ASSESSING THE RELATIONSHIP OF LST, NDVI AND EVI WITH LAND COVER CHANGES IN THE LAGOS LAGOON ENVIRONMENT

Main Article Content

Alfred S. Alademomi
Chukwuma J. Okolie
Olagoke E . Daramola
Raphael O . Agboola
Tosin J. Salami

Abstract

The Lagos Lagoon is under increased pressure from growth in human population, growing demands for natural resources, human activities, and socioeconomic factors. The degree of these activities and the impacts are directly proportional to urban expansion and growth. In the light of this situation, the objectives of this study were: (i) to estimate through satellite imagery analysis the extent of changes in the Lagos Lagoon environment for the periods 1984, 2002, 2013 and 2019 using Landsat-derived data on land cover, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI); and (ii) to evaluate the relationship between the derived data and determine their relative influence on the lagoon environment. The derived data were subjected to descriptive statistics, and relationships were explored using Pearson’s correlation and regression analysis. The effect of land cover on LST was measured using the Contribution Index and a trend analysis was carried out. From the results, the mean LSTs for the four years were 22.68°C (1984), 24.34°C (2002), 26.46°C (2013) and 28.40°C (2019). Generally, the mean LSTs is in opposite trend with the mean NDVIs and EVIs as associated with their dominant land cover type. The strongest positive correlations were observed between NDVI and EVI while NDVI had the closest fit with LST in the regression. Built-up areas have the highest contributions to LST while vegetation had a cooling influence. The depletion in vegetative cover has compromised the biodiversity of this environment and efforts are required to reverse this trend.

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Alademomi, A. S., Okolie, C. J., Daramola, O. E. ., Agboola, R. O. ., & Salami, T. J. (2020). ASSESSING THE RELATIONSHIP OF LST, NDVI AND EVI WITH LAND COVER CHANGES IN THE LAGOS LAGOON ENVIRONMENT. Quaestiones Geographicae, 39(3), 87–109. https://doi.org/10.2478/quageo-2020-0025
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References

  1. Abbas I.I., 2008. Use of satellite remote sensing and geographic information systems to monitor land degradation along Ondo Coastal Zone (Nigeria). Balwois, Orid, Macedonia 27: 1–13.
  2. Abbas I.I., Fasona M.J., 2012. Remote sensing and geographic information techniques: Veritable tools for land degrada¬tion assessment. American Journal of Geographic Information System 1(1): 1–6.
  3. Aboelnour M., Engel B., 2018. Application of remote sensing techniques and geographic information systems to analyze land surface temperature in response to land use/land cover change in Greater Cairo Region, Egypt. Journal of Geographic Information System 10: 57–88. DOI 10.4236/jgis.2018.101003.
  4. Adegboye K., 2013. Fashola highlights importance of greenery at 2013 tree planting campaign – Vanguard News. Vanguard Newspaper. Online: https://www.vanguardngr.com/2013/07/fashola-highlights-importance-of-greenery-at-2013-tree-planting-campaign/ (accessed 20 August 2020).
  5. Adegun O., Odunuga S., Appia Y., 2015. Dynamics in the landscape and ecological services in system I drainage area of Lagos. Ghana Journal of Geography 7(1): 75–96.
  6. Ajibola M., Adeleke A.M., Ogungbemi A., 2016. An assessment of wetland loss in Lagos Metropolis, Nigeria. De-veloping Country Studies 6(7): 1–7.
  7. Ajibola M., Adewale B., Ijasan K., 2012. Effects of urbanisation on Lagos wetlands. International Journal of Business and Social Science 3(17): 310–318.
  8. Akpootu D.O., Iliyasu M.I., Mustapha W., Aruna S., Yusuf S.O., 2017. The influence of meteorological parameters on atmospheric visibility over Ikeja, Nigeria. Archives of Current Research International 9(3): 1–12, Article no. ACRI.36010
  9. Anderson J.R., 1971. Land use classification schemes used in selected recent geographic applications of remote sensing. Photogrammetric Engineering 37(4): 379–387.
  10. Aribisala J.O., Ogundipe O.M., Akinkurolere O.O., 2016. The study of climate change. British Journal of Applied Science and Technology 13(6): 1–7.
  11. Boegh E., Soegaard H., Broge N., Hasager C.B., Jensen N.O., Schelde K., et al., 2002. Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sensing of Environment 81: 179–193.
  12. Bolstad P., Lillesand T.M., 1991. Rapid maximum likelihood classification. Photogrammetric Engineering and Remote Sensing 57(1): 67–74.
