Assessment of Land Surface Temperature Variations and Implications of Land Use/Land Cover Changes: A Case of Malappuram Urban Agglomeration Region, Kerala, India
DOI:
https://doi.org/10.11113/ijbes.v10.n3.1102Keywords:
Land Surface Temperature, Spatio-temporal analysis, Thermal hotspots, Urban Heat Stress, UrbanizationAbstract
Urbanization is taking place faster, and urban air temperatures are gradually rising in all cities across the world. Uncontrolled and unplanned urbanization leads to constant environmental threats and can alter local and regional climates. According to the survey published by Economist Intelligence Unit, in India, Kerala's Malappuram district ranks first among the fastest-growing urban areas globally, with a 44.05% growth rate. Hence, the present study aims to identify the hotspot regions of extreme heat within the Malappuram urban agglomeration region and suggest strategies for its improvement. The split-window algorithm retrieved land surface temperature (LST) for 1991, 1998, 2014, and 2020 using Landsat 5 ETM and Landsat 8 satellite imageries. A rising trend in LST has been detected in the last 30 years, and the mean value has increased by 1.70°C within the region. Among the selected hotspots, an LST increase of 1.84°C was observed for those areas with the highest increase in urban density with decreased vegetation. The increasing impact of urbanization and the subsequent change in land use patterns at the cost of greenery have caused a substantial effect on the local climate. Accordingly, planning and policy directions are proposed for the local government that can help provide awareness to the people through the effective implementation of mitigation measures.
References
Aik, J., Ismail, M. H., & Muharam, F. M. (2020). Land Use / Land Cover Changes and the Relationship. Land, 9(372): 1–23.
Avdan, U., & Jovanovska, G. (2016). Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. Journal of Sensors, 2016: 8, Article ID 1480307. https://doi.org/10.1155/2016/1480307
Banerji, H., Firoz, M., & Sen, J. (2014). A Methodology to Define the Typology of Rural Urban Continuum Settlements in Kerala. Journal of Regional Development and Planning, 3(1), 49.
Bhuvan. (2021). Thematic Data dissemination. Bhuvan ISRO/ NRSC. https://bhuvan-app1.nrsc.gov.in/thematic/thematic/index.php Retrieved September 14, 2021
Browne, D. (2019). Leaving no one behind. Highways, 88(5): 3. https://doi.org/10.4324/9781351006941-3
Browning, W., Ryan, C., & Clancy, J. (2014). 14 Patterns of Biophilic Design: Improving Health & Well-Being in the Built Environment. Terrapin Bright Green,LLC, 1–60. https://doi.org/10.1016/j.yebeh.2008.04.024
Business Standard News. (2022). 90 people died in 2022 due to heatwave spells in India, Pakistan: Study. https://www.business-standard.com/article/current-affairs/90-people-died-in-2022-due-to-heatwave-spells-in-india-pakistan-study-122052400052_1.html (Retrieved October 24, 2022)
Carbon Management Ask the Experts: The IPCC Fifth Assessment Report. (2014). https://doi.org/10.4155/cmt.13.80
Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3): 241–252. https://doi.org/10.1016/S0034-4257(97)00104-1
Chen, J., & Zhang, L. (2016). Joint multi-image saliency analysis for region of interest detection in optical multispectral remote sensing images. Remote Sensing, 8(6): 461. https://doi.org/10.3390/rs8060461
Cheval, S., & Dumitrescu, A. (2009). The July urban heat island of Bucharest as derived from modis images. Theoretical and Applied Climatology, 96(1–2): 145–153. https://doi.org/10.1007/S00704-008-0019-3
