Use of Remote Sensing in Comprehending the Influence of Urban Landscape’s Composition and Configuration on Land Surface Temperature at Neighbourhood Scale

Ifeanyi R. Ejiagha,M. Razu Ahmed,Anil Gupta,Elena Rangelova,Ashraf Dewan,Quazi K. Hassan

doi:10.3390/rs12152508

Abstract

The spatial composition and configuration of land use land cover (LULC) in the urban landscape impact the land surface temperature (LST). In this study, we assessed such impacts at the neighbourhood level of the City of Edmonton. In doing so, we employed Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) satellite images to derive LULC and LST maps, respectively. We used three classification methods, such as ISODATA, random forest, and indices-based, for mapping LULC classes including built-up, water, and green. We obtained the highest overall accuracy of 98.53 and 97.90% with a kappa value of 0.96 and 0.92 in the indices-based method for the 2018 and 2015 LULC maps, respectively. Besides, we estimated the LST map from the brightness temperature using a single-channel algorithm. Our analysis showed that the highest contributors to LST were the industrial (303.51 K in 2018 and 295.99 K in 2015) and residential (303.47 K in 2018 and 296.56 K in 2015) neighbourhoods, and the lowest contributor was the riverine/creek (298.77 K in 2018 and 292.89 K in 2015) during the 2018 late summer and 2015 early spring seasons. We also found that the residential neighbourhoods exhibited higher LST in comparison with the industrial with the same LULC composition. The result was also supported by our surface albedo analysis, where industrial and residential neighbourhoods were giving higher and lower albedo values, respectively. This indicated that the rooftop materials played further role in impacting the LST. In addition, our spatial autocorrelation (local Moran’s I) and proximity (near distance) analyses revealed that the structural configurations would additionally play an important role in contributing to the LST in the neighbourhoods. For example, the cluster pattern with a small gap of minimum 2.4 m between structures in the residential neighbourhoods were showing higher LST in compared with the sparse pattern, with large gaps between structures in the industrial areas. The wide passages for wind flow through the large gaps would be responsible for cooling the LST in the industrial neighbourhoods. The outcomes of this study would help planners in planning and designing urban neighbourhoods, and policymakers and stakeholders in developing strategies to balance surface energy and mitigate local warming.

Highlights

Urbanization has become one of the most critical issues across the earth
It is imperative to study the impact of urban land use and land cover (LULC) in terms of their composition and configuration on land surface temperature (LST)
We considered a set of three criteria: (i) use of residential and industrial neighbourhoods together, with the same percentages of combined water and green (e.g., I10 and R10 together); (ii) no use of I50+ and R50+ subcategories, because of their having more than 50% combined water and green; (iii) calculations of mean values using bins with a size of 0.03 and 5 m for local Moran’s I index and near distance, respectively

Summary

Introduction

Urbanization has become one of the most critical issues across the earth. about half of the world’s population and 75% population of developed nations are living in urban cities [1]. In order to accommodate the need of the growing population, it is common to convert forested land, agricultural land, wetland, water bodies, and open spaces into built-up/urban areas [3]. One of the major consequences of urbanization is the increase in local temperature as a result of a higher proportion of built-up/impervious surfaces in comparison to rural areas [4]. This is because built-up areas show higher sensible heat due to a lower amount of evaporative (i.e., water bodies) and transpirative (vegetation) surfaces [5]. It is imperative to study the impact of urban land use and land cover (LULC) in terms of their composition (i.e., percentage coverage) and configuration (i.e., spatial arrangement) on land surface temperature (LST)

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Discussion

Conclusion