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Remote Sensing and Spatial Analysis for Monitoring and Assessing Landscape...
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Remote Sensing and Spatial Analysis for Monitoring and Assessing Landscape and Ecosystem Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 6579

Special Issue Editors

Dr. Chao Zhang

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore
Interests: environmental remote sensing; LULCC monitoring and hydroclimatic effects; agricultural irrigation; machine learning
Dr. Ziqi Meng

E-Mail Website
Guest Editor
College of Resources and Environment, Shandong Agricultural University, Tai’an 271018, China
Interests: land use and land cover change; human activities; protected areas; conservation; effectiveness; One Health; sustainability
Dr. Nanshan You

E-Mail Website
Guest Editor
Key Laboratory for Resource Use and Environmental Remediation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: agricultural remote sensing; land system science; land–atmosphere feedback

Special Issue Information

Dear Colleagues,

The sustainability of ecological systems and landscapes is increasingly challenged by climate change and anthropogenic disturbances. In this evolving context, spatial analysis and remote sensing (RS) technologies have become indispensable tools for monitoring, understanding, and managing the dynamic processes that shape landscapes. Recent advancements in RS have significantly improved data acquisition across spatial, temporal, and spectral resolution, while platforms like Google Earth Engine (GEE) have democratized large-scale environmental analysis. When integrated with Geographical Information Systems (GIS), these technologies provide robust solutions for spatial modeling and informed decision-making, particularly in the realms of natural resource management and landscape sustainability.

This Special Issue aims to highlight cutting-edge research and reviews that illustrate the application of spatial analysis and remote sensing in promoting sustainable practices in ecology and landscape management. We invite contributions that demonstrate innovative use of these technologies for monitoring and modeling ecological processes, landscape transformations, and sustainable resource management. Additionally, studies that investigate the impacts of climate change on ecosystems and landscapes as well as how spatial tools can support the development of adaptive strategies and sustainable solutions are highly encouraged.

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

  • Spatial analysis of landscape changes and ecosystem services;
  • Remote sensing for biodiversity and ecosystem monitoring;
  • Ecological resilience and sustainability assessments;
  • Land use and land cover change detection;
  • Urban ecological planning and sustainable landscapes;
  • Natural resource management through GIS and remote sensing;
  • Vegetation and water responses to climate change;
  • Climatic and ecological impacts of landscape changes;
  • Urban heat island effects and mitigation strategies.

We look forward to receiving your high-quality contributions that push the boundaries of spatial analysis and remote sensing in fostering sustainable ecological and landscape management in a changing climate.

Dr. Chao Zhang
Dr. Ziqi Meng
Dr. Nanshan You
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. Sustainability 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 2400 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

  • spatial analysis
  • remote sensing
  • landscape sustainability
  • ecosystem services
  • natural resource management
  • climate change
  • ecological resilience
  • land use change
  • environmental monitoring

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Published Papers (3 papers)

