Analyzing the urban heat island using time series land surface temperature (LST) data

Weimin Wang; Hong Liang; Lijun Yang; Kai Liu; Hongbo Su; Xueke Li

doi:10.1109/IGARSS.2017.8128345

Analyzing the urban heat island using time series land surface temperature (LST) data

Weimin Wang
, Hong Liang
, Lijun Yang
, Kai Liu
, Hongbo Su
, Xueke Li

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

5 Citations (Scopus)

Abstract

This study aims to carefully analyze the urban heat island (UHI). Using the LST images from the MODIS and LANDSAT 8, high spatial-temporal resolution LST from May to November in 2013 were generated with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Furthermore, the relationships between urban LST and four Landscape Metrics were analyzed based on two GaoFen imageries. UHI effects were found accentuated in Beijing from May to October. Moreover, the UHI intensities were more obvious in the July and August. Study demonstrated that relatively weak linear relationships existed between urban impervious surface landscape metrics and remotely sensing LSTs. Contrarily, urban green landscape metrics exhibited well correlations with the LSTs. © 2017 IEEE.

Original language	English
Title of host publication	2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings
Publisher	IEEE
Pages	5874-5876
Volume	2017-July
ISBN (Print)	9781509049516
DOIs	https://doi.org/10.1109/IGARSS.2017.8128345
Publication status	Published - 1 Dec 2017
Externally published	Yes
Event	37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, United States Duration: 23 Jul 2017 → 28 Jul 2017

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2017-July

Conference

Conference	37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Place	United States
City	Fort Worth
Period	23/07/17 → 28/07/17

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Research Keywords

Landscape Metrics
Remote Sensing
STARFM fusion
Urban heat island

Access Output

10.1109/IGARSS.2017.8128345

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

Wang, W., Liang, H., Yang, L., Liu, K., Su, H., & Li, X. (2017). Analyzing the urban heat island using time series land surface temperature (LST) data. In 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings (Vol. 2017-July, pp. 5874-5876). Article 8128345 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2017-July). IEEE. https://doi.org/10.1109/IGARSS.2017.8128345

Wang, Weimin ; Liang, Hong ; Yang, Lijun et al. / Analyzing the urban heat island using time series land surface temperature (LST) data. 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Vol. 2017-July IEEE, 2017. pp. 5874-5876 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{b8364a87980b4e9fa3028e5c3c532093,

title = "Analyzing the urban heat island using time series land surface temperature (LST) data",

abstract = "This study aims to carefully analyze the urban heat island (UHI). Using the LST images from the MODIS and LANDSAT 8, high spatial-temporal resolution LST from May to November in 2013 were generated with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Furthermore, the relationships between urban LST and four Landscape Metrics were analyzed based on two GaoFen imageries. UHI effects were found accentuated in Beijing from May to October. Moreover, the UHI intensities were more obvious in the July and August. Study demonstrated that relatively weak linear relationships existed between urban impervious surface landscape metrics and remotely sensing LSTs. Contrarily, urban green landscape metrics exhibited well correlations with the LSTs. {\textcopyright} 2017 IEEE.",

keywords = "Landscape Metrics, Remote Sensing, STARFM fusion, Urban heat island",

author = "Weimin Wang and Hong Liang and Lijun Yang and Kai Liu and Hongbo Su and Xueke Li",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 ; Conference date: 23-07-2017 Through 28-07-2017",

year = "2017",

month = dec,

day = "1",

doi = "10.1109/IGARSS.2017.8128345",

language = "English",

isbn = "9781509049516",

volume = "2017-July",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "IEEE",

pages = "5874--5876",

booktitle = "2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings",

address = "United States",

}

Wang, W, Liang, H, Yang, L, Liu, K, Su, H & Li, X 2017, Analyzing the urban heat island using time series land surface temperature (LST) data. in 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. vol. 2017-July, 8128345, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2017-July, IEEE, pp. 5874-5876, 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017, Fort Worth, United States, 23/07/17. https://doi.org/10.1109/IGARSS.2017.8128345

Analyzing the urban heat island using time series land surface temperature (LST) data. / Wang, Weimin; Liang, Hong; Yang, Lijun et al.
2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Vol. 2017-July IEEE, 2017. p. 5874-5876 8128345 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2017-July).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Analyzing the urban heat island using time series land surface temperature (LST) data

AU - Wang, Weimin

AU - Liang, Hong

AU - Yang, Lijun

AU - Liu, Kai

AU - Su, Hongbo

AU - Li, Xueke

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2017/12/1

Y1 - 2017/12/1

N2 - This study aims to carefully analyze the urban heat island (UHI). Using the LST images from the MODIS and LANDSAT 8, high spatial-temporal resolution LST from May to November in 2013 were generated with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Furthermore, the relationships between urban LST and four Landscape Metrics were analyzed based on two GaoFen imageries. UHI effects were found accentuated in Beijing from May to October. Moreover, the UHI intensities were more obvious in the July and August. Study demonstrated that relatively weak linear relationships existed between urban impervious surface landscape metrics and remotely sensing LSTs. Contrarily, urban green landscape metrics exhibited well correlations with the LSTs. © 2017 IEEE.

AB - This study aims to carefully analyze the urban heat island (UHI). Using the LST images from the MODIS and LANDSAT 8, high spatial-temporal resolution LST from May to November in 2013 were generated with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Furthermore, the relationships between urban LST and four Landscape Metrics were analyzed based on two GaoFen imageries. UHI effects were found accentuated in Beijing from May to October. Moreover, the UHI intensities were more obvious in the July and August. Study demonstrated that relatively weak linear relationships existed between urban impervious surface landscape metrics and remotely sensing LSTs. Contrarily, urban green landscape metrics exhibited well correlations with the LSTs. © 2017 IEEE.

KW - Landscape Metrics

KW - Remote Sensing

KW - STARFM fusion

KW - Urban heat island

UR - http://www.scopus.com/inward/record.url?scp=85041856410&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85041856410&origin=recordpage

U2 - 10.1109/IGARSS.2017.8128345

DO - 10.1109/IGARSS.2017.8128345

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781509049516

VL - 2017-July

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 5874

EP - 5876

BT - 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings

PB - IEEE

T2 - 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017

Y2 - 23 July 2017 through 28 July 2017

ER -

Wang W, Liang H, Yang L, Liu K, Su H, Li X. Analyzing the urban heat island using time series land surface temperature (LST) data. In 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Vol. 2017-July. IEEE. 2017. p. 5874-5876. 8128345. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2017.8128345

Analyzing the urban heat island using time series land surface temperature (LST) data

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this