Effects of urban green spaces landscape pattern on carbon sink among urban ecological function areas at the appropriate scale: A case study in Xi’an
[Display omitted] •The effects of green spaces landscape pattern on carbon sink varied among UEFAs.•For each UEFA, the appropriate grid scale should be considered.•MLR and GWR were used to identify significant metrics and spatial relationships.•The relationship was heterogeneous across space, from g...
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| Published in | Ecological indicators Vol. 158; p. 111427 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.01.2024
Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | [Display omitted]
•The effects of green spaces landscape pattern on carbon sink varied among UEFAs.•For each UEFA, the appropriate grid scale should be considered.•MLR and GWR were used to identify significant metrics and spatial relationships.•The relationship was heterogeneous across space, from grids to UEFAs level.
At appropriate scales, optimizing landscape pattern in urban green spaces is shown to augment carbon sink. However, existing research primarily concentrates on regional or city-wide areas, often overlooking the nuanced effects of landscape pattern on carbon sink within specialized urban ecological function areas. Focusing on the city of Xi'an and emphasizing the forest within green spaces as the principal subject, this study investigated the spatial interplay between forest landscape pattern and carbon sink across the Ecological Control Area, the Urban Ecological Construction Buffer Area, and the Tsinling Mountains Ecological Protection Area. Grounded in field surveys, this study employed GF1-WFV remote sensing imagery to ascertain the 2021 land-use data in Xi'an and further classified forests into five distinct types: deciduous broad-leaved forest, evergreen coniferous forest, evergreen broad-leaved forest, coniferous and broad-leaved mixed forest, and evergreen-deciduous broad-leaved mixed forest. Employing the Carnegie-Ames-Stanford Approach model and landscape metrics methods, the study evaluated the carbon sink and landscape pattern at multi-scale. Through semi-variance analyses, we discerned the appropriate grid scales. Finally, multiple linear stepwise regression model elucidated the significant landscape metrics, while geographically weighted regression model were employed to scrutinize spatial relationships. Our findings suggested that grid scales of 5 km for the Ecological Control Area, 3 km for the Urban Ecological Construction Buffer Area, and 6 km for the Tsinling Mountains Ecological Protection Area are most appropriate for effective management and planning. Metrics related to area, edge, and fragmentation were pivotal in enhancing carbon sink, and their influence was spatially variable. This study furnished an in-depth analysis of how landscape pattern and carbon sink interact spatially within various urban ecological function areas at appropriate grid scales, thereby providing a scientific framework for the sustainable planning and management of green spaces. |
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| AbstractList | At appropriate scales, optimizing landscape pattern in urban green spaces is shown to augment carbon sink. However, existing research primarily concentrates on regional or city-wide areas, often overlooking the nuanced effects of landscape pattern on carbon sink within specialized urban ecological function areas. Focusing on the city of Xi'an and emphasizing the forest within green spaces as the principal subject, this study investigated the spatial interplay between forest landscape pattern and carbon sink across the Ecological Control Area, the Urban Ecological Construction Buffer Area, and the Tsinling Mountains Ecological Protection Area. Grounded in field surveys, this study employed GF1-WFV remote sensing imagery to ascertain the 2021 land-use data in Xi'an and further classified forests into five distinct types: deciduous broad-leaved forest, evergreen coniferous forest, evergreen broad-leaved forest, coniferous and broad-leaved mixed forest, and evergreen-deciduous broad-leaved mixed forest. Employing the Carnegie-Ames-Stanford Approach model and landscape metrics methods, the study evaluated the carbon sink and landscape pattern at multi-scale. Through semi-variance analyses, we discerned the appropriate grid scales. Finally, multiple linear stepwise regression model elucidated the significant landscape metrics, while geographically weighted regression model were employed to scrutinize spatial relationships. Our findings suggested that grid scales of 5 km for the Ecological Control Area, 3 km for the Urban Ecological Construction Buffer Area, and 6 km for the Tsinling Mountains Ecological Protection Area are most appropriate for effective management and planning. Metrics related to area, edge, and fragmentation were pivotal in enhancing carbon sink, and their influence was spatially variable. This study furnished an in-depth analysis of how landscape pattern and carbon sink interact spatially within various urban ecological function areas at appropriate grid scales, thereby providing a scientific framework for the sustainable planning and management of green spaces. At appropriate scales, optimizing landscape pattern in urban green spaces is shown to augment carbon sink. However, existing research primarily concentrates on regional or city-wide areas, often overlooking the nuanced effects of landscape pattern on carbon sink within specialized urban ecological function areas. Focusing on the city of Xi'an and emphasizing