Cyriac, S., & Firoz C, M. (2022). Dichotomous classification and implications in spatial planning: A case of the Rural-Urban Continuum settlements of Kerala, India. Land Use Policy, 114: 105992. https://doi.org/https://doi.org/10.1016/j.landusepol.2022.105992
Deng, C., & Wu, C. (2013). Examining the impacts of urban biophysical compositions on surface urban heat island: A spectral unmixing and thermal mixing approach. Remote Sensing of Environment, 131: 262–274. https://doi.org/10.1016/J.RSE.2012.12.020
Estoque, R. C., & Murayama, Y. (2017). Monitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987–2015). ISPRS Journal of Photogrammetry and Remote Sensing, 133: 18–29. https://doi.org/10.1016/j.isprsjprs.2017.09.008
Fathima Zehba, M. P., Mohammed Firoz, C., & Babu, N. (2021). Spatial Assessment of Quality of Life Using Composite Index. Quality of Life, 181–207. https://doi.org/10.1201/9781003009139-11
Feizizadeh, B., Blaschke, T., Nazmfar, H., Akbari, E., & Kohbanani, H. R. (2013). Monitoring land surface temperature relationship to land use/land cover from satellite imagery in Maraqeh County, Iran. Http://Dx.Doi.Org/10.1080/09640568.2012.717888, 56(9): 1290–1315. https://doi.org/10.1080/09640568.2012.717888
Gazi, M. Y., Rahman, M. Z., Uddin, M. M., & Rahman, F. M. A. (2021). Spatio-temporal dynamic land cover changes and their impacts on the urban thermal environment in the Chittagong metropolitan area, Bangladesh. GeoJournal, 86(5): 2119–2134. https://doi.org/10.1007/s10708-020-10178-4
Gohain, K. J., Mohammad, P., & Goswami, A. (2021). Assessing the impact of land use land cover changes on land surface temperature over Pune city, India. Quaternary International, 575–576: 259–269. https://doi.org/10.1016/j.quaint.2020.04.052
Grover, A., & Singh, R. B. (2015). Analysis of urban heat island (Uhi) in relation to normalized difference vegetation index (ndvi): A comparative study of delhi and mumbai. Environments - MDPI, 2(2): 125–138. https://doi.org/10.3390/environments2020125
Grover, A., & Singh, R. B. (2016). Monitoring Spatial patterns of land surface temperature and urban heat island for sustainable megacity: A case study of Mumbai, India, using landsat TM data. Environment and Urbanization ASIA, 7(1): 38–54. https://doi.org/10.1177/0975425315619722
Guha, S., & Govil, H. (2020). Land surface temperature and normalized difference vegetation index relationship: a seasonal study on a tropical city. SN Applied Sciences, 2(10): 1–14. https://doi.org/10.1007/s42452-020-03458-8
Haghighi, N., Liu, X. C., Zhang, G., & Porter, R. J. (2018). Impact of roadway geometric features on crash severity on rural two-lane highways. Accident Analysis and Prevention, 111(November 2017): 34–42. https://doi.org/10.1016/j.aap.2017.11.014
Hu, L., & Brunsell, N. A. (2013). The impact of temporal aggregation of land surface temperature data for surface urban heat island (SUHI) monitoring. Remote Sensing of Environment, 134: 162–174. https://doi.org/10.1016/j.rse.2013.02.022
Kafy, A. Al, Faisal, A. Al, Shuvo, R. M., Naim, M. N. H., Sikdar, M. S., Chowdhury, R. R., Islam, M. A., Sarker, M. H. S., Khan, M. H. H., & Kona, M. A. (2021). Remote sensing approach to simulate the land use/land cover and seasonal land surface temperature change using machine learning algorithms in a fastest-growing megacity of Bangladesh. Remote Sensing Applications: Society and Environment, 21(December 2020): 100463. https://doi.org/10.1016/j.rsase.2020.100463
Kaiser, E. A., Beatriz, S., Rolim, A., Efrain, A., Grondona, B., Hackmann, C. L., Linn, R. D. M., Käfer, P. S., & Souza, N. (2022). Spatiotemporal Influences of LULC Changes on Land Surface Temperature in Rapid Urbanization Area by Using Landsat-TM and TIRS Images. Atmosphere, 13(3):460.
