How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data
Urbanization has led to environmental challenges, with the urban heat island effect being a prominent concern. Understanding the influence of urban environmental characteristics (UECs) on land surface temperature (LST) is essential for addressing this issue and promoting sustainable urban developmen...
Gespeichert in:
Veröffentlicht in: | Sustainability 2023-11, Vol.15 (21), p.15511 |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 21 |
container_start_page | 15511 |
container_title | Sustainability |
container_volume | 15 |
creator | Wu, Zherong Zhang, Xinyang Ma, Peifeng Kwan, Mei-Po Liu, Yang |
description | Urbanization has led to environmental challenges, with the urban heat island effect being a prominent concern. Understanding the influence of urban environmental characteristics (UECs) on land surface temperature (LST) is essential for addressing this issue and promoting sustainable urban development. The spatiotemporal characteristics and influencing factors of LST have been investigated in past studies, but research that explicitly investigates the key factors and long-term spatial relationships affecting LST in city-scale urban areas is limited. Remote sensing techniques provide valuable insights into LST patterns and the relationship between urban environment and temperature dynamics. We utilized Landsat 8 images to derive the LST and six spectral indices from 2017 to 2022 in Hong Kong, a city characterized by high population density and rapid urban growth. We also acquired land use data to reflect Hong Kong’s dynamic urban landscape. The complex interactions between urban environment and LST were analyzed using various analytical techniques, including slope trend analysis, land use change detection, and correlation analysis. Finally, we constructed a random forest model to assess the importance of each environmental characteristic. Our findings provide three key insights for regions experiencing rapid urbanization. First, the LST showed an increasing trend in Hong Kong from 2017 to 2022, with the annual LST rising from 21.13 °C to 23.46 °C. Second, we identify negative relationships between LST and vegetation (−0.49)/water bodies (−0.49) and a positive correlation between LST and built-up areas (0.56) utilizing Pearson’s correlation. Third, the dominant influence of built-up areas was underscored, contributing as much as 53.4% to elevated LST levels, with specific attention to the substantial reclamation activities in Hong Kong. The insights from this study provide valuable guidance for policymakers, urban planners, and environmental researchers to formulate evidence-based strategies to achieve a resilient, livable urban future. |
doi_str_mv | 10.3390/su152115511 |
format | Article |
fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2888381210</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A772536663</galeid><sourcerecordid>A772536663</sourcerecordid><originalsourceid>FETCH-LOGICAL-c301t-9ba5cf8428b9c5667629ffeb32d60cc3af93154297089f6e3159319b9aab06c93</originalsourceid><addsrcrecordid>eNpVkc1OIzEMx0crVgKxnHiBSHtCq7L5YDKT0wqVQisqrUTpeeTJOCWok5Qkw8fD8K6kLQdIpDi2f_5bsovilNFzIRT9GwdWcsbKkrEfxRGnFRsxWtKDL__D4iTGR5qPEEwxeVS8T_0LubIdWYYWHJm4Zxu869ElWJPxAwTQCYONyepIZs6sB3QayRxcRxZDMJCde-w3GCANAYl1ZOrditxuHxN8TzhlFUk-W87_kcmz7XYKu9wd9j4hWaCLNvNb0Z3yMiK5ggS_ip8G1hFPPu1xsbye3I-no_n_m9n4cj7SgrI0Ui2U2tQXvG6VLqWsJFfGYCt4J6nWAowSrLzgqqK1MhKzkwOqVQAtlVqJ4-L3XncT_NOAMTWPfggut2x4XdeiZpzRTJ3vqRWssbHO-JTHk2-HvdXeobE5fllVvBRSSpELzr4VZCbha1rBEGMzW9x9Z__sWR18jAFNswm2h_DWMNps99t82a_4AC2dlSM</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2888381210</pqid></control><display><type>article</type><title>How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Wu, Zherong ; Zhang, Xinyang ; Ma, Peifeng ; Kwan, Mei-Po ; Liu, Yang</creator><creatorcontrib>Wu, Zherong ; Zhang, Xinyang ; Ma, Peifeng ; Kwan, Mei-Po ; Liu, Yang</creatorcontrib><description>Urbanization has led to environmental challenges, with the urban heat island effect being a prominent concern. Understanding the influence of urban environmental characteristics (UECs) on land surface temperature (LST) is essential for addressing this issue and promoting sustainable urban development. The spatiotemporal characteristics and influencing factors of LST have been investigated in past studies, but research that explicitly investigates the key factors and long-term spatial relationships affecting LST in city-scale urban areas is limited. Remote sensing techniques provide valuable insights into LST patterns and the relationship between urban environment and temperature dynamics. We utilized Landsat 8 images to derive the LST and six spectral indices from 2017 to 2022 in Hong Kong, a city characterized by high population density and rapid urban growth. We also acquired land use data to reflect Hong Kong’s dynamic urban landscape. The complex interactions between urban environment and LST were analyzed using various analytical techniques, including slope trend analysis, land use change detection, and correlation analysis. Finally, we constructed a random forest model to assess the importance of each environmental characteristic. Our findings provide three key insights for regions experiencing rapid urbanization. First, the LST showed an increasing trend in Hong Kong from 2017 to 2022, with the annual LST rising from 21.13 °C to 23.46 °C. Second, we identify negative relationships between LST and vegetation (−0.49)/water bodies (−0.49) and a positive correlation between LST and built-up areas (0.56) utilizing Pearson’s correlation. Third, the dominant influence of built-up areas was underscored, contributing as much as 53.4% to elevated LST levels, with specific attention to the substantial reclamation activities in Hong Kong. The insights from this study provide valuable guidance for policymakers, urban planners, and environmental researchers to formulate evidence-based strategies to achieve a resilient, livable urban future.