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Remote Sensing
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Monitoring and Managing Environmental Sustainability Using Remote Sensing
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Monitoring and Managing Environmental Sustainability Using Remote Sensing

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 5185

Special Issue Editors

Prof. Dr. Dongmei Chen

E-Mail Website
Guest Editor
Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, Canada
Interests: remote sensing; GIS; change detection; feature extraction; environmental modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuhong He

E-Mail Website
Guest Editor
Department of Geography, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada
Interests: remote sensing; geospatial analysis; ecosystem modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental sustainability is essential for maintaining the health and viability of our planet for current and future generations. As human activities are increasingly putting a strain on the environment, promoting sustainability becomes crucial to mitigate the adverse impacts of climate change, deforestation, habitat loss, and more. Sustainable practices not only protect natural habitats but also enhance quality of life by ensuring clean air, water, and soil, which are fundamental to human health and well-being. Remote sensing technology plays a pivotal role in monitoring and managing environmental sustainability. By utilizing satellites and airborne sensors, remote sensing provides comprehensive and real-time data on various environmental parameters, such as land use changes, deforestation rates, natural disasters, and the health of ecosystems with high precision and temporal resolution. The insights gained from remote sensing are invaluable for designing effective conservation strategies, implementing sustainable land management practices, and enforcing environmental regulations. As a result, remote sensing technology is indispensable for achieving and maintaining environmental sustainability in an era of rapid environmental change.

This Special Issue aims to compile a comprehensive collection of innovative research and insights into how remote sensing technology can be leveraged to monitor and manage environmental suitability, thus supporting sustainable development.

This Special Issue is mainly directed toward participants of the 31st International Conference on Geomatics, “Geospatial Sciences for Sustainability”. However, we also welcome other suitable manuscripts from around the world.

Authors are invited to submit their original research articles and review papers on topics including, but not limited to, the following:

  • Land use and land cover change: remote sensing applications in detecting and analyzing changes in land use and land cover.
  • Climate change monitoring: using remote sensing data to assess and model the impacts of climate change on different ecosystems.
  • Biodiversity and habitat mapping: techniques for monitoring biodiversity and habitat health through remote sensing data.
  • Agricultural monitoring: Remote sensing for assessing soil health, crop conditions, and agricultural sustainability.
  • Water resource management: Utilizing remote sensing technology for monitoring water quality, distribution, and management.
  • Urban environment analysis: Assessing urban heat islands, green spaces, and environmental quality in urban areas using remote sensing.
  • Disaster management: Remote sensing in predicting, monitoring, and managing natural disasters such as floods, droughts, and wildfires.
  • Forest and vegetation monitoring: Techniques for tracking forest health, deforestation, and reforestation efforts.
  • Environmental pollution monitoring: Monitoring air quality, atmospheric composition, and pollution using remote sensing technologies.
  • Innovative remote sensing technologies for environmental monitoring: Advances in sensors, platforms, and data processing techniques that enhance environmental monitoring capabilities.

Prof. Dr. Dongmei Chen
Prof. Dr. Yuhong He
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
  • environmental sustainability
  • land use/cover change
  • habitat health
  • climate change monitoring
  • satellite imagery
  • environmental monitoring
  • ecosystem assessment

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Published Papers (5 papers)

