Urban Land Surface Temperature and Vegetation Correlation in the Kumasi Metropolis
DOI:
https://doi.org/10.12974/2311-8741.2021.09.1Keywords:
Correlation, Land surface, Temperature, Urban heat, Vegetation, Vegetation index.Abstract
Urban heat is considered as a worrying issue in cities because of the unbearable feelings associated with heat especially on sunny days. Urban heat is not as a result of any one time event but a chain of processes associated with land use activities such as infrastructure development that replaces the green vegetation. This paper investigated how land surface temperature has changed in the Kumasi Metropolis in 10 years from an environment of low temperature to much warmer land surface temperature due to the loss of trees in the city. The objectives of the study were to assess the extent of Land Surface Temperature (LST) in the metropolis, determine the kind of correlation that exist between Land Surface Temperate and vegetation health and examine the extent to which vegetation has influenced the city temperature. Multiple methods were used to calculate Land Surface Temperature (LST) such as converting the digital numbers of Landsat 2009, 2015 and 2019 images to radiance, top of brightness temperature and at-sensor brightness temperature using the Plank’s inverse function. NDVI analysis was done by subtracting the near infrared bands from the red band, divided by addition of the near infrared band to the red Band. Study results showed a linear increase in LST from an average of about 18°C to 31°C. The NDVI result showed decline in vegetation cover as such the correlation analysis was a negative correlation showing places of high temperature had minimal vegetation cover while places of low temperature had more vegetation cover.
References
Abass K, Adanu SK & Gyasi RM. Urban sprawl and land use / land-cover transition probabilities in peri-urban Kumasi, Ghana 2018; 26: 118-132.
Abass K, Adanu SK and Agyeman S. Peri-urbanisation and loss of arable land in Kumasi Metropolis in three decades: Evidence from remote sensing image analysis, Land Use Policy, 2018; 72: 470-479 https://doi.org/10.1016/j.landusepol.2018.01.013
Alemu MM. Analysis of Spatio-Temporal Land Surface Temperature and Normalized Difference Vegetation Index Changes in the Andassa Watershed, Blue Nile Basin, Ethiopia. Journal of Resources and Ecology 2019; 10(1): 77. https://doi.org/10.5814/j.issn.1674-764x.2019.01.010
Appiah DO. Forkour FK & Bugri J. Land Surface Temperature Extracts for Peri-Urban Heat and Rural Cool Troughs in Ghana, International Journal of Advanced Remote Sensing and GIS 2017; 6(1): 2204-2222 ISSN 2320 - 0243. https://doi.org/10.23953/cloud.ijarsg.274
Agam N, Kustas WP, Anderson MC, Li, F & Neale CM. A vegetation index based technique for spatial sharpening of thermal imagery. Remote Sensing of Environment 2007; 107(4): 545-558. https://doi.org/10.1016/j.rse.2006.10.006
Bustos E, Meza FJ. 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 Climatology 2015; 120(1-2): 211- 226. https://doi.org/10.1007/s00704-014-1167-2
Corrêa JAJ, Andrade SCP, Pereira ICN, Borges TCS, Santos PV. Remote sensing applied to the detection of changes in land use and surface temperature in the Mojuí river watershed. Journal of Hyperspectral Remote Sensing 2016; 6: 250-261. https://doi.org/10.5935/2237-2202.20160025
Dousset B & Gourmelon F. Satellite multi-sensor data analysis of urban surface temperatures and landcover, ISPRS Journal of Photogrammetry and Remote Sensing 2003; 43-54. https://doi.org/10.1016/S0924-2716(03)00016-9
Fathian F, Prasad AD & Dehghan Z. Influence of land use / land cover change on land surface temperature using RS and GIS techniques. International Journal of Hydrology Science and Technology 2015; 5(3): 195-207. https://doi.org/10.1504/IJHST.2015.071348
Gusmão ACVL, Silva BB. da, Montenegro SMGL, Galvíncio JD, Oliveira LMM.de. Índice de vegetação e temperatura da superfície no ecótono Ilha do Bananal por sensoriamento remoto. Revista de Geografia (UFPE) 2013; 30: 209-225.
Ghana Statistical Service (2002). Ghana Population and Housing Census.
