Elsevier

Computer Communications

Volume 154, 15 March 2020, Pages 380-389
Computer Communications

Construction of complex network of green infrastructure in smart city under spatial differentiation of landscape

https://doi.org/10.1016/j.comcom.2020.02.042Get rights and content

Abstract

In the rapid urban and economic development, environmental problems have become prominent due to urban expansion and land imbalances. However, good historical and cultural landscapes are distributed in urban centers in large cities, and ecologically-friendly forests, lakes, or farmlands are distributed around these cities. It has the spatial differentiation of landscape resources. Landscape resources are used to build a composite green foundation, and the network of facility can improve urban environmental problems. The methods used in the work included landscape connectivity, minimum cumulative resistance model, Gravity model, Kernel Density Analysis, and Circuit theory. Through these methods, the ecological and cultural landscape networks were constructed, forming a composite network of green infrastructure by organic coupling of node composite, construction of composite landscape corridor, and corridor composite. The results showed that the subjective analysis and landscape connectivity analysis were used to quantitatively evaluate the importance of patches, thus selecting the superior ecological nodes. The minimum cumulative resistance analysis and gravity model were used to extract the GI corridors and their importance, respectively, with ecological landscapes constructed. Kernel density analysis and circuit theory could determine the cultural landscape nodes and extract cultural landscape corridors. Node composite, composite landscape corridor construction, and corridor composite are important methods to couple the composite green infrastructures. Compared to traditional ecological network, it can protect regional culture and ecology.

Introduction

In China, the high-density urban landscapes and good ecological resources are distributed around cities, while the distinctive and valuable cultural landscape resources are concentrated in urban areas. The distribution of landscape resources has spatial differentiation. These cause the challenges to urban space environment, including population concentration, high intensity of human production and living activities, great ecological demands of urban environment and human, and shortage of urban land resources. Good-quality ecological resources around cities, however, are often ignored. Urban living environment and education of nature and culture can be improved by digging the green resources in high-density urban areas, thus improving green space, service of ecological land, and use of suburban green space.

The concept of green infrastructure proposed in 1990s contains ecological and cultural landscapes [1]. Its essential connotation is to ensure the regional ecological processes and improve the species diversity by constructing green networks, focusing on ecosystem health and landscape resource protection. Methods are provided to improve urban ecological environment [2]. Some scholars used the green infrastructure networks to improve urban ecological service, such as hydrological regulation [3], air conditioning [4], climate regulation [5], and human health [6]. Based on current research, scholars focus on the ecological elements, and the cultural landscape elements are less integrated into network [7], [8]. Under the spatial distribution of urban ecological and cultural elements, it is difficult for green networks to enter the high-density urban areas, without improving urban green space. Therefore, under the spatial differentiation of urban landscape, in order to promote urban ecosystems, the work built a network system of green space integrating ecology and culture, namely a complex green infrastructure combining ecological and cultural landscape networks.

Therefore, under the spatial differentiation of landscape, the construction of composite green infrastructure is divided into the following aspects: The collection and analysis of basic data in the study area include the acquisition and interpretation of remote sensing images, data collection and analysis of urban cultural landscapes, etc. Subjective analysis and connectivity analysis are used to determine the primary and secondary ecological nodes. The minimum cumulative resistance and gravity model are used to extract important ecological corridors to form an ecological landscape network. Kernel density analysis is used to determine the cultural landscape nodes, and extract the cultural landscape corridors through circuit theory, thus building a cultural landscape network. The ecological landscape network is combined with the cultural landscape network through node composite, construction of composite landscape corridor, and corridor composite to obtain a composite network of green infrastructure.

