DERIVATION OF LAND SURFACE TEMPERATURE (LST) FROM LANDSAT 7 & 8 IMAGERIES AND ITS RELATIONSHIP WITH TWO VEGETATION INDICES (NDVI AND GNDVI)

Richard J. U.; Ibochi Andrew Abah

doi:10.29121/granthaalayah.v7.i2.2019.1013

Authors

Richard J. U. Department of Special Survey, Office of the Surveyor General, Moscow Road, Port Harcourt, Nigeria
Ibochi Andrew Abah Department of Surveying & Geoinformatics Federal Polytechnic, Bauchi, Nigeria

DOI:

https://doi.org/10.29121/granthaalayah.v7.i2.2019.1013

Keywords:

GNDVI, Land Surface Temperature, Isotherm Map, NDVI, OLI Landsat 8, Remote Sensing, TOA Radiance

Abstract [English]

Land surface temperature (LST) fluctuation is a global problem that is responsible for regional and global climate change. Abnormal LST causes drought, depletion of ozone layers, skin and lung diseases and also affect crops production. Spatial distribution of surface temperature has become an issue especially to less developed countries with little or absent of temperature stations to report daily or monthly temperature of the area. This study utilized remote sensing approach to estimate land surface temperature, NDVI and GNDVI of Port Harcourt, Rivers State, Nigeria. The study was conducted using Landsat imageries of two epochs (1990 and 2017) which was downloaded using path 188 and row 57. The images were converted from DN to TOA radiance using algorithm specified in the Landsat user’s guide. The highest estimated LST in 1990 and 2017 map was 28.77°C and 29.37°C. Also, in 1990 the highest LST was recorded in Diobu and part of Old Port Harcourt Township while in 2017 maximum temperature extended from Diobu to Trans-amadi. The increase LST was due to urbanization and industrialization in the city. The study observed mean difference between estimated LST of 1990 and 2017 and that observed by NIMET as 1.24°C and 2.91°C respectively. There was positive correlation coefficient of 0.09 and 0.4 between LST against NDVI in 1990 and in 2017 and between LST against GNDVI of 0.19 and 0.30 was also observed in both years. This study justified that remote sensing and GIS can be used to derive LST which can be used for environmental studies and research purposes. For further study, LST should be estimated in all the state and their values validate with in-situ observations.

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