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Updated: Jun 3, 2026
10:05
Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation
Published on: July 4, 2014
GeoAI for polar vegetation mapping and hydrological interactions: A systematic review
Narmilan Amarasingam1, Mariapina Vomero2, Arthur Platel3
1Securing Antarctica's Environmental Future (SAEF), University of Wollongong, Wollongong, New South Wales, 2522, Australia; Environmental Futures, University of Wollongong, Wollongong, New South Wales, 2522, Australia.
The Science of the Total Environment
|June 1, 2026
Summary
Artificial intelligence (AI) and remote sensing (RS) are revolutionizing polar ecological monitoring. This review synthesizes 116 studies, highlighting a shift towards integrated AI-enhanced RS frameworks for tracking climate-driven changes in vegetation and hydrology.
Area of Science:
- * Environmental Science, Remote Sensing, Artificial Intelligence, Polar Ecology.
- * Utilizes bibliometric and conceptual-network analyses for systematic synthesis.
Background:
- * Logistical and environmental challenges in polar regions necessitate advanced monitoring techniques.
- * Remote sensing (RS) and Artificial Intelligence (AI) offer novel solutions for ecological assessment.
- * Previous reviews have not systematically evaluated the scope and quality of AI-enhanced RS in polar environments.
Purpose of the Study:
- * To conduct the first systematic review of AI-enhanced RS (GeoAI) in Arctic and Antarctic environments (2005-2025).
- * To analyze trends, methodologies, and identify research gaps in polar GeoAI applications.
- * To establish a foundation for future polar monitoring strategies.
Main Methods:
- * Systematic literature search adhering to PRISMA 2020 guidelines, analyzing 116 studies.
- * Bibliometric and conceptual-network analyses to map publication trends and research connections.
- * Evaluation of methodologies, including machine learning, deep learning, and spectral-index applications.
Main Results:
- * Significant expansion of GeoAI research since 2018, driven by UAVs, multispectral imaging, and deep learning (DL).
- * Shift from isolated monitoring to integrated data-fusion frameworks linking vegetation, hydrology, and climate.
- * Emergence of DL, particularly convolutional neural networks, for fine-scale analysis; persistent gaps in UAV-to-satellite integration and validation.
- * Increasing use of advanced spectral indices beyond greenness for physiological insights.
Conclusions:
- * GeoAI is crucial for understanding and managing polar ecosystems under rapid climate change.
- * Future research should focus on hierarchical UAV-to-satellite fusion, open datasets, and explainable AI.
- * These advancements are essential for scalable, climate-adaptive conservation in Earth's sensitive polar regions.
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