scholarly journals Scenario Analysis on Climate Change Impacts of Urban Land Expansion under Different Urbanization Patterns: A Case Study of Wuhan Metropolitan

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Xinli Ke ◽  
Feng Wu ◽  
Caixue Ma
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Urban Expansion ◽  
Regional Climate ◽  
Urban Land ◽  
Urban Land Use ◽  
Rapid Urbanization ◽  
Urban Land Expansion ◽  
The Impact

Urban land expansion plays an important role in climate change. It is significant to select a reasonable urban expansion pattern to mitigate the impact of urban land expansion on the regional climate in the rapid urbanization process. In this paper, taking Wuhan metropolitan as the case study area, and three urbanization patterns scenarios are designed to simulate spatial patterns of urban land expansion in the future using the Partitioned and Asynchronous Cellular Automata Model. Then, simulation results of land use are adjusted and inputted into WRF (Weather Research and Forecast) model to simulate regional climate change. The results show that: (1) warming effect is strongest under centralized urbanization while it is on the opposite under decentralized scenario; (2) the warming effect is stronger and wider in centralized urbanization scenario than in decentralized urbanization scenario; (3) the impact trends of urban land use expansion on precipitation are basically the same under different scenarios; (4) and spatial distribution of rainfall was more concentrated under centralized urbanization scenario, and there is a rainfall center of wider scope, greater intensity. Accordingly, it can be concluded that decentralized urbanization is a reasonable urbanization pattern to mitigate climate change in rapid urbanization period.

scholarly journals Comprehensive Assessment of the Effect of Urban Built-Up Land Expansion and Climate Change on Net Primary Productivity

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Pengyan Zhang ◽  
Yanyan Li ◽  
Wenlong Jing ◽  
Dan Yang ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Use Change ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Urban Expansion ◽  
Ecosystem Functions ◽  
Rapid Urbanization ◽  
Important Indicator ◽  
The Impact

Urbanization is causing profound changes in ecosystem functions at local and regional scales. The net primary productivity (NPP) is an important indicator of global change, rapid urbanization and climate change will have a significant impact on NPP, and urban expansion and climate change in different regions have different impacts on NPP, especially in densely populated areas. However, to date, efforts to quantify urban expansion and climate change have been limited, and the impact of long-term continuous changes in NPP has not been well understood. Based on land use data, night light data, NPP data, climate data, and a series of social and economic data, we performed a comprehensive analysis of land use change in terms of type and intensity and explored the pattern of urban expansion and its relationship with NPP and climate change for the period of 2000–2015, taking Zhengzhou, China, as an example. The results show that the major form of land use change was cropland to built-up land during the 2000–2015 period, with a total area of 367.51 km2 converted. The NPP exhibited a generally increasing trend in the study area except for built-up land and water area. The average correlation coefficients between temperature and NPP and precipitation and NPP were 0.267 and 0.020, respectively, indicating that an increase in temperature and precipitation can promote NPP despite significant spatial differences. During the examined period, most expansion areas exhibited an increasing NPP trend, indicating that the influence of urban expansion on NPP is mainly characterized by an evident influence of the expansion area. The study can provide a reference for Zhengzhou and even the world's practical research to improve land use efficiency, increase agricultural productivity and natural carbon sinks, and maintain low-carbon development.

scholarly journals Analyzing Urban Growth Pattern and Driving Factors Using Remote Sensing and GIS: A Case Study of Banepa Municipality, Nepal

2020 ◽  
Vol 20 (1) ◽  
pp. 9-18
Author(s):  
Rabina Twayana ◽  
Sijan Bhandari ◽  
Reshma Shrestha
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Urban Growth ◽  
Growth Pattern ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Urban Land ◽  
Driving Factors ◽  
Rapid Urbanization ◽  
The Impact

