Urban Green Space Planning Based on Remote Sensing and Geographic Information Systems

Планирование городских зелёных пространств на основе дистанционного зондирования и географических информационных систем
Hua Bai, Ziwei Li, Hanlong Guo, Haopeng Chen, Pingping Luo
2022-08-26

BP neural networkecological networkgeographic information systemsminimum cumulative resistance modelremote sensing
Urban construction land expansion damages natural ecological patches, changing the relationship between residents and ecological land. This is widespread due to global urbanization. Considering nature and society in urban planning, we have established an evaluation system for urban green space construction to ensure urban development residents’ needs while considering natural resource distribution. This is to alleviate the contradiction of urban land use and realize the city’s sustainable development. Taking the Fengdong New City, Xixian New Area as an example, the study used seven indicators to construct an ecological source evaluation system, four types of factors to identify ecological corridors and ecological nodes using the minimum cumulative resistance model, and a Back Propagation neural network to determine the weight of the evaluation system, constructing an urban green space ecological network. We comprehensively analyzed and retained 11 ecological source areas, identified 18 ecological corridors, and integrated and selected 13 ecological nodes. We found that the area under the influence of ecosystem functions is 12.56 km2, under the influence of ecological demands is 1.40 km2, and after comprehensive consideration is 22.88 km2. Based on the results, this paper concludes that protecting, excavating, and developing various urban greening factors do not conflict with meeting the residents’ ecological needs. With consideration of urban greening factors, cities can achieve green and sustainable development. We also found that the BP neural network objectively calculates and analyzes the evaluation factors, corrects the distribution value of each factor, and ensures the validity and practicability of the weights. The main innovation of this study lies in the quantitative analysis and spatial expression of residents’ demand for ecological land and the positive and negative aspects of disturbance. The research results improve the credibility and scientificity of green space construction so that urban planning can adapt and serve the city and its residents.
1
A Back Propagation neural network objectively determined and corrected evaluation-factor weights, improving their validity and practical applicability.
2
Seven indicators identified 11 ecological source areas, while minimum cumulative resistance modeling identified 18 ecological corridors and 13 ecological nodes.
3
The areas influenced by ecosystem functions, ecological demands, and their comprehensive assessment were 12.56 km², 1.40 km², and 22.88 km², respectively.
4
The results indicate that protecting and developing urban greening factors can meet residents’ ecological needs without conflicting with urban development, supporting sustainable growth.
5
The study develops an urban green-space ecological network integrating remote sensing, GIS, ecological evaluation, and residents’ ecological demands.

Urban green space ecological network in Fengdong New City, Xixian New Area

Spatial configuration and evaluation of ecological sources, corridors, nodes, ecosystem functions, and residents’ ecological-land demands for sustainable urban green-space planning

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2022-08-26
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Hua Bai
Ziwei Li
Hanlong Guo
Haopeng Chen
Pingping Luo
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