Multi-scale thermal remote sensing, machine learning and radiative flux modeling to assess urban overheating
Kuupäev
2023-06-30
Autorid
Ajakirja pealkiri
Ajakirja ISSN
Köite pealkiri
Kirjastaja
Abstrakt
Linnas on palju soojust neelavaid pindu, mis muudavad linna ümbritsevast maapiirkonnast soojemaks. Kuumalaine ajal tõuseb linna õhutemperatuur tasemeni, mis kahjustab inimeste tervist ja heaolu: linn kuumeneb üle. Kliimaprognoosid näitavad, et kuumalained muutuvad sagedasemaks ja intensiivsusemaks. Ruumiliste soojusandmete puudumine takistab oluliselt kuuma leevendamise kavade väljatöötamist.
Väitekirjas kasutatakse kaugseiret, masinõpet ja kiirgusvoo erinevas mõõtkavas modelleerimist linnade ülekuumenemise hindamiseks. Kaugseire andmete kasutamisel on kaks probleemi: pilvede tõttu tekkivad andmelüngad ja erinevatelt satelliitidelt saadud andmete erineva ajasamm ruumiline täpsus. Töös loodi masinõppe mudelid, et täita lüngad satelliidi mõõdetud maapinna temperatuuri andmestikus ja luua suure lahutusvõimega sünteetilisi kaarte.
Uuriti ka, kuidas keskmist kiirgustemperatuuri ehk inimkeha soojuskoormust saab modelleerida suurtes linnades suure ruumilise lahutusvõimega ning kuidas jalakäijale majade ja puude varju hinnata kõnnitee varju katvust arvutades.
Töös genereeritud andmestik on oluline sisend linna ülekuumenemise vältimisel. Edasised uuringud peavad keskenduma masinõppemudelite täiustamisele, et ennustada maapinna temperatuuri ja modelleerida inimkeha soojuskoormust sõltuvalt liikumisharjumustest. Töös loodud andmestik koos sotsialmajanduslike ja demograafiliste andmetega võimaldab linnakeskkonda ülekuumenemisele paremini kohandada, tagamaks elanikkonna ohutuse ja heaolu.
Cities are warmer than their surrounding rural areas because of the abundance of impervious and heat-trapping surfaces. During a heatwave, a city’s ambient temperature rises to levels detrimental to the well-being of urban dwellers. This is known as urban overheating. Current climate projections indicate that heatwaves will increase in frequency with higher intensities. Lack of spatially explicit thermal data at various scales poses a significant challenge for city governments in developing comprehensive heat mitigation plans. This thesis proposes using remote sensing, machine learning, and radiative flux modeling to generate multi-scale thermal datasets for evaluating urban overheating. The thesis addresses two challenges when using satellite land surface temperature (LST) to assess urban overheating—the first concerns missing data due to clouds obscuring the satellite’s view of the Earth. The second is the trade-off between spatial and temporal resolution that must be made when using LST to assess overheating. The thesis created machine learning models to fill gaps in satellite LST and generate synthetic LST at local climate scales to address these challenges. The thesis also examines how Mean Radiant Temperature (TMRT) - the total heat load on a human body can be modeled for large cities at high spatial resolutions and how shade available to pedestrians can be quantified by calculating sidewalk shade coverage. When combined with socio-economic and demographic data, the datasets generated in the thesis will inform heat action plans in the respective study sites. Further research should improve machine learning models to predict LST and combine pedestrian heat exposure with travel patterns data. The thesis concludes by emphasizing the importance of making cities heat-ready to ensure the safety and well-being of urban dwellers, especially vulnerable populations.
Cities are warmer than their surrounding rural areas because of the abundance of impervious and heat-trapping surfaces. During a heatwave, a city’s ambient temperature rises to levels detrimental to the well-being of urban dwellers. This is known as urban overheating. Current climate projections indicate that heatwaves will increase in frequency with higher intensities. Lack of spatially explicit thermal data at various scales poses a significant challenge for city governments in developing comprehensive heat mitigation plans. This thesis proposes using remote sensing, machine learning, and radiative flux modeling to generate multi-scale thermal datasets for evaluating urban overheating. The thesis addresses two challenges when using satellite land surface temperature (LST) to assess urban overheating—the first concerns missing data due to clouds obscuring the satellite’s view of the Earth. The second is the trade-off between spatial and temporal resolution that must be made when using LST to assess overheating. The thesis created machine learning models to fill gaps in satellite LST and generate synthetic LST at local climate scales to address these challenges. The thesis also examines how Mean Radiant Temperature (TMRT) - the total heat load on a human body can be modeled for large cities at high spatial resolutions and how shade available to pedestrians can be quantified by calculating sidewalk shade coverage. When combined with socio-economic and demographic data, the datasets generated in the thesis will inform heat action plans in the respective study sites. Further research should improve machine learning models to predict LST and combine pedestrian heat exposure with travel patterns data. The thesis concludes by emphasizing the importance of making cities heat-ready to ensure the safety and well-being of urban dwellers, especially vulnerable populations.
Kirjeldus
Väitekirja elektrooniline versioon ei sisalda publikatsioone
Märksõnad
climate change, global warming, towns, overheating, air temperature, high temperatures, heat radiation, remote sensing, automatic learning, modelling (science)