Kallingal, F. R., & Joy, K. P. (2022). Regional Integrated Approach for Smart Master Planning: A Case of Kochi, Kerala, India. Advances in 21st Century Human Settlements, 231–247. https://doi.org/10.1007/978-981-19-2386-9_6
Kallingal, F. R., & Mohammed Firoz, C. (2022). Regional disparities in social development: A case of selected districts in Kerala, India. GeoJournal, 88: 1–28. https://doi.org/10.1007/s10708-022-10592-w
Kikon, N., Singh, P., Singh, S. K., & Vyas, A. (2016). Assessment of urban heat islands (UHI) of Noida City, India using multi-temporal satellite data. Sustainable Cities and Society, 22: 19–28. https://doi.org/10.1016/j.scs.2016.01.005
Madanian, M., Soffianian, A. R., Soltani Koupai, S., Pourmanafi, S., & Momeni, M. (2018). The study of thermal pattern changes using Landsat-derived land surface temperature in the central part of Isfahan province. Sustainable Cities and Society, 39: 650–661. https://doi.org/10.1016/j.scs.2018.03.018
Mathew, A., Sreekumar, S., Khandelwal, S., Kaul, N., & Kumar, R. (2016). Prediction of surface temperatures for the assessment of urban heat island effect over Ahmedabad city using linear time series model. Energy and Buildings, 128: 605–616. https://doi.org/10.1016/j.enbuild.2016.07.004
Ministry of Housing and Urban Affairs. (2017). National Capital Region Planning Board. https://ncrpb.nic.in/rationale.html (Retrieved November 20, 2022)
National Remote Sensing Agency. (2007). Natural Resources Census National Land Use and Land Cover Mapping Using Multi-Temporal AWiFS Data. June.
Norton, B., Bosomworth, K., Coutts, A., Williams, N., Livesley, S., Trundle, A., Harris, R., & Mcevoy, D. (2013). Planning for a Cooler Future : Green Infrastructure to Reduce Urban Heat: Climate Adaptation for Decision-makers. October, 1–29. http://www.vcccar.org.au/sites/default/files/publications/VCCCAR Green Infrastructure Guide Final.pdf
Praveen Lal, C. S., & Nair, S. B. (2017). Urbanization in Kerala—What Does the Census Data Reveal? Indian Journal of Human Development, 11(3): 356–386. https://doi.org/10.1177/0973703018763241
Revi, A., Satterthwaite, D., Aragón-Durand, F., Corfee-Morlot, Robert B R Kiunsi, J., Pelling, M., Roberts, D., Solecki, W., Pahwa, S., & Sverdlik, A. (2014). Towards transformative adaptation in cities: the IPCC’s Fifth Assessment. IIED), 26(1): 11–28. https://doi.org/10.1177/0956247814523539
Rimal, B. (2012). Urbanization and the Decline of Agricultural Land in Pokhara Sub-metropolitan City, Nepal. Journal of Agricultural Science, 5(1): 54–65. https://doi.org/10.5539/jas.v5n1p54
Rosenzweig, C.; Solecki, W.D.; Romero-Lankao, P.; Mehrotra, S.; Dhakal, S.; Ibrahim, S. A. (2018). Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network. Cambridge University Press: Cambridge, UK. https://doi.org/10.1080/23298758.1994.10685604
Ruefenacht, L. A., & Acero, J. A. (2017). Strategies for Cooling Singapore. Cooling Singapore (CS), 2017. https://doi.org/10.3929/ethz-b-000258216
Sannigrahi, S., Rahmat, S., Chakraborti, S., Bhatt, S., & Jha, S. (2017). Changing dynamics of urban biophysical composition and its impact on urban heat island intensity and thermal characteristics: the case of Hyderabad City, India. Modeling Earth Systems and Environment, 3(2): 647–667. https://doi.org/10.1007/s40808-017-0324-x
Sattari, F., & Hashim, M. (2014). A Breif Review of Land Surface Temperature Retrieval Methods from Thermal Satellite Sensors. Middle-East Journal of Scientific Research, 22(5): 757–768. https://doi.org/10.5829/idosi.mejsr.2014.22.05.21934.
Sekertekin, A., & Bonafoni, S. (2020). Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: Assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sensing 12(2): 294.https://doi.org/10.3390/rs12020294
Sholihah, R. I., & Shibata, S. (2019). Retrieving Spatial Variation of Land Surface Temperature Based on Landsat OLI/TIRS: A Case of Southern part of Jember, Java, Indonesia. IOP Conference Series: Earth and Environmental Science, 362(1). https://doi.org/10.1088/1755-1315/362/1/012125
Singh, P., Kikon, N., & Verma, P. (2017). Impact of land use change and urbanization on urban heat island in Lucknow city, Central India. A remote sensing based estimate. Sustainable Cities and Society, 32, 100–114. https://doi.org/10.1016/j.scs.2017.02.018
Singh, R. B., Grover, A., & Zhan, J. (2014). Inter-seasonal variations of surface temperature in the urbanized environment of Delhi using landsat thermal data. Energies, 7(3), 1811–1828. https://doi.org/10.3390/en7031811
Skymet Weather Services. (2016). Some more days of heatwave in Kerala before brief respite. https://www.skymetweather.com/content/weather-news-and-analysis/kerala-records-highest-temperature-in-almost-3 decades/ (Retrieved January 08, 2022)
Sobrino, J. A., Jiménez-Muñoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4): 434–440. https://doi.org/10.1016/j.rse.2004.02.003
Sreenath, G. (2013). Ground Water Information Booklet of Malappuram District. December, 1–29.
