Statistical Relation between Vegetation Cover and Land Surface Temperature in Phnom Penh City

Authors: Gulam Mohiuddin, Jan-Peter Mund

Abstract:

This study assessed the correlation between Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) in Phnom Penh City (Cambodia) from 2016 to 2020. Understanding the LST and NDVI can be helpful to understand the Urban Heat Island (UHI) scenario, and it can contribute to planning urban greening and combating the effects of UHI. The study used Landsat-8 images as the data for analysis. They have 100 m spatial resolution (per pixel) in the thermal band. The current study used an approach for the statistical analysis that considers every pixel from the study area instead of taking few sample points or analyzing descriptive statistics. Also, this study is examining the correlation between NDVI and LST with a spatially explicit approach. The study found a strong negative correlation between NDVI and LST (coefficient range -0.56 to -0.59), and this relationship is linear. This study showed a way to avoid the probable error from the sample-based approach in examining two spatial variables. The method is reproducible for a similar type of analysis on the correlation between spatial phenomena. The findings of this study will be used further to understand the causation behind LST change in that area triangulating LST, NDVI and land-use changes.

Keywords: Land Surface Temperature, NDVI, Normalized Difference Vegetation Index, remote sensing, methodological development.

References:

[1] UN, 2015. Goal 11 | Department of Economic and Social Affairs. (online) Sdgs.un.org. Available at: (Accessed 21 February 2021).
[2] UN DESA | United Nations Department of Economic and Social Affairs (2018): 68% of the world population projected to live in urban areas by 2050, says UN | UN DESA | United Nations Department of Economic and Social Affairs.
[3] Arya, S. Pal (2001): Introduction to micrometeorology. 2nd ed. San Diego: Academic Press (This is volume 79 in the International geophysics series).
[4] Kunst, A.E., Looman, C.W. and Mackenbach, J.P., 1993. Outdoor air temperature and mortality in the Netherlands: a time-series analysis. American Journal of epidemiology, 137(3), pp.331-341.
[5] Tan, Jianguo; Zheng, Youfei; Tang, Xu; Guo, Changyi; Li, Liping; Song, Guixiang et al. (2010): The urban heat island and its impact on heat waves and human health in Shanghai. In International journal of biometeorology 54 (1), pp. 75–84. DOI: 10.1007/s00484-009-0256-x.
[6] Sarrat, C.; Lemonsu, A.; Masson, V.; Guedalia, D. (2006): Impact of urban heat island on regional atmospheric pollution. In Atmospheric Environment 40 (10), pp. 1743–1758. DOI: 10.1016/j.atmosenv.2005.11.037.
[7] Copernicus, n.d. Land surface temperature. (online) Copernicus global land service. Available at: (Accessed 24 February 2021).
[8] Molina, Mario J.; Molina, Luisa T. (2004): Megacities and atmospheric pollution. In Journal of the Air & Waste Management Association (1995) 54 (6), pp. 644–680. DOI: 10.1080/10473289.2004.10470936.
[9] Khan, Mobarak Hossain (2012): Urban health in megacities of developing countries. In Public Health Forum 20 (2). DOI: 10.1016/j.phf.2012.03.012.
[10] Gill, S.E., Handley, J.F., Ennos, A.R., & Pauleit, S. (2007). Adapting Cities for Climate Change: The Role of the Green Infrastructure. Built Environment, 33(1), 115–133. https://doi.org/10.2148/benv.33.1.115
[11] Bowler, D. E., Buyung-Ali, L., Knight, T. M., & Pullin, A. S. (2010). Urban greening to cool towns and cities: A systematic review of the empirical evidence. Landscape and urban planning, 97(3), 147-155.
[12] Hiemstra, J. A., Saaroni, H., & Amorim, J. H. (2017). The Urban Heat Island: Thermal Comfort and the Role of Urban Greening. In D. Pearlmutter, C. Calfapietra, R. Samson, L. O'Brien, S. Krajter Ostoić, G. Sanesi, & R. Alonso del Amo (Eds.), Future City. The Urban Forest (Vol. 7, pp. 7–19). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-50280-9_2
