Modeling the Relationship between Land Surface Temperature, Topography and Vegetation Cover Using Landsat 8 Satellite Imagery

Document Type : Full length article


1 Assistant Professor of of Remote Sensing and GIS, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

2 MSc in GIS & RS, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

3 PhD Candidate in GIS & RS, Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran


Land surface temperature plays a vital role in a wide range of scientific researches including climatology, meteorology, hydrology, ecology, geology, medical sciences, design and optimization of transportation services, fire location and especially in calculating the real evaporation and transpiration. There are some determining factors affecting the land surface temperature, such as, the kind of surface elements, topography conditions, environmental conditions, climate condition and the amount of emitted energy from the sun. Recognition and analysis of the relation between the land surface temperature and various factors are so critical. The remote sensing method has a widespread application in preparing the land surface temperature images due to the extensive covering and continuous data. The purpose of this study was to investigate the effects of vegetation cover indices and topographic factors on land surface temperature and modeling the relationship between land surface temperature, topographic conditions and vegetation cover using Landsat 8 satellite imagery.
Materials and Methods
In this study, sensor reflective bands OLI, Jimenez and Sobrino method were utilized to calculate the emissivity of the available phenomena in the area. By using TIRS Landsat 8 sensor thermal bands 10 and 11 and utilizing Split-window algorithm, the land surface temperature was calculated. Topography parameters, such as elevation, slope, aspect and vegetation were extracted using digital elevation model and NDVI index, respectively. Then, the relation between the land surface temperature and topography factors in diverse conditions was investigated by statistical analysis, and then, the validity of relations was analyzed with a confidence level of 95%. For this purpose, we employed ENVI 5.3, ArcGIS 10.4, ERDAS IMAGING 2014 software as well as SPSS statistical software.
Results and Discussion
The obtained results indicates that the study area has a uniform vegetation cover in most areas and a high percentage of the areas have the NDVI of 0.45-0.6. Nonetheless, due to the diversity in topographic and climatic conditions the area surface temperature is inhomogeneous and non-uniform. Consequently, there is no relation with a high correlation coefficient between the land surface temperature and vegetation in the area. However, there is a reverse linear relationship between the land surface temperature and vegetation in the area. This relation gains a higher correlation coefficient in the form of linear relation compared with second order polynomial, Pearson, and logarithmic equations. The areas with southern and southeast slope have higher land surface average temperature compared to other aspects during imaging due to their position exposed to direct sun radiation. The temperature average is different in various slopes. Investigating the relations of temperature and elevation independent of slope parameters and slope aspect, gives rise to an increase in the correlation coefficient between the two parameters. The relation of the land surface temperature and elevation, regardless of slope and aspect for the studied area, is a reverse linear relation with the correlation coefficient of 0.54, whereas for the relation between the land surface temperature and elevation in the western slope and slope of 40-50 degree, there is a reverse linear relation with the correlation coefficient of 0.76. Moreover, in investigating the relation between the land surface temperatures with topographic conditions, simultaneous consideration of both elevation and slope variables as independent variables for modeling the dependent variable of surface temperature reveals a strong relation. The addition of vegetation index parameter to relation independent parameters brings about a rise in relation’s correlation coefficient. For instance, relation’s correlation coefficient of the land surface temperature with elevation independent variables, slope, and vegetation in the western, northwest and southeast direction, are 0.84, 0.81 and 0.8, respectively. All the obtained relations are investigated in the confidence level of 95%, and validity of relations was confirmed by “t” statistic and search for relations’ coefficients.
Results of this research have indicated that taking elevation, slope, aspect and NDVI parameters independently for modeling the land surface temperature, can give adverse results and by simultaneous application of both topographic parameters and vegetation and also their combination, as dependent parameters, the land surface temperature can be precisely calculated. In addition, for accurate modeling of the land surface temperature all topographic, climatic and environmental conditions for the area should be taken into account. The thermal and reflective remote sensing technology are economical, fast and effective due to several positive aspects to provide uniform topographic, vegetation data and environmental parameters. So, further researches and investigation are necessary.


