Next Article in Journal
Spatial Pattern of the Seasonal Drought/Burned Area Relationship across Brazilian Biomes: Sensitivity to Drought Metrics and Global Remote-Sensing Fire Products
Previous Article in Journal
Elevational Trends in Usnic Acid Concentration of Lichen Parmelia flexilis in Relation to Temperature and Precipitation
Open AccessArticle

Towards Systematic Prediction of Urban Heat Islands: Grounding Measurements, Assessing Modeling Techniques

Toulan School of Urban Studies and Planning, Portland State University, 1825 SW Broadway, Portland, OR 97201, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Yang Zhang
Climate 2017, 5(2), 41; https://doi.org/10.3390/cli5020041
Received: 20 April 2017 / Revised: 30 May 2017 / Accepted: 1 June 2017 / Published: 10 June 2017
While there exists extensive assessment of urban heat, we observe myriad methods for describing thermal distribution, factors that mediate temperatures, and potential impacts on urban populations. In addition, the limited spatial and temporal resolution of satellite-derived heat measurements may limit the capacity of decision makers to take effective actions for reducing mortalities in vulnerable populations whose locations require highly-refined measurements. Needed are high resolution spatial and temporal information for urban heat. In this study, we ask three questions: (1) how do urban heat islands vary throughout the day? (2) what statistical methods best explain the presence of temperatures at sub-meter spatial scales; and (3) what landscape features help to explain variation in urban heat islands? Using vehicle-based temperature measurements at three periods of the day in the Pacific Northwest city of Portland, Oregon (USA), we incorporate LiDAR-derived datasets, and evaluate three statistical techniques for modeling and predicting variation in temperatures during a heat wave. Our results indicate that the random forest technique best predicts temperatures, and that the evening model best explains the variation in temperature. The results suggest that ground-based measurements provide high levels of accuracy for describing the distribution of urban heat, its temporal variation, and specific locations where targeted interventions with communities can reduce mortalities from heat events. View Full-Text
Keywords: urban heat island; ground-based vehicle traverse; random forest; modeling; urban planning urban heat island; ground-based vehicle traverse; random forest; modeling; urban planning
Show Figures

Graphical abstract

MDPI and ACS Style

Voelkel, J.; Shandas, V. Towards Systematic Prediction of Urban Heat Islands: Grounding Measurements, Assessing Modeling Techniques. Climate 2017, 5, 41. https://doi.org/10.3390/cli5020041

AMA Style

Voelkel J, Shandas V. Towards Systematic Prediction of Urban Heat Islands: Grounding Measurements, Assessing Modeling Techniques. Climate. 2017; 5(2):41. https://doi.org/10.3390/cli5020041

Chicago/Turabian Style

Voelkel, Jackson; Shandas, Vivek. 2017. "Towards Systematic Prediction of Urban Heat Islands: Grounding Measurements, Assessing Modeling Techniques" Climate 5, no. 2: 41. https://doi.org/10.3390/cli5020041

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop