Loading…
Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data
The impacts of climate change such as extreme heat waves are exacerbated in cities where most of the world's population live. Quantifying urbanization impacts on ambient air temperatures (Tair) has relevance for human health risk, building energy use efficiency, vector-borne disease control and...
Saved in:
| Published in: | Remote sensing of environment 2020-06, Vol.242, p.111791, Article 111791 |
|---|---|
| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c368t-b14e66699ade95d6e44392b65836bbb3f7cfc40ed41dafd47046f6bc0fc5a99d3 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c368t-b14e66699ade95d6e44392b65836bbb3f7cfc40ed41dafd47046f6bc0fc5a99d3 |
| container_end_page | |
| container_issue | |
| container_start_page | 111791 |
| container_title | Remote sensing of environment |
| container_volume | 242 |
| creator | Venter, Zander S. Brousse, Oscar Esau, Igor Meier, Fred |
| description | The impacts of climate change such as extreme heat waves are exacerbated in cities where most of the world's population live. Quantifying urbanization impacts on ambient air temperatures (Tair) has relevance for human health risk, building energy use efficiency, vector-borne disease control and urban biodiversity. Remote sensing of urban climate has been focused on land surface temperature (LST) due to a scarcity of data on Tair which is usually interpolated at 1 km resolution. We assessed the efficacy of mapping hyperlocal Tair (spatial resolutions of 10–30 m) over Oslo, Norway, by integrating Sentinel, Landsat and LiDAR data with crowd-sourced Tair measurements from 1310 private weather stations during 2018. Using Random Forest regression modelling, we found that annual mean, daily maximum and minimum Tair can be mapped with an average RMSE of 0.52 °C (R2 = 0.5), 1.85 °C (R2 = 0.05) and 1.46 °C (R2 = 0.33), respectively. Mapping accuracy decreased sharply with |
| doi_str_mv | 10.1016/j.rse.2020.111791 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2440490139</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0034425720301619</els_id><sourcerecordid>2440490139</sourcerecordid><originalsourceid>FETCH-LOGICAL-c368t-b14e66699ade95d6e44392b65836bbb3f7cfc40ed41dafd47046f6bc0fc5a99d3</originalsourceid><addsrcrecordid>eNp9UMtOwzAQtBBIlMIHcLPEOcVOHCcWJ1QBRarEBa5Yjr0GR80D26Hq3-MSzpxWo53ZnRmErilZUUL5bbvyAVY5yROmtBL0BC1oXYmMVISdogUhBctYXlbn6CKElhBa1hVdoPfNYQS_G7Ta4U6No-s_8GDx5BvVY-U8jtAlgoqTBzyF49pDN0TAAfpfqHqDtR_2JgyT12DwHlT8BI-NiuoSnVm1C3D1N5fo7fHhdb3Jti9Pz-v7baYLXsesoQw450IoA6I0HBgrRN7wsi540zSFrbTVjIBh1ChrWMrELW80sbpUQphiiW7mu6MfviYIUbbJTZ9eypwxwgShhUgsOrOS3xA8WDl61yl_kJTIY42ylalGeaxRzjUmzd2sgWT_24GXQTvoU1DnQUdpBveP-gdJD3xk</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2440490139</pqid></control><display><type>article</type><title>Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data</title><source>ScienceDirect Journals</source><creator>Venter, Zander S. ; Brousse, Oscar ; Esau, Igor ; Meier, Fred</creator><creatorcontrib>Venter, Zander S. ; Brousse, Oscar ; Esau, Igor ; Meier, Fred</creatorcontrib><description>The impacts of climate change such as extreme heat waves are exacerbated in cities where most of the world's population live. Quantifying urbanization impacts on ambient air temperatures (Tair) has relevance for human health risk, building energy use efficiency, vector-borne disease control and urban biodiversity. Remote sensing of urban climate has been focused on land surface temperature (LST) due to a scarcity of data on Tair which is usually interpolated at 1 km resolution. We assessed the efficacy of mapping hyperlocal Tair (spatial resolutions of 10–30 m) over Oslo, Norway, by integrating Sentinel, Landsat and LiDAR data with crowd-sourced Tair measurements from 1310 private weather stations during 2018. Using Random Forest regression modelling, we found that annual mean, daily maximum and minimum Tair can be mapped with an average RMSE of 0.52 °C (R2 = 0.5), 1.85 °C (R2 = 0.05) and 1.46 °C (R2 = 0.33), respectively. Mapping accuracy decreased sharply with <250 weather stations (approx. 1 station km−2) and remote sensing data averaged within a 100-500 m buffer zone around each station maximized accuracy. Further, models performed best outside of summer months when the spatial variation in temperatures were low and wind velocities were high. Finally, accuracies were not evenly distributed over space and we found the lowest mapping errors in the local climate zone characterized by compact lowrise buildings which are most relevant to city residents. We conclude that this method is transferable to other cities given there was little difference (0.02 °C RMSE) between models trained on open- (satellite and terrain) vs closed-source (LiDAR) remote sensing data. These maps can provide a complement to and validation of traditional urban canopy models and may assist in identifying hyperlocal hotspots and coldspots of relevance to urban planners.