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Research

19 pages, 7245 KiB  
Article
Integrating Drone Truthing and Functional Classification of Remote Sensing Time Series for Supervised Vegetation Mapping
by Giacomo Quattrini, Simone Pesaresi, Nicole Hofmann, Adriano Mancini and Simona Casavecchia
Remote Sens. 2025, 17(2), 330; https://doi.org/10.3390/rs17020330 - 18 Jan 2025
Viewed by 499
Abstract
Accurate vegetation mapping is essential for monitoring biodiversity and managing habitats, particularly in the context of increasing environmental pressures and conservation needs. Ground truthing plays a crucial role in ensuring the accuracy of supervised remote sensing maps, as it provides the high-quality reference [...] Read more.
Accurate vegetation mapping is essential for monitoring biodiversity and managing habitats, particularly in the context of increasing environmental pressures and conservation needs. Ground truthing plays a crucial role in ensuring the accuracy of supervised remote sensing maps, as it provides the high-quality reference data needed for model training and validation. However, traditional ground truthing methods are labor-intensive, time-consuming and restricted in spatial coverage, posing challenges for large-scale or complex landscapes. The advent of drone technology offers an efficient and cost-effective solution to these limitations, enabling the rapid collection of high-resolution imagery even in remote or inaccessible areas. This study proposes an approach to enhance the efficiency of supervised vegetation mapping in complex landscapes, integrating Multivariate Functional Principal Component Analysis (MFPCA) applied to the Sentinel-2 time series with drone-based ground truthing. Unlike traditional ground truthing activities, drone truthing enabled the generation of large, spatially balanced reference datasets, which are critical for machine learning classification systems. These datasets improved classification accuracy by ensuring a comprehensive representation of vegetation spectral variability, enabling the classifier to identify the key phenological patterns that best characterize and distinguish different vegetation types across the landscape. The proposed methodology achieves a classification accuracy of 92.59%, significantly exceeding the commonly reported thresholds for habitat mapping. This approach, characterized by its efficiency, repeatability and adaptability, aligns seamlessly with key environmental monitoring and conservation policies, such as the Habitats Directive. By integrating advanced remote sensing with drone-based technologies, it offers a scalable and cost-effective solution to the challenges of biodiversity monitoring, enabling timely updates and supporting effective habitat management in diverse and complex environments. Full article
(This article belongs to the Special Issue Monitoring and Managing Environmental Sustainability Using Remote Sensing)
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28 pages, 127916 KiB  
Article
A Pine Wilt Disease Detection Model Integrated with Mamba Model and Attention Mechanisms Using UAV Imagery
by Minhui Bai, Xinyu Di, Lechuan Yu, Jian Ding and Haifeng Lin
Remote Sens. 2025, 17(2), 255; https://doi.org/10.3390/rs17020255 - 13 Jan 2025
Viewed by 730
Abstract
Pine wilt disease (PWD) is a highly destructive worldwide forest quarantine disease that has the potential to destroy entire pine forests in a relatively brief period, resulting in significant economic losses and environmental damage. Manual monitoring, biochemical detection and satellite remote sensing are [...] Read more.
Pine wilt disease (PWD) is a highly destructive worldwide forest quarantine disease that has the potential to destroy entire pine forests in a relatively brief period, resulting in significant economic losses and environmental damage. Manual monitoring, biochemical detection and satellite remote sensing are frequently inadequate for the timely detection and control of pine wilt disease. This paper presents a fusion model, which integrates the Mamba model and the attention mechanism, for deployment on unmanned aerial vehicles (UAVs) to detect infected pine trees. The experimental dataset presented in this paper comprises images of pine trees captured by UAVs in mixed forests. The images were gathered primarily during the spring of 2023, spanning the months of February to May. The images were subjected to a preprocessing phase, during which they were transformed into the research dataset. The fusion model comprised three principal components. The initial component is the Mamba backbone network with State Space Model (SSM) at its core, which is capable of extracting pine wilt features with a high degree of efficacy. The second component is the attention network, which enables our fusion model to center on PWD features with greater efficacy. The optimal