Land Use and Land Cover Changes and Prediction Based on Multi-Scenario Simulation: A Case Study of Qishan County, China

Abstract

Research on land use change is helpful to better understand the processes and mechanisms of land use changes and provide a decision base for reasonable land development. However, studies on LUCC were mainly conducted for megalopolises and urban agglomerations in China, but there is a gap in the scholarly community when it comes to shrinking small cities where the population decreased sharply under the influence of the urban expansion of megacities. Hence, it is necessary to investigate the evolution rule of land use in these regions. This study takes Qishan County in Shanxi Province as the research subject and analyzes the land use change over the last 20 years with remote sensing technology. Comparing the two LUCC models of the CA-Markov Model and the LCM Model, an optimal model is used to predict and simulate land use change under three potential scenarios in 2030. The conclusions are stated as follows: (1) From 2000 to 2020, the cultivated land area increased originally and subsequently decreased, and forest land continued to decrease at a progressively slower speed. In contrast, the urban land area expanded significantly. (2) The comprehensive dynamic change in water land is the most significant, indicating that this is an unstable land resource in the region and more attention should be given to this matter. (3) The scenario of water area protection indicates that the inhibition of the transition of water areas can protect their vulnerable ecological environment without negatively impacting economic development. Furthermore, the ongoing focus on economic development in the region is related to the rapid disappearance of cultivated land, which is not an optimistic perspective for the area’s ecosystem. The results of this study implied land transition features and mechanisms in Qishan County, providing novel insights for decision support for county-level land use planning.

Assessment and Management Zoning of Ecosystem Service Trade-Off/Synergy Based on the Social–Ecological Balance: A Case of the Chang-Zhu-Tan Metropolitan Area

“…According to the land use planning texts of various cities in the CZTMA, the maximum increase in construction land is set at 50%, and nature reserve is considered a constraint. Land conversions with high economic benefits are increased: conversion from grassland, water, unused land, cropland, and woodland to construction land are increased by 30%, 40%, and 60%, respectively [46,47].…”

Spatiotemporal Variation and Quantitative Attribution of Carbon Storage Based on Multiple Satellite Data and a Coupled Model for Jinan City, China

“…The FLUS (future land use simulation) model is suitable for the simulation research of future land-use change scenarios and is an effective model for geospatial simulation, space optimization, and auxiliary decision-making. This model uses an Artificial Neural Network (ANN) to obtain the suitability probability of various land uses and then improves the applicability of the model by coupling the Markov model and the Cellular Automaton (CA) model [19][20][21]. In the CA model, an adaptive inertial competition mechanism is introduced to address the complexity and uncertainty of the conversion of various land-use types under the joint influence of nature and human activities [19,22,23].…”

Considering Regional Connectivity and Policy Factors in the Simulation of Land Use Change in New Areas: A Case Study of Nansha New District, China

“…Cellular automata (CA) has emerged as one of the most dominant models for land use simulation due to its remarkable ability to capture the interplay between natural and human-driven factors [1,5,11] . However, despite the prominence of CA models, existing studies on land use and land cover change (LUCC) have primarily focused on cities, urban agglomerations, or larger scales, with relatively fewer investigations specifically examining land use simulation within new areas [3,7,8,12,13] . The rapid and intense land use changes often observed in new areas within cities necessitate close monitoring and thorough understanding.…”

“…Traditional drivers of land use change have been extensively studied, encompassing natural factors, transportation factors, and location factors [17] . Among these, particular attention has been given to natural factors, including terrain conditions represented by the digital elevation model (DEM) [6,7,9,13,[18][19][20][21] , elevation [10,16,22,23] , slope [6,7,10,12,13,16,18,19,[21][22][23] , and aspect [6,7,16,18] , among others. Notably, slope stands out as one of the most significant topographic factors influencing urban sprawl [7] .…”