Applications of Remote Sensing and Artificial Intelligence in Land Cover Mapping and Ecosystem Monitoring
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 159
Special Issue Editors
Interests: soil pollution and contamination; land degradation; climate change; water resource management; remote sensing and GIS
Interests: machine learning and data science; reservoirs; groundwater and river and water quality modeling; application of remote sensing (RS) in water resources and environmental management; water quality monitoring and water resources systems analysis and planning; optimization and simulation techniques and water quality assessment; integration with numerical, hydrological and climate change models for different water resource and water quality assessments; floods; seawater intrusion; costal management
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Special Issue Information
Dear Colleagues,
Remote sensing has changed the way we view and study the Earth's surface in a very cost- and time-effective manner. The synergistic application of remote sensing techniques with artificial intelligence (AI) and machine learning (ML) amplifies its effectiveness, allowing for automated and precise analysis of satellite and aerial imagery. This improves the detection of land cover changes, water quality, and other ecosystem changes, as well as the prediction of environmental trends, thus offering invaluable insights for the sustainable management of natural resources and transforming the way we map land cover and evaluate ecosystem health.
Additionally, these advancements are crucial in tackling the growing challenges caused by climate change and human activities. Real-time and historical data on land cover changes help in identifying areas affected by deforestation, desertification, agricultural expansion, and urban sprawl, which are key drivers of greenhouse gas emissions and environmental degradation. By handling big data, AI and machine learning models revolutionize predictions of climate-related phenomena, such as rising sea levels, extreme weather events, and shifting agricultural zones, enabling policymakers to develop effective mitigation and adaptation strategies. Additionally, the predictive capabilities of AI and machine learning enable the detection of long-term climate trends and early warnings of extreme weather events, which aid in disaster preparedness and resilience planning.
This Special Issue considers remote sensing techniques in combination with AI and machine learning in detecting and monitoring landcover and land use changes, as well as ecosystem dynamics, especially in the context of climate change and anthropogenic activities.
Dr. Malik Mohammed Al-Wardy
Dr. Mohammad Reza Nikoo
Guest Editors
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Keywords
- remote sensing
- artificial intelligence
- machine learning
- land cover changes
- climate change
- ecosystem health
- environmental trends
- resilience planning
- sustainable management
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