[Display omitted]
•Hyperlocal air temperatures mapped at 10-30 m resolution with RMSE of 0.52 °C•Little difference between maps with open- vs closed-source data inputs•Mapping accuracy decreases with <1 station km−2•Accuracies are highest when taking a 100-500 m neighbourhood into account</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2020.111791</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Air temperature ; Biodiversity ; Buffer zones ; Climate change ; Climate models ; Crowdsourcing ; Detection ; Disease control ; Energy consumption ; Environmental impact ; Extreme heat ; Extreme high temperatures ; Health risks ; Heat waves ; High resolution ; Infectious diseases ; Land surface temperature ; Landsat ; Landsat satellites ; Lidar ; Local climates ; Mapping ; Meteorological data ; Model accuracy ; Remote control ; Remote sensing ; Satellite ; Satellites ; Sentinel ; Spatial variations ; Surface temperature ; Urban air ; Urban areas ; Urban climates ; Urban heat island ; Urban planning ; Urbanization ; Vector-borne diseases ; Weather ; Weather stations ; Wind speed ; Wind velocities</subject><ispartof>Remote sensing of environment, 2020-06, Vol.242, p.111791, Article 111791</ispartof><rights>2020 The Authors</rights><rights>Copyright Elsevier BV Jun 1, 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-b14e66699ade95d6e44392b65836bbb3f7cfc40ed41dafd47046f6bc0fc5a99d3</citedby><cites>FETCH-LOGICAL-c368t-b14e66699ade95d6e44392b65836bbb3f7cfc40ed41dafd47046f6bc0fc5a99d3</cites><orcidid>0000-0003-2638-7162 ; 0000-0003-4122-6340</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Venter, Zander S.</creatorcontrib><creatorcontrib>Brousse, Oscar</creatorcontrib><creatorcontrib>Esau, Igor</creatorcontrib><creatorcontrib>Meier, Fred</creatorcontrib><title>Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data</title><title>Remote sensing of environment</title><description>The impacts of climate change such as extreme heat waves are exacerbated in cities where most of the world's population live. Quantifying urbanization impacts on ambient air temperatures (Tair) has relevance for human health risk, building energy use efficiency, vector-borne disease control and urban biodiversity. Remote sensing of urban climate has been focused on land surface temperature (LST) due to a scarcity of data on Tair which is usually interpolated at 1 km resolution. We assessed the efficacy of mapping hyperlocal Tair (spatial resolutions of 10–30 m) over Oslo, Norway, by integrating Sentinel, Landsat and LiDAR data with crowd-sourced Tair measurements from 1310 private weather stations during 2018. Using Random Forest regression modelling, we found that annual mean, daily maximum and minimum Tair can be mapped with an average RMSE of 0.52 °C (R2 = 0.5), 1.85 °C (R2 = 0.05) and 1.46 °C (R2 = 0.33), respectively. Mapping accuracy decreased sharply with <250 weather stations (approx. 1 station km−2) and remote sensing data averaged within a 100-500 m buffer zone around each station maximized accuracy. Further, models performed best outside of summer months when the spatial variation in temperatures were low and wind velocities were high. Finally, accuracies were not evenly distributed over space and we found the lowest mapping errors in the local climate zone characterized by compact lowrise buildings which are most relevant to city residents. We conclude that this method is transferable to other cities given there was little difference (0.02 °C RMSE) between models trained on open- (satellite and terrain) vs closed-source (LiDAR) remote sensing data. These maps can provide a complement to and validation of traditional urban canopy models and may assist in identifying hyperlocal hotspots and coldspots of relevance to urban planners.