configuration was determined through an evaluation of various attention mechanism modules, including four attention modules. The third component, Path Aggregation Feature Pyramid Network (PAFPN), facilitates the fusion and refinement of data at varying scales, thereby enhancing the model’s capacity to detect multi-scale objects. Furthermore, the convolutional layers within the model have been replaced with depth separable convolutional layers (DSconv), which has the additional benefit of reducing the number of model parameters and improving the model’s detection speed. The final fusion model was validated on a test set, achieving an accuracy of 90.0%, a recall of 81.8%, a map of 86.5%, a parameter counts of 5.9 Mega, and a detection speed of 40.16 FPS. In comparison to Yolov8, the accuracy is enhanced by 7.1%, the recall by 5.4%, and the map by 3.1%. These outcomes demonstrate that our fusion model is appropriate for implementation on edge devices, such as UAVs, and is capable of effective detection of PWD. Full article
(This article belongs to the Special Issue Monitoring and Managing Environmental Sustainability Using Remote Sensing)
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23 pages, 4376 KiB  
Article
Spatial Characteristics and Driving Mechanisms of Carbon Neutrality Progress in Tourism Attractions in the Qinghai–Tibet Plateau Based on Remote Sensing Methods
by Bing Xia
Remote Sens. 2024, 16(23), 4481; https://doi.org/10.3390/rs16234481 - 29 Nov 2024
Cited by 1 | Viewed by 624
Abstract
This research employs multi-source data including big data, remote sensing raster data, and statistical vector data. Through the superposition of tourism activity points of interest with remotely sensed inversion raster data like human carbon emissions, net primary productivity, and kilometer-grid GDP, the carbon [...] Read more.
This research employs multi-source data including big data, remote sensing raster data, and statistical vector data. Through the superposition of tourism activity points of interest with remotely sensed inversion raster data like human carbon emissions, net primary productivity, and kilometer-grid GDP, the carbon emissions, carbon sinks, and economic output of tourism attractions are obtained. Data envelopment analysis and econometric models are utilized to assess the “carbon emissions–carbon sinks–economic output” coupling efficiency relationship and driving mechanism under the framework of the tourism carbon neutrality process. This research takes Gannan Tibetan Autonomous Prefecture in the Qinghai–Tibet Plateau region, which has had a severe response to global climate change and is particularly deficient in statistical and monitoring data, as an example. It is found that in Gannan Prefecture, which is at the primary stage of tourism development, with a high degree of dependence on the location and regional economic development level, the challenge of decoupling carbon emissions from the economy is significant. The carbon neutrality process in natural tourism attractions is marginally superior to that in cultural tourism attractions. However, even among natural tourism attractions, the number of spots achieving high carbon sink efficiency is extremely limited. There remains considerable scope for achieving carbon neutrality process through carbon sinks in the future. The location and vegetation conditions can exert a direct and positive influence on the improvement of carbon efficiency in tourist destinations. Establishing natural tourism attractions near cities is more conducive to facilitating carbon neutrality. This research highlights the advantages of remote sensing methods in specific sectors such as tourism where quality monitoring facilities and methods are lacking and provides a reference for evaluating the tourism carbon neutrality process and managing environmental sustainability on tourism attractions in similar regions and specific sectors worldwide. Full article
(This article belongs to the Special Issue Monitoring and Managing Environmental Sustainability Using Remote Sensing)
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18 pages, 4574 KiB  
Article
Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves
by Carlos Augusto Alves Cardoso Silva, Rodnei Rizzo, Marcelo Andrade da Silva, Matheus Luís Caron and Peterson Ricardo Fiorio
Remote Sens. 2024, 16(22), 4250; https://doi.org/10.3390/rs16224250 - 14 Nov 2024
Viewed by 806
Abstract
Nitrogen fertilization is a challenging task that usually requires intensive use of resources, such as fertilizers, management and water. This study explored the potential of VIS-NIR-SWIR remote sensing for quantifying leaf nitrogen content (LNC) in sugarcane from different regions and vegetative stages. Conducted [...] Read more.
