The Effects of Urbanization and Vegetation Cover on Urban Heat Island: A Case Study in Osmaniye Province

Year 2023, Volume: 10 Issue: 1, 120 - 131, 19.03.2023

Deniz Çolakkadıoğlu

https://doi.org/10.30897/ijegeo.1144167

Cited By: 2

Abstract

This study analyzed the changes in the urban heat island effect in the 30 years (from 1990 to 2021) in the central district of Osmaniye. In this sense, there were two primary goals. Firstly, Land use/land cover change (LULC), land surface temperature (LST), normalized difference built-up index (NDBI), and normalized difference vegetation index (NDVI) were analyzed by using remote sensing methods between 1990 and 2021. Secondly, a linear regression analysis was conducted to determine the factors associated with LST, NDVI, and NDBI. The study results revealed increases in urban surfaces and the average land surface temperature values in the past 30 years and showed a decline in the vegetation with low, medium, and high NDVI values. The regression analysis results indicated a strong negative relationship between LST and NDVI and a strong positive relationship between LST and NDBI. It was also found a robust negative relationship between NDBI and NDVI. In light of the findings, it was stated that the amount of open and green areas should be increased in order to prevent the negative effects of the urban heat island in the central district of Osmaniye. For this purpose, it has been proposed to encourage green roof systems throughout the city, to create city parks and to create a green belt system. In addition, as a result of the study, the importance of preventing forest destruction caused by over-settlement in the Amanos Mountains, which is one of the rare habitats of the world with different plant species, was emphasized. In this sense, legal sanctions should be employed to protect those areas and prevent construction.

Keywords

Land surface temperature, Normalized difference built-up index, Normalized difference vegetation index, Urban heat island

