).
Meng, C., Z. L.Li., X.Zhan, J. Shi & C. Liu. Land surface temperature data assimilation and its impact on evapotranspiration estimates from the Common Land Model. Water Resources Research45: (2009).
Akinbode, O., A. Eludoyin & O. Fashae. Temperature and relative humidity distributions in a medium-size administrative town in southwest Nigeria. Journal of environmental management87: 95-105 (2008).
Akhoondzadeh, M. & M. Saradjian. Comparison of land surface temperature mapping using MODIS and ASTER images in semi-arid area. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences37: 873-876 (2008).
Alavipanah, S., C. B. Komaki, R. M. Karimpour, M. Sarajian, F. G. R. Savaghebi & E. Moghimi. Land surface temperature in the Yardang region of Lut Desert (Iran) based on field measurements and Landsat thermal data.Journal of Agricultural Science Technology 9: 287-303(2007).
Oluseyi, I. O. & A. J. Olusegun. Managing land use transformation and land surface temperature change in Anyigba Town, Kogi State, Nigeria. Journal of Geography and Geology 3: 77-85 (2011).
Rajeshwari, A. & N. Mani. Estimation of land surface temperature of Dindigul district using Landsat 8 data. International Journal of Research in Engineering and Technology3: 122-126 (2014).
GoP. District Census Report of Lahore 1998. Islamabad: Population Census Organization, Statistics Division. Govt. of Pakistan. (2000).
GoP. Provisional summary results of 6th population and housing census-2017. Population Census Organization, Statistics Division. Govt. of Pakistan. (2017).
Rana, I. A. &S. S. Bhatti. Lahore, Pakistan– Urbanization challenges and opportunities. Cities72: 348-355 (2018).
Foody, G. M. Status of land cover classification accuracy assessment. Remote sensing of environment80: 185-201 (2002).
Alrababah, M. & M. Alhamad. Land use/cover classification of arid and semi-arid Mediterranean landscapes using Landsat ETM. International journal of remote sensing 27: 2703-2718 (2006).
Avdan, U. & G. Jovanovska. Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of Sensors 2016: 1-18 (2016).
Jiménez-Muñoz, J. C. & J. A. Sobrino. A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research: Atmospheres 108:1-12 (2003).
Qin, Z., Karnieli, A. & P. Berliner. 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 sensing22: 3719-3746 (2001).
Bustos, E. & F. J. Meza. A method to estimate maximum and minimum air temperature using MODIS surface temperature and vegetation data: application to the Maipo Basin, Chile. Theoretical and Applied Climatology120: 211-226 (2015).
Ding, H. & A. J. Elmore.Spatio-temporal patterns in water surface temperature from Landsat time series data in the Chesapeake Bay, USA. Remote Sensing of Environment168: 335-348 (2015).
Fu, B. & I. Burgher. Riparian vegetation NDVI dynamics and its relationship with climate, surface water and groundwater. Journal of Arid Environments113: 59-68 (2015).
Sobrino, J. A., J. C. Jiménez-Muñoz.&L. Paolini. Land surface temperature retrieval from Landsat TM 5. Remote Sensing of environment90: 434-440 (2004).
Kolokotroni, M., I. Giannitsaris & R. Watkins. The effect of the London urban heat island on building summer cooling demand and night ventilation strategies. Solar Energy80: 383-392 (2006).
Liu, W., C.Ji, J.Zhong, X. Jiang & Z. Zheng. Temporal characteristics of the Beijing urban heat island. Theoretical and Applied Climatology87: 213-221 (2007).
Fujibe, F. Urban warming in Japanese cities and its relation to climate change monitoring. International Journal of Climatology31: 162-173 (2011).
Mohan, M., Y.Kikegawa, B.Gurjar, S.Bhati & N. R. Kolli. Assessment of urban heat island effect for different land use–land cover from micrometeorological measurements and remote sensing data for megacity Delhi. Theoretical and applied climatology 112: 647-658 (2013).
Chen, L., R. Jiang & W. N. Xiang. Surface heat island in Shanghai and its relationship with urban development from 1989 to 2013. Advances in Meteorology 2016:1-15 (2016).