  13. Brendel A.S., Ferrelli F., Piccolo M.C., Perillo G.M.E., 2019. Assessment of the effectiveness of supervised and unsupervised methods: Maximizing land-cover classification accuracy with spectral indices data. Journal of Applied Remote Sensing 13(1): 014503. DOI 10.1117/1.JRS.13.014503.
  14. Butuc B.R., Moldovean G., 2011. Environmental impact scenario of an azimuthal tracked PV platform based on CO2 emissions reduction. Environmental Engineering and Management Journal 10: 271–276.
  15. Chen P., Fedosejevs G., Tiscareño-LóPez M., Arnold, J. G., 2006b. Assessment of MODIS-EVI, MODIS-NDVI and vegetation-NDVI composite data using agricultural measurements: An example at corn fields in Western Mexico. Environmental Monitoring and Assessment 119: 69–82. DOI 10.1007/s10661-005-9006-7.
  16. Chen X-L., Zhao H-M., Li P.-X., Yin Z.-Y., 2006a. Remote sensing image based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment 104: 133–146.
  17. David A.R., 2008. A re-interpretation of Landsat TM data on Chernobyl. International Journal of Remote Sensing 10(8): 1423–1427.
  18. Deng Y., Wang S., Bai X., Tian Y., Wu L., Xiao J., Chen F., Qian Q., 2018. Relationship among land surface temperature and LUCC, NDVI in typical karst area. Scientific Reports 8(1): 1–12. DOI 10.1038/s41598-017-19088-x.
  19. Dewan A.M., Corner, R.J., 2012. The impact of land use and land cover changes on land surface temperature in a rapidly urbanizing megacity. In: IGARSS, Munich, Germany, 22–27 July 2012; pp. 6337–6339.
  20. Farina A., 2012. Exploring the relationship between land surface temperature and vegetation abundance for urban heat island mitigation in Seville, Spain. M.Sc. Thesis, Lund University.
  21. Fasona M., Omojola A., Odunuga S., Tejuoso O., Amogu N., 2005. An appraisal of sustainable water management solutions for large cities in developing countries through GIS: The case of Lagos, Nigeria. In: Proceeding of the Symposium S2 Held during the 7th IAHS Scientific Assembly, Foz do Iguacu, Brazil, 3–9 April 2005; pp. 49–57.
  22. Ferreira L.S., Duarte D.H.S., 2019. Exploring the relationship between urban form, land surface temperature and veg¬etation indices in a subtropical megacity. Urban Climate 27: 105–123.
  23. Ferrelli F., Bustos M., Huamantinco-Cisneros M., Piccolo M., 2015. Utilization of satellite images to study the thermal distribution in different soil covers in Bahia Blanca city (Argentina). Revista de Teledetección 44: 31–42.
  24. Ferrelli F., Cisneros M.A.H., Delgado A.L., Piccolo M.C., 2018. Spatial and temporal analysis of the LST-NDVI relationship for the study of land cover changes and their contribution to urban planning in Monte Hermoso, Argentina. Documents d’Analisi Geografica 2018, 64/1: 25–47. DOI 10.5565/rev/dag.355
  25. Gao X., Huete A.R., Didan K., 2003. Multisensor comparisons and validation of MODIS vegetation indices at the semiarid Jornada experimental range. IEEE Transactions on Geoscience and Remote Sensing 41: 2368–2381.
  26. Gao X., Huete A.R., Ni W., Miura T., 2000. Optical–biophysical relationships of vegetation spectra without background contamination. Remote Sensing of Environment, 74: 609–620.
  27. Ghulam A., 2010. Calculating surface temperature using landsat thermal imagery. Online: https://serc.carleton.edu/files/NAGTWorkshops/gis/activities2/student_handout_calculating_te.pdf (accessed 25 March 2016).
  28. Gilbert R.O., 1987. Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold Company Inc., New York.
  29. Gocic M., Trajkovic S., 2013. Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Global and Planetary Change 100: 172–182.
  30. Green E., Mumby P., Edwards A., Clark C., 1996. A review of remote sensing for the assessment and management of tropical coastal resources. Coastal Management 24(1): 1–40.
  31. Guha S., Govil H., Dey A., Gill N., 2020. A case study on the relationship between land surface temperature and land surface indices in Raipur City, India. Geografisk Tidsskrift-Danish Journal of Geography 120(1): 35–50. DOI 10.1080/00167223.2020.1752272.