Sruthi Krishnan, V., & Mohammed Firoz, C. (2020). Regional urban environmental quality assessment and spatial analysis. Journal of Urban Management, 9(2): 191–204. https://doi.org/10.1016/j.jum.2020.03.001
Sterling, S., & Duchame, A. (2008). Comprehensive data set of global land cover change for land surface model applications. Global Biogeochemical Cycles, 22(3): GB3017. https://doi.org/10.1029/2007GB002959
T.S., S., Mohammed Firoz, C., & Bhagyanathan, A. (2022). The impact of upstream land use land cover change on downstream flooding: A case of Kuttanad and Meenachil River Basin, Kerala, India. Urban Climate, 41(April 2021): 101089. https://doi.org/10.1016/j.uclim.2022.101089
The Hindu. (2019). Sunstroke claims three lives in Kerala. https://www.thehindu.com/news/national/kerala/two-more-suspected-sunstroke-deaths/article26627812.ece (Retrieved October 18, 2021)
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. https://doi.org/10.1016/j.isprsjprs.2017.01.001
Twumasi, Y. A., Merem, E. C., Namwamba, J. B., Mwakimi, O. S., Ayala-Silva, T., Frimpong, D. B., Ning, Z. H., Asare-Ansah, A. B., Annan, J. B., Oppong, J., Loh, P. M., Owusu, F., Jeruto, V., Petja, B. M., Okwemba, R., McClendon-Peralta, J., Akinrinwoye, C. O., & Mosby, H. J. (2021). Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. A Comparative Analysis of Two Cities in Ghana. Advances in Remote Sensing, 10(04): 131–149. https://doi.org/10.4236/ars.2021.104009
United Nations. (2019). The Climate Crisis – A Race We Can Win | United Nations. https://www.un.org/en/un75/climate-crisis-race-we-can-win (Retrieved December 30, 2021)
United States Geological Survey. (2022). EarthExplorer. https://earthexplorer.usgs.gov/ (Retrieved August 10, 2021)
Vancutsem, C., Ceccato, P., Dinku, T., & Connor, S. J. (2010). Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sensing of Environment, 114(2): 449–465. https://doi.org/10.1016/j.rse.2009.10.002
Varughese, A., & Purushothaman, C. (2021). Climate Change and Public Health in India: The 2018 Kerala Floods. World Medical and Health Policy, 13(1), 16–35. https://doi.org/10.1002/wmh3.429. World Medical and Health Policy, 13(1): 16–35. https://doi.org/10.1002/wmh3.429
Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3): 370–384. https://doi.org/10.1016/S0034-4257(03)00079-8
Weng, Q., & Fu, P. (2014). Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 97: 78–88. https://doi.org/10.1016/j.isprsjprs.2014.08.009
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. https://doi.org/10.1016/S0034-4257(03)00084-1
World Health Organisation. (2022). Heatwaves. https://www.who.int/health-topics/heatwaves#tab=tab_1
Yeo, L. B., Hoh, G., Ling, T., & Tan, M. L. (2021). Interrelationships between Land Use Land Cover ( LULC ) and Human Thermal Comfort ( HTC ): A Comparative Analysis of Different Spatial Settings, Sustainability, 13(1): 382. https://doi.org/10.3390/su13010382
Zullo, F., Fazio, G., Romano, B., Marucci, A., & Fiorini, L. (2019). Effects of urban growth spatial pattern (UGSP) on the land surface temperature (LST): A study in the Po Valley (Italy). Science of The Total Environment, 650: 1740–1751. https://doi.org/10.1016/j.scitotenv.2018.09.331
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright of articles that appear in International Journal of Built Environment and Sustainability belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions or any other reproductions of similar nature.
Authors who publish with this journal agree to the following terms:
- This Journal applies Creative Commons Licenses of CC-BY-NC-SA
- Authors retain copyright and grant the journal right of publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
