Developing land-cover driver model for estimating the intensity of surface urban heat islands using landsat 8 satellite imagery

Nguyen Thanh Hoan; Nguyen Van Dung; Ho Le Thu; Hoa Thuy Quynh

doi:10.15625/0866-7187/41/3/13829

Author affiliations

Authors

Nguyen Thanh Hoan Institute of Geography, VAST, Hanoi, Vietnam
Nguyen Van Dung Institute of Geography, VAST, Hanoi, Vietnam
Ho Le Thu Institute of Geography, VAST, Hanoi, Vietnam
Hoa Thuy Quynh Institute of Geography, VAST, Hanoi, Vietnam

DOI:

https://doi.org/10.15625/0866-7187/41/3/13829

Keywords:

Surface urban heat island, land cover, Landsat 8, regression, land management unit, tropics

Abstract

It is of utmost importance to understand and monitor the impact of urban heat islands on ecosystems and overall human health in the context of climate change and global warming. This research was conducted in a tropical city, Hanoi, with a major objective of assessing the quantitative relationships between the composition of the main land-cover types and surface urban heat island phenomenon. In this research, we analyzed the correlation between land-cover composition, percentage coverage of the land cover types, and land surface temperature for different moving window sizes or urban land management units. Landsat 8 OLI (Operational Land Imager) satellite data was utilized for preparing land-cover composition datasets in inner Hanoi by employing the unsupervised image clustering method. High-resolution (30m) land surface temperature maps were generated for different days of the years 2016 and 2017 using Landsat 8 TIRS (Thermal Infrared Sensor) images. High correlations were observed between percentage coverage of the land-cover types and land surface temperature considering different window sizes. A new model for estimating the intensity of surface urban heat islands from Landsat 8 imagery is developed, through recursively analyzing the correlation between land-cover composition and land surface temperature at different moving window sizes. This land-cover composition-driven model could predict land surface temperature efficiently not only in the case of different window sizes but also on different days. The newly developed model in this research provides a wonderful opportunity for urban planners and designers to take measures for adjusting land surface temperature and the associated effects of surface urban heat islands by managing the land cover composition and percentage coverage of the individual land-cover types.

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