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su152115511</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Artificial intelligence ; Bahrain ; Earth resources technology satellites ; Heat ; Iran ; Land use ; Morphology ; Remote sensing ; Socioeconomic factors ; South China Sea ; Sustainability ; Sustainable urban development ; Urban areas ; Urban climatology ; Urban development ; Urban heat islands ; Urban planning ; Vegetation</subject><ispartof>Sustainability, 2023-11, Vol.15 (21), p.15511</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c301t-9ba5cf8428b9c5667629ffeb32d60cc3af93154297089f6e3159319b9aab06c93</citedby><cites>FETCH-LOGICAL-c301t-9ba5cf8428b9c5667629ffeb32d60cc3af93154297089f6e3159319b9aab06c93</cites><orcidid>0000-0002-5284-9343 ; 0000-0001-8602-9258</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>Wu, Zherong</creatorcontrib><creatorcontrib>Zhang, Xinyang</creatorcontrib><creatorcontrib>Ma, Peifeng</creatorcontrib><creatorcontrib>Kwan, Mei-Po</creatorcontrib><creatorcontrib>Liu, Yang</creatorcontrib><title>How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data</title><title>Sustainability</title><description>Urbanization has led to environmental challenges, with the urban heat island effect being a prominent concern. Understanding the influence of urban environmental characteristics (UECs) on land surface temperature (LST) is essential for addressing this issue and promoting sustainable urban development. The spatiotemporal characteristics and influencing factors of LST have been investigated in past studies, but research that explicitly investigates the key factors and long-term spatial relationships affecting LST in city-scale urban areas is limited. Remote sensing techniques provide valuable insights into LST patterns and the relationship between urban environment and temperature dynamics. We utilized Landsat 8 images to derive the LST and six spectral indices from 2017 to 2022 in Hong Kong, a city characterized by high population density and rapid urban growth. We also acquired land use data to reflect Hong Kong’s dynamic urban landscape. The complex interactions between urban environment and LST were analyzed using various analytical techniques, including slope trend analysis, land use change detection, and correlation analysis. Finally, we constructed a random forest model to assess the importance of each environmental characteristic. Our findings provide three key insights for regions experiencing rapid urbanization. First, the LST showed an increasing trend in Hong Kong from 2017 to 2022, with the annual LST rising from 21.13 °C to 23.46 °C. Second, we identify negative relationships between LST and vegetation (−0.49)/water bodies (−0.49) and a positive correlation between LST and built-up areas (0.56) utilizing Pearson’s correlation. Third, the dominant influence of built-up areas was underscored, contributing as much as 53.4% to elevated LST levels, with specific attention to the substantial reclamation activities in Hong Kong. The insights from this study provide valuable guidance for policymakers, urban planners, and environmental researchers to formulate evidence-based strategies to achieve a resilient, livable urban future.</description><subject>Artificial intelligence</subject><subject>Bahrain</subject><subject>Earth resources technology satellites</subject><subject>Heat</subject><subject>Iran</subject><subject>Land use</subject><subject>Morphology</subject><subject>Remote sensing</subject><subject>Socioeconomic factors</subject><subject>South China Sea</subject><subject>Sustainability</subject><subject>Sustainable urban development</subject><subject>Urban areas</subject><subject>Urban climatology</subject><subject>Urban development</subject><subject>Urban heat islands</subject><subject>Urban planning</subject><subject>Vegetation</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpVkc1OIzEMx0crVgKxnHiBSHtCq7L5YDKT0wqVQisqrUTpeeTJOCWok5Qkw8fD8K6kLQdIpDi2f_5bsovilNFzIRT9GwdWcsbKkrEfxRGnFRsxWtKDL__D4iTGR5qPEEwxeVS8T_0LubIdWYYWHJm4Zxu869ElWJPxAwTQCYONyepIZs6sB3QayRxcRxZDMJCde-w3GCANAYl1ZOrditxuHxN8TzhlFUk-W87_kcmz7XYKu9wd9j4hWaCLNvNb0Z3yMiK5ggS_ip8G1hFPPu1xsbye3I-no_n_m9n4cj7SgrI0Ui2U2tQXvG6VLqWsJFfGYCt4J6nWAowSrLzgqqK1MhKzkwOqVQAtlVqJ4-L3XncT_NOAMTWPfggut2x4XdeiZpzRTJ3vqRWssbHO-JTHk2-HvdXeobE5fllVvBRSSpELzr4VZCbha1rBEGMzW9x9Z__sWR18jAFNswm2h_DWMNps99t82a_4AC2dlSM</recordid><startdate>20231101</startdate><enddate>20231101</enddate><creator>Wu, Zherong</creator><creator>Zhang, Xinyang</creator><creator>Ma, Peifeng</creator><creator>Kwan, Mei-Po</creator><creator>Liu, Yang</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0002-5284-9343</orcidid><orcidid>https://orcid.org/0000-0001-8602-9258</orcidid></search><sort><creationdate>20231101</creationdate><title>How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data</title><author>Wu, Zherong ; Zhang, Xinyang ; Ma, Peifeng ; Kwan, Mei-Po ; Liu, Yang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c301t-9ba5cf8428b9c5667629ffeb32d60cc3af93154297089f6e3159319b9aab06c93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial intelligence</topic><topic>Bahrain</topic><topic>Earth resources technology satellites</topic><topic>Heat</topic><topic>Iran</topic><topic>Land use</topic><topic>Morphology</topic><topic>Remote sensing</topic><topic>Socioeconomic factors</topic><topic>South China Sea</topic><topic>Sustainability</topic><topic>Sustainable urban development</topic><topic>Urban areas</topic><topic>Urban climatology</topic><topic>Urban development</topic><topic>Urban heat islands</topic><topic>Urban planning</topic><topic>Vegetation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Zherong</creatorcontrib><creatorcontrib>Zhang, Xinyang</creatorcontrib><creatorcontrib>Ma, Peifeng</creatorcontrib><creatorcontrib>Kwan, Mei-Po</creatorcontrib><creatorcontrib>Liu, Yang</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Zherong</au><au>Zhang, Xinyang</au><au>Ma, Peifeng</au><au>Kwan, Mei-Po</au><au>Liu, Yang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data</atitle><jtitle>Sustainability</jtitle><date>2023-11-01</date><risdate>2023</risdate><volume>15</volume><issue>21</issue><spage>15511</spage><pages>15511-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>Urbanization has led to environmental challenges, with the urban heat island effect being a prominent concern. Understanding the influence of urban environmental characteristics (UECs) on land surface temperature (LST) is essential for addressing this issue and promoting sustainable urban development. The spatiotemporal characteristics and influencing factors of LST have been investigated in past studies, but research that explicitly investigates the key factors and long-term spatial relationships affecting LST in city-scale urban areas is limited. Remote sensing techniques provide valuable insights into LST patterns and the relationship between urban environment and temperature dynamics. We utilized Landsat 8 images to derive the LST and six spectral indices from 2017 to 2022 in Hong Kong, a city characterized by high population density and rapid urban growth. We also acquired land use data to reflect Hong Kong’s dynamic urban landscape. The complex interactions between urban environment and LST were analyzed using various analytical techniques, including slope trend analysis, land use change detection, and correlation analysis. Finally, we constructed a random forest model to assess the importance of each environmental characteristic. Our findings provide three key insights for regions experiencing rapid urbanization. First, the LST showed an increasing trend in Hong Kong from 2017 to 2022, with the annual LST rising from 21.13 °C to 23.46 °C. Second, we identify negative relationships between LST and vegetation (−0.49)/water bodies (−0.49) and a positive correlation between LST and built-up areas (0.56) utilizing Pearson’s correlation. Third, the dominant influence of built-up areas was underscored, contributing as much as 53.4% to elevated LST levels, with specific attention to the substantial reclamation activities in Hong Kong. The insights from this study provide valuable guidance for policymakers, urban planners, and environmental researchers to formulate evidence-based strategies to achieve a resilient, livable urban future.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su152115511</doi><orcidid>https://orcid.org/0000-0002-5284-9343</orcidid><orcidid>https://orcid.org/0000-0001-8602-9258</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2071-1050 |
ispartof | Sustainability, 2023-11, Vol.15 (21), p.15511 |
issn | 2071-1050 2071-1050 |
language | eng |
recordid | cdi_proquest_journals_2888381210 |
source | MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals |
subjects | Artificial intelligence Bahrain Earth resources technology satellites Heat Iran Land use Morphology Remote sensing Socioeconomic factors South China Sea Sustainability Sustainable urban development Urban areas Urban climatology Urban development Urban heat islands Urban planning Vegetation |
title | How Did Urban Environmental Characteristics Influence Land Surface Temperature in Hong Kong from 2017 to 2022? Evidence from Remote Sensing and Land Use Data |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T17%3A01%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=How%20Did%20Urban%20Environmental%20Characteristics%20Influence%20Land%20Surface%20Temperature%20in%20Hong%20Kong%20from%202017%20to%202022?%20Evidence%20from%20Remote%20Sensing%20and%20Land%20Use%20Data&rft.jtitle=Sustainability&rft.au=Wu,%20Zherong&rft.date=2023-11-01&rft.volume=15&rft.issue=21&rft.spage=15511&rft.pages=15511-&rft.issn=2071-1050&rft.eissn=2071-1050&rft_id=info:doi/10.3390/su152115511&rft_dat=%3Cgale_proqu%3EA772536663%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2888381210&rft_id=info:pmid/&rft_galeid=A772536663&rfr_iscdi=true |