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Local Climate Zone Classification System Using Web GIS Approach
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Orientador(es)
Resumo(s)
Climate change along with population growth and urbanization trends across the coastal areas
are pressuring our cities to become more and more resilient to the ''new normal''. Our
resilience is a function of hazards’ severity and probability, people’s and assets’ exposure and
societies' sensitivity, and our ability in responding not only to a warmer climate, but also and
foremost to extreme climate events will determine our resilience, in the future. Midlatitude
countries such as Portugal have been experiencing warmer summers, in which heatwaves are
becoming more frequent and severe, exposing an increasingly ageing population to correlated
health and energy poverty issues. Part of the challenge moving forward is ensuring our ability
to monitor and predict extreme heatwaves in space and time, with enough level of detail as
to allow to highlight hot spots within cities and larger metropolitan areas which should be
prioritized. The temporal aspect, and the spatial accuracy are something that the national and
international weather services already take well care of - but there is a gap in what concerns
managing cities, especially in more complex and coastal environments, where temperatures
asymmetries are greater due to the complex land-sea interactions. To overcome this issue,
remote sensing now offers spatially complete time series of observational data from which to
derive insights into what concerns to the way we occupy these territories, as well as the
thermal footprint of that land use land cover. When processed using machine learning and
artificial intelligence, time series of geospatial data obtained from satellite imagery hence
allows us to downscale weather and climate up to the neighborhood scale. But to do that we
need to ensure consistency, precision, and standardization of processes and benchmarking of
climate-relevant land use land cover data. Local Climate Zones (LCZ) is now the gold standard
scheme for Land Use/Land Cover (LULC) classification; nevertheless, while the LCZ’s overall
criteria are well-established, the methods employed for classification are very diverse, often
local-specific. Alternatively, some highly scalable satellite imagery-based classification
approaches have been attempted, based on open-source data – but these lack sufficient local
detail, and may lead to misleading urban climate modelling results. This thesis aims to build
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upon pre-existent attempts at transforming geospatial data products into accurate and subkilometric resolution LCZ classification maps, to optimize them in terms of precision and
scalability using open-source tools and libraries and a WEB GIS interface. The GIS-based
classification is implemented using Portuguese and Denmark cities for benchmark with
previous works. The objective is to attain the best-performing algorithm without overfitting it
to a specific location, while its usability to tackle urban climate shall be tested, particularly the
predictability of the Urban Heat Island (UHI) effect using such LCZ as a predictor, but open for
scientific community to use.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and Science, specialization in Geospatial Data Science
Palavras-chave
Land Use/Land Cover Urban Heat Island Local Climate Zone Web GIS Climate Adaptation SDG 3 - Good health and well-being SDG 10 - Reduced inequalities SDG 11 - Sustainable cities and communities SDG 13 - Climate action