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Remote Sensing
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Monitoring Urban Environment from Space
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Monitoring Urban Environment from Space

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1707

Editors

Prof. Dr. Yong Xue

E-Mail Website
Guest Editor
Emergency Management College, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: atmospheric remote sensing; quantitative remote sensing; big earth data; emergency management
Prof. Dr. Pinliang Dong

E-Mail Website
Guest Editor
Department of Geography and the Environment, University of North Texas, Denton, TX 76201, USA
Interests: LiDAR remote sensing; forestry and vegetation mapping; urban environments; disaster damage assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Linlin Lu

E-Mail Website
Guest Editor
1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2. International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Interests: urban land use; machine learning/deep learning; thermal infrared remote sensing; urban heat island effect; urban green infrastructure; disaster risk assessment; sustainable development goals (SDGs)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid urbanization has become a defining feature of the 21st century, bringing both opportunities and challenges for sustainable development. As cities expand, monitoring urban environments—including land use changes, air and water quality, heat island effects, and infrastructure development—is critical for informed policymaking and resilient urban planning. Satellite remote sensing provides a powerful tool for observing these dynamics at various spatial and temporal scales, offering cost-effective, consistent, and large-coverage data. Advances in Earth observation technologies, including high-resolution optical sensors, synthetic aperture radar (SAR), LiDAR, and hyperspectral imaging, along with AI-driven data analytics, have revolutionized urban monitoring. This Special Issue seeks to showcase cutting-edge research leveraging space-based remote sensing to understand and manage urban environments more effectively. 

This Special Issue aims to compile high-quality research that demonstrates the integration of multi-source remote sensing data, machine learning, and geospatial analytics to address urban environmental challenges. Contributions may cover methodological advancements, case studies, or reviews highlighting the role of remote sensing in urban sustainability, disaster resilience, and climate adaptation. 

Topics of interest include (but are not limited to) the following:

  • Urban land cover/land use change detection;
  • Monitoring urban air pollution and greenhouse gas emissions;
  • Remote sensing data collection and processing techniques for urban air quality assessment;
  • Development and validation of models that utilize remote sensing data to predict air pollution levels;
  • Case studies illustrating the role of remote sensing in policymaking and urban planning for improved air quality;
  • Evaluation of the accuracy and effectiveness of remote sensing methods in monitoring urban air;
  • Urban heat island effect and thermal environment analysis;
  • Water resource management and flood risk assessment in cities;
  • AI and big data analytics for urban remote sensing;
  • Multi-sensor fusion and high-resolution urban mapping.

Prof. Dr. Yong Xue
Prof. Dr. Pinliang Dong
Dr. Linlin Lu
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • land use/land cover change
  • urban heat island
  • air pollution mapping
  • sustainable cities
  • earth observation
  • machine learning
  • multi-sensor fusion
  • climate resilience
  • big data analytics
  • disaster risk assessment

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

25 pages, 12058 KB  
Article
DMSF-Net: A Dual-Encoder Multi-Source Feature Fusion Network for Fine-Grained Urban Green Space Segmentation
by Longzhen Jiao, Xiaoyong Zhang, Linlin Lu, Wei Cai, Shusheng Yin, Manbin Yuan, Zhengchao Chen and Qingting Li
Remote Sens. 2026, 18(14), 2308; https://doi.org/10.3390/rs18142308 - 9 Jul 2026
Viewed by 516
Abstract
The mapping and monitoring of urban green space (UGS) are of great significance for ecological assessment and sustainable development in urban settlements. However, for fine-grained classification tasks in high-resolution remote sensing imagery, existing methods suffer from significant challenges due to the high spectral [...] Read more.
The mapping and monitoring of urban green space (UGS) are of great significance for ecological assessment and sustainable development in urban settlements. However, for fine-grained classification tasks in high-resolution remote sensing imagery, existing methods suffer from significant challenges due to the high spectral similarity between low-growing and dense vegetation, as well as the complexity of spatial structures. To overcome these challenges, this paper proposes a Dual-Encoder Multi-Source Feature Fusion Network (DMSF-Net) for fine-grained urban green space segmentation. The proposed method constructs a parallel encoding structure for RGB and auxiliary features (NDVI and LBP), introduces an Adaptive Feature Fusion Module (AFFM) during the encoding phase to achieve dynamic weighted fusion of cross-source features, and designs a Boundary-Aware Up-Sampling Module (BAM) during the decoding phase to strengthen the representation of complex boundary regions through joint modeling of regional semantics and boundary information. Experimental results on a self-constructed UrbanGreen dataset and the publicly available Vaihingen dataset demonstrate the superior performance of DMSF-Net over existing mainstream methods across several evaluation metrics, achieving mIoU values of 82.27% and 74.73%, with improvements of 1.07% and 0.57% over the best baselines, respectively. The model demonstrates particularly strong discrimination capability for the fine-grained category of low vegetation. Ablation experiments further validate the usefulness of each structural module, with AFFM playing a key role in overall performance improvement, while the BAM improves boundary delineation as observed in visual comparisons. Through the synergistic integration of multi-source feature information and structural optimization, DMSF-Net effectively enhances fine-grained UGS segmentation in complex urban scenes, thereby providing an effective approach for high-resolution remote sensing-based urban ecological monitoring. Full article
(This article belongs to the Special Issue Monitoring Urban Environment from Space)
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24 pages, 14540 KB  
Article
Investigating Ozone Formation Regimes in the Metropolitan Area of São Paulo Using Five Years of TROPOMI HCHO/NO2 Ratios
by Arthur Dias Freitas, Daniel Constantino Zacharias, Bruna Lüdtke Paim, Agnès Borbon and Adalgiza Fornaro
Remote Sens. 2026, 18(10), 1603; https://doi.org/10.3390/rs18101603 - 16 May 2026
Cited by 1 | Viewed by 431
Abstract
The Metropolitan Area of São Paulo (MASP), located in southeastern Brazil, faces significant air quality challenges due to its large vehicle fleet and complex fuel composition, including widespread ethanol use. Air pollution dynamics in this context are investigated, focusing on spatio-temporal variations in [...] Read more.
The Metropolitan Area of São Paulo (MASP), located in southeastern Brazil, faces significant air quality challenges due to its large vehicle fleet and complex fuel composition, including widespread ethanol use. Air pollution dynamics in this context are investigated, focusing on spatio-temporal variations in formaldehyde (HCHO) and nitrogen dioxide (NO2), and their role in ozone (O3) formation. High-resolution data from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite are used to analyze HCHO and NO2 vertical column densities (VCDs) over a 5-year period (2019–2023). Results reveal high HCHO and NO2 VCDs over MASP, with spatial patterns related to land use and higher concentrations during the dry season, with HCHO mean VCD reaching 14.21 × 1015 molecules cm−2 and NO2 mean VCD reaching 8.91 × 1015 molecules cm−2. The Formaldehyde to Nitrogen dioxide Ratio (FNR) thresholds were derived based on observations from 24 CETESB surface O3 monitoring stations, providing region-specific constraints for O3 sensitivity classification in MASP, with lower and upper thresholds of 1.6 and 2.4. Based on these thresholds, the analysis indicates a predominance of VOC-sensitive conditions in the urban core, alongside transition and NOx-limited regimes in other areas. Full article
(This article belongs to the Special Issue Monitoring Urban Environment from Space)
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