References

• Ahmad, N., Waqas, T., Shafique, M., and Khattak, I. (2022). The land surface temperature dynamics and its impact on land cover in district Peshawar, Khyber Pakhtunkhwa, International Journal of Environment and Geoinformatics (IJEGEO), 9(3): 097-107, doi. 10.30897/ijegeo. 890206
• Akyürek, Ö. (2020). Determination of Surface Temperatures with Thermal Remote Sensing Images: A Case Study of Kocaeli, Artvin Coruh University. Natural Disasters Application, and Research Center Journal of Natural Hazards and Environment. 6(2): 377-390. doi:10.21324/dacd.667594.
• Alhawiti, R. H., and Mitsova, D. (2016). Using landsat-8 data to explore the correlation between urban heat island and urban land uses. IJRET: International Journal of Research in Engineering and Technology 5(3): 457-466. eISSN: 2319-1163 pISSN: 2321-7308. http://www.ijret.org.
• Asgarian, A., Amiri, B., and Sakieh, Y. (2014). Assessing the effect of green cover spatial patterns on urban land surface temperature using landscape metrics approach. Urban Ecosyst. 18: 209–222. doi:10.1007/s11252-014-0387-7.
• Aygün, C., Sever, A. L.,. İsmail, K.A.R.A., Erdoğdu, İ., and Atalay, A.K. (2016). Use of NDVI Data on Planning of Grazing in Eskisehir Grasslands. Journal of Field Crops Central Research Institute, 25(1).
• Aytaç, A.S., and Semenderoğlu, A. (2011) . Vegetation geography of the central part of the Amanos Mountains. Journal of Anatolian Natural Sciences, 2(2): 34-47.
• Bakar, S.B., Pradhan, B., Lay, U.S., and Abdullahi, S. (2016). Spatial assessment of land surface temperature and land use/land cover in Langkawi Island. 8th IGRSM International Conference and Exhibition on Remote Sensing & GIS (IGRSM 2016), IOP Conf. Series: Earth and Environmental Science 37 (2016) 012064. doi: 10.1088/1755-1315/37/1/012064.
• Balew, A., and Korme, T. (2020). Monitoring land surface temperature in Bahir Dar City and its surrounding using Landsat images. The Egyptian Journal of Remote Sensing and Space Science. doi:10.1016/j.ejrs.2020.02.001.
• Carlson, T., and Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3): 241-252.doi: 10.1016/S0034-4257(97)00104-1.
• Chen, X., Zhao, H., Li, P., and Yin, Z. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2):133-146. Doi:10.1016/j.rse.2005.11.016.
• Das, S. (2022). A Review of UHI formation over changing climate and its impacts on Urban Land Use and Environments and Adaptation Measures. International Journal of Environment and Geoinformatics (IJEGEO), 9(1): 064-073. doi. 10.30897/ijegeo.938231
• Das, N., Mondal, P., Sutradhar, S., and Ghosh, R. (2021). Assessment of variation of land use/land cover and its impact on land surface temperature of Asansol subdivision. The Egyptian Journal of Remote Sensing and Space Sciences, 24(1): 131-149. doi: 10.1016/j.ejrs.2020.05.001.
• Gauquelin, T., Michon, G., Joffre, R., Duponnois, R., Génin, D., Fady, B., Dagher-Kharrat, M.B., Derridj, A., Slimani, S., Badri, W., Alifriqui, M., Auclair, L., Simenel, R., Aderghal, M., Baudoin, E., Galiana, A., Prin, Y., Sanguin, H., Fernandez, C., and Baldy, V. (2016). Mediterranean forests, land use and climate change: a social-ecological perspective. Regional Environmental Change, 18: 623-636.doi: 10.1007/s10113-016-0994-3.
• General Directorate of Meteorology. (2021). T.R. Ministry of Agriculture and Forestry, General Directorate of Meteorology 1987-2020 data.https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=OSMANIYE (accessed 11/03/2021).
• Guha, S., Govil, H., Dey, A., and Gill, N. (2018). Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples City, Italy. European Journal of Remote Sensing 51 (1): 667–678. doi: 10.1080/22797254.2018.1474494.
• Guo, G., Wu, Z., Xio, R., Chen, Y., Liu, X., and Zhang, X. (2015). Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landscape and Urban Planning 135 (2015): 1–10. Doi: 10.1016/j.landurbplan.2014.11.007.
• Huang, L., Zhao, D., Wang, J., Zhu, J., and Li, J. (2008). Scale impacts of land cover and vegetation corridors on urban thermal behavior in Nanjing, China. Theoretical and Applied Climatology, 94(3-4): 241-257.doi: 10.1007/s00704-007-0359-4.
• Jamei, Y., Rajagopalan, P., and Sun, Q. (2019). Spatial structure of surface urban heat island and its relationship with vegetation and built-up areas in Melbourne, Australia. Science of the Total Environment 659 (2019): 1335–1351. doi: 10.1016/j.scitotenv.2018.12.308.
• Jamei, E., Rajagopalan, P., Seyedmahmoudian, M., and Jamei, Y. (2016). Review on the impact of urban geometry and pedestrian level greening on outdoor thermal comfort. Renewable and Sustainable Energy Reviews 54: 1002–1017. Doi: 10.1016/j.rser.2015.10.104.
• Jin, M.S., Kessomkiat, W., and Pereira, G. (2011). Satellite-observed urbanization characters in Shanghai, China: aerosols, urban heat island effect, and land–atmosphere interactions. Remote Sensing, 3(1): 83-99. Doi:10.3390/rs3010083.
• Kesgin Atak, B., and Ersoy Tonyaloğlu, E. (2020). Evaluation of the effect of land use/land cover and vegetation cover change on land surface temperature: The case of Aydın province. Turkish Journal of Forestry, 21(4): 489- 497. Doi: 10.18182/tjf.786827.
• Khanal, N., Uddin, K., Matin, M., and Tenneson, K. (2019). Automatic Detection of Spatiotemporal Urban Expansion Patterns by Fusing OSM and Landsat Data in Kathmandu. Remote Sensing 11 (19): 2296.doi: 10.3390/rs11192296.
• Kikon, N., Singh, P., Singh, S.K., and 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. doi:10.1016/J.SCS.2016.01.005.
• Koday, S., and Kızılkan, Y. (2019). Determining the Changes in the use of the Lands in Unye District through the LULC and NDVI Analyses by means of multi-time Landsat satellite imaging. Journal of Atatürk University Institute of Social Sciences, 23 (3): 1301-1312.
