File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Artificial intelligence to evaluate the impact of urban green and blue spaces on chlorophyll-a concentrations

TitleArtificial intelligence to evaluate the impact of urban green and blue spaces on chlorophyll-a concentrations
Authors
KeywordsArtificial intelligence
Chlorophyll-a concentrations
Random forest classification
Urban green and blue spaces
Urbanization
Issue Date1-Jan-2025
PublisherSpringer
Citation
Environmental Science and Pollution Research, 2025 How to Cite?
Abstract

Urbanization is accelerating rapidly, highlighting the critical role of aligning with sustainable development goals, urban green and blue spaces (UGS and UBS). These spaces play a crucial role in enhancing the health and well-being of city residents in terms of ecology. Acknowledging the importance of urban ecology, this study utilizes Sentinel-2A data and support vector machine classification, aimed to identify UGS and UBS. To examine the connections between UGS and UBS, specific indices, spectral bands, and textures were calculated. Additionally, the concentration of chlorophyll, a vital indicator of ecological health, was assessed using three indices. Structural equation modeling was employed to elucidate the relationship between UGS and UBS and their impact on chlorophyll concentration for the years 2017 and 2023. In the 2017 model, UGS exhibited a positive path coefficient (0.25) with chlorophyll-a, indicating that an increase in UGS is associated with an increase in chlorophyll levels. Conversely, in 2023, the path coefficient turned negative (− 0.83), presenting a stark contrast to the 2017 model. This shift suggests potential environmental or urban development changes, such as alterations in the quality or type of urban green spaces, potentially including more non-native or ornamental plants that contribute less to overall chlorophyll levels. UGS can be subjected to pollution, soil compaction, and other stressors that reduce plant health. Similarly, the UBS showed an increase in its path coefficient from − 0.99 in 2017 to − 1.8 in 2023, suggesting improvements such as cleaner water or urban planning strategies aimed at reducing water pollution. The consistent negative relationship across both years suggests that urban water bodies are not contributing to Chl levels due to complex interactions of water bodies with their urban surroundings. However, further research is essential to delve into these dynamics and comprehend the implications for urban ecological planning and sustainability.


Persistent Identifierhttp://hdl.handle.net/10722/360531
ISSN
2022 Impact Factor: 5.8
2023 SCImago Journal Rankings: 1.006

 

DC FieldValueLanguage
dc.contributor.authorFonseka, Panchali U.-
dc.contributor.authorMampitiya, Lakindu-
dc.contributor.authorRathnayake, Namal-
dc.contributor.authorZhang, Hongsheng-
dc.contributor.authorSamarasuriya, Chaminda-
dc.contributor.authorPremasiri, Ranjith-
dc.contributor.authorRathnayake, Upaka-
dc.date.accessioned2025-09-12T00:36:37Z-
dc.date.available2025-09-12T00:36:37Z-
dc.date.issued2025-01-01-
dc.identifier.citationEnvironmental Science and Pollution Research, 2025-
dc.identifier.issn0944-1344-
dc.identifier.urihttp://hdl.handle.net/10722/360531-
dc.description.abstract<p>Urbanization is accelerating rapidly, highlighting the critical role of aligning with sustainable development goals, urban green and blue spaces (UGS and UBS). These spaces play a crucial role in enhancing the health and well-being of city residents in terms of ecology. Acknowledging the importance of urban ecology, this study utilizes Sentinel-2A data and support vector machine classification, aimed to identify UGS and UBS. To examine the connections between UGS and UBS, specific indices, spectral bands, and textures were calculated. Additionally, the concentration of chlorophyll, a vital indicator of ecological health, was assessed using three indices. Structural equation modeling was employed to elucidate the relationship between UGS and UBS and their impact on chlorophyll concentration for the years 2017 and 2023. In the 2017 model, UGS exhibited a positive path coefficient (0.25) with chlorophyll-a, indicating that an increase in UGS is associated with an increase in chlorophyll levels. Conversely, in 2023, the path coefficient turned negative (− 0.83), presenting a stark contrast to the 2017 model. This shift suggests potential environmental or urban development changes, such as alterations in the quality or type of urban green spaces, potentially including more non-native or ornamental plants that contribute less to overall chlorophyll levels. UGS can be subjected to pollution, soil compaction, and other stressors that reduce plant health. Similarly, the UBS showed an increase in its path coefficient from − 0.99 in 2017 to − 1.8 in 2023, suggesting improvements such as cleaner water or urban planning strategies aimed at reducing water pollution. The consistent negative relationship across both years suggests that urban water bodies are not contributing to Chl levels due to complex interactions of water bodies with their urban surroundings. However, further research is essential to delve into these dynamics and comprehend the implications for urban ecological planning and sustainability.</p>-
dc.languageeng-
dc.publisherSpringer-
dc.relation.ispartofEnvironmental Science and Pollution Research-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectArtificial intelligence-
dc.subjectChlorophyll-a concentrations-
dc.subjectRandom forest classification-
dc.subjectUrban green and blue spaces-
dc.subjectUrbanization-
dc.titleArtificial intelligence to evaluate the impact of urban green and blue spaces on chlorophyll-a concentrations-
dc.typeArticle-
dc.identifier.doi10.1007/s11356-025-36292-9-
dc.identifier.scopuseid_2-s2.0-105000508999-
dc.identifier.eissn1614-7499-
dc.identifier.issnl0944-1344-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats