Exploring an integrated spatially model for land-use scenarios simulation in a metropolitan region

Abstract

Spatial simulation of land-use change scenarios in metropolitan areas is essential for analyzing both the causes and consequences of various future scenarios and is also valuable for land-use planning and management. However, current simulation models primarily focus on spatial and rarely on quantitative driving factors. This article aims to simulate future scenarios of land-use changes in the Tehran metropolitan region (TMR) by combining different models to fill this gap. Thus, in the first step, land-use changes were analyzed in the period 1985, 2000, and 2015. Then, by identifying the impact of driving factors and land-use transition potentials with Logistic regression (LR), land-use changes were allocated using the Cellular Automata (CA) method. Finally, with the validation of the model, four scenarios of the current trend(CT), socioeconomic growth(SEG), ecologicaloriented(EO), and integrated development(ID) were suggested with the combination of the System Dynamic (SD) model. The results show that the trend of land-use changes in TMR has led to the destruction of grassland, agricultural, and uncultivated lands and the continuation of this trend will increase the damage of built-up areas on valuable natural and ecological resources. In this way, proximity to roads, distance from built-up areas, and natural factors had the greatest impact on changes. Based on future scenarios in 2030, the change in the SEG-scenario shows a rapid increase in built-up areas (2858km 2) and encroachment on agricultural lands (2171km 2) . In the EO-scenario, destruction of grassland and agricultural lands and the growth of built-up areas will be limited, while CT-scenario leads to the high growth of built-up areas along with destructive impacts on natural and open spaces. In the ID-scenario, the built-up areas and grasslands will increase to 2808km 2 and 7438km 2 , respectively. Accordingly, policy-makers can use simulation of different scenarios to mitigate probable consequences of land-use changes in the metropolitan regions.

An Integrated Modelling Approach to Urban Growth and Land Use/Cover Change

“…LR has been used as an experimental model in deforestation analysis, groundwater potential, agriculture, landslide probability mapping, and urban growth modelling [4,15,53,56,98]. The LR is such that it considers several explanatory factors as independent (X) variables and measures their association with a dependent variable (Y).…”

“…It takes the form of a never-ending increase in urban sprawl, which poses problems, including pollution and depletion of Land 2022, 11, 1715 2 of 26 natural resources, in rapidly developing cities [9][10][11][12][13][14]. With these enormous LUCCs, the absence of accurate insight into the environmental impacts of urbanisation and the absence of suitable planning processes undermines the balance of sustainable development [15].…”

“…Scholars have chosen a wide variety of spatial driving elements according to the magnitude and unique characteristics of the investigations [12,49,51,[62][63][64]. However, a comprehensive knowledge base of the study region is necessary for analysing the driving forces since they might indirectly influence LUCC at many spatial-temporal scales [13,15,43]. Despite the aforementioned models' usefulness in spatial simulation, quantitative data are lacking throughout the simulation phase [30].…”

“…Despite the aforementioned models' usefulness in spatial simulation, quantitative data are lacking throughout the simulation phase [30]. To address the need for an integrated model that simulates land use changes in spatial and quantitative dimensions, Dadashpoor and Panahi [15], using the Tehran metropolitan region (TMR) as a case study, applied socioeconomic factors as a driving force and combined System Dynamics (SD), Logistic Regression (LR), and CA models. Aburas and Ho [30] were also interested in reducing the CA model's limitations, so they looked to integrate it with other quantitative models, such as the Analytic Hierarchy Process (AHP) and the Markov Chain.…”

Research on land‐use evolution and ecosystem services value response in mountainous counties based on the SD‐PLUS model

“…Current research focuses on analyzing the spatio‐temporal changes in land use and the response of ESV. However, the majority put their emphasis on land‐use change and ESVs of big cities (Dadashpoor & Panahi, 2021), ecologically sensitive areas, and developed coastal areas (Liu et al, 2019). Research on small‐scale counties, especially mountain areas, remains inadequate.…”