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First published in applied sciences, v14 (2024) published by MDPI. DOI: https://doi.org/10.3390/app142210576

Document Type

Article

Publication Version

Published Version

Keywords

LiDAR, mobile sensors, random forest, urban morphology, urban temperature patterns

Journal/Book/Conference Title

Applied Sciences

Volume

14

Issue

22

First Page

1

Last Page

16

Abstract

Thorough investigations into air temperature variation across urban environments are essential to address concerns about city livability. With limited research on smaller cities, especially in the American Midwest, the goal of this research was to examine the spatial patterns of air temperature across multiple small to medium-sized cities in Iowa, a relatively rural US state. Extensive fieldwork was conducted utilizing manually built mobile temperature sensors to collect air temperature data at a high temporal and spatial resolution in ten Iowa urban areas during the afternoon, evening, and night on days exceeding 32 °C from June to September 2022. Using the random forest machine-learning algorithm and estimated urban morphological variables at varying neighborhood distances derived from 1 m2 aerial imagery and derived products from LiDAR data, we created 24 predicted surface temperature models that demonstrated R2 coefficients ranging from 0.879 to 0.997 with the majority exceeding an R2 of 0.95, all with p-values < 0.001. The normalized vegetation index and 800 m neighbor distance were found to be the most significant in explaining the collected air temperature values. This study expanded upon previous research by examining different sized cities to provide a broader understanding of the impact of urban morphology on air temperature distribution while also demonstrating utility of the random forest algorithm across cities ranging from approximately 10,000 to 200,000 inhabitants. These findings can inform policies addressing urban heat island effects and climate resilience.

Department

Department of Geography

Original Publication Date

11-16-2024

Object Description

1 PDF File

DOI of published version

10.3390/app142210576

Repository

UNI ScholarWorks, Rod Library, University of Northern Iowa

Copyright

©2024 The Author(s) This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Language

en

File Format

application/pdf

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