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Smart Monitoring of Urban Environment Using Remote Sensing

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A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Urban Remote Sensing".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 12576

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Editors

Dr. Darshana Athukorala

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Guest Editor

Institute of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8572, Japan
Interests: urban wetlands; remote sensing; geospatial modeling; ecosystem service; urban climate; urban forestry; urban hydrology; climate impacts
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yuji Murayama

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Guest Editor

Institute of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8572, Japan
Interests: human geography; GIScience; geospatial analysis; spatial modeling; urban geography
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Takehiro Morimoto

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Guest Editor

Institute of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8572, Japan
Interests: spatial analysis; urbanization studies; rural geography; land use; GIS; remote sensing; natural disasters
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the 21st century progresses, urbanization continues to change the land into the primary habitat for most of the global population. This rapid urban expansion requires a deeper discussion of the changing urban environment, which is critical in achieving sustainable development across global and regional scales. Metropolitan areas are a system where people and nature are connected, and we look at how socio-economic processes interact with the surrounding rural and natural areas. Therefore, understanding how the urban environment supports their functioning becomes crucial for sustainable urban development.

Recently, advancements in computer science, internet communication technology (ICT), remote sensing, and data science have revolutionized urban monitoring and management. Smart monitoring systems using cutting-edge technologies enable a more dynamic and precise investigation of urban environments. These advancements help to deeper examine the trajectories of spatial and temporal changes, urban population exposure to extreme heat waves, human exposure to green space during urbanization, changes in hydrological patterns in urban areas, climate-smart agriculture in urban and urban-rural fringes, and trade-offs and synergies, in which these factors play an essential role in sophisticating urban planning and policy.

This Special Issue aims to gather high-quality and innovative scientific articles that combine remote sensing and data science to improve the smart monitoring of cities. Studies should encompass the latest global trends and focus on innovative methods for addressing critical urban challenges, such as heat mitigation, water resource management, and the role of urban agriculture in enhancing food security, with the overall goal of improving the quality of life and offering valuable insights for policy recommendations and sustainable urban planning.

We invite contributions that provide innovative solutions and integrate various interdisciplinary approaches to enhance our understanding of how technology can develop urban ecosystem management. Topics of interest include but are not limited to the following:

Global trends in the integration of smart monitoring of urban environments;
Applications of remote sensing in monitoring urban heat islands;
Urban agriculture as a tool for sustainability and climate resilience;
Integrating urban forestry and hydrology for sustainable city planning;
Landscape sustainability through spatiotemporal urban ecosystem monitoring;
Smart systems for managing urban risk and environmental trade-offs;
The role of big data in enhancing urban sustainability and resilience.

We encourage submissions that explore these key issues and present cutting-edge approaches to ensuring sustainable urban futures.

Dr. Darshana Athukorala
Prof. Dr. Yuji Murayama
Prof. Dr. Takehiro Morimoto
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

smart monitoring
future predictions
scenario simulations
land use change
climate impacts
agriculture in urban areas
urban forestry
hydrology in urban areas
landscape sustainability
urban climate
quality of life
public health
ecosystem services

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Published Papers (5 papers)

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34 pages, 2614 KB

Open AccessArticle

From Earth Observation to Landscape Units: A Framework for Urban Cooling Assessment in Polish Functional Urban Area

by Anna Markowska, Konrad Wróblewski, Julia Baranowska, Wiktor Mróz, Szymon Jakubiak and Katarzyna Dąbrowska-Zielińska

