Land use change and driving factors in rural China during the period 1995-2015

2020 ◽  
Vol 99 ◽  
pp. 105048
Author(s):  
Yang Zhou ◽  
Xunhuan Li ◽  
Yansui Liu
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Rural China ◽  
Driving Factors

A study of the system dynamics coupling model of the driving factors for multi-scale land use change

2016 ◽  
Vol 75 (6) ◽  
Author(s):  
J. S. Zhao ◽  
L. Yuan ◽  
M. Zhang
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
System Dynamics ◽  
Coupling Model ◽  
Driving Factors ◽  
Multi Scale

scholarly journals Analysis of Characteristics and Driving Factors of Land-Use Changes in the Tarim River Basin from 1990 to 2018

2021 ◽  
Vol 13 (18) ◽  
pp. 10263
Author(s):  
Yang Wang ◽  
Tingting Xia ◽  
Remina Shataer ◽  
Shuai Zhang ◽  
Zhi Li
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
River Basin ◽  
Center Of Gravity ◽  
Driving Factors ◽  
Tarim River ◽  
Tarim River Basin ◽  
Cultivated Land ◽  
The Tarim River ◽  
The Tarim River Basin

Land-use and cover change is an important indicator for exploring global change trends, with in-depth research on land use and its driving factors being of particular significance in forging ecologically sustainable development. The present work used the Tarim River Basin as the study area, while the land-use transfer matrix, normalized difference vegetation index (NDVI), regional center-of-gravity model, and night-time-light remote-sensing mutual correction method were employed to explore temporal and spatial characteristics of land-use change and its driving factors. The results show the following. (1) From 1990 to 2018, land-use types in the study area significantly changed, with the cultivated land increasing by 73.9% and grassland area decreasing at a rate of 6.38 × 104 hm2 per year. (2) Areas with a natural vegetation NDVI above 0.2 appeared to follow a growth trend, with an area growth of 259.12 × 104 hm2 at a rate of 14.39 × 104 hm2/a. Average annual temperature and precipitation showed a fluctuating upward trend. (3) The center of gravity of land-use type area moved significantly. The center of gravity of cultivated land was moving in the same direction as the GDP and population center of gravity, migrating to the northeast. The migration distance of the center of gravity of cultivated land area was 212.59 km, the center of gravity migration rate of GDP was 14.44 km/a, and the population center of gravity was 812.21 km. (4) During the study period, the brightness of night lights in the study area was distributed in a circular shape, with more in the northwest and less in the southeast. Brightness gradually increased and showed an expansion trend, increasing from 0.3% to 6.3%. Among the influencing factors of spatial change of land-use change, natural factors such as climate change were related to the process of land-use/-cover change in the Tarim River Basin. Overall, human activities had the most obvious impact on land-use change.

Spatial Analysis of Cultural Heritage Landscapes in Rural China: Land Use Change and Its Risks for Conservation

2016 ◽  
Vol 57 (6) ◽  
pp. 1304-1318 ◽  
Author(s):  
Huirong Yu ◽  
Peter H. Verburg ◽  
Liming Liu ◽  
David A. Eitelberg
Keyword(s):  
Land Use ◽  
Spatial Analysis ◽  
Land Use Change ◽  
Cultural Heritage ◽  
Rural China

scholarly journals Land-use change simulation and assessment of driving factors in the loess hilly region—a case study as Pengyang County

2009 ◽  
Vol 164 (1-4) ◽  
pp. 133-142 ◽  
Author(s):  
Zhanqiang Zhu ◽  
Liming Liu ◽  
Zhantao Chen ◽  
Junlian Zhang ◽  
Peter H. Verburg
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Driving Factors ◽  
Hilly Region ◽  
Loess Hilly Region

Implications of land-use change in rural China: A case study of Yucheng, Shandong province

2014 ◽  
Vol 40 ◽  
pp. 111-118 ◽  
Author(s):  
Yansui Liu ◽  
Ren Yang ◽  
Hualou Long ◽  
Jay Gao ◽  
Jieyong Wang
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Rural China ◽  
Shandong Province

Participatory Bayesian network modeling to understand driving factors of land-use change decisions: insights from two case studies in northeast Madagascar