Some scholars use the network of green infrastructure to improve the ecological environment. Liu Xingbo [9] selected 18 protected areas in Shanghai as ecological network nodes according to biodiversity conservation. Wang Haizhen [10] selected 50 green areas with an area greater than 6 ha. as the nodes of ecological network. Kong Fanhua [11] selected 12 green space patches as ecological nodes. Although being convenient and fast, this method is not objective. When the area difference between plaques is not large, it is difficult to judge the quality of plaque. Huang He [12] and Gao Yaling [13] used the area size and connectivity analysis to quantitatively evaluate the importance of ecological plaques, thus determining ecological nodes. Therefore, the work analyzed the connectivity to determine the quality of plaques based on the area and size of plaques. With better ecological plaques selected as nodes, the minimum cumulative resistance model is used to extract ecological corridors and build the networks of ecological landscape. Based on the networks of cultural landscape, Yu Kongjian [14], Li Heping [15] and Wang Sisi [16] used the Delphi method and Kernel density analysis to determine cultural nodes, and the minimum cumulative resistance model and manual selection to extract cultural corridors. A cultural landscape network is established. However, the redundant corridors generated in extracting the cultural landscape corridors by minimum cumulative resistance model cannot be eliminated, which is objective. The circuit theory has been applied to identify the connectivity and construct the corridors, with good applicability in studying green networks [17]. Song Lili [18] used the circuit theory to identify the importance of corridors, which can recognize more important corridors in the network. In addition, Liu Jia [19] used the circuit theory to evaluate the network corridors of green infrastructure in Nanjing. Therefore, the work used the model of minimum cumulative resistance, and the circuit theory to extract important corridors. Then, a network of cultural landscape was established.

The composite green infrastructure network was formed by coupling the networks of ecological landscape and cultural landscape. There is currently no relevant coupling method. The work integrated the cultural and ecological landscape nodes through node composition to build a network of ecological and cultural landscape. The corridors were connected to form a complex network of green infrastructure through integrating corridor.

Section snippets

Study-area overview

Fuzhou, capital of Fujian Province, is located on the southeast coast, and its geographical environment is characterized by “leaning against mountain, adjacent to river, and facing the sea”. Jiancheng District has a flat terrain with an altitude of 10–30 m, whose main urban area is the core area of cultural landscapes, especially Wushishan Mountain, Yushan Mountain and Pingshan Mountain. There are land-scale natural scenic areas in suburbs with superior environment, including Lianhuashan

Selecting ecological nodes

Ecological nodes are selected based on the subjective analysis and quantitative evaluation. Large ecological green areas around cities, such as scenic spots, forest parks and nature reserves, have natural features with less human-induced disturbance, large areas, rich species, and high levels of protection. In terms of quantity and quality, they can be selected as network nodes, ranking as the first-level ecological nodes. For other green lands, the corresponding quantitative evaluation should

Ecological node selection results

(1) Primary ecological nodes. According to the land use classification map, the areas of Lianhua Mountain, Gushan, Qishan, and Wuhu Mountain around Fuzhou are 12,500.6, 16,764.3, 18,278.7, and 18,193.9 hm2, respectively. As the four largest green space patches in the area and the natural reserve of Fuzhou, the four mountains above own rich species, less man-made interference, and large area. It made them an important ecological source of study area. Selecting four green space patches as primary

Conclusions

The work constructed a composite green infrastructure network coupling the ecological and cultural landscapes of Fuzhou based on the current status of spatial distribution of landscapes common in large and medium-sized cities in China. Meanwhile, using theories of landscape ecology and heritage corridors as the foundation, the construction of the network involved connectivity analysis, minimum cumulative resistance model, nuclear density analysis, and circuit theory. The conclusion is as

CRediT authorship contribution statement

He Huang: Conceptualization, Methodology, Investigation, Writing - original draft. Minghuan Zhang: Data curation, Writing - original draft. Kunyong Yu: Resources, Software, Validation. Yaling Gao: Formal analysis. Jian Liu: Supervision, Project administration.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was funded by Educational Research Project for Young and Middle-aged Teachers in Fujian Province (Grant No. JAT170199); Entrepreneurial and Innovative Foundation (Grant No. 115-K8615001A), National Training Program of Innovation for Undergraduates (Grant No. 201814046001), and National Natural Science Foundation of China (Grant No. 31770760).

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