Nitrogen fertilization is a challenging task that usually requires intensive use of resources, such as fertilizers, management and water. This study explored the potential of VIS-NIR-SWIR remote sensing for quantifying leaf nitrogen content (LNC) in sugarcane from different regions and vegetative stages. Conducted in three regions of São Paulo, Brazil (Jaú, Piracicaba and Santa Maria), the research involved three experiments, one per location. The spectral data were obtained at 140, 170, 200, 230 and 260 days after cutting (DAC). From the hyperspectral data, clustering analysis was performed to identify the patterns between the spectral bands for each region where the spectral readings were made, using the Partitioning Around Medoids (PAM) algorithm. Then, the LNC values were used to generate spectral models using Partial Least Squares Regression (PLSR). Subsequently, the generalization of the models was tested with the leave-one-date-out cross-validation (LOOCV) technique. The results showed that although the variation in leaf N was small, the sensor demonstrated the ability to detect these variations. Furthermore, it was possible to determine the influence of N concentrations on the leaf spectra and how this impacted cluster formation. It was observed that the greater the average variation in N content in each cluster, the better defined and denser the groups formed were. The best time to quantify N concentrations was at 140 DAC (R2 = 0.90 and RMSE = 0.74 g kg−1). From LOOCV, the areas with sandier soil texture presented a lower model performance compared to areas with clayey soil, with R2 < 0.54. The spatial generalization of the models recorded the best performance at 140 DAC (R2 = 0.69, RMSE = 1.18 g kg−1 and dr = 0.61), decreasing in accuracy at the crop-maturation stage (260 DAC), R2 of 0.05, RMSE of 1.73 g kg−1 and dr of 0.38. Although the technique needs further studies to be improved, our results demonstrated potential, which tends to provide support and benefits for the quantification of nutrients in sugarcane in the long term. Full article
(This article belongs to the Special Issue Monitoring and Managing Environmental Sustainability Using Remote Sensing)
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18 pages, 4132 KiB  
Article
Assessing Air Quality Dynamics during Short-Period Social Upheaval Events in Quito, Ecuador, Using a Remote Sensing Framework
by Cesar Ivan Alvarez, Santiago López, David Vásquez and Dayana Gualotuña
Remote Sens. 2024, 16(18), 3436; https://doi.org/10.3390/rs16183436 - 16 Sep 2024
Viewed by 1301
Abstract
This study uses a remote sensing approach to investigate air quality fluctuations during two short-period social upheaval events caused by civil protests in 2019 and the COVID-19 pandemic in 2020 in Quito, Ecuador. We used data from the TROPOMI Sentinel-P5 satellite to evaluate [...] Read more.
This study uses a remote sensing approach to investigate air quality fluctuations during two short-period social upheaval events caused by civil protests in 2019 and the COVID-19 pandemic in 2020 in Quito, Ecuador. We used data from the TROPOMI Sentinel-P5 satellite to evaluate the concentrations of two greenhouse gases, namely O3 and NO2. TROPOMI Sentinel-P5 satellite data are becoming essential in air quality monitoring, particularly for countries that lack ground-based monitoring systems. For a better approximation of satellite data with ground data, we related the remotely sensed data using ground station data and Pearson correlation analysis, which revealed a significant association between the two sources (0.43 ≤ r ≤ 0.78). Using paired t-test comparisons, we evaluated the differences in mean gas concentrations at 30 randomly selected intervals to identify significant changes before and after the events. The results indicate noticeable changes in the two gases over the three analysis periods. O3 significantly decreased between September and November 2019 and between March and May 2020, while NO2 significantly increased. NO2 levels decreased by 18% between February and March 2020 across the study area, as indicated by remote sensing data. The geovisualization of remotely sensed data over these periods supports these patterns, suggesting a potential connection with population density. The results show the complexity of drawing global conclusions about the impact of social disruptions on the atmosphere and emphasize the advantages of using remote sensing as an effective framework to address air quality changes over short periods of time. This study also highlights the advantages of a remote sensing approach to monitor atmospheric conditions in countries with limited air quality monitoring infrastructure and provides a valuable approach for the evaluation of short-term alterations in atmospheric conditions due to social disturbance events. Full article
(This article belongs to the Special Issue Monitoring and Managing Environmental Sustainability Using Remote Sensing)
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