A GIS Spatial Analysis Model for Land Use Change (Volume III)

A special issue of Geographies (ISSN 2673-7086).

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 1517

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WAT Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warszawa, Poland
Interests: spatial analysis; mapping; geoinformation; geomatics; geographical analysis
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Special Issue Information

Dear Colleagues,

Land use change is one of the most important types of environmental change, which is occurring rapidly worldwide. Land use is generally driven by demographic changes such as population growth, migration, and economic changes. Land use changes include urban sprawl, the conversion of agricultural land, land abandonment, deforestation, and reforestation. The reason for changes in LU/LC varies considerably from region to region, and covers many environmental, economic, political, and social issues. Documenting and simulating land use changes and identifying their environmental impact is becoming increasingly important, as the results may be useful for sustainable land management on a local, regional, national, or even global scale. GIS-based spatial analysis and GIS modeling have been widely used to monitor and forecast land use/land cover changes and their impact on the environment and human wellbeing. Geospatial technology also plays a key role in monitoring progress towards the Sustainable Development Goals, particularly Goal 11, as well as land use efficiency (SDG 11.3.1).

This Special Issue of Geographies aims to disseminate state-of-the-art research articles and review papers exploring GIS-based spatial analysis and models for land use/land cover change using Earth observation data (in situ and remote sensing), topographic maps, and any additional sources of information on land cover/land use. Contributions related to geography, geology, and geosciences are welcome.

Prof. Dr. Elzbieta Bielecka
Guest Editor

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Keywords

  • change detection of land use and land cover
  • urban sprawl modeling
  • deforestation modeling and monitoring
  • multitemporal spatial analysis
  • accuracy assessment
  • spatial relation between land use and population distribution
  • SDG 11.3.1
  • data mining and machine learning

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Published Papers (1 paper)

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Research

32 pages, 16539 KiB  
Article
Spatiotemporal Variation of Summertime Urban Heat Island (UHI) and Its Correlation with Particulate Matter (PM2.5) over Metropolitan Cities in Alabama
by Gamal El Afandi and Hossam Ismael
Geographies 2023, 3(4), 622-653; https://doi.org/10.3390/geographies3040033 - 2 Oct 2023
Viewed by 1290
Abstract
More than half of the global population lives in urban areas, which can cause the phenomenon known as Urban Heat Island (UHI). UHI is a phenomenon where urban areas experience higher temperatures compared to their rural surroundings. The occurrence of UHI in large [...] Read more.
More than half of the global population lives in urban areas, which can cause the phenomenon known as Urban Heat Island (UHI). UHI is a phenomenon where urban areas experience higher temperatures compared to their rural surroundings. The occurrence of UHI in large cities is primarily due to urbanization and increased vehicular emissions. Factors such as wind speed and direction, solar flux, and the thermodynamic properties of surface materials determine the intensity of UHI. It can cause thermal air circulation, leading to high concentrations of urban air pollutants such as fine particulate matter (PM2.5). These pollutants can remain suspended in the air and cause asthma and allergies. It is essential to understand the characteristics of UHI intensity and its effect on air quality. This study aims to analyze the spatiotemporal variations of UHI and their correlation with PM2.5 concentration in three Alabama cities, namely Birmingham, Montgomery, and Mobile, during the summer seasons of 2002, 2012, and 2022. The study also compares UHI in these cities with nearby rural areas to determine the effect of urbanization by calculating the Normalized Difference Building Index (NDBI). To achieve these objectives, the Land Surface Temperature (LST), UHI intensity, and NDBI Datasets were analyzed. The results showed that PM2.5 concentrations in the cities have been decreasing annually since 2002, leading to an improvement in air quality. There was a negative linear correlation between UHI intensity and PM2.5 concentration. However, LST remained consistently high throughout the study period. The correlation between UHI intensity and NDBI was positive. The findings of this study can help us better understand the dynamics and driving mechanisms of the urban heat environment. Furthermore, they can assist urban metropolitan planners in developing more efficient mitigation strategies that reduce the negative impacts of UHI and PM2.5 concentrations on the environment. Full article
(This article belongs to the Special Issue A GIS Spatial Analysis Model for Land Use Change (Volume III))
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