Deconstructing Spatial Connectivity of Multiple Ecosystem Services in the Guangdong–Hong Kong–Macao Greater Bay Area: A Spatial Network Approach.
Saved in:
| Title: | Deconstructing Spatial Connectivity of Multiple Ecosystem Services in the Guangdong–Hong Kong–Macao Greater Bay Area: A Spatial Network Approach. |
|---|---|
| Authors: | Wu, Linlin1 (AUTHOR), Fan, Fenglei1 (AUTHOR) fanfenglei@scnu.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1966. 32p. |
| Subjects: | Ecosystem services, Geographic spatial analysis, Image segmentation |
| Geographic Terms: | Hong Kong (China), Guangdong Sheng (China) |
| Abstract: | Highlights: What are the main findings? A framework for constructing spatial networks of multiple ecosystem services was proposed. The framework is implemented by the InVEST model, the minimum cumulative resistance model and a multiresolution segmentation method. What are the implications of the main findings? The proposed framework is feasible for capturing the spatial connectivity of multiple ecosystem services. Different ecosystem service networks exhibited conspicuous spatial heterogeneity but generally maintained relatively high connectivity. Exploring the interaction relationship among multiple ecosystem services is vital for maintaining ecosystem function. However, traditional approaches are limited in their ability to: (i) characterize complex interactions and (ii) visualize the spatial connectivity of various ecosystem services delivered by social–ecological systems. To address these challenges, a framework for constructing spatial networks of multiple ecosystem services was proposed. The framework is implemented by: (i) estimating the spatial distribution of multiple ecosystem services using the InVEST model, and (ii) generating network nodes and edges with geographical attributes based on the minimum cumulative resistance model and a multiresolution segmentation method. We conducted a case study in the Guangdong–Hong Kong–Macao Greater Bay Area and examined the topological features of the spatial networks using complex network indicators. For each network, winding and multiple edges connected adjacent nodes and formed continuous linkages across the entire study area, indicating that the proposed framework is feasible for capturing the spatial connectivity of multiple ecosystem services. The different ecosystem service networks exhibited conspicuous spatial heterogeneity and generally maintained relatively high connectivity, as evidenced by their tree-like structure with winding pathways and the distribution of multi-edge nodes, indicating that each ES was predominantly connected with multiple other ecosystem services. Meanwhile, nodes with high values of degree centrality and clustering coefficient were mainly concentrated in coastal and mountainous regions. This study advances the representation of complex interactions among multiple ecosystem services from a spatial perspective, thereby facilitating a deeper understanding of the interaction mechanisms underlying ecosystem functioning. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? A framework for constructing spatial networks of multiple ecosystem services was proposed. The framework is implemented by the InVEST model, the minimum cumulative resistance model and a multiresolution segmentation method. What are the implications of the main findings? The proposed framework is feasible for capturing the spatial connectivity of multiple ecosystem services. Different ecosystem service networks exhibited conspicuous spatial heterogeneity but generally maintained relatively high connectivity. Exploring the interaction relationship among multiple ecosystem services is vital for maintaining ecosystem function. However, traditional approaches are limited in their ability to: (i) characterize complex interactions and (ii) visualize the spatial connectivity of various ecosystem services delivered by social–ecological systems. To address these challenges, a framework for constructing spatial networks of multiple ecosystem services was proposed. The framework is implemented by: (i) estimating the spatial distribution of multiple ecosystem services using the InVEST model, and (ii) generating network nodes and edges with geographical attributes based on the minimum cumulative resistance model and a multiresolution segmentation method. We conducted a case study in the Guangdong–Hong Kong–Macao Greater Bay Area and examined the topological features of the spatial networks using complex network indicators. For each network, winding and multiple edges connected adjacent nodes and formed continuous linkages across the entire study area, indicating that the proposed framework is feasible for capturing the spatial connectivity of multiple ecosystem services. The different ecosystem service networks exhibited conspicuous spatial heterogeneity and generally maintained relatively high connectivity, as evidenced by their tree-like structure with winding pathways and the distribution of multi-edge nodes, indicating that each ES was predominantly connected with multiple other ecosystem services. Meanwhile, nodes with high values of degree centrality and clustering coefficient were mainly concentrated in coastal and mountainous regions. This study advances the representation of complex interactions among multiple ecosystem services from a spatial perspective, thereby facilitating a deeper understanding of the interaction mechanisms underlying ecosystem functioning. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121966 |
