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Special Issue Editor

Dr. Maomao Zhang

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Guest Editor

College of Public Administration, Huazhong University of Science and Technology, Wuhan 430079, China
Interests: land use and management; urban climate; regional Development and sustainability; environmental economics

Special Issue Information

Dear Colleagues,

Urbanization is growing rapidly across the globe, and cities are facing enormous challenges, including efficient management of land resources, the impacts of climate change, and the pressure of sustainable development. Under this background, big data and machine learning technologies are coming to the fore, providing powerful tools to address these complex issues. It can analyze urban land use and help urban planners better allocate land resources, optimize the spatial layout of cities, and improve the sustainable utilization of land. In addition, climate change has become a global challenge, and urban land use change is a key factor affecting urban climate change. The use of big data and machine learning to monitor and predict the impact of land use on urban climate change is a new option for formulating response strategies. Through real-time data collection and analysis, urban authorities can track various sustainable development indicators, such as land use change, carbon emissions, air quality, and green coverage. It enables planning departments to adjust policies and plans in a timely manner to ensure that cities develop in a sustainable direction.

Therefore, we believe that these technologies can not only provide better decision-making support for urban planners, but also help build smarter and more sustainable cities to meet the growing challenges of urbanization. We encourage authors to share new technologies and theories in the study of land use change simulation, urban heat island effect, and environmental issues in sustainable development, as well as case studies on land use change, urban climate, and sustainability in typical regions.

The main topics of this special issue include but are not limited to:

(1)Response of Land Use to Climate in Urban Areas|
(2)Application of Machine Learning Algorithms in Land Use Simulation
(3)Impact of Urban Climate Change on Urban Sustainable Development
(4)The Challenge of Extreme Climate on Improving Urban Resilience
(5)The great potential of big data and machine learning algorithms in land use and climate change research
(6)Effective measures to alleviate changes in urban thermal environment

We very much look forward to your submissions.

Best regards,

Dr. Maomao Zhang
Guest Editor

Manuscript Submission Information

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Keywords

big data and machine learning
land resources and regional development
land use simulation
optimization of urban land use structure
satellite remote sensing
monitoring and assessment of urban climate change
urban environmental governance
urban sustainable development
governance of urban extreme climate issues
SUHI (Surface Urban Heat Island)
changes in urban thermal environment
heat mitigation

Published Papers (2 papers)

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Research

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19 pages, 7378 KiB

Open AccessArticle

Integrated Predictive Modeling and Policy Factor Analysis for the Land Use Dynamics of the Western Jilin

by Shibo Wen, Yongzhi Wang, Haohang Song, Hengxi Liu, Zhaolong Sun and Muhammad Atif Bilal

Atmosphere 2024, 15(3), 288; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15030288 - 27 Feb 2024

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Abstract

The external environment in the transitional zone of the ecological barrier is fragile, and economic growth has resulted in a series of land degradation issues, significantly impacting regional economic development and the ecological environment. Therefore, monitoring, assessing, and predicting land use changes are crucial for ecological security and sustainable development. This study developed an integrated model comprising convolutional neural network, cellular automata, and Markov chain to forecast the land use status of western Jilin, located in the transitional zone of the ecological barrier, by the year 2030. Additionally, the study evaluated the role of land use policies in the context of land use changes in western Jilin. The findings demonstrate that the coupled modeling approach exhibits excellent predictive performance for land use prediction in western Jilin, yielding a Kappa coefficient of 93.26%. Policy drivers play a significant role in shaping land use patterns in western Jilin, as evidenced by the declining farmland accompanied by improved land utilization, the sustained high levels of forest aligning with sustainable development strategies, the ongoing restoration of waters and grassland, which are expected to show positive growth by 2030, and the steady growth in built-up areas. This study contributes to understanding the dynamics of land use in the transitional zone of the ecological barrier, thereby promoting sustainable development and ecological resilience in the region. Full article

(This article belongs to the Special Issue Impacts of Land Use and Climate Change in Urban Area: Big Data and Machine Learning)

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Review

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22 pages, 12108 KiB

Open AccessReview

A Systematic Review of the Potential Influence of Urbanization on the Regional Thunderstorm Process and Lightning Activity

by Tao Shi, Gaopeng Lu, Xiangcheng Wen, Lei Liu and Ping Qi

Atmosphere 2024, 15(3), 374; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15030374 - 19 Mar 2024

Viewed by 704

Abstract

In the context of global climate change, lightning disasters have emerged as a serious environmental factor that restricts the sustainable development of megacities. This paper provides a review of the research on the impact of urbanization on thunderstorm processes and lightning activity, exploring various aspects, such as aerosols, urban thermal effects, urban dynamic effects, and building morphology. Despite numerous significant achievements in the study of the impact of air pollutants on lightning activity, there is no consensus on whether aerosols serve to enhance or inhibit lightning activity. The temperature difference between the urban underlying surface and the natural underlying surface could sustain and promote the occurrence and development of convective systems, thus enhancing lightning activity. In terms of urban dynamics, the barrier effect has led to the maximum center of lightning appearing at the edge of a built-up area, which might be associated with factors, such as urban heat island (UHI) intensity, wind speed, synoptic background, and city size. Additionally, the size of a city and the height of the buildings was also an influencing factor on lightning activity. In summary, scholars have made progress in understanding the characteristics and drivers of urban lightning activity in recent years, but there are still some urgent problems that need to be solved: (1) How to analyze, comprehensively, the spatiotemporal patterns of urban lightning activity under different thunderstorm intensity backgrounds? (2) How to conduct analysis to investigate the influence of alterations in the boundary layer structure, water–heat energy balance, and water vapor circulation processes on urban lightning activity in the context of urbanization? (3) How to couple numerical models of different scales to enhance the understanding of the impact of complex underlying surfaces on urban lightning activity? Future studies could investigate the relationship between urbanization and thunderstorm/lightning activity using a combination of observational data, numerical modeling, and laboratory experiments, which holds promise for providing valuable theoretical insights and technical support to enhance the prediction, nowcasting, early warning, and risk assessment of thunderstorms and lightning in urban areas. Full article

(This article belongs to the Special Issue Impacts of Land Use and Climate Change in Urban Area: Big Data and Machine Learning)

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