Document Type
Article
Language
eng
Publication Date
7-20-2019
Publisher
MDPI
Source Publication
Remote Sensing
Source ISSN
2072-4292
Abstract
Urbanization and climate change are driving increases in urban land surface temperatures that pose a threat to human and environmental health. To address this challenge, we must be able to observe land surface temperatures within spatially complex urban environments. However, many existing remote sensing studies are based upon satellite or aerial imagery that capture temperature at coarse resolutions that fail to capture the spatial complexities of urban land surfaces that can change at a sub-meter resolution. This study seeks to fill this gap by evaluating the spatial variability of land surface temperatures through drone thermal imagery captured at high-resolutions (13 cm). In this study, flights were conducted using a quadcopter drone and thermal camera at two case study locations in Milwaukee, Wisconsin and El Paso, Texas. Results indicate that land use types exhibit significant variability in their surface temperatures (3.9–15.8 °C) and that this variability is influenced by surface material properties, traffic, weather and urban geometry. Air temperature and solar radiation were statistically significant predictors of land surface temperature (R2 0.37–0.84) but the predictive power of the models was lower for land use types that were heavily impacted by pedestrian or vehicular traffic. The findings from this study ultimately elucidate factors that contribute to land surface temperature variability in the urban environment, which can be applied to develop better temperature mitigation practices to protect human and environmental health.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Naughton, Joseph and McDonald, Walter M., "Evaluating the Variability of Urban Land Surface Temperatures Using Drone Observations" (2019). Civil and Environmental Engineering Faculty Research and Publications. 246.
https://epublications.marquette.edu/civengin_fac/246
Comments
Published version. Remote Sensing, Vol. 11, No. 14 (July 20, 2019): 1722. DOI. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. Used with permission.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).