Ilayaraja K, Wasi R, Vivek K, Sourov P & Ravindra C. Estimation of Land Surface Temperature of Chennai Metropolitan Area using Landsat Images Journal of Chemical and Pharmaceutical Sciences 2015; ISSN: 0974-2115
Jawak SD & Luis AJ. A Rapid Extraction of Water Body Features from Antarctic Coastal Oasis Using Very High- Resolution Satellite Remote Sensing Data. Aquatic Procedia, 4(Icwrcoe), 2015; 125-132. https://doi.org/10.1016/j.aqpro.2015.02.018
Jesus JB De, Santana IDM. Estimation of land surface temperature in caatinga area using Landsat 8 data. Journal of Hyper spectral Remote Sensing 2017; 7: 150-157.
Kumar KS, Bhaskar PU & Padmakumari K. Estimation of Land Surface Temperature To Study Urban Heat Island Effect Using Landsat Etm+ Image. International Journal of Engineering Science and Technology 2012; 4(02): 771-778.
Kumar KS, Bhaskar PU & Padmakumari K. Estimation of Land Surface Temperature To Study Urban Heat Island Effect Using Landsat Etm+ Image. International Journal of Engineering Science and Technology 2012; 4(02): 771-778.
Kustas WP, Norman JM, Anderson MC & French AN. Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship. Remote sensing of Environment 2003; 85(4): 429-440. https://doi.org/10.1016/S0034-4257(03)00036-1
Mallick J, kant Y and Bharath BD. Estimation of land surface temperature over Delhi using Landsat-7 ETM+, J. Ind. Geophys. Union 2008; 12(3): 131-140
Mensah CA. Destruction of Urban Green Spaces: A Problem Beyond Urbanization in Kumasi City (Ghana). American Journal of Environmental Protection 2014; 3(1): 1. https://doi.org/10.11648/j.ajep.20140301.11
Molnar G. Analysis of land surface temperature and ndvi distribution for budapest using landsat 7 etm+ data. Acta Climatologica Et Chorologica 2016; 49-61.
Nichol JE & To PH. Temporal characteristics of thermal satellite images for urban heat stress and heat island mapping. ISPRS journal of photogrammetry and remote sensing 2012; 74: 153-162. https://doi.org/10.1016/j.isprsjprs.2012.09.007
Ramachandra TV, Bharath H. Aithal and Durgappa Sanna D. Land Surface Temperature Analysis in an Urbanising Landscape through Multi- Resolution Data, Research & Reviews: Journal of Space Science & Technology 2012; 1(1): 1-10
Running SW, Loveland TR, Pierce LL, Nemani RR & Hunt Jr, ER. A remote sensing based vegetation classification 1995.
Sahana M, Ahmed R & Sajjad H. 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, 2016; 2(2). https://doi.org/10.1007/s40808-016-0135-5
Tucker CJ, Slayback DA, Pinzon JE, Los SO, Myneni RB & Taylor MG. Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. International journal of biometeorology 2001; 45(4): 184-190. https://doi.org/10.1007/s00484-001-0109-8
Weng Q, Lu D, Schubring J. Estimation of land surface temperature-vegetation abundance relationship for urban heat is- land studies, Remote Sens. Environ 2004; 89: 467- 483, https://doi.org/10.1016/j.rse.2003.11.005
World Bank Group. Doing Business 2015: Going Beyond Efficiency: Comparing Business Regulations for Domestic Firms in 189 Economies: a World Bank Group Flagship Report. World Bank Publications 2014.
Yamashita S. Detail Structure of Heat Island Phenomena from Moving Observations from Electric Trans Cars in Metropolitan Tokyo, Atmospheric Environment 1996; 30: 429 435. https://doi.org/10.1016/1352-2310(95)00010-0
Koranteng C. Simons B & Nyame-Tawiah D. “Green to Grey: An Urban Heat Assessment of Kumasi, Ghana”. International Journal of Environment and Climate Change 2019; 9(12): 751-63. https://doi.org/10.9734/ijecc/2019/v9i1230155
Kumasi Metropolitant Assembly (KMA), 2006: http://kma.ghanadistricts.gov.gh/
Anon., 2019. https://en.wikipedia.org/wiki/Kumasihttps https://doi.org/10.5194/hess-16-1833-2012
Anderson MCJM. Norman JR. Mecikalski JA. Otkin W & Kustas P. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation. J. Geophysics, Res, 2004; 112: D10117. https://doi:10.1029/2006/JD007506