Remote Sens. 2026, 18(19), 3390; https://doi.org/10.3390/rs18193390 - 2 Oct 2026

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Abstract

Urban environmental monitoring increasingly requires methods that translate Earth observation (EO) data into spatial units relevant to ecosystem-service assessment and planning. This study develops a landscape-unit framework for smart monitoring of urban cooling in two Polish Functional Urban Areas (FUAs): Zielona Góra and Gorzów Wielkopolski. Landsat-derived land surface temperature and surface urban heat island (SUHI) intensity were integrated with vegetation, water, imperviousness and NDVI indicators, together with the characteristics of directly adjacent landscape units. Spearman correlations, ordinary least squares and Spatial Error Models (SEMs) were used to diagnose the spatial structure of thermal conditions. The SEM variants removed residual spatial autocorrelation and showed the strongest full-sample fit, with RMSE values of approximately 0.85 SUHI units and MAE values of approximately 0.66; these statistics are used as model-fit diagnostics rather than as measures of out-of-sample predictive performance. The results were translated into a continuous weighted HeatCool Index and seven Jenks natural-break classes that represent the relative capacity of landscape units and their neighbourhoods to support cooling or reinforce warming. The classification is interpreted as an ecosystem-service screening product rather than as an independent validation of its component variables. Independently modelled annual and seasonal NO₂ indicators were then aggregated to the same landscape units as a complementary pressure layer. After EDM correction, the NO₂ model achieved R² = 0.77, RMSE = 3.98 µg m⁻³ and MAE = 2.36 µg m⁻³ on the held-out validation subset. Across the HCI gradient, Classes 1–2 showed the lowest and least variable annual NO₂ conditions, whereas Classes 6–7 exhibited the strongest winter deterioration and Class 7 contained the highest annual NO₂ concentrations. Because NO₂ was not used to construct the HCI, these gradients represent an independent comparison of co-occurring thermal and air-pollution pressures rather than evidence of a causal relationship. The proposed framework extends pixel-based thermal assessment by integrating landscape-unit classification, neighbourhood context and independently modelled air-pollution pressure within a common planning-oriented spatial framework. Full article

(This article belongs to the Special Issue Smart Monitoring of Urban Environment Using Remote Sensing)

27 pages, 11214 KB

Open AccessArticle

Study on Spatiotemporal Coupling Between Urban Form and Carbon Footprint from the Perspective of Color Nighttime Light Remote Sensing

by Jingwen Li, Xinyi Gong, Yanling Lu and Jianwu Jiang

Remote Sens. 2025, 17(18), 3208; https://doi.org/10.3390/rs17183208 - 17 Sep 2025

Viewed by 1490

Abstract

This study addresses the limitations of traditional nighttime light remote sensing data in ground object feature recognition and carbon emission monitoring by proposing a fusion framework based on Nonsubsampled Contourlet Transform (NSCT) and Intensity-Hue-Saturation (IHS). This framework successfully generates a high-resolution color nighttime light remote sensing imagery (color-NLRSI) dataset. Focusing on Guangzhou, an important city in the Guangdong-Hong Kong-Macao Greater Bay Area, the study systematically analyzes the spatiotemporal coupling mechanism between urban form evolution and carbon footprint by integrating multiple remote sensing data sources and socio-economic statistical information. Key findings include: (i) The color-NLRSI dataset outperforms traditional NPP-VIIRS data in built-up area extraction, providing more accurate spatial information by refining urban boundary recognition logic. (ii) Spatial correlation analysis reveals a remarkably strong positive relationship between built-up area expansion and carbon emissions, with the correlation coefficient for numerous districts exceeding 0.9. High-density built-up areas are strongly associated with a carbon lock-in effect, hindering low-carbon transformation efficiency. (iii) Geographically Weighted Regression analysis demonstrates that in population-polarized regions, the impact coefficient of built-up area expansion on carbon emissions is notably high at 0.961. This factor’s association (22.43%) surpasses economic development (10.34%) and urbanization rate (14.91%). The established “data fusion—dynamic monitoring—mechanism analysis” technical system, which generates a novel high-resolution color-NLRSI dataset and reveals a distinct ‘core-periphery’ heterogeneity pattern in Guangzhou, demonstrating that urban expansion is the dominant driver of carbon emissions. This approach offers a scientific basis for tailored urban low-carbon development strategies, spatial optimization, and enhanced precision in carbon emission monitoring. Full article

(This article belongs to the Special Issue Smart Monitoring of Urban Environment Using Remote Sensing)

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27 pages, 42290 KB

Open AccessArticle

Study on the Dynamic Changes in Land Cover and Their Impact on Carbon Stocks in Karst Mountain Areas: A Case Study of Guiyang City

by Rui Li, Zhongfa Zhou, Jie Kong, Cui Wang, Yanbi Wang, Rukai Xie, Caixia Ding and Xinyue Zhang

Remote Sens. 2025, 17(15), 2608; https://doi.org/10.3390/rs17152608 - 27 Jul 2025