2020 ◽  
Vol 15 (1) ◽  
pp. 69-90 ◽  
Author(s):  
R. Ntsiva N. Andriatsitohaina ◽  
Enrico Celio ◽  
Jorge C. Llopis ◽  
Zo H. Rabemananjara ◽  
Bruno S. Ramamonjisoa ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Bayesian Network ◽  
Case Studies ◽  
Network Modeling ◽  
Driving Factors ◽  
Bayesian Network Modeling

scholarly journals Land Use Change and Driving Force Analysis of Wetland in Poyang Lake Based on Remote Sensing

2021 ◽  
Author(s):  
Chunqiu Ren ◽  
Bin Dong ◽  
Ping Wang ◽  
Lei Fang ◽  
Xiang Gao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Paddy Field ◽  
Poyang Lake ◽  
Driving Factors ◽  
Land Resource ◽  
Wetland Protection ◽  
Dry Land ◽  
Construction Land

Abstract As an important wetland in the world, Poyang Lake wetland is constantly changing its land use mode due to economic development and human activities, thus affecting the ecological environment of wetland.Landsat remote sensing images from 1986 to 2020 are utilized to obtain land use information data through supervised classification and interpretation. Combined with ENVI and ArcGIS software, five land use type maps are generated by taking 8 years or so as the interval period. Land use transfer matrix and land use dynamic attitude are adopted.This paper analyzes the wetland land use change in Poyang Lake and explores the driving factors of wetland land use change in Poyang Lake.The results show that the wetland land use types in Poyang Lake will change greatly from 1985 to 2020, and the change range of construction land, mudflat, paddy field and dry land is relatively large and shows an increasing trend.The area of water and grassland decreased on the whole, and the transferred area was large, which was mainly transferred to construction land, paddy field and dry land.The woodland area increased slowly, but the change range was not large from the perspective of dynamic attitude.The change of wetland area of reed flat decreased first and then increased, and the overall land use change was relatively gentle.According to the grey correlation analysis, the total population and annual precipitation are the main driving factors of land use change in Poyang Lake wetland.Through the above research, it provides important reference for wetland protection and land resource management of Poyang Lake.

scholarly journals How Does ICT Expansion Drive “Smart” Urban Growth? A Case Study of Nanjing, China

2020 ◽  
Vol 5 (1) ◽  
pp. 129-139
Author(s):  
Zipan Cai ◽  
Vladimir Cvetkovic ◽  
Jessica Page
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Urban Growth ◽  
Driving Forces ◽  
Linear Regression Analysis ◽  
Urban Land ◽  
Urban Land Use ◽  
Driving Factors ◽  
Utilization Efficiency ◽  
Urban Land Use Change

In the context of accelerated urbanization, socioeconomic development, and population growth, as well as the rapid advancement of information and communication technology (ICT), urban land is rapidly expanding worldwide. Unplanned urban growth has led to the low utilization efficiency of land resources. Also, ecological and agricultural lands are continuously sacrificed for urban construction, which in the long-term may severely impact the health of citizens in cities. A thorough understanding of the mechanisms and driving forces of a city’s urban land use changes, including the influence of ICT development, is therefore crucial to the formation of optimal and feasible urban planning in the new era. Taking Nanjing as a study case, this article attempts to explore the measurable “smart” driving indicators of urban land use change and analyze the tapestry of the relationship between these and urban land use change. Different from the traditional linear regression analysis method of driving force of urban land use change, this study focuses on the interaction relationship and the underlying causal relationship among various “smart” driving factors, so it adopts a fuzzy statistical method, namely the grey relational analysis (GRA). Through the integration of literature research and known effective data, five categories of “smart” indicators have been taken as the primary driving factors: industry and economy, transportation, humanities and science, ICT systems, and environmental management. The results show that these indicators have different impacts on driving urban built-up land growth. Accordingly, optimization possibilities and recommendations for development strategies are proposed to realize a “smarter” development direction in Nanjing. This article confirms the effectiveness of GRA for studies on the driving mechanisms of urban land use change and provides a theoretical basis for the development goals of a smart city.

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