Quantifying urban heat exposure at fine scale - modeling outdoor and indoor temperatures using citizen science and VHR remote sensing

[1]  P. Michalak.  Thermal Network Model for an Assessment of Summer Indoor Comfort in a Naturally Ventilated Residential Building , 2022, Energies.

[2]  J. Toftum,et al.  A large field study of relationship between indoor and outdoor climate in residential buildings , 2021, Journal of Physics: Conference Series.

[3]  C. Beck,et al.  Heat adaptation measures in private households: an application and adaptation of the protective action decision model , 2021, Humanities and Social Sciences Communications.

[4]  S. Cheval,et al.  Defining local extreme heat thresholds and Indoor Cooling Degree Necessity for vulnerable residential dwellings during the 2020 summer in Ankara – Part I: Air temperature , 2021, Solar Energy.

[5]  M. Hart,et al.  Combining High-Resolution Land Use Data With Crowdsourced Air Temperature to Investigate Intra-Urban Microclimate , 2021, Frontiers in Environmental Science.

[6]  S. Brönnimann,et al.  Modelling the spatial pattern of heatwaves in the city of Bern using a land use regression approach , 2021, Urban Climate.

[7]  X. Lee,et al.  Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms , 2021, Science Advances.

[8]  M. Bilal,et al.  Utilizing world urban database and access portal tools (WUDAPT) and machine learning to facilitate spatial estimation of heatwave patterns , 2021 .

[9]  C. Beck,et al.  Threshold temperatures for subjective heat stress in urban apartments—Analysing nocturnal bedroom temperatures during a heat wave in Germany , 2021, Climate Risk Management.

[10]  Huanfeng Shen,et al.  Spatially Continuous and High-Resolution Land Surface Temperature Product Generation: A review of reconstruction and spatiotemporal fusion techniques , 2021, IEEE Geoscience and Remote Sensing Magazine.

[11]  Weiqi Zhou,et al.  Within-city spatial and temporal heterogeneity of air temperature and its relationship with land surface temperature , 2021 .

[12]  Hannes Taubenböck,et al.  Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling , 2021, ISPRS Int. J. Geo Inf..

[13]  M. Breil,et al.  The heat-health nexus in the urban context: A systematic literature review exploring the socio-economic vulnerabilities and built environment characteristics , 2020 .

[14]  A. Gustavsen,et al.  A systematic review of urban climate research in cold and polar climate regions , 2020 .

[15]  Seth D. Guikema,et al.  Night and day: The influence and relative importance of urban characteristics on remotely sensed land surface temperature , 2020, Remote Sensing of Environment.

[16]  Stenka Vulova,et al.  Summer Nights in Berlin, Germany: Modeling Air Temperature Spatially With Remote Sensing, Crowdsourced Weather Data, and Machine Learning , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  F. Meier,et al.  Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data , 2020 .

[18]  Y. Kamarianakis,et al.  Spatial interpolation of urban air temperatures using satellite-derived predictors , 2020, Theoretical and Applied Climatology.

[19]  Sugie Lee,et al.  Diurnal and Seasonal Variations in the Effect of Urban Environmental Factors on Air Temperature: A Consecutive Regression Analysis Approach , 2020, International journal of environmental research and public health.

[20]  G. A. Pagani,et al.  Sky view factor calculations and its application in urban heat island studies , 2019, Urban Climate.

[21]  Xiao Xiang Zhu,et al.  Has Dongying developed to a ghost city? - Evidence from multi-temporal population estimation based on VHR remote sensing and census counts , 2019, Comput. Environ. Urban Syst..

[22]  Tzu-Ping Lin,et al.  The application of a high-density street-level air temperature observation network (HiSAN): The relationship between air temperature, urban development, and geographic features. , 2019, The Science of the total environment.

[23]  Katja Berger,et al.  Statistical modelling of spatial patterns of the urban heat island intensity in the urban environment of Augsburg, Germany , 2019, Urban Climate.

[24]  S. Dech,et al.  Inferring floor area ratio thresholds for the delineation of city centers based on cognitive perception , 2019, Environment and Planning B: Urban Analytics and City Science.

[25]  W. Wong,et al.  Assessing spatial variability of extreme hot weather conditions in Hong Kong: A land use regression approach , 2019, Environmental research.

[26]  P. Ramamurthy,et al.  The Harlem Heat Project: A Unique Media–Community Collaboration to Study Indoor Heat Waves , 2018, Bulletin of the American Meteorological Society.

[27]  A. Berg,et al.  Present and future Köppen-Geiger climate classification maps at 1-km resolution , 2018, Scientific Data.

[28]  Alexandra Schneider,et al.  Air temperature characteristics of local climate zones in the Augsburg urban area (Bavaria, southern Germany) under varying synoptic conditions , 2018, Urban Climate.

[29]  F. V. D. Hoeven,et al.  Hotterdam: Mapping the social, morphological, and land-use dimensions of the Rotterdam urban heat island , 2018, Urbani izziv.

[30]  Yan Liu,et al.  Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[31]  D. Haase,et al.  The effect of multi-dimensional indicators on urban thermal conditions , 2018 .

[32]  M. Davies,et al.  Comparison of built environment adaptations to heat exposure and mortality during hot weather, West Midlands region, UK. , 2017, Environment international.

[33]  Mikhail Chester,et al.  Building Thermal Performance, Extreme Heat, and Climate Change , 2017 .

[34]  C. McAlpine,et al.  The impact of urbanization and climate change on urban temperatures: a systematic review , 2017, Landscape Ecology.

[35]  E. Hawkins,et al.  Global risk of deadly heat , 2017 .

[36]  Kathrin J. Ward,et al.  Heat waves and urban heat islands in Europe: A review of relevant drivers. , 2016, The Science of the total environment.

[37]  Yuming Guo,et al.  Impact of heatwave on mortality under different heatwave definitions: A systematic review and meta-analysis. , 2016, Environment international.

[38]  Anders Knudby,et al.  A Spatial Framework to Map Heat Health Risks at Multiple Scales , 2015, International journal of environmental research and public health.

[39]  P. A. Mirzaei,et al.  Recent challenges in modeling of urban heat island , 2015 .

[40]  Ki-Young Lee,et al.  The Relationship Between Indoor and Outdoor Temperature in Two Types Of Residence , 2015 .

[41]  Giles M. Foody,et al.  Crowdsourcing for climate and atmospheric sciences: current status and future potential , 2015 .

[42]  Matthew Carroll,et al.  The relationship between housing and heat wave resilience in older people , 2015, International Journal of Biometeorology.

[43]  H. Ho,et al.  Mapping maximum urban air temperature on hot summer days , 2014 .

[44]  J. Jacobson,et al.  Predicting indoor heat exposure risk during extreme heat events. , 2014, The Science of the total environment.

[45]  F. Haghighat,et al.  Indoor thermal condition in urban heat island: Comparison of the artificial neural network and regression methods prediction , 2014 .

[46]  Larissa Larsen,et al.  How factors of land use/land cover, building configuration, and adjacent heat sources and sinks explain Urban Heat Islands in Chicago , 2014 .

[47]  Dan Zhang,et al.  Estimation of the Relationship between Urban Park Characteristics and Park Cool Island Intensity by Remote Sensing Data and Field Measurement , 2013 .

[48]  D. Dockery,et al.  The relationship between indoor and outdoor temperature, apparent temperature, relative humidity, and absolute humidity. , 2013, Indoor air.

[49]  Hannes Taubenböck,et al.  Analysis of Surface Thermal Patterns in Relation to Urban Structure Types: A Case Study for the City of Munich , 2013 .

[50]  Lee Chapman,et al.  Sensors and the city: a review of urban meteorological networks , 2013 .

[51]  J Shaman,et al.  Socioeconomic and Outdoor Meteorological Determinants of Indoor Temperature and Humidity in New York City Dwellings. , 2013, Weather, climate, and society.

[52]  U. Schlink,et al.  Heat stress in urban areas: Indoor and outdoor temperatures in different urban structure types and subjectively reported well-being during a heat wave in the city of Leipzig , 2013 .

[53]  T. Oke,et al.  Local Climate Zones for Urban Temperature Studies , 2012 .

[54]  F. Haghighat,et al.  Indoor thermal condition in urban heat Island – Development of a predictive tool , 2012 .

[55]  Thomas Esch,et al.  Object-based image information fusion using multisensor earth observation data over urban areas , 2011 .

[56]  I. Stewart,et al.  A systematic review and scientific critique of methodology in modern urban heat island literature , 2011 .

[57]  F. Haghighat,et al.  Approaches to study Urban Heat Island – Abilities and limitations , 2010 .

[58]  T. Esch,et al.  Object-based feature extraction using high spatial resolution satellite data of urban areas , 2010 .

[59]  E. Fischer,et al.  Consistent geographical patterns of changes in high-impact European heatwaves , 2010 .

[60]  S. Tong,et al.  Influence of Prenatal Lead Exposure on Genomic Methylation of Cord Blood DNA , 2009, Journal of Epidemiology & Community Health.

[61]  J. Gulliver,et al.  A review of land-use regression models to assess spatial variation of outdoor air pollution , 2008 .

[62]  S. Hajat,et al.  Heat stress and public health: a critical review. , 2008, Annual review of public health.

[63]  J. Robine,et al.  Death toll exceeded 70,000 in Europe during the summer of 2003. , 2008, Comptes rendus biologies.

[64]  Ron Wehrens,et al.  The pls Package: Principal Component and Partial Least Squares Regression in R , 2007 .

[65]  R. Kovats,et al.  Heatwaves and public health in Europe. , 2006, European journal of public health.

[66]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[67]  T. Oke,et al.  Thermal remote sensing of urban climates , 2003 .

[68]  S. Liang.  Narrowband to broadband conversions of land surface albedo I Algorithms , 2001 .

[69]  T. Oke,et al.  Scale Modelling of Nocturnal Cooling in Urban Parks , 1999 .

[70]  D. Artis,et al.  Survey of emissivity variability in thermography of urban areas , 1982 .

[71]  R. Knutti,et al.  Mapping urban temperature using crowd-sensing data and machine learning , 2021 .

[72]  S. Vardoulakis,et al.  Assessing urban population vulnerability and environmental risks across an urban area during heatwaves - Implications for health protection. , 2018, The Science of the total environment.

[73]  K. Doick,et al.  The impact of greenspace size on the extent of local nocturnal air temperature cooling in London , 2016 .

[74]  C. Gronlund,et al.  Vulnerability to extreme heat by socio-demographic characteristics and area green space among the elderly in Michigan, 1990-2007. , 2015, Environmental research.

[75]  Britta Jänicke,et al.  Assessment of indoor heat stress variability in summer and during heat warnings: a case study using the UTCI in Berlin, Germany , 2015, International Journal of Biometeorology.