[Display omitted]
•Hyperlocal air temperatures mapped at 10-30 m resolution with RMSE of 0.52 °C•Little difference between maps with open- vs closed-source data inputs•Mapping accuracy decreases with <1 station km−2•Accuracies are highest when taking a 100-500 m neighbourhood into account</description><subject>Air temperature</subject><subject>Biodiversity</subject><subject>Buffer zones</subject><subject>Climate change</subject><subject>Climate models</subject><subject>Crowdsourcing</subject><subject>Detection</subject><subject>Disease control</subject><subject>Energy consumption</subject><subject>Environmental impact</subject><subject>Extreme heat</subject><subject>Extreme high temperatures</subject><subject>Health risks</subject><subject>Heat waves</subject><subject>High resolution</subject><subject>Infectious diseases</subject><subject>Land surface temperature</subject><subject>Landsat</subject><subject>Landsat satellites</subject><subject>Lidar</subject><subject>Local climates</subject><subject>Mapping</subject><subject>Meteorological data</subject><subject>Model accuracy</subject><subject>Remote control</subject><subject>Remote sensing</subject><subject>Satellite</subject><subject>Satellites</subject><subject>Sentinel</subject><subject>Spatial variations</subject><subject>Surface temperature</subject><subject>Urban air</subject><subject>Urban areas</subject><subject>Urban climates</subject><subject>Urban heat island</subject><subject>Urban planning</subject><subject>Urbanization</subject><subject>Vector-borne diseases</subject><subject>Weather</subject><subject>Weather stations</subject><subject>Wind speed</subject><subject>Wind velocities</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9UMtOwzAQtBBIlMIHcLPEOcVOHCcWJ1QBRarEBa5Yjr0GR80D26Hq3-MSzpxWo53ZnRmErilZUUL5bbvyAVY5yROmtBL0BC1oXYmMVISdogUhBctYXlbn6CKElhBa1hVdoPfNYQS_G7Ta4U6No-s_8GDx5BvVY-U8jtAlgoqTBzyF49pDN0TAAfpfqHqDtR_2JgyT12DwHlT8BI-NiuoSnVm1C3D1N5fo7fHhdb3Jti9Pz-v7baYLXsesoQw450IoA6I0HBgrRN7wsi540zSFrbTVjIBh1ChrWMrELW80sbpUQphiiW7mu6MfviYIUbbJTZ9eypwxwgShhUgsOrOS3xA8WDl61yl_kJTIY42ylalGeaxRzjUmzd2sgWT_24GXQTvoU1DnQUdpBveP-gdJD3xk</recordid><startdate>20200601</startdate><enddate>20200601</enddate><creator>Venter, Zander S.</creator><creator>Brousse, Oscar</creator><creator>Esau, Igor</creator><creator>Meier, Fred</creator><general>Elsevier Inc</general><general>Elsevier BV</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TG</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0003-2638-7162</orcidid><orcidid>https://orcid.org/0000-0003-4122-6340</orcidid></search><sort><creationdate>20200601</creationdate><title>Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data</title><author>Venter, Zander S. ; Brousse, Oscar ; Esau, Igor ; Meier, Fred</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-b14e66699ade95d6e44392b65836bbb3f7cfc40ed41dafd47046f6bc0fc5a99d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Air temperature</topic><topic>Biodiversity</topic><topic>Buffer zones</topic><topic>Climate change</topic><topic>Climate models</topic><topic>Crowdsourcing</topic><topic>Detection</topic><topic>Disease control</topic><topic>Energy consumption</topic><topic>Environmental impact</topic><topic>Extreme heat</topic><topic>Extreme high temperatures</topic><topic>Health risks</topic><topic>Heat waves</topic><topic>High resolution</topic><topic>Infectious diseases</topic><topic>Land surface temperature</topic><topic>Landsat</topic><topic>Landsat satellites</topic><topic>Lidar</topic><topic>Local climates</topic><topic>Mapping</topic><topic>Meteorological data</topic><topic>Model accuracy</topic><topic>Remote control</topic><topic>Remote sensing</topic><topic>Satellite</topic><topic>Satellites</topic><topic>Sentinel</topic><topic>Spatial variations</topic><topic>Surface temperature</topic><topic>Urban air</topic><topic>Urban areas</topic><topic>Urban climates</topic><topic>Urban heat island</topic><topic>Urban planning</topic><topic>Urbanization</topic><topic>Vector-borne diseases</topic><topic>Weather</topic><topic>Weather stations</topic><topic>Wind speed</topic><topic>Wind velocities</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Venter, Zander S.</creatorcontrib><creatorcontrib>Brousse, Oscar</creatorcontrib><creatorcontrib>Esau, Igor</creatorcontrib><creatorcontrib>Meier, Fred</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Venter, Zander S.