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

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.

Land Use/Land Cover Changes and the Relationship with Land Surface Temperature Using Landsat and MODIS Imageries in Cameron Highlands, Malaysia

“…We classified these areas as secondary forests despite their signature showing as an urban zone due to surface reflectance. Notably, these areas have a similar pattern in their retention of heat to that of urban areas because plantations are situated below aluminum roofs [67]. Thus, heat will further radiate in these areas, thus contributing to an increase in the value of the overall LST, because these manmade objects do not regulate LST in the same way as natural objects [68,69].…”

Using Wavelet Transforms to Fuse Nighttime Light Data and POI Big Data to Extract Urban Built-Up Areas

“…The scope of urban built-up areas is directly related to the level of urbanization [3], that is, the accurate identification of urban build-up area can greatly contribute to the accurate understanding of rapid urbanization. In the cities with a higher urbanization level, the urban build-up area is one of the most important factors that affect the temperature of surface [4,5], therefore, the accurate extraction of urban built-up areas can provide a research basis for the study of urban heat island phenomena [6,7].…”

Combinative Study of Urban Heat Island in Ascoli Piceno City with Remote Sensing and CFD Simulation—Climate Change and Urban Health Resilience—CCUHRE Project

“…In this paper, a transdisciplinary approach was used to develop a method of transscalar investigation to estimate/simulate the effects of climate change through digital processing and innovative geo-informatics tools referring to GIScience [8][9][10] and CFD (computational fluid dynamics) [11][12][13], which accompanied more traditional multi-system urban-planning analysis. In particular, remote sensing methods and processes were used to identify urban heat islands (UHI) on the neighbourhood scale based on medium-low spatial resolution images on which there is wide literature [14]. In a constant macro-micro dialogue, further medium-high data resolution investigations were based on digital terrain models (3d) and simulation algorithms in a GIS environment, as shown in some studies [15].…”