[13] Carlson, Toby N.; Gillies, Robert R.; Perry, Eileen M. (1994): A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. In Remote Sensing Reviews 9 (1-2), pp. 161–173. DOI: 10.1080/02757259409532220.
[14] Clarke, T. R. (1997). An Empirical Approach for Detecting Crop Water Stress Using Multispectral Airborne Sensors. HortTechnology horttech 7, 1, 9-16, available from: < https://doi.org/10.21273/HORTTECH.7.1.9> (Accessed 21 February 2021)
[15] Gillies, R. R.; Kustas, W. P.; Humes, K. S. (1997): A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e. In International Journal of Remote Sensing 18 (15), pp. 3145–3166. DOI: 10.1080/014311697217026.
[16] Goetz, S. J. (1997): Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site. In International Journal of Remote Sensing 18 (1), pp. 71–94. DOI: 10.1080/014311697219286.
[17] Sandholt, I., Rasmussen, K. and Andersen, J., 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment, 79(2-3), pp.213-224.
[18] Weng, Qihao; Lu, Dengsheng; Schubring, Jacquelyn (2004): Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. In Remote Sensing of Environment 89 (4), pp. 467–483. DOI: 10.1016/j.rse.2003.11.005.
[19] Amiri, Reza; Weng, Qihao; Alimohammadi, Abbas; Alavipanah, Seyed Kazem (2009): Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. In Remote Sensing of Environment 113 (12), pp. 2606–2617. DOI: 10.1016/j.rse.2009.07.021.
[20] Wei, M. A., Yun-hao, C., & Ji, Z. (2008). Quantitative analysis of land surface temperature–vegetation indexes relationship based on remote sensing. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII ((Part31 B6b)), 261–264.
[21] Mallick J., Kant Y., & Bharath B.D. (2008): Estimation of land surface temperature over Delhi using Landsat-7 ETM+. In J. Ind. Geophys. Union Vol.12, No.3, pp. 131– 140.
[22] Wan, Z.; Wang, P.; Li, X. (2004): Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA. In International Journal of Remote Sensing 25 (1), pp. 61–72. DOI: 10.1080/0143116031000115328.
[23] Orhan, Osman; Ekercin, Semih; Dadaser-Celik, Filiz (2014): Use of Landsat land surface temperature and vegetation indices for monitoring drought in the Salt Lake Basin Area, Turkey. In The Scientific World Journal 2014, p. 142939. DOI: 10.1155/2014/142939.
[24] Zhibin, Ren; Haifeng, Zheng; Xingyuan, He; Dan, Zhang; Xingyang, Yu (2015a): Estimation of the Relationship Between Urban Vegetation Configuration and Land Surface Temperature with Remote Sensing. In J Indian Soc Remote Sens 43 (1), pp. 89–100. DOI: 10.1007/s12524-014-0373-9.
[25] Kumar, Deepak; Shekhar, Sulochana (2015): Statistical analysis of land surface temperature-vegetation indexes relationship through thermal remote sensing. In Ecotoxicology and environmental safety 121, pp. 39–44. DOI: 10.1016/j.ecoenv.2015.07.004.
[26] Guha, Subhanil; Govil, Himanshu (2020): An assessment on the relationship between land surface temperature and normalized difference vegetation index. In Environ Dev Sustain 23 (2), pp. 1944–1963. DOI: 10.1007/s10668-020-00657-6.
[27] Encyclopedia Britannica (2020): Phnom Penh | national capital, Cambodia. Available online at https://www.britannica.com/place/Phnom-Penh, updated on 9/25/2020, checked on 9/25/2020.
[28] Phnom Penh Population 2020 (Demographics, Maps, Graphs) (2020). Available online at https://worldpopulationreview.com/world-cities/phnom-penhpopulation, updated on 9/25/2020, checked on 9/25/2020.
[29] Cambodia: Phnom Penh (City Districts and Communes) - Population Statistics, Charts and Map (2020). Available online at https://www.citypopulation.de/en/cambodia/phnompenh/admin/, updated on 9/25/2020, checked on 9/25/2020.
[30] Nicholas E. Young, Ryan S. Anderson, Stephen M. Chignell, Anthony G. Vorste, Rick Lawrence, Paul H. Evangelista (2017): A survival guide to Landsat preprocessing. Ecology 98 (4), pp. 920–932.
[31] Avdan, U., & Jovanovska, G. (2016). Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. Journal of Sensors, 2016, 1–8. https://doi.org/10.1155/2016/1480307.