</au><au>Brousse, Oscar</au><au>Esau, Igor</au><au>Meier, Fred</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data</atitle><jtitle>Remote sensing of environment</jtitle><date>2020-06-01</date><risdate>2020</risdate><volume>242</volume><spage>111791</spage><pages>111791-</pages><artnum>111791</artnum><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>The impacts of climate change such as extreme heat waves are exacerbated in cities where most of the world's population live. Quantifying urbanization impacts on ambient air temperatures (Tair) has relevance for human health risk, building energy use efficiency, vector-borne disease control and urban biodiversity. Remote sensing of urban climate has been focused on land surface temperature (LST) due to a scarcity of data on Tair which is usually interpolated at 1 km resolution. We assessed the efficacy of mapping hyperlocal Tair (spatial resolutions of 10–30 m) over Oslo, Norway, by integrating Sentinel, Landsat and LiDAR data with crowd-sourced Tair measurements from 1310 private weather stations during 2018. Using Random Forest regression modelling, we found that annual mean, daily maximum and minimum Tair can be mapped with an average RMSE of 0.52 °C (R2 = 0.5), 1.85 °C (R2 = 0.05) and 1.46 °C (R2 = 0.33), respectively. Mapping accuracy decreased sharply with <250 weather stations (approx. 1 station km−2) and remote sensing data averaged within a 100-500 m buffer zone around each station maximized accuracy. Further, models performed best outside of summer months when the spatial variation in temperatures were low and wind velocities were high. Finally, accuracies were not evenly distributed over space and we found the lowest mapping errors in the local climate zone characterized by compact lowrise buildings which are most relevant to city residents. We conclude that this method is transferable to other cities given there was little difference (0.02 °C RMSE) between models trained on open- (satellite and terrain) vs closed-source (LiDAR) remote sensing data. These maps can provide a complement to and validation of traditional urban canopy models and may assist in identifying hyperlocal hotspots and coldspots of relevance to urban planners.
[Display omitted]
•Hyperlocal air temperatures mapped at 10-30 m resolution with RMSE of 0.52 °C•Little difference between maps with open- vs closed-source data inputs•Mapping accuracy decreases with <1 station km−2•Accuracies are highest when taking a 100-500 m neighbourhood into account</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2020.111791</doi><orcidid>https://orcid.org/0000-0003-2638-7162</orcidid><orcidid>https://orcid.org/0000-0003-4122-6340</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0034-4257 |
| ispartof | Remote sensing of environment, 2020-06, Vol.242, p.111791, Article 111791 |
| issn | 0034-4257 1879-0704 |
| language | eng |
| recordid | cdi_proquest_journals_2440490139 |
| source | ScienceDirect Journals |
| subjects | Air temperature Biodiversity Buffer zones Climate change Climate models Crowdsourcing Detection Disease control Energy consumption Environmental impact Extreme heat Extreme high temperatures Health risks Heat waves High resolution Infectious diseases Land surface temperature Landsat Landsat satellites Lidar Local climates Mapping Meteorological data Model accuracy Remote control Remote sensing Satellite Satellites Sentinel Spatial variations Surface temperature Urban air Urban areas Urban climates Urban heat island Urban planning Urbanization Vector-borne diseases Weather Weather stations Wind speed Wind velocities |
| title | Hyperlocal mapping of urban air temperature using remote sensing and crowdsourced weather data |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T11%3A40%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Hyperlocal%20mapping%20of%20urban%20air%20temperature%20using%20remote%20sensing%20and%20crowdsourced%20weather%20data&rft.jtitle=Remote%20sensing%20of%20environment&rft.au=Venter,%20Zander%20S.&rft.date=2020-06-01&rft.volume=242&rft.spage=111791&rft.pages=111791-&rft.artnum=111791&rft.issn=0034-4257&rft.eissn=1879-0704&rft_id=info:doi/10.1016/j.rse.2020.111791&rft_dat=%3Cproquest_cross%3E2440490139%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c368t-b14e66699ade95d6e44392b65836bbb3f7cfc40ed41dafd47046f6bc0fc5a99d3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2440490139&rft_id=info:pmid/&rfr_iscdi=true |