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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 8333

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 (6 papers)

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Research

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19 pages, 6796 KiB  
Article
Performance Assessment of Advanced Daily Surface Soil Moisture Products in China for Sustainable Land and Water Management
by Dai Chen, Zhounan Dong and Jingnan Chen
Sustainability 2025, 17(14), 6482; https://doi.org/10.3390/su17146482 - 15 Jul 2025
Viewed by 174
Abstract
This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic [...] Read more.
This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic Soil Moisture Monitoring Network. All products were standardized to a 0.25° × 0.25° grid in the WGS-84 coordinate system through reprojection and resampling for consistent comparison. Daily averaged station observations were matched to product pixels using a 10 km radius buffer, with the mean station value as the reference for each time series after rigorous quality control. Results reveal distinct performance rankings, with SMAP-based products, particularly the SMAP_IB descending orbit variant, achieving the lowest unbiased root mean square deviation (ubRMSD) and highest correlation with in situ data. Blended products like ESA CCI and NOAA SMOPS, alongside reanalysis datasets such as ERA5 and MERRA2, outperformed SMOS and China’s FY3 products. The SoMo.ml product showed the broadest spatial coverage and strong temporal consistency, while FY3-based products showed limitations in spatial reliability and seasonal dynamics capture. These findings provide critical insights for selecting appropriate soil moisture datasets to enhance sustainable agricultural practices, optimize water resource allocation, monitor ecosystem resilience, and support climate adaptation strategies, therefore advancing sustainable development across diverse geographical regions in China. 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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20 pages, 12984 KiB  
Article
Spatial and Temporal Characterization of the Development and Pollution Emissions of Key Heavy Metal-Related Industries in Typical Regions of China: A Case Study of Hunan Province
by Liying Yang, Xia Li, Jianan Luo, Xuechun Ma, Xiaoyan Zhang, Jiamin Zhao, Zhicheng Shen and Jingwen Xu
Sustainability 2025, 17(14), 6275; https://doi.org/10.3390/su17146275 - 9 Jul 2025
Viewed by 302
Abstract
At present, there is a lack of in-depth knowledge of the effects of heavy metal-related industries (HMIs) in China on the environment. Hunan Province, as a representative gathering place of HMIs, is among the regions in China that are the most severely polluted [...] Read more.
At present, there is a lack of in-depth knowledge of the effects of heavy metal-related industries (HMIs) in China on the environment. Hunan Province, as a representative gathering place of HMIs, is among the regions in China that are the most severely polluted with heavy metals. This paper selected Hunan Province as the study area to analyze the development trend, characteristics of pollution emissions, and environmental impacts of seven HMIs based on emission permit information data from Hunan Province. The results of this study show that (1) from 2000 to 2022, the number of heavy metal-related enterprises in Hunan Province increased overall. Among the seven industries, the chemical product manufacturing industry (CPMI) had the largest number of enterprises, whereas the nonferrous metal smelting and rolling industry (NSRI) had the highest gross industrial product (27.6%). (2) HMIs in Hunan Province had significant emissions of cadmium (Cd), arsenic (As), and hydargyrum (Hg) from exhaust gas and wastewater. Heavy metal-related exhaust gas and wastewater outlets from the NSRI constituted 43.9% and 35.3%, respectively, of all outlets of the corresponding type. The proportions of exhaust gas outlets involving Cd, Hg, and As from the NSRI to total exhaust gas outlets were 44.27%, 60.54%, and 34.23%, respectively. The proportions of wastewater outlets involving Cd, Hg, and As from the NSRI to total wastewater outlets were 61.13%, 57.89%, and 75.30%, respectively. (3) The average distances of heavy metal-related enterprises from arable land, rivers, and flooded areas in Hunan Province were 256 m, 1763 m, and 3352 m, respectively. Counties with high environmental risk (H-L type) were situated mainly in eastern Hunan. Among them, Chenzhou had the most heavy metal-related wastewater outlets (22.7%), and Hengyang had the most heavy metal-related exhaust gas outlets (23.1%). The results provide a scientific basis for the prevention and control of heavy metal pollution and an enhancement in environmental sustainability in typical Chinese areas where HMIs are concentrated. 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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19 pages, 3344 KiB  
Article
Terrestrial LiDAR Technology to Evaluate the Vertical Structure of Stands of Bertholletia excelsa Bonpl., a Species Symbol of Conservation Through Sustainable Use in the Brazilian Amazon
by Felipe Felix Costa, Raimundo Cosme de Oliveira Júnior, Danilo Roberti Alves de Almeida, Diogo Martins Rosa, Kátia Emídio da Silva, Hélio Tonini, Troy Patrick Beldini, Darlisson Bentes dos Santos and Marcelino Carneiro Guedes
Sustainability 2025, 17(13), 6049; https://doi.org/10.3390/su17136049 - 2 Jul 2025
Viewed by 260
Abstract
The Amazon rainforest hosts a diverse array of forest types, including those where Brazil nut (Bertholletia excelsa) occurs, which plays a crucial ecological and economic role. The Brazil nut is the second most important non-timber forest product in the Amazon, a [...] Read more.
The Amazon rainforest hosts a diverse array of forest types, including those where Brazil nut (Bertholletia excelsa) occurs, which plays a crucial ecological and economic role. The Brazil nut is the second most important non-timber forest product in the Amazon, a symbol of development and sustainable use in the region, promoting the conservation of the standing forest. Understanding the vertical structure of these forests is essential to assess their ecological complexity and inform sustainable management strategies. We used terrestrial laser scanning (TLS) to assess the vertical structure of Amazonian forests with the occurrence of Brazil nut (Bertholletia excelsa) at regional (Amazonas, Mato Grosso, Pará, and Amapá) and local scales (forest typologies in Amapá). TLS allowed high-resolution three-dimensional characterization of canopy layers, enabling the extraction of structural metrics such as canopy height, rugosity, and leaf area index (LAI). These metrics were analyzed to quantify the forest vertical complexity and compare structural variability across spatial scales. These findings demonstrate the utility of TLS as a precise tool for quantifying forest structure and highlight the importance of integrating structural data in conservation planning and forest monitoring initiatives involving B. excelsa. 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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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 412
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 2263
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 5 | Viewed by 4298
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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