  32. Gutman G., Huang C., Chander G., Noojipady P., Masek J. G., 2013. Assessment of the NASA-USGS global land survey (GLS) datasets. Remote Sensing of Environment 134: 249–265.
  33. Hamed K.H., 2008. Trend detection in hydrologic data: The Mann–Kendall trend test under the scaling hypothesis. Journal of Hydrology 349(3–4): 350–363. DOI 10.1016/j.jhydrol.2007.11.009
  34. Hamoodi M.N., Corner R., Dewan A., 2019. Thermophysical behaviour of LULC surfaces and their effect on the urban thermal environment. Journal of Spatial Science 64(1): 111–130. DOI 10.1080/14498596.2017.1386598
  35. Hasmadi M., Pakhriazad H.Z., Shahrin M.F., 2009. Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data. Geografia: Malaysian Journal of Society and Space 5(1): 1–10.
  36. Hoek van Dijke A.J., Mallick K., Teuling A.J., Schlerf M., Machwitz M., Hassler S.K., Blume T., Herold M., 2019. Does the normalized difference vegetation index explain spatial and temporal variability in sap velocity in temperate forest ecosystems? Hydrology and Earth System Sciences 23: 2077–2091. DOI 10.5194/hess-23-2077-2019.
  37. Hou G.L., Zhang H.Y., Wang Y.Q., Qiao Z.H., Zhang, Z.X., 2010. Retrieval and spatial distribution of land surface temperature in the middle part of Jilin province based on MODIS data. Scientia Geographica Sinica 30, 421–427.
  38. Huete A.R., 1988. A soil adjusted vegetation index (SAVI). Remote Sensing of Environment 25, 295–309.
  39. Huete A.R., Didan K., Miura T., Rodriguez E.P., Gao X., Ferreira L.G., 2002. Overview of the radiometric and bio¬physical performance of the MODIS Vegetation indices’. Remote Sensing of Environment 83: 195–213.
  40. Huete A.R., Justice C., Van Leeuwen W., 1999. MODIS vegetation index (MOD13). Algorithm Theoretical Basis Document. Version 3.
  41. James G.K., Adegoke J.O., Saba E., Nwilo P., Akinyede, J., 2007. Satellite-based assessment of the extent and changes in the mangrove ecosystem of the Niger Delta. Marine Geodesy 30(3), 249–267.
  42. Jeevalakshmi D., Narayana Reddy S., Manikiam B., 2017. Land surface temperature retrieval from Landsat data using Emissivity Estimation. International Journal of Applied Engineering Research 12(20): 9679–9687.
  43. Ji C., Liu Q., Sun D., Wang S., Lin P., Li X., 2001. Monitoring urban expansion with remote sensing in China. International Journal of Remote Sensing 22(8): 1441–1455.
  44. Jimenez-Munoz J.C., Sobrino J.A., 2003. A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research 108, DOI 10.1029/2003JD003480.
  45. Kaufmann R.K., Seto K.C., Shneider A., Liu Z., Zhou L., Wang W., 2007. Climate response to rapid urban growth: Evidence of human-induced precipitation deficit. Journal of Climate 20: 2299–2306. DOI 10.1175/JCLI4109.1
  46. Kindscher K., Fraser A., Jakubauskas M., Debinski D., 1997. Identifying wetland meadows in Grand Teton National Park using remote sensing and average wetland values. Wetlands Ecology and Management 5(4): 265–273. DOI 10.1023/A:1008265324575.
  47. Kolios S., Stylios C.D., 2013. Identification of land cover/land use changes in the greater area of the Preveza peninsula in Greece using Landsat satellite data. Applied Geography 40: 150–160.
  48. Li W.F., Cao Q.W., Kun L., Wu J.S., 2017. Linking potential heat source and sink to urban heat island: Heterogeneous effects of landscape pattern on land surface temperature. Science of the Total Environment 586: 457–465.
  49. Li Z., Li X., Wei D., Xu X., Wang H., 2010. An assessment of correlation on MODIS-NDVI and EVI with natural vegetation coverage in Northern Hebei Province, China. Procedia Environmental Sciences 2: 964–969.
  50. Luque S., 2000. Evaluating temporal changes using multi-spectral scanner and thematic Mapper data on the landscape of a natural reserve: The New Jersey Pine Barrens, a case study. International Journal of Remote Sensing 21(13–14): 2589–2610.
  51. Luyssaert S., Ciais P., Piao S.L., Schulze E.D., Jung M., Zaehle S., et al., 2010. The European Carbon Balance: Part 3: Forests. Global Change Biology 2010. DOI 10.1111/j.1365-2486.2009.02056.x.