Nepal is considered one of the rapidly urbanizing countries in south Asia. Most of the urbanization is dominated in large and medium cities i.e., metropolitan, sub-metropolitan, and municipalities. Remote Sensing and Geographic Information System (GIS) technologies in the sector of urban land governance are growing day by day due to their capability of mapping, analyzing, detecting changes, etc. The main aim of this paper is to analyze the urban growth pattern in Banepa Municipality during three decades (1992-2020) using freely available Landsat imageries and explore driving factors for change in the urban landscape using the AHP model. The Banepa municipality is taken as a study area as it is one of the growing urban municipalities in the context of Nepal. The supervised image classification was applied to classify the acquired satellite image data. The generated results from this study illustrate that urbanization is gradually increasing from 1992 to 2012 while, majority of the urban expansion happened during 2012-2020, and it is still growing rapidly along the major roads in a concentric pattern. This study also demonstrates the responsible driving factors for continuous urban growth during the study period. Analytical Hierarchy Process (AHP) was adopted to analyze the impact of drivers which reveals that, Internal migration (57%) is major drivers for change in urban dynamics whereas, commercialization (25%), population density (16%), and real estate business (5%) are other respective drivers for alteration of urban land inside the municipality. To prevent rapid urbanization in this municipality, the concerned authorities must take initiative for proper land use planning and its implementation on time. Recently, Nepal Government has endorsed Land Use Act 2019 for preventing the conversion of agricultural land into haphazard urban growth.

scholarly journals Urban Expansion and Farmland Loss in Beijing during 1980–2015

2018 ◽  
Vol 10 (11) ◽  
pp. 3927 ◽  
Author(s):  
Yingjie Hu ◽  
Xiangbin Kong ◽  
Ji Zheng ◽  
Jin Sun ◽  
Linlin Wang ◽  
...  
Keyword(s):  
Land Use ◽  
Urban Expansion ◽  
Urban Land ◽  
Urban Land Use ◽  
Average Quality ◽  
The Past ◽  
Land Use Efficiency ◽  
Farmland Loss ◽  
Use Efficiency ◽  
Urban Land Expansion

The analysis of urban land expansion and farmland loss is essential to adequately understand the land use change in a rapidly urbanizing China. We found that both urban expansion and farmland loss in Beijing experienced high- and low-speed stages and their spatial patterns were consistent during the past 35 years as most of the newly expanded urban land was converted from farmland. The area of farmland loss by urban expansion in Beijing is 12.6 km2/year, 39.86 km2/year, 23.38 km2/year, and 41.11 km2/year during the period of 1980–1990, 1990–2000, 2000–2010, and 2010–2015, respectively. The urban expansion in Beijing continuously preferred to consume “above average” quality farmland during 1980–2015. Meanwhile, although the urban expansion in Beijing was highly dependent on occupying farmland, the dependence of urban expansion on farmland consumption has declined over time. However, the contribution of urban expansion on farmland loss increased during 1980–2010 and decreased afterward. In order to protect the farmland from urban expansion, we call for more effort to improve the urban land use efficiency with rigid controls over areas of urban expansion.

scholarly journals Effects of Hierarchical City Centers on the Intensity and Direction of Urban Land Expansion: A Case Study of Beijing

Land ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 312 ◽  
Author(s):  
Daquan Huang ◽  
Xin Tan ◽  
Tao Liu ◽  
Erxuan Chu ◽  
Fanhao Kong
Keyword(s):  
Land Use ◽  
Spatial Patterns ◽  
Driving Forces ◽  
Land Development ◽  
Urban Land ◽  
Urban Land Use ◽  
Urban Land Expansion ◽  
Land Use And Management ◽  
Spatial Analysis Method

Worldwide urban spatial expansion has become a hot topic in recent decades. To develop effective urban growth containment strategies, it is important to understand the spatial patterns and driving forces of urban sprawl. By employing a spatial analysis method and land use survey data for the years 1996–2010, this study explores the effects of hierarchical administrative centers on the intensity and direction of urban land expansion in a Beijing municipality. The results are as follows: (1) land development intensity and expansion speeds are both affected significantly by the municipal and district and county centers where the governments hold a lot of administrative, public, and economic resources. (2) The distances to the administrative centers are determinant factors for the direction of urban land expansion. Except for several subregions adjacent to the municipal center, the closer the area is to an administrative center, the more likely it is that the expansion direction points toward the center. (3) The spatial patterns of urban land development are shaped jointly by governments at different levels, and transportation lines also play a role in remote areas. These findings are expected to have consulting value for future policymaking on urban land use and management in mega-cities, especially those with strong local government powers in other transition economies and developing countries.

scholarly journals Urban Land Expansion and Sustainable Land Use Policy in Shenzhen: A Case Study of China’s Rapid Urbanization

