JOURNAL ARTICLE

Using Google Earth to evaluate the climate change resilience of Detroit, Michigan.

  • Published In: Bios (0005-3155), 2024, v. 95, n. 4. P. 239 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Candela, Matteo; Thomas, Sarah; Dally, Angeleena; Abouleila, Sarah; Scheys, Caleb; Carmona-Galindo, Víctor 3 of 3

Abstract

Adapting to climate change is an important multidisciplinary challenge for cities and metropolitan areas around the world. The availability of online mapping tools that analyze climate change patterns and their impacts may play a significant role in adapting urban systems. The interactive Fitzlab map tool used in this study predicts changes in temperature and rainfall for a given city in the year 2080 and then matches it to a sister city with a similar climate in 2020. The objective of our study was to evaluate climate change adaptation in Detroit, Michigan, by using Google Earth to contrast greenery and urban infrastructure with a comparator city presently experiencing the future climate of Detroit per the Fitzlab map tool. We hypothesized that tree canopy area, infrastructure space, and green space would all differ between Detroit and its comparator city. The Fitzlab map tool found that the 2080 climate of Detroit will be 4.88C warmer and 63.6% wetter than present, and that its future climate is a match with the present climate of Chester, Pennsylvania. Relative to Chester, Detroit has significantly lower percentages of tree canopy area, significantly higher percentages of infrastructure space, and significantly more variable green space. Based on the present levels of urban vegetation and infrastructure, our study suggests that Detroit is unprepared for the predicted increases in temperature and rainfall over the next 60 years. We propose a citizen science approach for empowering high school STEM programs to support local cities in their exploration of short-term adaptation strategies via Google Earth. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Bios (0005-3155). 2024/12, Vol. 95, Issue 4, p239
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2024
  • ISSN:0005-3155
  • DOI:10.1893/BIOS-D-21-00021
  • Accession Number:181671184
  • Copyright Statement:Copyright of Bios (0005-3155) is the property of Beta Beta Beta Biological Honor Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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