Multi-Scenario Dynamic Simulation of Urban Agglomeration Development on the Northern Slope of the Tianshan Mountains in Xinjiang, China, with the Goal of High-Quality Urban Construction

Abstract

The construction of high-quality urban agglomeration has become a guiding strategy for future urban development. Based on the current development status of urban agglomeration on the northern slope of the Tianshan Mountains, the concepts of environmental protection, harmonious coexistence, and sustainable development were combined in the present study. Land cover data for 2010 and 2020 as well as data on various driving factors and limiting factors were selected to simulate and forecast the land change of the urban agglomeration under environmental constraints. At the same time, to simulate the natural development scenario, farmland protection scenario, and ecological protection scenario for the land development of urban agglomeration on the northern slope of the Tianshan Mountains in 2030, the future land use simulation and Markov (FLUS-Markov) model and the urban growth boundary (UGB) model were combined. The following conclusions may be drawn from the results. (1) Using the land cover in 2010 to simulate the land cover in 2020, the kappa value was 0.724, the overall accuracy was 82.9%, and the FOM value was 0.245, exhibiting a high accuracy. (2) Under the three scenarios, the degree of expansion varied significantly from 2020 to 2030, but the proportion of construction area remained stable at 3%. Under the natural development scenario, urban land expansion was the most obvious, followed by the farmland protection scenario, while under the ecological protection scenario, construction land expansion was the least obvious. (3) Under the three scenarios, the expansion of construction land was mainly dominated by the encroachment of grassland, and the edge expansion mode was characterized by concentrated contiguous land. (4) The kernel density results show that the urban area exhibited a year-by-year expansion, and the best suitable development area was the surrounding farmland. (5) Under the three scenarios, the delineation of UGB in urban agglomeration at the northern slope of the Tianshan Mountains was reasonable and effective, and it can provide a relevant reference for the government’s future urban development and layout planning.

Multi-Scenario Prediction of Land-Use Changes and Ecosystem Service Values in the Lhasa River Basin Based on the FLUS-Markov Model

“…Considering that land-use change is affected by physical and chemical conditions and multiple factors, such as natural factors, socio-economic conditions, and location [38,39], we selected three types of driving factors: physical geography, socioeconomic, and location factors. Then, we processed the driver data by using a GIS platform so as to make them consistent with the projected coordinate system and spatial resolution of the land-use data [25,40]. The driving factors for Model 1 were selected with reference to previous research experience and totaled 13, and the driving factors for Model 2 were selected based on Model 1 for the characteristics of the plateau basins and totaled 20 (Table 1 and Figure 2).…”

“…At present, the existing land-use change prediction models mainly include the CA-Markov model [22], CLUS-S model [23,24], and FLUS-Markov model [25,26]. The first two models have good spatial extensibility and can predict the spatial distribution pattern of future land use, but the CA-Markov model lacks a cellular state transition restriction module and can simulate only a single land-use type [27], while the CLUS-S model easily ignores the possibility of the conversion of nondominant land-use types and has shortcomings regarding the land-use allocation process [26].…”

Land Use Change and Landscape Ecological Risk Prediction in Urumqi under the Shared Socio-Economic Pathways and the Representative Concentration Pathways (SSP-RCP) Scenarios

“…where 𝑛 is the total number of rasters in Urumqi, 𝑝 is the number of rasters in which the simulated value of the ith land use type is the same as the actual value, 𝑘 is the number of land use types (𝑘 = 6), 𝑝 is the number of actual rasters of the ith land use type, and 𝑝 ' is the number of simulated rasters of the i-th land use type. The Kappa coefficient is calculated as follows [46]:…”