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Application of Remote Sensing-Based Monitoring of Local Climate in Urban Areas

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: 20 November 2024 | Viewed by 1928

Special Issue Editors

Department of Geographic Information Science, Nanjing University, Nanjing 210046, China
Interests: land cover mapping; urban remote sensing; machine learning; deep learning; geoinformation; very high resolution; object-based image analysis; big data; automation; change detection; uncertainty; human geography
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Guest Editor
State Key Laboratory of Space-Ground Integrated Information Technology, Space Star Technology Company Ltd., Beijing 100095, China
Interests: remote sensing image interpretation; target detection; semantic segmentation; machine learning; spatial information fusion

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Guest Editor
Natural Resources and Ecosystem Services, Institute for Global Environmental Strategies, Kanagawa 240-0115, Japan
Interests: geographic information systems (GIS); remote sensing; spatial modeling; and data mining for urban and environmental analysis and planning; mapping urban land cover (green space, impervious surfaces, etc.); monitoring forest health using fine resolution satellite imagery
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Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: urban landscapes; thermal environment; human comfort; remote sensing; numerical simulation
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Guest Editor
Beidou Research Institute, Faculty of Engineering, South China Normal University, Foshan 528225, China
Interests: geospatial big data; urban landscape; land cover/use

Special Issue Information

Dear Colleagues,

Understanding and monitoring the local climate in urban areas is crucial for addressing the challenges posed by rapid urbanization and climate change. Remote sensing-based monitoring has emerged as a powerful tool to collect and analyze valuable data related to urban climate dynamics. This Special Issue aims to highlight the significance of remote sensing in studying local climate patterns and its application in urban planning, environmental management, and climate change mitigation efforts, in order to address climate-related challenges and foster sustainable urban development.

The primary objective of this Special Issue is to explore the diverse applications and potential of remote sensing-based monitoring of local climate in urban areas. By examining the latest research findings and advancements in the field, this Special Issue seeks to advance our understanding of urban climate dynamics and provide insights into sustainable urban development practices. The subject matter aligns closely with the journal's scope, as it focuses on interdisciplinary research in environmental sciences and emphasizes the integration of remote sensing technology.

Contributions to this Special Issue are encouraged to cover, but are not limited to, the following themes:

a. Urban heat island and local climate zones mapping:

  • Detection and characterization of urban heat islands using remote sensing techniques.
  • Assessment of urban morphology and land cover changes influencing heat island formation.
  • Methods for local climate zones mapping using remote sensing and GIS techniques.

b. Vegetation monitoring and green infrastructure:

  • Remote sensing-based approaches for monitoring vegetation cover and health in urban areas.
  • Quantifying the benefits of urban green spaces in terms of microclimate regulation.
  • Assessing the effectiveness of urban greening initiatives on local climate and human well-being.

c. Air quality assessment and population mapping:

  • Remote sensing methods for monitoring air pollutants in urban areas.
  • Mapping and analysis of pollution sources and their spatial distribution using remote sensing data.
  • Integrating remote sensing with population estimate models for promoting population management and policy-making.

d. Urban resilience and risk management:

  • Remote sensing applications for various risk mapping and monitoring in urban areas.
  • Assessing vulnerability to climate-related hazards using remote sensing data with multi source geographic big data.
  • Integrating remote sensing with urban planning for enhancing resilience and disaster prevention capacity.

Authors are encouraged to submit original research articles, reviews, case studies, and technical notes that contribute to the understanding and application of remote sensing-based monitoring of local climate in urban areas. 

