Remote Sensing of Surface Urban Cool
and Heat Island Dynamics in Erbil, Iraq,
between 1992 and 2013
Thesis submitted for the degree of
Doctor of Philosophy
at the University of Leicester
by
Azad Othman Rasul (MSc)
Department of Geography
University of Leicester
2016
Abstract
The variation between surface and air temperature within a city and its surrounding area is a result of variations in surface cover, thermal capacity and 3-dimensional geometry. This study examines the spatiotemporal formation of the daytime Surface Urban Cool Island (SUCI) and night-time Surface Urban Heat Island (SUHI) effect in Erbil, Iraq, as a case study for cities in semi-arid climates more generally. It furthermore quantifies the influence of rapid urban expansion on the urban heat/cool island effect over a 20 year period.
Satellite images acquired by Landsat 4, 5, 7 and 8 between 1992 and 2013 are used to retrieve Land Surface Temperature (LST). Normalised Ratio Scale (NRS) is applied to the multi-mission Landsat data, which is used to adjust the temperature range for different acquisition times of images within the same temporal range. In addition, LST data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Aqua and Terra from January 2003 to December 2014 are analysed. In order to establish the drivers of the observed patterns of LST and SUCI/SUHI, the relationships of LST with wetness, greenness, NDVI, soil moisture and other variables are assessed.
The results indicate that during the daytime in summer, autumn and winter, densely built-up areas had lower LST acting as cool islands (SUCI) compared to the non-urbanised area around the city. In contrast, at night-time, Erbil experienced higher LST and demonstrated a significant SUHI effect. The mean LST of the newly urbanised and vegetated areas between 1992 and 2013 decreased by 2.28°C and 7.29°C respectively. Soil moisture (wetness) is the main marker of the SUCI/SUHI effect, whilst urban expansion may cause a decrease of daytime LST in dry climate zones. The NRS method is appropriate for detecting temperature trends greater than 2°C in Landsat data.
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