File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1016/j.rse.2014.08.012
- Scopus: eid_2-s2.0-84906835900
- WOS: WOS:000345201900004
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Mapping maximum urban air temperature on hot summer days
Title | Mapping maximum urban air temperature on hot summer days |
---|---|
Authors | |
Keywords | Urban heat island Statistical model Landsat Urban Random forest Remote sensing application Spatial modeling Air temperature |
Issue Date | 2014 |
Citation | Remote Sensing of Environment, 2014, v. 154, p. 38-45 How to Cite? |
Abstract | Air temperature is an essential component in microclimate and environmental health research, but difficult to map in urban environments because of strong temperature gradients. We introduce a spatial regression approach to map the peak daytime air temperature relative to a reference station on typical hot summer days using Vancouver, Canada as a case study. Three regression models, ordinary least squares regression, support vector machine, and random forest, were all calibrated using Landsat TM/ETM. + data and field observations from two sources: Environment Canada and the Weather Underground. Results based on cross-validation indicate that the random forest model produced the lowest prediction errors (RMSE. = 2.31. °C). Some weather stations were consistently cooler/hotter than the reference station and were predicted well, while other stations, particularly those close to the ocean, showed greater temperature variability and were predicted with greater errors. A few stations, most of which were from the Weather Underground data set, were very poorly predicted and possibly unrepresentative of air temperature in the area. The random forest model generally produced a sensible map of temperature distribution in the area. The spatial regression approach appears useful for mapping intra-urban air temperature variability and can easily be applied to other cities. © 2014 Elsevier Inc. |
Persistent Identifier | http://hdl.handle.net/10722/265489 |
ISSN | 2023 Impact Factor: 11.1 2023 SCImago Journal Rankings: 4.310 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ho, Hung Chak | - |
dc.contributor.author | Knudby, Anders | - |
dc.contributor.author | Sirovyak, Paul | - |
dc.contributor.author | Xu, Yongming | - |
dc.contributor.author | Hodul, Matus | - |
dc.contributor.author | Henderson, Sarah B. | - |
dc.date.accessioned | 2018-12-03T01:20:49Z | - |
dc.date.available | 2018-12-03T01:20:49Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | Remote Sensing of Environment, 2014, v. 154, p. 38-45 | - |
dc.identifier.issn | 0034-4257 | - |
dc.identifier.uri | http://hdl.handle.net/10722/265489 | - |
dc.description.abstract | Air temperature is an essential component in microclimate and environmental health research, but difficult to map in urban environments because of strong temperature gradients. We introduce a spatial regression approach to map the peak daytime air temperature relative to a reference station on typical hot summer days using Vancouver, Canada as a case study. Three regression models, ordinary least squares regression, support vector machine, and random forest, were all calibrated using Landsat TM/ETM. + data and field observations from two sources: Environment Canada and the Weather Underground. Results based on cross-validation indicate that the random forest model produced the lowest prediction errors (RMSE. = 2.31. °C). Some weather stations were consistently cooler/hotter than the reference station and were predicted well, while other stations, particularly those close to the ocean, showed greater temperature variability and were predicted with greater errors. A few stations, most of which were from the Weather Underground data set, were very poorly predicted and possibly unrepresentative of air temperature in the area. The random forest model generally produced a sensible map of temperature distribution in the area. The spatial regression approach appears useful for mapping intra-urban air temperature variability and can easily be applied to other cities. © 2014 Elsevier Inc. | - |
dc.language | eng | - |
dc.relation.ispartof | Remote Sensing of Environment | - |
dc.subject | Urban heat island | - |
dc.subject | Statistical model | - |
dc.subject | Landsat | - |
dc.subject | Urban | - |
dc.subject | Random forest | - |
dc.subject | Remote sensing application | - |
dc.subject | Spatial modeling | - |
dc.subject | Air temperature | - |
dc.title | Mapping maximum urban air temperature on hot summer days | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.rse.2014.08.012 | - |
dc.identifier.scopus | eid_2-s2.0-84906835900 | - |
dc.identifier.volume | 154 | - |
dc.identifier.spage | 38 | - |
dc.identifier.epage | 45 | - |
dc.identifier.isi | WOS:000345201900004 | - |
dc.identifier.issnl | 0034-4257 | - |