Multi-scenario simulation of urban growth boundaries with an ESP-FLUS model: A case study of the Min Delta region, China

Liu, Xiaoyang; Wei, Ming; Li, Peilin; Zeng, Jian

doi:10.1016/j.ecolind.2022.108538

Cited by 82 publications

(41 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with conventional logical reasoning and mathematical derivations, cellular automata (CA) simulations can clarify the spatiotemporal dynamics of land use and land cover change (LUCC). The dynamic simulation of urban development based on CA continues to attract research, and CA-based urban models (e.g., SLEUTH, CLUE-S, and FLUS) are widely used in fields such as ecology and environmental science [ 32 , 33 , 34 ]. Markov chains, system dynamics, and multi-objective constrained optimization are the main approaches for predicting land demand in urban CA models [ 35 , 36 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scenario Simulation to Predict Ecological Risk Posed by Urban Sprawl with Spontaneous Growth: A Case Study of Quanzhou

Liao

Tang

Shao

2022

IJERPH

View full text Add to dashboard Cite

The rapid expansion of different types of urban land continues to erode natural and semi-natural ecological space and causes irreversible ecological damage to rapidly industrialized and urbanized areas. This work considers Quanzhou, a typical industrial and trade city in southeastern China as the research area and uses a Markov chain integrated into the patch-generating land use simulation (PLUS) model to simulate the urban expansion of Quanzhou from 2005 to 2018. The PLUS model uses the random forest algorithm to determine the contribution of driving factors and simulate the organic and spontaneous growth process based on the seed generation mechanism of multi-class random patches. Next, leveraging the importance of ecosystem services and ecological sensitivity as indicators of evaluation endpoints, we explore the temporal and spatial evolution of ecological risks from 2018 to 2031 under the scenarios of business as usual (BAU), industrial priority, and urban transformation scenarios. The evaluation endpoints cover water conservation service, soil conservation service, biodiversity maintenance service, soil erosion sensitivity, riverside sensitivity, and soil fertility. The ecological risk studied in this work involves the way in which different types of construction land expansion can possibly affect the ecosystem. The ecological risk index is divided into five levels. The results show that during the calibration simulation period from 2005 to 2018 the overall accuracy and Kappa coefficient reached 91.77% and 0.878, respectively. When the percent-of-seeds (PoS) parameter of random patch seeds equals 0.0001, the figure of merit of the simulated urban construction land improves by 3.9% compared with the logistic-based cellular automata model (Logistic-CA) considering organic growth. When PoS = 0.02, the figure of merit of the simulated industrial and mining land is 6.5% higher than that of the Logistic-CA model. The spatial reconstruction of multiple types of construction land under different urban development goals shows significant spatial differentiation on the district and county scale. In the industrial-priority scenario, the area of industrial and mining land is increased by 20% compared with the BAU scenario, but the high-level risk area is 42.5% larger than in the BAU scenario. Comparing the spatial distribution of risks under the BAU scenario, the urban transition scenario is mainly manifested as the expansion of medium-level risk areas around Quanzhou Bay and the southern region. In the future, the study area should appropriately reduce the agglomeration scale of urban development and increase the policy efforts to guide the development of industrial land to the southeast.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Scenario Simulation to Predict Ecological Risk Posed by Urban Sprawl with Spontaneous Growth: A Case Study of Quanzhou

Liao

Tang

Shao

2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…Therefore, it is necessary to justify the importance and identify the contribution weights of these factors for a case study. In previous studies, forest methods (e.g., random forest and extremely randomized forest) have been widely used to calculate the feature importance by examining the total reduction of impurity in internal nodes of decision trees brought by the variable (Liu, Wei, Li, & Zeng, 2022; Zhang, Zhou, et al., 2019). Since the deep forest model is composed of random forests and extremely randomized forests, we took the average of variable importance calculated by internal forests as contribution weights of driving factors to urban land change.…”

Section: Case Studymentioning

confidence: 99%

Integrating a deep forest algorithm with vector‐based cellular automata for urban land change simulation

Zhuang

Yan

et al. 2022

Transactions in GIS

View full text Add to dashboard Cite

With the capacity to represent irregular geographical entities precisely, vector‐based cellular automata (VCA) have been extensively employed in urban land change simulation at the land parcel level. However, while more driving factors are considered, modeling the complicated nonlinear relationship between land parcel attributes and multiple land‐use changes is increasingly difficult. Moreover, in VCA, the driving factors are unstructured and cannot be directly modeled by traditional deep learning methods, which can only be applied to structured data processing. In order to address these problems, a new VCA model DF‐VCA that adopts the deep forest (DF) algorithm for mining CA transition rules was proposed to simulate urban land‐use change at the parcel level. The DF algorithm is a new deep learning method that can directly mine high‐level features from unstructured data and obtain accurate land‐use transition rules. The proposed DF‐VCA model was applied to simulate the urban land change in Shenzhen, China. Compared with several traditional VCA models, the DF‐VCA model achieved the best simulation performance at parcel‐level (Figure of Merit = 39.88%), pattern‐level (similarity = 96.47%), and community‐level (correlation coefficient = 0.9269). The results show that the DF‐VCA model with strong representative learning ability could simulate urban land change at fine scales precisely. Furthermore, the proposed DF‐VCA model was applied in Shenzhen's future land change simulations to guide sustainable urban development.

show abstract

“…Gumber and Ghosh [36] further divided the ecological space into three levels (i.e., basic, intermediate, and optimal). Liu et al [37] then divided the area into integrated evolution, basic farmland protection, construction land control, and priority for ecological protection to meet different needs. The second type of literature is more flexible and based on the assessment of the carrying capacity of urban areas, excluding areas with low land carrying capacity from the UGB, thus adjusting the delineation of the UGB [38].…”

Section: Literature Reviewmentioning

confidence: 99%

Can the Urban Growth Boundary Reduce Urban Industrial Air Pollution in China? A Study from the Perspective of the Sustainability of Land Urbanization

Li¹,

Zuo²

2022

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

Using the panel data on 93 prefecture-level cities in China from 2005 to 2018, this paper takes the UGB implementation in pilot cities as a quasi-natural experiment and uses the difference in difference model to empirically test the impact and mechanism of urban growth boundary (UGB) on urban industrial air pollution. The results show that the UGB policy has a significant role in reducing urban industrial air pollution. This conclusion remains valid after a series of robustness tests. Furthermore, we found that UGB reduces industrial air pollution through governance effects rather than spillover effects, and the negative impact is dynamic and sustainable. Hence, UGB contributes to the sustainable development of land urbanization in terms of reducing industrial air pollution. This research provides empirical evidence for the sustainable development of land urbanization and green development in China.

show abstract

Multi-scenario simulation of urban growth boundaries with an ESP-FLUS model: A case study of the Min Delta region, China

Cited by 82 publications

References 52 publications

Multi-Scenario Simulation to Predict Ecological Risk Posed by Urban Sprawl with Spontaneous Growth: A Case Study of Quanzhou

Multi-Scenario Simulation to Predict Ecological Risk Posed by Urban Sprawl with Spontaneous Growth: A Case Study of Quanzhou

Integrating a deep forest algorithm with vector‐based cellular automata for urban land change simulation

Can the Urban Growth Boundary Reduce Urban Industrial Air Pollution in China? A Study from the Perspective of the Sustainability of Land Urbanization

Contact Info

Product

Resources

About