Assessing the Impact of Land Cover Changes on Surface Urban Heat Islands with High-Spatial-Resolution Imagery on a Local Scale: Workflow and Case Study

Abstract

Low-altitude remote sensing platform has been increasingly applied to observing local thermal environments due to its obvious advantage in spatial resolution and apparent flexibility in data acquisition. However, there is a general lack of systematic analysis for land cover (LC) classification, surface urban heat island (SUHI), and their spatial and temporal change patterns. In this study, a workflow is presented to assess the LC’s impact on SUHI, based on the visible and thermal infrared images with high spatial resolution captured by an unmanned airship in the central area of the Sino-Singapore Guangzhou Knowledge City in 2012 and 2015. Then, the accuracy assessment of LC classification and land surface temperature (LST) retrieval are performed. Finally, the commonly-used indexes in the field of satellites are applied to analyzing the spatial and temporal changes in the SUHI pattern on a local scale. The results show that the supervised maximum likelihood algorithm can deliver satisfactory overall accuracy and Kappa coefficient for LC classification; the root mean square error of the retrieved LST can reach 1.87 °C. Moreover, the LST demonstrates greater consistency with land cover type (LCT) and more fluctuation within an LCT on a local scale than on an urban scale. The normalized LST classified by the mean and standard deviation (STD) is suitable for the high-spatial situation; however, the thermal field level and the corresponded STD multiple need to be judiciously selected. This study exhibits an effective pathway to assess SUHI pattern and its changes using high-spatial-resolution images on a local scale. It is also indicated that proper landscape composition, spatial configuration and materials on a local scale exert greater impacts on SUHI.

Detecting and Analyzing Land Use and Land Cover Changes in the Region of Al-Jabal Al-Akhdar, Libya Using Time-Series Landsat Data from 1985 to 2017

“…However, the maximum likelihood, minimum distance, parallopiped, and Mahalanobis are the most popular classifiers [45]. While reviewing the literature, it transpired that the maximum likelihood classifier is widely used in remote sensing research as it is quick, is simple to implement, allows a clear interpretation of the outcomes, and mostly delivers a satisfactory accuracy [46][47][48]. The technique presumes that the statistics for each LULC category in each band are subject to the normal distribution and computes the likelihood for each pixel to belong to a particular category [4].…”

Investigating the Spatial Autocorrelation of Surface-Air Temperature for Different Ground Materials in Hot Desert Climate

“…According to earlier literature, many studies investigated the correlations between land cover materials, surface temperature, and ambient air temperature on a largescale area using satellite images, with little attention to the possibility of having multiple flooring materials on one spatial scale [4,[21][22][23][24]. In a different approach, this study focuses on the relationship between ground materials and different microclimate factors in a restricted urban boundary area.…”

“…II) In terms of the scale of the study region, various studies focused on thermal environments in largescale, high densely urbanized areas and megacities which showed how the size and type of different land use and land cover affect thermal environment [4,6,[26][27][28]. III) Much of the literature into the thermal impacts of surface materials and land cover relies on large-scale techniques that employ satellites images from remote sensing data to estimate land surface temperature [6,7,[22][23][24][29][30][31][32]. Field experiments were performed in conjunction with remote sensing data to investigate the influence of land cover changes on air temperature across a large region [3,5,22,24,25,33].…”

A New Approach for Surface Urban Heat Island Monitoring Based on Machine Learning Algorithm and Spatiotemporal Fusion Model

“…Therefore, better monitoring and understanding the variation of UHI intensity (UHII) at various temporal (interannual, seasonal, diurnal) and spatial (from local to global) scales is of critical importance for global environmental and urban climate research and impact studies, which has strong implications for designing effective measure to mitigate the UHI [3], [4]. Satellite remote sensing provides an unhindered tool for studying UHI, especially the Surface Urban Heat Island (SUHI), which represents the spatial temporal structures of land surface temperature (LST) differences between urban and suburban areas [5]. So far, various studies have been conducted using satellite derived LST to study SUHI for hundreds of cities around the world [6].…”