the forest within green spaces as the principal subject, this study investigated the spatial interplay between forest landscape pattern and carbon sink across the Ecological Control Area, the Urban Ecological Construction Buffer Area, and the Tsinling Mountains Ecological Protection Area. Grounded in field surveys, this study employed GF1-WFV remote sensing imagery to ascertain the 2021 land-use data in Xi'an and further classified forests into five distinct types: deciduous broad-leaved forest, evergreen coniferous forest, evergreen broad-leaved forest, coniferous and broad-leaved mixed forest, and evergreen-deciduous broad-leaved mixed forest. Employing the Carnegie-Ames-Stanford Approach model and landscape metrics methods, the study evaluated the carbon sink and landscape pattern at multi-scale. Through semi-variance analyses, we discerned the appropriate grid scales. Finally, multiple linear stepwise regression model elucidated the significant landscape metrics, while geographically weighted regression model were employed to scrutinize spatial relationships. Our findings suggested that grid scales of 5 km for the Ecological Control Area, 3 km for the Urban Ecological Construction Buffer Area, and 6 km for the Tsinling Mountains Ecological Protection Area are most appropriate for effective management and planning. Metrics related to area, edge, and fragmentation were pivotal in enhancing carbon sink, and their influence was spatially variable. This study furnished an in-depth analysis of how landscape pattern and carbon sink interact spatially within various urban ecological function areas at appropriate grid scales, thereby providing a scientific framework for the sustainable planning and management of green spaces. [Display omitted] •The effects of green spaces landscape pattern on carbon sink varied among UEFAs.•For each UEFA, the appropriate grid scale should be considered.•MLR and GWR were used to identify significant metrics and spatial relationships.•The relationship was heterogeneous across space, from grids to UEFAs level. At appropriate scales, optimizing landscape pattern in urban green spaces is shown to augment carbon sink. However, existing research primarily concentrates on regional or city-wide areas, often overlooking the nuanced effects of landscape pattern on carbon sink within specialized urban ecological function areas. Focusing on the city of Xi'an and emphasizing the forest within green spaces as the principal subject, this study investigated the spatial interplay between forest landscape pattern and carbon sink across the Ecological Control Area, the Urban Ecological Construction Buffer Area, and the Tsinling Mountains Ecological Protection Area. Grounded in field surveys, this study employed GF1-WFV remote sensing imagery to ascertain the 2021 land-use data in Xi'an and further classified forests into five distinct types: deciduous broad-leaved forest, evergreen coniferous forest, evergreen broad-leaved forest, coniferous and broad-leaved mixed forest, and evergreen-deciduous broad-leaved mixed forest. Employing the Carnegie-Ames-Stanford Approach model and landscape metrics methods, the study evaluated the carbon sink and landscape pattern at multi-scale. Through semi-variance analyses, we discerned the appropriate grid scales. Finally, multiple linear stepwise regression model elucidated the significant landscape metrics, while geographically weighted regression model were employed to scrutinize spatial relationships. Our findings suggested that grid scales of 5 km for the Ecological Control Area, 3 km for the Urban Ecological Construction Buffer Area, and 6 km for the Tsinling Mountains Ecological Protection Area are most appropriate for effective management and planning. Metrics related to area, edge, and fragmentation were pivotal in enhancing carbon sink, and their influence was spatially variable. This study furnished an in-depth analysis of how landscape pattern and carbon sink interact spatially within various urban ecological function areas at appropriate grid scales, thereby providing a scientific framework for the sustainable planning and management of green spaces. |
| ArticleNumber | 111427 |
| Author | Shi, Nuo Yu, Yang Liu, Mengqi Liang, Shuting Ren, Yichen |
| Author_xml | – sequence: 1 givenname: Nuo surname: Shi fullname: Shi, Nuo email: shinuo@xauat.edu.cn – sequence: 2 givenname: Yang orcidid: 0009-0000-8275-0144 surname: Yu fullname: Yu, Yang email: yangy_1@xauat.edu.cn – sequence: 3 givenname: Shuting surname: Liang fullname: Liang, Shuting – sequence: 4 givenname: Yichen surname: Ren fullname: Ren, Yichen – sequence: 5 givenname: Mengqi surname: Liu fullname: Liu, Mengqi |
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•The effects of green spaces landscape pattern on carbon sink varied among UEFAs.•For each UEFA, the appropriate grid scale should be... At appropriate scales, optimizing landscape pattern in urban green spaces is shown to augment carbon sink. However, existing research primarily concentrates on... |
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| SubjectTerms | carbon sinks case studies coniferous forests conservation buffers deciduous forests ecological function Forest land use Landscape metric Landscape planning landscapes mixed forests Net primary productivity regression analysis Spatial correlation |
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| Title | Effects of urban green spaces landscape pattern on carbon sink among urban ecological function areas at the appropriate scale: A case study in Xi’an |
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