Towards a satellite based monitoring of urban air temperatures

Bechtel, Benjamin; Zakšek, Klemen; Oßenbrügge, Jürgen; Kaveckis, Giedrius; Böhner, Jürgen

doi:10.1016/j.scs.2017.05.018

Cited by 30 publications

(9 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cristobal (2008) added remote sensing variables such as α, LST and NDVI to a multiple regression model based on altitude, latitude, continentality, solar radiation and cloudiness [76]. The use of multiple predictors was previously mentioned in the outlook section of several publications, e.g., Bechtel (2017) and Vogt (1997), and was applied before in other studies [76][77][78][79].…”

Section: State Of the Artmentioning

confidence: 99%

“…Modelling diurnal (or nocturnal) courses are seldom done. Bechtel (2014 and 2017) has applied MLR based on multi-temporal Meteosat SEVIRI data to predict short term T a with very high correlation (R 2 between 0.97 to 0.98) [77,92]. Nichol (2005 and used ASTER nighttime data in Hong Kong and compared the remote sensing data to T a -measured during vehicle traverses and at measurement stations with R 2 between 0.80 and 0.94 [59,69].…”

Section: Statistical Measuresmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation and Modeling of Urban Heat Island Intensity in Basel, Switzerland

Wicki

Parlow

Feigenwinter

2018

Climate

View full text Add to dashboard Cite

An increasing number of people living in urban environments and the expected increase in long lasting heat waves makes the study of temperature distribution one of the major tasks in urban climatology, especially considering human health and heat stress. This excess heat is often underestimated because stations from national meteorological services are limited in numbers and are not representing the entire urban area with typically higher nocturnal temperatures, especially in densely built-up environments. For a majority of the population, heat stress is consequently monitored insufficiently. In this study, the factors influencing the nocturnal urban heat island have been evaluated in detail and have been tested using different spatial resolutions. A multiple linear regression model has been developed with predictors resulting from different data sources to model the urban air temperature distribution continuously. Results show that various datasets can be used for the prediction of the heat island distribution with comparable results, ideally run on a 200 m grid. Validation using random sampling indicated a RMSE clearly below the standard deviation of the measurements with an average around~0.15 • C. The regression coefficients are varying within the nocturnal runs with best results around 22:00 CET (R 2 > 0.9).

show abstract

Section: State Of the Artmentioning

confidence: 99%

Section: Statistical Measuresmentioning

confidence: 99%

Evaluation and Modeling of Urban Heat Island Intensity in Basel, Switzerland

Wicki

Parlow

Feigenwinter

2018

Climate

View full text Add to dashboard Cite

show abstract

“…Nevertheless, caution is required as many parameters may affect the results, such as material surface reflections, airborne particles, the angle of perspective, etc. Remote sensing has been used to collect surface temperature data, by satellite images (Bechtel et al, 2017) and aerial and on-ground thermographic images (Alchapar et al, 2014). Toparlar et al (2015) used satellite thermographic images provided by Klok et al (2012) to validate simulated surface temperatures for the area of Bergpolder Zuid, Rotterdam, for five consequent days.…”

Section: Introductionmentioning

confidence: 99%

CFD simulation of urban microclimate: Validation using high-resolution field measurements

Antoniou

Montazeri

Neophytou

et al. 2019

Science of The Total Environment

130

View full text Add to dashboard Cite

DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

show abstract

“…In order to improve the prediction of 999 call numbers and illness types in severe weather, the use of satellite air temperature estimates, to give a more accurate spatial prediction, should be considered. Recent improvements in estimating air temperature from satellite data are available [29,30]. The urban heat island in London and other cities compounds the health impact of heatwaves for ambulance and other health services [31,32].…”

Section: Discussionmentioning

confidence: 99%

Impact of Air Temperature on London Ambulance Call-Out Incidents and Response Times

Mahmood

Thornes

Pope

et al. 2017

Climate

View full text Add to dashboard Cite

Ambulance services are in operation around the world and yet, until recently, ambulance data has only been used for operational purposes rather than for assessing public health. Ambulance call-out data offers new and valuable (near) real-time information that can be used to assess the impact of environmental conditions, such as temperature, upon human health. A detailed analysis of London ambulance data at a selection of dates between 2003 and 2015 is presented and compared to London temperature data. In London, the speed of ambulance response begins to suffer when the mean daily air temperature drops below 2 • C or rises above 20 • C. This is explained largely by the increased number of calls past these threshold temperatures. The baseline relationships established in this work will inform the prediction of likely changes in ambulance demand (and illness types) that may be caused by seasonal temperature changes and the increased frequency and intensity of extreme/severe weather events, exacerbated by climate change, in the future.

show abstract

Towards a satellite based monitoring of urban air temperatures

Cited by 30 publications

References 58 publications

Evaluation and Modeling of Urban Heat Island Intensity in Basel, Switzerland

Evaluation and Modeling of Urban Heat Island Intensity in Basel, Switzerland

CFD simulation of urban microclimate: Validation using high-resolution field measurements

Impact of Air Temperature on London Ambulance Call-Out Incidents and Response Times

Contact Info

Product

Resources

About