Main Subjects

جهانبخش، س.؛ زاهدی، م. و ولیزاده کامران، خ. (1390). محاسبة دمای سطح زمین با استفاده از روش سبال و درخت تصمیم‏گیری در محیط،GIS RS در بخش مرکزی منطقة مراغه، جغرافیا و برنامه‏ریزی، 16(38): 19ـ42.
علوی‏پناه، س. ک. (1386). سنجش از دور حرارتی و کاربردهای آن در علوم زمین، تهران: انتشارات دانشگاه تهران.
Agam, N.; Kustas, W.P.; Anderson, M.C.; Li, F. and Neale, C.M. (2007). A vegetation index based technique for spatial sharpening of thermal imagery, Remote Sensing of Environment, 107(4): 545-558.
Alavipanah, S.K. (2007). Thermal remote sensing and its application in earth sciences, Tehran University Press.
Becker, F. and Li, A.L. (1990). Towards a local split window method over land surfaces, Internatoinal Journal of Remote Sensing, 11: 369-393.
Boori, M.S.; Balzter, H.; Choudhary, K.; Kovelskiy, V. and Vozenilek, V. (2015). A Comparison of Land Surface Temperature, Derived from AMSR-2, Landsat and ASTER Satellite Data, Journal of Geography and Geology, 7(3): 61.
Ding, H. and Shi, W. (2013). Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City, International Journal of Remote Sensing, 34(15): 5503-5517.
Gillespie, A.; Rokugawa, S.; Matsunaga, T.; Cothern, J.S.; Hook, S. and Kahle, A.B. (1998). A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, IEEE transactions on geoscience and remote sensing, 36(4): 1113-1126.
Herb, W.R.; Janke, B.; Mohseni, O. and Stefan, H.G. (2008). Ground surface temperature simulation for different land covers, Journal of Hydrology, 356(3): 327-343.
He, J.; Zhao, W.; Li, A.; Wen, F. and Yu, D. (2018). The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas, International Journal of Remote Sensing, 1-20.
Jahanbakhsh, S.; Zahedi, M. and Valizadeh Kamran, KH. (2012). Land surface temperature Calculation using SEBAL and Decision Tree Methods Based on ETM+ Image in RS, GIS Environment in the Maragh central region, Quarterly Geography and Planning, 16(38): 19-42.
Jain, S.K.; Goswami, A. and Saraf, A.K. (2008). Determination of land surface temperature and its lapse rate in the Satluj River basin using NOAA data, International Journal of Remote Sensing, 29(11): 3091-3103.
Jiang, J. and Tian, G. (2010). Analysis of the impact of land use/land cover change on land surface temperature with remote sensing, Procedia environmental sciences, 2: 571-575.
Jiménez-Muñoz, J.C. and Sobrino, J. A. (2003). A generalized single‐channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research: Atmospheres, 108(D22).
Jiménez-Muñoz, J.C.; Sobrino, J.A.; Skoković, D.; Mattar, C. and Cristóbal, J. (2014). Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data, IEEE Geoscience and Remote Sensing Letters, 11(10): 1840-1843.
Kotchi, S.O.; Barrette, N.; Viau, A.A.; Jang, J.D.; Gond, V. and Mostafavi, M.A. (2016). Estimation and uncertainty assessment of surface microclimate indicators at local scale using airborne infrared thermography and multispectral imagery, In Geospatial Technology-Environmental and Social Applications, InTech.
Kustas, W.P.; Norman, J.M.; Anderson, M.C. and French, A.N. (2003). Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship, Remote sensing of environment, 85(4): 429-440.
Li, Z.L.; Wu, H.; Wang, N.; Qiu, S.; Sobrino, J.A.; Wan, Z. ... and Yan, G. (2013). Land surface emissivity retrieval from satellite data, International Journal of Remote Sensing, 34(9-10): 3084-3127.
Lu, D. and Weng, Q. (2006). Spectral mixture analysis of ASTER images for examining the relationship between urban thermal features and biophysical descriptors in Indianapolis, Indiana, USA. Remote Sensing of Environment, 104(2): 157-167.
Okamoto, K. (2001). Global Environment Remote Sensing, IOS Press.
Peters, J.; De Baets, B.; De Clercq, E.M.; Ducheyne, E. and Verhoest, N.E. (2012). Influence of topographic normalization on the vegetation index–surface temperature relationship, Journal of Applied Remote Sensing, 6(1): 063518-1.
Qin, Z.; Karnieli, A. and Berliner, P. (2001). 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): 3719-3746.
Rott, H. (2000). Physical Principles and Technical Aspects of Remote sensing, In: Schultz, G.A. & Engman, E.T. (Eds.), Remote Sensing in Hydrology and Water Management, Springer-Verlag, Berlin- Heidelberg, Germany, pp. 15-39.
Rozenstein, O.; Qin, Z.; Derimian, Y. and Karnieli, A. (2014). Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm, Sensors, 14(4): 5768-5780.
Running, S.W.; Justice, C.O.; Salomonson, V.; Hall, D.; Barker, J.; Kaufmann, Y.J. ... and Wan, Z.M. (1994). Terrestrial remote sensing science and algorithms planned for EOS/MODIS, International journal of remote sensing, 15(17): 3587-3620.
Sahana, M.; Ahmed, R. and Sajjad, H. (2016). Analyzing land surface temperature distribution in response to land use/land cover change using split window algorithm and spectral radiance model in Sundarban Biosphere Reserve, India, Modeling Earth Systems and Environment, 2(2): 81.
Sobrino, J.; Coll, C. and Caselles, V. (1991). Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5, Remote sensing of environment, 38(1): 19-34.
Sobrino, J. A.; Jiménez-Muñoz, J.C.; Sòria, G.; Romaguera, M.; Guanter, L.; Moreno, J. ... and Martínez, P. (2008). Land surface emissivity retrieval from different VNIR and TIR sensors, IEEE Transactions on Geoscience and Remote Sensing, 46(2): 316-327.
Sobrino, J.A.; Li, Z.L.; Stoll, M.P. and Becker, F. (1996). Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data, International Journal of Remote Sensing, 17(11): 2089-2114.
Sobrino, J.A.; Del Frate, F.; Drusch, M.; Jiménez-Muñoz, J.C.; Manunta, P. and Regan, A. (2016). Review of thermal infrared applications and requirements for future high-resolution sensors, IEEE Transactions on Geoscience and Remote Sensing, 54(5): 2963-2972.
Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation, Remote sensing of Environment, 8(2): 127-150.
Weng, Q. (2003). Fractal analysis of satellite-detected urban heat island effect. Photogrammetric engineering & remote sensing, 69(5): 555-566.
Weng, Q. Liu, H. and Lu, D. (2007). Assessing the effects of land use and land cover patterns on thermal conditions using landscape metrics in city of Indianapolis, United States. Urban ecosystems, 10(2): 203-219.
Xian, G. and Crane, M. (2006). An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data. Remote Sensing of environment, 104(2): 147-156.
Xiao, J. and Moody, A. (2005). A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sensing of Environment, 98(2): 237-250.
Yu, X.; Guo, X. and Wu, Z. (2014). Land surface temperature retrieval from Landsat 8 TIRS- Comparison between radiative transfer equation-based method, split window algorithm and single channel method, Remote Sensing, 6(10): 9829-9852.