scholarly journals Quantifying the Speed, Landscape Pattern Changes and Its Driving Factors of Shenzhen China

2021 ◽  
Vol 308 ◽  
pp. 02004
Author(s):  
Qinxue He ◽  
Yuhong Chen ◽  
Yunlei Su
Keyword(s):  
Land Use ◽  
Urban Area ◽  
Urban Growth ◽  
Urban Expansion ◽  
Urban Land ◽  
Driving Factors ◽  
Cultivated Land ◽  
Land Use Types ◽  
University Enrolment ◽  
Main Factors

Urban expansion has always been a topic of great concern. The purpose of this study is to explore land use change and the types of urban expansion in Shenzhen from 1995 to 2015, and to indicate the driving factors of this change, so as to provide a paradigm for other similar studies. By analysing the landscape expansion index and the correlation coefficient between urban area and various factors in Shenzhen, the following conclusions are obtained: 1) The main changes of land use types are the decrease of cultivated land and the increase of urban land. The land cover type changed most dramatically from 2000 to 2005, and the urban land transformed from cultivated land and grassland occupied most of the area. 2) Analysis shows that during the 20 years from 1995 to 2015, the main expansion type is edge-expansion. In detail, during the period from 1995 to 2010, the proportion of infilling has been increasing, while that of the outlying has been decreasing. After 2010, the urban area of Shenzhen increased slightly. Besides, according to the landscape expansion index, Shenzhen experienced dramatic urban expansion from 2000 to 2005. 3) Education and population growth are the main factors of urban growth in Shenzhen, which is reflected in the strongest correlation between university enrolment rate and urban area.

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 growth dynamics based on surface Albedo changes in Petrolina, Brazil

2020 ◽  
Vol 42 ◽  
pp. e46270
Author(s):  
Michele Laurentino de Oliveira ◽  
Iana Alexandra Alves Rufino ◽  
John Elton de Brito Leite Cunha ◽  
Rochele Sheila Vasconcelos ◽  
Higor Costa de Brito
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Urban Growth ◽  
Land Use Planning ◽  
Quantitative Methods ◽  
Surface Albedo ◽  
Urban Expansion ◽  
Temporal Analysis ◽  
Urban Land ◽  
Urban Land Use

Cities keep growing, and in most of the cases this expansion process is hard to model and describe for planning actions. Quantitative methods are increasingly used to help planning, monitoring, and regulating urban land-use processes. Remote sensing images series are making possible different types of spatial-temporal analysis of the Earth surface. Surface albedo is a remote sensing product acquired in a long series of satellite images such as Landsat (more than 40 years of observation). Those analyses allow measuring waterproofed areas for urban drainage studies, as well as monitoring urban spreading patterns, growth vectors, and issues related to comfort and environmental quality, as well as about land use and land-use planning (directives for master plans) among others. This article shows the direct applicability of surface albedo changes as an indicator of urban land-cover changes. The current study analyzed the urban area of Petrolina County (PE) in the following periods: 2001 and 2006, 2006 and 2011, and 2011 and 2017. Such analysis uses the surface albedo variation along the time and results showed a strong correlation between increased surface albedo and urban expansion. Besides, it enabled to observe the relation between the high urban growth in the 2011-2017 period and the urban spot expansion by 14% (approximately 590 thousand square meters of territorial extension). The Urban development stood out in the Northern and Southwestern regions of Petrolina County.

scholarly journals Spatial Monitoring of Urban Expansion Using Satellite Remote Sensing Images: A Case Study of Amman City, Jordan

2019 ◽  
Vol 11 (8) ◽  
pp. 2260 ◽  
Author(s):  
Hussam Al-Bilbisi
Keyword(s):  
Land Use ◽  
Urban Area ◽  
Urban Growth ◽  
Urban Areas ◽  
Urban Expansion ◽  
Capital City ◽  
Annual Rate ◽  
Average Annual Rate ◽  
Rate Of Increase ◽  
Landsat Satellite

Amman, the capital city of Jordan, faces urbanization challenges and lacks reliable data for urban planning. This study is aimed at assessing, monitoring, and mapping urban land cover using multitemporal Landsat satellite images. Four different land use/cover maps were produced; periods of over ten years between 1987 and 2017 (i.e., in 1987, 1997, 2007, and 2017) were used to evaluate and analyze urban expansion visually and quantitatively. Supervised classification technique followed by the post classification comparison change detection approach was used to analyze images. Over the past three decades, the urban area has increased rapidly in Amman. It increased by 90.78 km2, from 149.08 km2 in 1987 to 237.86 km2 in 2017, with an average annual rate of increase of 2.03%. Urban area increases were significantly higher in the first 10 years of the study period (i.e., from 1987 to 1997), during which the average annual rate of increase reached 3.33%, while it was 2.04% for the last two decades of the study period (i.e., from 1997 to 2017). Urban growth in Amman generally occurred along transport routes away from the core of Amman, and as a result, this growth led to the expansion of urban areas into other types of land use/cover classes, particularly vegetation areas. The spatial analysis of urban expansion and trends of urban growth in Amman could provide the required input data for the urban modeling of the city.

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.

scholarly journals Spatiotemporal Patterns of Urban Land Use Change in Typical Cities in the Greater Mekong Subregion (GMS)

2019 ◽  
Vol 11 (7) ◽  
pp. 801 ◽  
Author(s):  
Hui Cao ◽  
Jian Liu ◽  
Jianglong Chen ◽  
Jinlong Gao ◽  
Guizhou Wang ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Urban Expansion ◽  
Urban Land ◽  
Urban Land Use ◽  
Phnom Penh ◽  
Land Use Types ◽  
Natural Land ◽  
Urban Land Use Change ◽  
Greater Mekong Subregion

The Greater Mekong Subregion (GMS) has experienced rapid economic growth and urbanization. However, few studies have paid attention to urban land use dynamics, especially spatiotemporal patterns of urban expansion and land use change, in this region. This research aimed to conduct a comprehensive study of urban land use change in Xishuangbanna, Yangon, Vientiane, Phnom Penh, Bangkok, and Ho Chi Minh City, from 1990 to 2015. The analysis was based on land use maps derived from Landsat satellite products and employed urban expansion intensity, sector analysis, gradient-direction analysis, and landscape metrics. The results show Xishuangbanna, Yangon, Vientiane, Phnom Penh, Bangkok, and Ho Chi Minh City all experienced dramatic urban expansion and land use change since 1990, with urban expansion intensities of 15.01, 5.26, 9.15, 1.56, 11.88 and 11.91, respectively. The landscape metrics analysis indicated that urban areas were always aggregated and self-connected, while other land use types showed trends of disaggregation and fragmentation. In the process of urban expansion, paddy and natural land use types were commonly transformed to built up area. The results further reveal several common issues in urban land use, e.g. land fragmentation and loss of natural land use types. Finally, the discussion on the relationship between government policy and land use change for these cities shows land reform and attitude toward foreign direct investments played important roles in urban land use change in GMS.

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.

scholarly journals REMOTE SENSING AND MULTIVARIATE LOGISTIC REGRESSION MODEL FOR THE ESTIMATION OF URBAN EXPANSION (CASE OF DARKHAN CITY, MONGOLIA)

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 721-726
Author(s):  
P. Myagmartseren ◽  
D. Ganpurev ◽  
I. Myagmarjav ◽  
G. Byambakhuu ◽  
G. Dabuxile
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Regression Model ◽  
Urban Area ◽  
Urban Growth ◽  
Logistic Regression Model ◽  
Urban Expansion ◽  
Landsat Tm ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression

Abstract. Urban expansion and land use and land cover change (LUCC) studies are a key knowledge of efficient local governance and urban planning and a lot contributing to the future sustainable development of the city. The main goal of the paper is to model a future urban spatial expansion by 2029 and 2039 of Darkhan city using Landsat TM satellite imagery (land use and cover change map of 1999, 2009, and 2019) and multivariate logistic regression model. Clark Lab’s (Clark University) IDRISI & TerrSet software applied for the urban expansion prediction and the correlation between expansion and driving factors. On account of multivariate logistics regression modelling, eight physical factors influencing urban expansion identified to predict urban expansion based on USGS Landsat TM imageries (Landsat Multispectral Scanner with 60 m resolution). The regression statistic accounted for the probability of future urban expansion was positive. Overall, the LUCC has estimated the transition of natural cover to the impervious surface in Darkhan city. Our result estimates an increase in the built-up area and slum area during the period 1999–2009 and 2009–2019, represents LUCC was characterized by an external transformation from natural to urban area. According to the future urban growth prediction, the urban area would be significantly spread into the open space and natural vegetation area. The main findings stated here are that Darkhan city is expanding in an unsystematic way, even though the urban growth has not been analysed in detail and has a bad case of urban unregulated sprawl.

THE IMPACT OF DIFFERENT URBAN LAND USE TYPES ON AIR POLLUTION IN THE MEGACITY OF SÃO PAULO

2020 ◽  
Vol 7 (1) ◽  
pp. 91
Author(s):  
Júlio Barboza Chiquetto ◽  
Maria Elisa Siqueira Silva ◽  
Rita Yuri Ynoue ◽  
Flávia Noronha Dutra Ribieiro ◽  
Débora Souza Alvim ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Urban Land ◽  
Sao Paulo ◽  
Urban Land Use ◽  
São Paulo ◽  
Land Use Types ◽  
The Impact

