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18 September 2018 Green areas and urban heat island: combining remote sensed data with ground observations
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Climate Change is now an undisputed fact (IPCC, 2007). There is a broad consensus on fact that cities have a special role in Climate Change, occupying an especially relevant role in Urban Heat Island (Oke, 1973). This scientific and technical consensus, however, does not seem to have influenced urban planning practice. The analysis of the UHI is today a fundamental element for the proper understanding of the primary factors of the contribution of cities to CC. The analysis of the structure of climate in Metropolitan Areas should enable the adoption of measures to mitigate the adverse effects of CC[J1].

This paper proposes the construction of a set of explanatory models of the UHI of the Metropolitan Region of Barcelona (MRB) aimed at assisting planners in taking measures that serve, at the level of territorial and urban planning, to mitigate the effects of climate change. The general objective of the research is to study, using remote sensing techniques as well as "in situ" measurements, how urban design affects in the generation of the Urban Heat Island (UHI), as well as the urban microclimate in general. Specifically, this paper seeks to clarify whether the design of green areas can mitigate the UHI.

The hypothesis is that morphology of public space plays a key role to control UHI. The research methodology consisted in: a) studying the urban and climatic parameters of selected areas; b) analyzing the spatial distribution of the LST using remote sensing technologies (Landsat 8); c) obtaining LST and LSAT through field work, during day and night time; and d) constructing a model of surface and air temperatures as a function of the different types of land cover, combining Remote Sensed data and in situ measurements, for each of the areas of analysis.
Conference Presentation
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Blanca Arellano, Josep Roca, and Enric Batlle "Green areas and urban heat island: combining remote sensed data with ground observations", Proc. SPIE 10767, Remote Sensing and Modeling of Ecosystems for Sustainability XV, 1076705 (18 September 2018);

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