Evaluating the Effect of Green Infrastructure in Mitigating the Urban Heat Island Effect Using Remote Sensing
Authors: Fidani Sofia , Daliakopoulos Ioannis , Manios Thrassyvoulos , Grillakis Manolis , Charalampopoulou Vasiliki , Nektarios Panagiotis 
Published: March 2021

&lt;p&gt;Urban green infrastructure in the form of green roofs and vertical gardens is gradually becoming a mainstream development option to mitigate the negative impacts of dense urbanization, and primarily those associated with the urban heat island effect and the consequent vulnerability due to climate change (Nektarios and Ntoulas, 2017). Nevertheless, the quantification of the effect of green infrastructure in comparison to conventional infrastructure as well as tree parks and gardens, can be a challenge in a rapidly changing urban environment, especially due to historical gaps in environmental parameter monitoring. Here we propose the use of land surface temperature (LST) [&lt;sup&gt;o&lt;/sup&gt;C] produced using freely available LandSat imagery at 30 m resolution, to evaluate the effect of green infrastructure on urban surface temperature. The method relies on the comparison of historical LST timeseries of an area of interest which has undergone urban greening interventions with adjacent city blocks that have retained their conventional urban character. The method is applied to evaluate the impact of the recently constructed Eco Campus Orange (ECO) garden, which has resulted from the renovation of 4 city blocks in Paris, France. Within an area over 3 ha, ECO employs environmentally friendly materials and 100,000 plants to feature 2,300 m&lt;sup&gt;2&lt;/sup&gt; of green wall and &amp;#8220;the largest green roof of Europe&amp;#8221;. For the area of interest, over 250 LandSat 5, 7, and 8 multispectral images dating from 2010 to 2020, were analyzed after Ermida et al. (2020). Results show that, since its construction, LST at ECO quickly dropped by over 2 &lt;sup&gt;o&lt;/sup&gt;C, reaching the LST levels of adjacent urban parks. The method is ideal for ambient temperature timeseries reconstruction where long-term monitoring is sparce and can be applied to evaluate drastic landscape changes such as urban greening or vegetation thinning.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Ermida, S.L., Soares, P., Mantas, V., G&amp;#246;ttsche, F.M., Trigo, I.F., 2020. Google earth engine open-source code for land surface temperature estimation from the landsat series. Remote Sens. https://doi.org/10.3390/RS12091471&lt;/p&gt;&lt;p&gt;Nektarios, P.A., Ntoulas, N., 2017. Designing green roofs for arid and semi-arid climates. The route towards the adaptive approach, in: Acta Horticulturae. International Society for Horticultural Science, pp. 197&amp;#8211;202. https://doi.org/10.17660/ActaHortic.2017.1189.39&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgements&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;The research was co-financed by the European Union and Greek national funds through the Operational Program RIS3Crete (COMPOLIVE: &amp;#922;&amp;#929;&amp;#919;&amp;#929;3-0028773)&lt;/p&gt;&lt;p&gt;The research of MG was co-financed by the European Union and Greek national funds through the Operational Program &quot;Human Resource Development, Education and Lifelong Learning&quot;, under the Act &quot;STRENGTHENING post-doctoral fellows / researchers - B cycle&quot; (MIS 5033021) implemented by the State Scholarship Foundation.&lt;/p&gt;