An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data

Fangzheng Lyu; Shaohua Wang; Su Yeon Han; Charlie Catlett; Shaowen Wang

doi:10.1007/s44212-022-00002-4

An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data

Urban Inform. 2022;1(1):6. doi: 10.1007/s44212-022-00002-4. Epub 2022 Sep 9.

Authors

Fangzheng Lyu^{1

2}, Shaohua Wang^{3

4}, Su Yeon Han^{1

2}, Charlie Catlett⁵, Shaowen Wang^{1

2}

Affiliations

¹ cyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois at Urbana-Champaign, Urbana, IL USA.
² Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL USA.
³ Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094 China.
⁴ International Research Center of Big Data for Sustainable Development Goals, Beijing, 100094 China.
⁵ Computing, Environment, and Life Sciences, Argonne National Laboratory, Chicago, IL USA.

Abstract

Due to climate change and rapid urbanization, Urban Heat Island (UHI), featuring significantly higher temperature in metropolitan areas than surrounding areas, has caused negative impacts on urban communities. Temporal granularity is often limited in UHI studies based on satellite remote sensing data that typically has multi-day frequency coverage of a particular urban area. This low temporal frequency has restricted the development of models for predicting UHI. To resolve this limitation, this study has developed a cyber-based geographic information science and systems (cyberGIS) framework encompassing multiple machine learning models for predicting UHI with high-frequency urban sensor network data combined with remote sensing data focused on Chicago, Illinois, from 2018 to 2020. Enabled by rapid advances in urban sensor network technologies and high-performance computing, this framework is designed to predict UHI in Chicago with fine spatiotemporal granularity based on environmental data collected with the Array of Things (AoT) urban sensor network and Landsat-8 remote sensing imagery. Our computational experiments revealed that a random forest regression (RFR) model outperforms other models with the prediction accuracy of 0.45 degree Celsius in 2020 and 0.8 degree Celsius in 2018 and 2019 with mean absolute error as the evaluation metric. Humidity, distance to geographic center, and PM_2.5 concentration are identified as important factors contributing to the model performance. Furthermore, we estimate UHI in Chicago with 10-min temporal frequency and 1-km spatial resolution on the hottest day in 2018. It is demonstrated that the RFR model can accurately predict UHI at fine spatiotemporal scales with high-frequency urban sensor network data integrated with satellite remote sensing data.

Keywords: Machine learning; Remote sensing; Urban Heat Island; Urban sensor network; cyberGIS.