Cited by 5 | Viewed by 2146

Abstract

Investigating land cover patterns, changes in carbon stocks, and forecasting future conditions are essential for formulating regional sustainable development strategies and enhancing ecological and environmental quality. This study centers on Guiyang, a mountainous urban area in southwestern China, to analyze the dynamic changes in land cover and their effects on carbon stocks from 2000 to 2035. A carbon stocks assessment framework was developed using a cellular automaton-based artificial neural network model (CA-ANN), the InVEST model, and the geographical detector model to predict future land cover changes and identify the primary drivers of variations in carbon stocks. The results indicate that (1) from 2000 to 2020, impervious surfaces expanded significantly, increasing by 199.73 km². Compared to 2020, impervious surfaces are projected to increase by 1.06 km², 13.54 km², and 34.97 km² in 2025, 2030, and 2035, respectively, leading to further reductions in grassland and forest areas. (2) Over time, carbon stocks in Guiyang exhibited a general decreasing trend; spatially, carbon stocks were higher in the western and northern regions and lower in the central and southern regions. (3) The level of greenness, measured by the normalized vegetation index (NDVI), significantly influenced the spatial variation of carbon stocks in Guiyang. Changes in carbon stocks resulted from the combined effects of multiple factors, with the annual average temperature and NDVI being the most influential. These findings provide a scientific basis for advancing low-carbon development and constructing an ecological civilization in Guiyang. Full article

(This article belongs to the Special Issue Smart Monitoring of Urban Environment Using Remote Sensing)

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25 pages, 15537 KB

Open AccessArticle

Exploring the Cooling Effects of Urban Wetlands in Colombo City, Sri Lanka

by Darshana Athukorala, Yuji Murayama, N. S. K. Herath, C. M. Madduma Bandara, Rajeev Kumar Singh and S. L. J. Fernando

Remote Sens. 2025, 17(11), 1919; https://doi.org/10.3390/rs17111919 - 31 May 2025

Cited by 10 | Viewed by 5280

Abstract

An urban heat island (UHI) refers to urban areas that experience higher temperatures due to heat absorption and retention by impervious surfaces compared to the surrounding rural areas. Urban wetlands are crucial in mitigating the UHI effect and improving climate resilience via their cooling effect. This study examines Colombo, Sri Lanka, the RAMSAR-accredited wetland city in South Asia, to assess the cooling effect of urban wetlands based on 2023 dry season data for effective sustainable management. We used Landsat 8 and 9 data to create Land Use/Cover (LUC), Land Surface Temperature (LST), and surface-reflectance-based maps using the Google Earth Engine (GEE). The Enhanced Vegetation Index (EVI), Modified Normalized Difference Water Index (mNDWI), topographic wetness, elevation, slope, and impervious surface percentage were identified as the influencing variables. The results show that urban wetlands in Colombo face tremendous pressure due to rapid urban expansion. The cooling intensity positively correlates with wetland size. The threshold value of efficiency (TVoE) of urban wetlands in Colombo was 1.42 ha. Larger and more connected wetlands showed higher cooling effects. Vegetation- and water-based wetlands play an important role in <10 km urban areas, while more complex shape configuration wetlands provide better cooling effects in urban and peri-urban areas due to edge effects. Urban planners should prioritize protecting wetland areas and ensuring hydrological connectivity and interconnected wetland clusters to maximize the cooling effect and sustain ecosystem services in rapidly urbanizing coastal cities. Full article

(This article belongs to the Special Issue Smart Monitoring of Urban Environment Using Remote Sensing)

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18 pages, 5589 KB

Open AccessTechnical Note

Dual-Task Supervised Network for SAR and Road Vector Image Matching

by Hanyu Cai, Yong Xian, Shaopeng Li and Decao Ma

Remote Sens. 2025, 17(20), 3504; https://doi.org/10.3390/rs17203504 - 21 Oct 2025

Viewed by 1204

Abstract

We propose using Synthetic Aperture Radar (SAR) images as real-time images and road vector images as reference images for matching navigation, and propose a Siamese U-Net dual-task supervised network for solving the problem, called SUDS. Unlike existing methods of heterogenous image matching, which extract common features and eliminate saliency differences for matching, we exploit the advantages of the vector images themselves to reduce the matching difficulty from the reference image selection. Firstly, we extract the common road features between SAR images and road vector images using a weight-sharing U-Net feature extraction network. Then, we propose to weight the sum of segmentation loss and matching loss as the network loss to optimize the feature extraction efficiency from both segmentation and matching perspectives. We prepare a specialized SAR-VEC dataset for experiments. Experiments show that the method is able to obtain high matching correctness, with 80.2% correctness within 5 pixels of matching error and 91.0% correctness within 10 pixels of matching error. Compared to existing methods, this method is able to identify the differences in similar roads and better eliminate the influence of imaging interference in SAR images on the matching results, obtaining more accurate matching results with better robustness. And we explore the effect of different weighting parameters

β

on the matching accuracy, and the best matching results are obtained when

β = 0.8

. Full article

(This article belongs to the Special Issue Smart Monitoring of Urban Environment Using Remote Sensing)

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