Analysing the effect of tree canopy and urban form on urban surface heat using street IR imagery
Time: Mon 2026-06-15 13.00
Location: H1, Teknikringen 33, Stockholm
Video link: https://kth-se.zoom.us/j/63024958953
Language: English
Subject area: Land and Water Resources Engineering
Doctoral student: Elina Merdymshaeva , Hållbarhet, utvärdering och styrning, Environmental Management and Assessment
Opponent: Professor Anders Brandt, Högskolan i Gävle, Gävle, Sweden
Supervisor: Professor Ulla Mörtberg, Hållbarhet, utvärdering och styrning; Professor Anne Håkansson, Datatekniska och lärande system
QC 20260525
Abstract
Urban heat island (UHI) effects are intensifying due to climate change and urbanisation, posing increasing risks to human health, energy demand, and environmental sustainability. Understanding how urban morphology and vegetation influence thermal conditions at street level remains challenging due to the limited spatial resolution of traditional measurement approaches. This thesis investigates urban heat dynamics in Stockholm using opportunistic drive-by sensing (DS) combined with spatial machine learning methods to analyse hyperlocal relationships between urban form, tree canopy, and surface temperature within the urban canopy layer. High-resolution air and surface temperature data were collected during the summers of 2021 and 2022 using DS platforms mounted on electric vehicles, generating more than one million spatially distributed observations. These data were integrated with geospatial datasets describing urban morphology, greenery, and water bodies. Relationships between environmental variables and thermal patterns were analysed using statistical methods and machine learning models, including XGBoost and GPBoost, across multiple spatial scales. The results demonstrate that DS measurements capture substantial hyperlocal variability in surface temperature that is not fully captured by weather stations or satellite-derived land-surface temperatures. Surface temperatures were generally higher and more spatially heterogeneous than air temperature, reflecting strong dependence on immediate land cover and urban geometry. Tree canopy and reduced sun exposure were consistently associated with lower surface temperature differences, while higher building density increased heat accumulation. Machine learning models showed the highest explanatory power at a hyperlocal scale, with GPBoost outperforming XGBoost due to its ability to account for spatial dependencies. The findings highlight the importance of integrating fine-scale sensing with advanced spatial modelling to improve understanding of urban heat processes. The proposed methodological framework supports evidence-based urban planning strategies to enhance climate resilience through vegetation, shading, and urban design.