2015 ◽  
Vol 8 (1) ◽  
pp. 16 ◽  
Author(s):  
Jing Qian ◽  
Yunfei Peng ◽  
Cheng Luo ◽  
Chao Wu ◽  
Qingyun Du
Keyword(s):  
Land Use ◽  
Urban Land ◽  
Land Use Policy ◽  
Sustainable Land Use ◽  
Rapid Urbanization ◽  
Urban Land Expansion

scholarly journals ESTIMATING BUILT-UP AREA CHANGE IN ULAANBAATAR CITY, MONGOLIA

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 143-147
Author(s):  
P. Myagmartseren ◽  
I. Myagmarjav ◽  
N. Enkhtuya ◽  
G. Byambakhuu ◽  
T. Bazarkhand
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Urban Expansion ◽  
Land Development ◽  
Urban Land ◽  
Urban Land Use ◽  
Chain Model ◽  
Rapid Urbanization ◽  
Study Results

Abstract. Long-term urban built-up area changes of the Ulaanbaatar city has accelerated since the 1950s and due to rapid urbanization most of the Mongolian population, or about 68%, live in urban areas. The systematic understanding of urban land expansion is a crucial clue for urban land use planning and sustainable land development. Therefore, in this paper, we used a Markov chain model and cellular automata (CA) to simulate and predict current and future built-up areas expansion is Ulaanbaatar. Landsat imageries (Landsat TM 5, Landsat ETM 7 and Landsat OLI 8) of 1988, 1998, 2008, and 2017 were used to derive main land use classes. Clark Lab’s (Clark University) Geospatial Monitoring and Model software had been used for the urban expansion prediction. The results are innovated to comparable to validate with other study results by using a different kind of methods. Built-up area expansion modeled and predicted 2028’s trends based on a historical expansion of the Ulaanbaatar city between 1988 and 2017, which are prepared according to input model requirements. The built-up area was 7282 hectares (ha) in 1988 and has expanded to 31144 ha in 2017. The built-up area growth of the Ulaanbaatar city has reached 4.3 times over the past 30 years, and from 2017 to 2028 the expansion of the built-up area will be 1.5 times. A comparison of urban expansion from 1988 to 2017 has revealed a rapid built-up invasion to the previous areas of agriculture, grassland, and forest. Simulation performance of Markov chain with the cellular automata model can be used for an improvement in the understanding of the urban expansion processes while allowing helpful for better planning of Ulaanbaatar city, as well as for other rapidly developing towns of Mongolia.

scholarly journals Spatiotemporal Evolution of Urban Expansion Using Landsat Time Series Data and Assessment of Its Influences on Forests

2020 ◽  
Vol 9 (2) ◽  
pp. 64 ◽  
Author(s):  
Meng Zhang ◽  
Huaqiang Du ◽  
Fangjie Mao ◽  
Guomo Zhou ◽  
Xuejian Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Decision Tree ◽  
Urban Expansion ◽  
Urban Land ◽  
Urban Land Use ◽  
Series Data ◽  
Cultivated Land ◽  
The Impact ◽  
C5.0 Decision Tree

Analysis of urban land use dynamics is essential for assessing ecosystem functionalities and climate change impacts. The focus of this study is on monitoring the characteristics of urban expansion in Hang-Jia-Hu and evaluating its influences on forests by applying 30-m multispectral Landsat data and a machine learning algorithm. Firstly, remote sensed images were preprocessed with radiation calibration, atmospheric correction and topographic correction. Then, the C5.0 decision tree was used to establish classification trees and then applied to make land use maps. Finally, spatiotemporal changes were analyzed through dynamic degree and land use transfer matrix. In addition, average land use transfer probability matrix (ATPM) was utilized for the prediction of land use area in the next 20 years. The results show that: (1) C5.0 decision tree performed with precise accuracy in land use classification, with an average total accuracy and kappa coefficient of more than 90.04% and 0.87. (2) During the last 20 years, land use in Hang-Jia-Hu has changed extensively. Urban area expanded from 5.84% in 1995 to 21.32% in 2015, which has brought about enormous impacts on cultivated land, with 198,854 hectares becoming urban, followed by forests with 19,823 hectares. (3) Land use area prediction based on the ATPM revealed that urbanization will continue to expand at the expense of cultivated land, but the impact on the forests will be greater than the past two decades. Rationality of urban land structure distribution is important for economic and social development. Therefore, remotely sensed technology combined with machine learning algorithms is of great significance to the dynamic detection of resources in the process of urbanization.

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