• Kumar, D., and Shekhar, S. (2015). Statistical analysis and surface temperature–vegetation indexes relationship through thermal remote sensing. Ecotoxicology and Environmental Safety, 121(2015): 39-44. doi: 10.1016/j.ecoenv.2015.07.004.
• Li, J., Wang, X., Wang, X., and Ma, W. (2009). Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area, China. Ecological Complexity, 6(4): 413–420. Doi: 10.1016/j.ecocom.2009.02.002.
• Ma, X., Li, C., Tong, X., and Liu, S. (2019). A new fusion approach for extracting urban built-up areas from multisource remotely sensed data. Remote Sensing 11 (21): 2516. doi: 10.3390/rs11212516.
• Malik, M.S., Shukla, J.P., and 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 (01): 25-31.
• Maskooni, E.K., Hashemi, H., Berndtsson, R., Arasteh, P.D., and M. Kazemi. (2021). Impact of spatio temporal land-use and land cover changes on surface urban heat islands in a semi arid region using Landsat data. International Journal of Digital Earth, 14(2): 250-270, doi:10.1080/17538947.2020.1813210.
• Pal, S., and Ziaul, S.K. (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban centre. The Egyptian Journal of Remote Sensing and Space Science, 20(1): 125-145. doi: 10.1016/j.ejrs.2016.11.003
• Petropoulos, G.P., Griffiths, H.M., and Kalivas, D.P. (2014). Quantifying spatial and temporal vegetation recovery dynamics following a wildfire event in a Mediterranean landscape using EO data and GIS. Applied Geography, 50:120–131. doi:10.1016/j.apgeog.2014.02.006.
• Provincial Environmental Status Report Osmaniye. (2019). Osmaniye Governorship Provincial Directorate of Environment and Urbanization, Department of EIA, and Environmental Permits. pp:212. https://webdosya.csb.gov.tr/db/ced/icerikler/osman-ye_2018_-cdr_son-20190828111313.pdf.
• Ranagalage, M., Estoque, R.C., Zhang, X., and Murayama, Y. (2018). Spatial changes of urban heat island formation in the Colombo District, Sri Lanka: Implications for sustainability planning. Sustainability, 10(5): 1367. doi: 10.3390/su10051367.
• Rogan, J., Ziemer, M., Martin, D., Ratick, S., Cuba, N., and DeLauer, V. (2013). The impact of tree cover loss on land surface temperature: A case study of central Massachusetts using Landsat Thematic Mapper thermal data. Applied Geography, 45: 49–57. doi: 10.1016/j.apgeog.2013.07.004.
• Roy, S., Pandit, S., Eva, E.A. S., Bagmar, H., Papia, M., Banik, L., Dube, T., Rahman, F., and Razi, M.A. (2020). Examining the nexus between land surface temperature and urban growth in Chattogram Metropolitan Area of Bangladesh using long term Landsat series data. Urban Climate 32 (2020) 100593. doi: 10.1016/j.uclim.2020.100593.
• Sahebjalal, E., and Dashtekian, K. (2013). Analysis of land use-land covers changes using normalized difference vegetation index (NDVI) differencing and classification methods. African Journal of Agricultural, 8(37): 4614-4622. doi: 10.5897/AJAR11.1825.
• Sharma, R., and Joshi, P.K. (2016). Mapping environmental impacts of rapid urbanization in the National Capital Region of India using remote sensing inputs. Urban Climate, 15(2016): 70-82. doi:10.1016/j.uclim.2016.01.004.
• Sobrino, J.A., Jimenez-Muoz, J.C., Soria, G., Romaguera, M., Guanter, L., Moreno, J., Plaza, A., and Martinez, P. (2008). Land surface emissivity retrieval from different VNIR and TIR sensors. Transactions on Geoscience and Remote Sensing, 46(2): 316-327. Doi: 10.1109/TGRS.2007.904834.
• Sobrino, J.A., Jiménez-Muñoz, J.C., and Paolini, L. (2004). Land surface temperature retrieval from Landsat TM 5. Remote Sensing of Environment, 90: 434–440. doi: 10.1016/j.rse.2004.02.003.
• Sutariya, S., Hirapara, A., and Tiwari, M. K., (2022). Development of Modeler for Automated Mapping of Land Surface Temperature Using GIS and LANDSAT8 Satellite Imagery. International Journal of Environment and Geoinformatics (IJEGEO), 9(2):054-059, doi. 10.30897/ijegeo.820906
• Tıraş, M., and Besnek, F. (2017). A tourism potential of Osmaniye. Ataturk University, Journal of Social Sciences Institute, June (2017), 21 (2): 757-777.
• Türkeş, M. (2012). Observed and projected climate change, drought, and desertification in Turkey. Ankara University Journal of Environmental Sciences 4(2): 1-32. doi:10.1501/Csaum_0000000063.
• USGS. (2019). United States Geological Survey (USGS). Landsat 8 (L8) Data Users Handbook, LSDS-1574.2019. Version 5.0, URL: http://landsat.usgs.gov/Landsat8_Using_Product.php (accessed 11/08/2021).
• Varshney, A. (2013). Improved NDBI differencing algorithm for built-up regions change detection from remote-sensing data: an automated approach. Remote Sensing Letters, 4(5): 504-512. doi: 10.1080/2150704X.2013.763297.
• Weng, Q., Lu, D., and Schubring, J. (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4): 467–483. doi: 10.1016/j.rse.2003.11.005.
• Xiong, Y., Huang, S., Chen, F., Ye, H., Wang, C., and Zhu, C. (2012). The impacts of rapid urbanization on the thermal environment: A remote sensing study of Guangzhou, South China. Remote Sensing, 4: 2033-2056. doi:10.3390/rs4072033.
• Xu, C., and Zhang, Y. (2017). Impacts of urban surface characteristics on spatiotemporal pattern of land surface temperature in Kunming of China. Sustainable Cities and Society 32 (July): 87–99. doi:10.1016/j.scs. 2017.03.013.
• Zha, Y., Gao, J., and Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from tm imagery. International Journal of Remote Sensing 24(3): 583–594. doi: 10.1080/01431160304987.
• Zhang, Y., Odeh, I.O.A., and Han, C. (2009). Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation 11 (2009): 256–264. doi:10.1016/j.jag.2009.03.00.