Dr. Lei Ma
Dr. Guangjun He
Dr. Brian Alan Johnson
Prof. Dr. Qian Cao
Prof. Dr. Hanfa Xing
Dr. Zhaowu Yu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • urban areas
  • local climate
  • risk assessment
  • sustainable urban development
  • green space
  • geographic big data

Published Papers (2 papers)

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Research

33 pages, 64449 KiB  
Article
Spatiotemporal Evolution in the Thermal Environment and Impact Analysis of Drivers in the Beijing–Tianjin–Hebei Urban Agglomeration of China from 2000 to 2020
by Haodong Liu, Hui Zheng, Liyang Wu, Yan Deng, Junjie Chen and Jiaming Zhang
Remote Sens. 2024, 16(14), 2601; https://doi.org/10.3390/rs16142601 - 16 Jul 2024
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Abstract
As urbanization advances, the issue of urban heat islands (UHIs) grows increasingly serious, with UHIs gradually transitioning into regional urban heat islands. There is still a lack of research on the evolution and drivers of the thermal environment in urban agglomerations; therefore, in [...] Read more.
As urbanization advances, the issue of urban heat islands (UHIs) grows increasingly serious, with UHIs gradually transitioning into regional urban heat islands. There is still a lack of research on the evolution and drivers of the thermal environment in urban agglomerations; therefore, in this study, we used trend analysis methods and spatial statistical analysis tools to investigate these issues in the Beijing–Tianjin–Hebei (BTH) urban agglomeration. The results demonstrated the following: (1) The land surface temperature (LST) exhibited low fluctuation, while the relative land surface temperature (RLST) fluctuated significantly. In Zhangjiakou and Chengde, the LST and RLST evolution trends were complex, and the results differed between daytime and nighttime, as well as between the annual and seasonal scales. In other regions, the trends of LST and RLST evolution were more obvious. (2) During the daytime, the high UHI clusters centered on “BJ–TJ–LF” and “SJZ–XT–HD” formed gradually; during the nighttime, the high UHI clusters were mainly observed in built-up areas. The distribution range and direction of UHIs showed greater degrees of evolution during the daytime in summer. (3) The total UHI area showed an increasing trend, and the intensity of heat stress suffered by the BTH agglomeration was increasing. (4) In BTH and Hebei, aerosol optical depth, surface solar radiation, population density, and gross domestic product were the dominant factors influencing UHIs; moreover, in Beijing and Tianjin, all factors showed an basically equal impact. The methodology and findings of this study hold significant implications for guiding urban construction, optimizing urban structure, and improving urban thermal comfort in the BTH urban agglomeration. Full article
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17 pages, 12123 KiB  
Article
Evaluating the Reconstructed All-Weather Land Surface Temperature for Urban Heat Island Analysis
by Xuepeng Zhang, Chunchun Meng, Peng Gou, Yingshuang Huang, Yaoming Ma, Weiqiang Ma, Zhe Wang and Zhiheng Hu
Remote Sens. 2024, 16(2), 373; https://doi.org/10.3390/rs16020373 - 17 Jan 2024
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Abstract
With the continuous improvement of urbanization levels in the Lhasa area, the urban heat island effect (UHI) has seriously affected the ecological environment of the region. However, the satellite-based thermal infrared land surface temperature (LST), commonly used for UHI research, is affected by [...] Read more.
With the continuous improvement of urbanization levels in the Lhasa area, the urban heat island effect (UHI) has seriously affected the ecological environment of the region. However, the satellite-based thermal infrared land surface temperature (LST), commonly used for UHI research, is affected by cloudy weather, resulting in a lack of continuous spatial and temporal information. In this study, focusing on the Lhasa region, we combine simulated LST data obtained by the Weather Research and Forecasting (WRF) model with remote sensing-based LST data to reconstruct the all-weather LST for March, June, September, and December of 2020 at a resolution of 0.01° while using the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST as a reference (in terms of accuracy). Subsequently, based on the reconstructed LST, an analysis of the UHI was conducted to obtain the spatiotemporal distribution of UHI in the Lhasa region under all-weather LST conditions. The results demonstrate that the reconstructed LST effectively captures the expected spatial distribution characteristics with high accuracy, with an average root mean square error of 2.20 K, an average mean absolute error of 1.51 K, and a correlation coefficient consistently higher than 0.9. Additionally, the heat island effect in the Lhasa region is primarily observed during the spring and winter seasons, with the heat island intensity remaining relatively stable in winter. The results of this study provide a new reference method for the reconstruction of all-weather LST, thereby improving the research accuracy of urban thermal environment from the perspective of foundational data. Additionally, it offers a theoretical basis for the governance of UHI in the Lhasa region. Full article
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