  52. Malik M.S., Shukla J.P., Mishra S., 2019. Relationship of LST, NDBI and NDVI using Landsat-8 data in Kandaihimmat Watershed, Hoshangabad, India. Indian Journal of Geo Marine Sciences 48(1): 25–31.
  53. Masek J.G., Vermote E.F., Saleous N.E., Wolfe R., Hall F.G., Huemmrich K.F., Gao F., Kutler J., Lim T.-K., 2006. A Landsat surface reflectance data set for North America, 1990-100. IEEE Geoscience and Remote Sensing Letters 3: 68–72.
  54. Matsushita B., Yang W., Chen J., Onda Y., Qiu G., 2007. Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to topographic effects: A case study in high-density Cypress forest. Sensors 2007(7): 2636–2651.
  55. Meera G.G., Parthiban S., Nagaraj T., Christy A., 2015. NDVI: Vegetation change detection using remote sensing and GIS – A case study of Vellore District. 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015). Procedia Computer Science 57(2015): 1199–1210. DOI 10.1016/j.procs.2015.07.415.
  56. Mildrexler D.J., Zhao M., Heinsch F.A., Running S.W., 2007. A new satellite-based methodology for continental-scale disturbance detection. Ecological Applications 17(1): 235–250.
  57. Mildrexler D.J., Zhao M., Running S.W., 2009. Testing a MODIS global disturbance index across North America. Remote Sensing of Environment 113(10): 2103–2117.
  58. Mukherjee F., Singh D., 2020. Assessing land use–land cover change and its impact on land surface temperature using LANDSAT data: A comparison of two urban areas in India. Earth Systems and Environment 4, 385–407. DOI 10.1007/s41748-020-00155-9.
  59. Mushtaq A.G., Asima, N., 2016. Determining the Vegetation Indices (NDVI) from landsat 8 satellite data. Article DOI 10.21474/IJAR01/1348. International Journal of Advance Research 4(8): 1459–1463. DOI 10.21474/IJAR01/1348
  60. Ngie A., Abutaleb K., Ahmed F., Taiwo O.J., Darwish A.A., Ahmed M., 2016. An estimation of land surface temperatures from landsat ETM+ images for Durban, South Africa. Rwanda Journal 1(1): 17p. DOI: 10.4314/rj.v1i2S.2D.
  61. Nwilo P.C., Ayodele E.G., Okolie C.J., Orji M.J., Marve M.F., Oyelade E.A., et al., 2020. An assessment of seasonal variations in the CREF CORS at the University of Lagos. Geomatics, Landmanagement and Landscape No. 1, 2020: 63–77. DOI 10.15576/GLL/2020.1.63.
  62. Nwilo P.C., Olayinka D.N., Atagbaza A.O., Adzandeh A.E., 2012. Determination of Land Surface Temperature (LST) and potential urban heat Island effect in parts of Lagos state using satellite imageries. FUTY Journal of the Environment 7(1): 19–33. DOI 10.4314/fje.v7i1.2.
  63. Obiefuna J.N., Nwilo P.C., Atagbaza A.O., Okolie C.J., 2013a. Spatial Changes in the Wetlands of Lagos/Lekki Lagoons of Lagos, Nigeria. Journal of Sustainable Development 6(7): 123–133. DOI 10.5539/jsd.v6n7p123.
  64. Obiefuna J.N., Nwilo P.C., Atagbaza A.O., Okolie C.J., 2013b. Land Cover Dynamics Associated with the Spatial Changes in the Wetlands of Lagos/Lekki Lagoon System of Lagos, Nigeria. Journal of Coastal Research 29(3): 671–679. DOI 10.2112/JCOASTRES-D-12-00038.1.
  65. Obiefuna J.N., Nwilo P.C., Okolie C.J., Emmanuel E.I., Daramola O.E., 2018. Dynamics of land surface temperature in response to land cover changes in Lagos metropolis. Nigerian Journal of Environmental Sciences and Technology 2(2): 148–159. DOI 10.36263/nijest.2018.02.0074.
  66. Odindi J.O., Bangamwabo V., Mutanga O., 2015. Assessing the value of urban green spaces in mitigating multi-seasonal urban heat using MODIS Land Surface Temperature (LST) and landsat 8 data. International Journal of Environmental Research 9(1): 9–18.
