Spatiotemporal Dynamics and Driving Forces of Urban Land-Use Expansion: A Case Study of the Yangtze River Economic Belt, China

Zhong, Yanfei; Lin, Aiming; He, Ling‐Yun; Zhou, Zhigao; Yuan, Moxi

doi:10.3390/rs12020287

Cited by 68 publications

(33 citation statements)

References 46 publications

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“…Data were strictly processed to remove effects of fire, sunlight, moonlight, clouds, and aurora. Stable lighting data include lights from cities, towns, and other places with long-lasting light sources with the removal of background noise [56].…”

Section: Data Sourcesmentioning

confidence: 99%

Spatially Explicit Evaluation and Driving Factor Identification of Land Use Conflict in Yangtze River Economic Belt

Cui

Kong

Chen

et al. 2021

Land

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Regional land use transitions driven by the adaptive reconciliation of existing land use conflict with socioeconomic development can lead to positive economic effects as well as new land use conflict. Although research on land use transition has progressed considerably, limited studies have explored the spatiotemporal dynamic pattern of land use conflict during the land use transition period. Previous evaluation approaches on land use conflict that mainly focus on status or potential conflict lack conflict intensity evaluation during the land use transition process. A new spatially explicit evaluation framework of land use conflict that directly examines three aspects of conflict, namely, ecological and agricultural (EAC), agricultural and construction (ACC), and ecological and construction (ECC) land conflicts based on ecological quality and agricultural suitability, is proposed in this study. The spatiotemporal dynamic pattern and driving factors of land use conflict in the Yangtze River Economic Belt of China in the period of 2000–2018 are evaluated. The results indicated that comprehensive land use conflict (CLUC) intensity slightly decreased by 9.91% and its barycenter showed a trend toward the west during 2000–2018. ACC is the most drastic conflict among the three aspects of conflict. The mean intensity of ACC reduced remarkably by 38.26%, while EAC increased by 33.15% and ECC increased by 28.28% during the research periods. The barycenter of EAC moved toward the east while the barycenter of ACC and ECC moved toward the west. The changes in the intensity and spreading pattern of land use conflict indices demonstrated the changes in the pattern of territorial space development. Total population, population density, per capita GDP, number of mobile phone users, and road density were strong drivers that influenced the land use conflict of territorial space. Multiple policy recommendations including improving territorial space planning and governance ability, and improving land use efficiency, were proposed to manage and resolve the land use conflict of territorial space. The results and conclusions of this study will help improve future regional land use policies and reduce land use conflict.

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Section: Data Sourcesmentioning

confidence: 99%

Spatially Explicit Evaluation and Driving Factor Identification of Land Use Conflict in Yangtze River Economic Belt

Cui

Kong

Chen

et al. 2021

Land

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“…The impervious surface area increased from 808 km 2 to 1179 km 2 over the past 17 years with the net increase rate of 45.90% (Table A1). There was large spatial variation in the area of impervious surface increase among subregions: from 211 to 246 km 2 The impervious surface area increased from 808 km 2 to 1179 km 2 over the past 17 years with the net increase rate of 45.90% (Table A1). There was large spatial variation in the area of impervious surface increase among subregions: from 211 to 246 km 2 (Table A4).…”

Section: Temporal Lulc Change From 2001 To 2017mentioning

confidence: 99%

“…There was large spatial variation in the area of impervious surface increase among subregions: from 211 to 246 km 2 The impervious surface area increased from 808 km 2 to 1179 km 2 over the past 17 years with the net increase rate of 45.90% (Table A1). There was large spatial variation in the area of impervious surface increase among subregions: from 211 to 246 km 2 (Table A4). Figure 5 demonstrates the annual increment (AI) and annual growth rate (AGR) of the impervious surface for nine regions over the past 17 years.…”

Section: Temporal Lulc Change From 2001 To 2017mentioning

confidence: 99%

“…Earlier national policies for urbanization in China mainly focused on coastal megacities and land-locked cities did not catch up with the pace of development, therefore some cities in CZTUA were relatively backward. Until the year 2002, the Rise of Central China policy was adopted by the central government, mainly aimed at improving the central China's capacity for the coordinated development [2,94]. The local government of Hunan province, promoting the integration of CZTUA under a scientific and systematic urban planning, shaped up the first urban agglomeration plan in 2005.…”

Section: Urban Expansion Under the Guidance Of Policymentioning

confidence: 99%

“…Nowadays, more than 50% of China's population lives in cities, and this rate will continue to rise to 75% by 2030 [1]. For this reason, urbanization has become one of the most important factors affecting ecosystem services and environmental quality, especially in some emerging cities or city clusters [2,3]. To address the adverse impacts and promote sustainable urban development, the Chinese government has issued the "New Urbanization Plan" with the intent to emphasize ecological progress, urbanization quality, domestic demand expansion, and rural-urban coordination simultaneously [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Co-Evolution of Emerging Multi-Cities: Rates, Patterns and Driving Policies Revealed by Continuous Change Detection and Classification of Landsat Data

et al. 2020

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The co-evolution of multi-cities has emerged as the primary form of urbanization in China in recent years. However, the processes, patterns, and coordination are not well characterized and understood, which hinders the understanding of the driving forces, consequences, and management of polycentric urban development. We used the Continuous Change Detection and Classification (CCDC) algorithm to integrate all available Landsat 5, 7, and 8 images and map annual land use and land cover (LULC) from 2001 to 2017 in the Chang–Zhu–Tan urban agglomeration (CZTUA), a typical urban agglomeration in China. Results showed that the impervious surface in the study area expanded by 371 km2 with an annual growth rate of 2.25%, primarily at the cost of cropland (169 km2) and forest (206 km2) during the study period. Urban growth has evolved from infilling being the dominant type in the earlier period to mainly edge-expansion and leapfrogging in the core cities, and from no dominant type to mainly leapfrogging in the satellite cities. The unfolding of the “cool center and hot edge” urban growth pattern in CZTUA, characterized by higher expansion rates in the peripheral than in the core cities, may signify a new form of the co-evolution of multi-cities in the process of urbanization. Detailed urban management and planning policies in CZTUA were analyzed. The co-evolution of multi-cities principles need to be studied in more extensive regions, which could help policymakers to promote sustainable and livable development in the future.

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Ecological risk assessment and restoration area identification of Pengyang County on the basis of the landscape pattern and function

Li¹,

Ai²,

Liu³

et al. 2023

Environ Monit Assess

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Spatiotemporal Dynamics and Driving Forces of Urban Land-Use Expansion: A Case Study of the Yangtze River Economic Belt, China

Cited by 68 publications

References 46 publications

Spatially Explicit Evaluation and Driving Factor Identification of Land Use Conflict in Yangtze River Economic Belt

Spatially Explicit Evaluation and Driving Factor Identification of Land Use Conflict in Yangtze River Economic Belt

Co-Evolution of Emerging Multi-Cities: Rates, Patterns and Driving Policies Revealed by Continuous Change Detection and Classification of Landsat Data

Ecological risk assessment and restoration area identification of Pengyang County on the basis of the landscape pattern and function

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