A poluição do ar é influenciada por fatores naturais e antropogênicos. Quatro pontos de monitoramento (veicular, comercial, residencial e background urbano (BGU))da poluição do ar em São Paulo foram avaliados durante 16 anos, revelando diferenças significativas devidoao uso do solo em todas as escalas temporais. Na escala diurna, as concentrações de poluentes primários são duas vezes mais altas nos pontos veicular e residencial do que no ponto BGU, onde a concentração de ozonio (O3) é 50% mais alta. Na escala sazonal, as concentrações de monóxido de carbono(CO) variaram em 80% devido ao uso do solo, e 55% pela sazonalidade.As variações sazonais ede uso do solo exercem impactos similares nas concentrações de O3 e monóxido de nitrogênio (NO). Para o material particulado grosso (MP10) e o dióxido de nitrogênio(NO2), as variações sazonais são mais intensas do que as por uso do solo. Na série temporal de 16 anos, o ponto BGU apresentou correlações mais fortes e significativas entre a média mensal de ondas longas (ROL) e o O3 (0,48) e o MP10 (0,37), comparadas ao ponto veicular (0,33 e 0,22, respectivamente). Estes resultados confirmam que o uso do solo urbano tem um papel significativo na concentração de poluentes em todas as escalas de análise, embora a sua influência se torne menos pronunciada em escalas maiores, conforme a qualidade do ar transita de um sistema antropogênico para um sistema natural. Isto poderá auxiliar decisões sobre políticas públicas em megacidades envolvendo a modificação do uso do solo.

scholarly journals Optimization of a Novel Urban Growth Simulation Model Integrating an Artificial Fish Swarm Algorithm and Cellular Automata for a Smart City

2021 ◽  
Vol 13 (4) ◽  
pp. 2338
Author(s):  
Xinxin Huang ◽  
Gang Xu ◽  
Fengtao Xiao
Keyword(s):  
Land Use ◽  
Sustainable Development ◽  
Urban Growth ◽  
Smart City ◽  
Urban Land ◽  
Urban Land Use ◽  
Growth Simulation ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Ca Model

As one of the 17 Sustainable Development Goals, it is sensible to analysis historical urban land use characteristics and project the potentials of urban sustainable development for a smart city. The cellular automaton (CA) model is the widely applied in simulating urban growth, but the optimum parameters of variables driving urban growth in the model remains to be continued to improve. We propose a novel model integrating an artificial fish swarm algorithm (AFSA) and CA for optimizing parameters of variables in the urban growth model and make a comparison between AFSA-CA and other five models, which is used to study a 40-year urban land growth of Wuhan. We found that the urban growth types from 1995 to 2015 appeared relatively consistent, mainly including infilling, edge-expansion and distant-leap types in Wuhan, which a certain range of urban land growth on the periphery of the central area. Additionally, although the genetic algorithms (GA)-CA model and the AFSA-CA model among the six models due to the distance variables, the parameter value of the GA-CA model is −15.5409 according to the fact that the population (POP) variable should be positively. As a result, the AFSA-CA model regardless of the initial parameter setting is superior to the GA-CA model and the GA-CA model is superior to all the other models. Finally, it is projected that the potentials of urban growth in Wuhan for 2025 and 2035 under three scenarios (natural urban land growth without any restrictions (NULG), sustainable urban land growth with cropland protection and ecological security (SULG), and economic urban land growth with sustainable development and economic development in the core area (EULG)) focus mainly on existing urban land and some new town centers based on AFSA-CA urban growth simulation model. An increasingly precise simulation can determine the potential increase area and quantity of urban land, providing a basis to judge the layout of urban land use for urban planners.

scholarly journals Analysis and prediction of LUCC change in Huang-Huai-Hai river basin

2020 ◽  
Vol 12 (1) ◽  
pp. 1406-1420
Author(s):  
Jianwei Wang ◽  
Kun Wang ◽  
Tianling Qin ◽  
Hanjiang Nie ◽  
Zhenyu Lv ◽  
...  
Keyword(s):  
Land Use ◽  
River Basin ◽  
Land Use Planning ◽  
Climate Model ◽  
Dynamic Change ◽  
Cultivated Land ◽  
Emission Scenarios ◽  
Future Change ◽  
Land Use Types ◽  
Downstream Area

AbstractLand use/cover change plays an important role in human development and environmental health and stability. Markov chain and a future land use simulation model were used to predict future change and simulate the spatial distribution of land use in the Huang-Huai-Hai river basin. The results show that cultivated land and grassland are the main land-use types in the basin, accounting for about 40% and 30%, respectively. The area of cultivated land decreased and artificial surfaces increased from 1980 to 2010. The degree of dynamic change of land use after the 1990s was greater than that before the 1990s. There is a high probability of exchange among cultivate land, forest and grassland. The area of forest decreased before 2000 and increased after 2000. Under the three emission scenarios (RCP2.6, RCP4.5, and RCP8.5) of IPSL-CM5A-LR climate model, the area of cultivated land will decrease and that of grassland will increase in the upstream area while it will decrease in the downstream area. The above methods and rules will be of great help to future land use planning.

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