  67. Odindi J.O., Mutanga O., Abdel-Rahman E.M., Adam E., Bangamwabo V., 2017. Determination of urban land-cover types and their implication on thermal characteristics in three South African coastal metropolitans using remotely sensed data. South African Geographical Journal 99: 52–67.
  68. Odunuga S., Oyebande L., 2007. Change detection and hydrological implications in the Lower Ogun flood plain, SW Nigeria. Remote Sensing for Environmental Monitoring and Change Detection, IAHS-AISH Publications 316: 91–99.
  69. Oguz H., 2013. LST calculator: A program for retrieving land surface temperature from Landsat TM/ETM+ imagery. Environmental Engineering and Management Journal 12(3): 549–555.
  70. Ojeh V.N., Balogun A.A., Okhimamhe A.A., 2016. Urban-rural temperature differences in Lagos. Climate 4: 29. DOI 10.3390/cli4020029.
  71. Panda U., Mohanty, P., 2008. Monitoring and modelling of Chilika environment using remote sensing data. Proceedings of Taal 2007: The 12th World Lake Conference: 617–638.
  72. Panigrahi S., Acharya B.C., Panigrahy R.C., Nayak B.K., Banarjee K., Sarkar S.K., 2007. Anthropogenic impact on wa¬ter quality of Chilika lagoon RAMSAR site: A statistical approach. Wetlands Ecology and Management 15(2): 113–126.
  73. Phompila C., Lewis M., Ostendorf B., Clarke K., 2015. MODIS EVI and LST temporal response for discrimination of tropical land covers. Remote Sensing 7(5): 6026–6040.
  74. Qiu J., Yang J., Wang Y., Su H., 2018. A comparison of NDVI and EVI in the DisTrad model for thermal sub-pixel mapping in densely vegetated areas: A case study in Southern China. International Journal of Remote Sensing 39(8): 2105–2118. DOI 10.1080/01431161.2017.1420929.
  75. Rankine C., Sánchez-Azofeifa G.A., AntonioGuzmán J., Espirito-Santo M.M., Sharp I., 2017. Comparing MODIS and near-surface vegetation indexes for monitoring tropical dry forest phenology along a successional gradient using optical phenology towers. Environmental Research Letters 12(2017): 105007. DOI 10.1088/1748-9326/aa838c
  76. Roth M., 2008. Urban climate considerations for the development of sustainable cities. In: Proceedings for Recent Findings on Planning and Designing Sustainable Cities, Singapore, November 2008. National University of Singapore.
  77. Salau O.R., Fasuba A., Aduloju K.A., Adesakin G.E., Fatigun A.T., 2016. Effects of changes in ENSO on temperature and rainfall distribution in Nigeria. Climate 2016, 4(1): 1–12. DOI 10.3390/cli4010005.
  78. Schott J.R., Volchok W.J., 1985. Thematic Mapper thermal infrared calibration. Photogrammetric Engineering and Remote Sensing 51: 1351–1357.
  79. Semeraro T., Luvisi A., Lillo A.O., Aretano R., Buccolieri R., Marwan N., 2020. Recurrence analysis of vegetation indices for highlighting the ecosystem response to drought events: An application to the Amazon forest. Remote Sensing 12: 907. DOI 10.3390/rs12060907
  80. Sen P.K., 1968. Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association 63(324): 1379–1389.
  81. Sharma A., Boroevich K.A., Shigemizu D., Kamatani Y., Kubo M., Tsunoda T., 2017. Hierarchical maximum likelihood clustering approach. IEEE Transactions on Biomedical Engineering 64(1): 112–122.
  82. Sharma M., Gupta R., Kumar D., Kapoor R., 2011. Efficacious approach for satellite image classification. Journal of Electrical and Electronics Engineering Research 3(8): 143–150.
  83. Sobrino J.A., Julien Y., 2013. Trend analysis of global MODIS-Terra vegetation indices and land surface temperature between 2000 and 2011. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6(5): 2139–2145.
  84. Soladoye O., Oromakinde O.O., 2013. Assessment of Tree Planting Efforts in Lagos Island Local Government Area of Lagos State, Nigeria. Environment and Natural Resources Research 3(4): 12–18. DOI 10.5539/enrr.v3n4p12.
  85. Streutker D.R., 2003. Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sensing of Environment 85(3): 282–289.
  86. Sun H., Sun X., Wang H., Li Y., Li X., 2011. Automatic target detection in high-resolution remote sensing images using spatial sparse coding bag-of-words model. IEEE Geoscience and Remote Sensing Letters 9(1): 109–113.