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Research

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25 pages, 34970 KiB  
Article
Spatial Impact Dynamics of the “Mountain–City–Sea” Pattern on the Urban Thermal Environment and Adaptive Zoning Regulation
by Lingyi Ouyang, Hao Guo, Xiujin Song and Tingting Hong
Sustainability 2025, 17(10), 4459; https://doi.org/10.3390/su17104459 - 14 May 2025
Viewed by 321
Abstract
Optimizing urban patterns is increasingly recognized as an effective technological strategy to mitigate the urban heat island (UHI) effect. Taking Xiamen City as a case study, this research extracts and quantifies city spatial characteristics from multiple data sources. Key factors influencing the urban [...] Read more.
Optimizing urban patterns is increasingly recognized as an effective technological strategy to mitigate the urban heat island (UHI) effect. Taking Xiamen City as a case study, this research extracts and quantifies city spatial characteristics from multiple data sources. Key factors influencing the urban thermal environment were integrated into three primary urban pattern elements: Mountain, City, and Sea. The spatial autocorrelation and heterogeneous impacts of these urban pattern elements on the thermal environment were analyzed using Moran’s I and Geographically Weighted Regression (GWR) modeling, followed by impact-based zoning using K-means clustering algorithms. The results revealed a significant positive correlation between Mountain and City elements and the thermal environment, whereas Sea elements exhibited a notable cooling effect. Furthermore, each factor demonstrated significant spatial heterogeneity. Based on local GWR regression coefficients and spatial variations in factor intensity and directionality, Xiamen was partitioned into four distinct regulatory zones: City-dominated zones, Sea-dominated zones, Mountain–Sea co-dominated zones, and Comprehensive transitional zones influenced by Mountain–City–Sea interactions. Customized, spatially targeted regulatory strategies were subsequently proposed for each zone. This study provides an innovative methodological framework for targeted, region-specific policy interventions to alleviate urban thermal stress under climate change, thereby contributing to the optimization of future urban planning and promoting sustainable and adaptive development. Full article
(This article belongs to the Special Issue Remote Sensing and Spatial Analysis for Monitoring and Assessing Landscape and Ecosystem Sustainability)
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15 pages, 4980 KiB  
Article
Modeling the Impact of Socio-Economic and Environmental Factors on Air Quality in the City of Kabul
by Mohammad Shahab Sharifi, Alyas Aslami, Hameedullah Zaheb, Imran Abed, Abdul Wahab Shokoori and Atsushi Yona
Sustainability 2024, 16(24), 10969; https://doi.org/10.3390/su162410969 - 13 Dec 2024
Cited by 1 | Viewed by 2128
Abstract
Air pollution is a vital concern for developing countries, and the growing concentration of air pollutants in Kabul—the most polluted city in Afghanistan—has raised concerns about the health of its citizens. This study examines Kabul’s ambient air quality from a socio-economic and environmental [...] Read more.
Air pollution is a vital concern for developing countries, and the growing concentration of air pollutants in Kabul—the most polluted city in Afghanistan—has raised concerns about the health of its citizens. This study examines Kabul’s ambient air quality from a socio-economic and environmental perspective, primarily focusing on some crucial parameters, such as the Air Quality Index (AQI), nitrogen dioxide (NO2), particulate matter (PM2.5), and carbon monoxide (CO). Using multiple regression analysis modeling in R and data from satellite imagery, air quality monitoring stations, and Geographic Information Systems (GIS), this study demonstrates a strong relationship between air quality and urban green spaces, population growth, vehicle count, temperature, and electricity availability. Key results indicate that increasing urban green areas improves air quality, but that population growth and heat make air pollution worse. This study suggests that airborne pollutants could be reduced through efficient emissions management, green energy sources, and urban planning. These observations provide policymakers and urban planners with practical recommendations to enhance Kabul’s air quality and general public health. Full article
(This article belongs to the Special Issue Remote Sensing and Spatial Analysis for Monitoring and Assessing Landscape and Ecosystem Sustainability)
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Review

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41 pages, 1464 KiB  
Review
Integration of Machine Learning and Remote Sensing for Water Quality Monitoring and Prediction: A Review
by Shashank Mohan, Brajesh Kumar and A. Pouyan Nejadhashemi
Sustainability 2025, 17(3), 998; https://doi.org/10.3390/su17030998 - 26 Jan 2025
Cited by 2 | Viewed by 3592
Abstract
Aquatic ecosystems play a crucial role in sustaining life and supporting key green and blue economic sectors globally. However, the growing population and increasing anthropogenic pressures are significantly degrading terrestrial water resources, threatening their ability to provide essential socioeconomic services. To safeguard these [...] Read more.
Aquatic ecosystems play a crucial role in sustaining life and supporting key green and blue economic sectors globally. However, the growing population and increasing anthropogenic pressures are significantly degrading terrestrial water resources, threatening their ability to provide essential socioeconomic services. To safeguard these ecosystems and their benefits, it is critical to continuously monitor changes in water quality. Remote sensing technologies, which offer high-resolution spatial and temporal data over large geographic areas, including surface water bodies, have become indispensable for these monitoring efforts. They enable the observation of various physical, chemical, and biological water quality indicators, which are essential for assessing ecosystem health. Machine learning algorithms are well suited to handle the complex and often non-linear relationships between remote sensing data and water quality parameters. By integrating remote sensing with machine learning techniques, it is possible to develop predictive models that enhance the accuracy and efficiency of water quality assessments. These models can identify and predict trends in water quality, supporting timely interventions to protect aquatic ecosystems. This paper provides a thorough review of the major remote sensing techniques for estimating water quality indicators (e.g., chlorophyll-a, turbidity, temperature, total nitrogen and total phosphorous, dissolved organic, total suspended solids, dissolved oxygen, and hydrogen power). It examines how machine learning can improve water quality assessments. Additionally, it identifies key research gaps in current methodologies and suggests future directions to address challenges in water quality monitoring, aiming to improve the precision and scope of these critical efforts. Full article
(This article belongs to the Special Issue Remote Sensing and Spatial Analysis for Monitoring and Assessing Landscape and Ecosystem Sustainability)
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