  87. Tarawally M., Wenbo X., Weiming H., Terence D.M., 2018. Comparative analysis of responses of land surface temperature to long-term land use/cover changes between a coastal and Inland City: A case of freetown and Bo town in Sierra Leone. Remote Sensing 10: 112, 18p. DOI 10.3390/rs10010112.
  88. Tatem A.J., Nayar A., Hay S.I., 2006. Scene selection and the use of NASA’s global orthorectified Landsat dataset for land cover and land use change monitoring. International Journal of Remote Sensing 27(14): 3073–3078.
  89. Tran D.X., Pla F., Latorre-Carmona P., Myint S.W., Caetano M., Kieu H.V., 2017. Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing 124: 119–132. DOI 10.1016/j.isprsjprs.2017.01.001.
  90. Ullah S., Tahir A.A., Akbar T.A., Hassan Q.K., Dewan A., Khan A.J., et al., 2019. Remote sensing-based quantification of the relationships between land use land cover changes and surface temperature over the Lower Himalayan Region. Sustainability 11: 5492. DOI 10.3390/su11195492.
  91. USGS [United States Geological Survey], 2015. Landsat 8 (L8) Data Users Handbook, Version 1.0. LSDS-1574. Department of the Interior, U.S. Geological Survey.
  92. USGS [United States Geological Survey], 2019. Landsat 8 Surface Reflectance Code (LASRC) Product Guide. Version 2.0. Online: https://www.usgs.gov/media/files/landsat-8-collection-1-land-surface-reflectance-code-product-guide (accessed 20 August 2020).
  93. USGS [United States Geological Survey], 2020. Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA) on-demand interface user guide. Release 3.0.0. Version 4.0
  94. Uyeda K.A., Stow D.A., Roberts D.A., Riggan P.J., 2017. Combining ground-based measurements and MODIS-based spectral vegetation indices to track biomass accumulation in post-fire chaparral. International Journal of Remote Sensing 38(3): 728–741. DOI 10.1080/01431161.2016.1271477.
  95. Vermote E., Justice C., Claverie M., Franch B., 2016. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment 185: 46–56.
  96. Weng Q., 2003. Fractal analysis of satellite-detected urban heat island effect. Photogrammetric Engineering and Remote Sensing 69(5): 555–566.
  97. Weng Q., Lu D., Schubring J., 2004. Estimation of land surface temperature – vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment 89(4): 467–483.
  98. Wilson J.S., Clay M., Martin E., Stuckey D., Vedder-Risch K., 2003. Evaluating environmental influences of zoning in urban ecosystems with remote sensing. Remote Sensing of Environment 86(3): 303–321.
  99. WWO [World Weather Online], 2020. Lagos monthly climate averages. Online: www.worldweatheronline.com/lagos-weather-averages/lagos/ng.aspx (accessed 1 August 2020).
  100. Xian G., 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.
  101. Xiao R., Ouyang Z., Zheng H., Li W., Schienke E.W., Wang X., 2007. Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. Journal of Environmental Sciences 19(2): 250–256.
  102. Xiao X., Zhang Q., Braswell B., Urbanski S., Boles S., Wofsy S., Moore III B., Ojima D., 2004. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment 91(2): 256–270.
  103. Yuan X., Wang W., Cui J., Meng F., Kurban A., De Maeyer P., 2017. Vegetation changes and land surface feedbacks drive shifts in local temperatures over Central Asia. Scientific Reports 7(1): 1–8.
  104. Yue W., Xu J., Tan W., Xu L., 2007. The relationship between land surface temperature and NDVI with remote sensing: Application to Shanghai Landsat 7 ETM+ data’. International Journal of Remote Sensing 28(15): 3205–3226.
  105. Zaharaddeen I., Ibrahim I.B., Zachariah A., 2016. Estimation of land surface temperature of Kaduna metropolis, Nigeria using Landsat images. Science World Journal 11(3): 36–42.
  106. Zareie S., Khosravi H., Nasiri A., 2016. Derivation of land surface temperature from landsat thematic mapper (TM) sensor data and analysing relation between land use changes and surface temperature. Solid Earth Discussions: 1–15. DOI 10.5194/se-2016-22.
  107. Zhang J., Wang Y., Li Y., 2006. A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band 6. Computers & Geosciences 32(10): 1796–1805.
  108. Zhang Y., Balzter H., Liu B., Chen Y., 2016. Analyzing the impacts of urbanization and seasonal variation on land surface temperature based on subpixel fractional covers using landsat images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(4): 1344–1356.