Automated Mapping of the Freshwater Ecosystem Functional Groups of the International Union for Conservation of Nature Global Ecosystem Typology in a Large Region of Arid Australia

The classification of freshwater ecosystems is essential for effective biodiversity conservation and ecosystem management, particularly with increasing threats. We developed an automated approach to mapping and classifying freshwater ecosystem functional groups based on the IUCN Global Ecosystem Typology (GET), offering a scalable, dynamic and efficient alternative to current manual methods. Our method leveraged remote sensing data and thresholding algorithms to classify ecosystems into distinct ecosystem functional groups, accounting for challenges such as the temporal and spatial complexities of dynamic freshwater ecosystems and inconsistencies in manual classification. Unlike traditional approaches, which rely on manual cross-referencing to adapt existing maps and contain subjective biases, our system is repeatable, transparent and adaptable to new incoming satellite data. We demonstrate the applicability of this method in the Paroo–Warrego region of Australia (~14,000,000 ha), highlighting the automated classification’s capacity to process large areas with diverse ecosystems. Although some functional groups require static datasets due to current limitations in satellite data, the overall approach had high accuracy (84%). This work provides a foundation for future applications to other freshwater ecosystems around the world, underpinning biodiversity management, monitoring and reporting worldwide.

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Publication Details

Journal
Remote Sensing
Published
2025-04-22
DOI
https://doi.org/10.3390/rs17091488
Primary Topic
Soil Geostatistics and Mapping
Type
article
Field-Weighted Citation Impact
0.00

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article

Automated Mapping of the Freshwater Ecosystem Functional Groups of the International Union for Conservation of Nature Global Ecosystem Typology in a Large Region of Arid Australia

Roxane J. Francis, José R. Ferrer‐Paris, Richard T. Kingsford, Hedley S. Grantham et al.
Remote Sensing
Soil Geostatistics and Mapping
article

Automated Mapping of the Freshwater Ecosystem Functional Groups of the International Union for Conservation of Nature Global Ecosystem Typology in a Large Region of Arid Australia

Roxane J. Francis, José R. Ferrer‐Paris, Richard T. Kingsford, Hedley S. Grantham, David A. Keith
article en

Abstract

The classification of freshwater ecosystems is essential for effective biodiversity conservation and ecosystem management, particularly with increasing threats. We developed an automated approach to mapping and classifying freshwater ecosystem functional groups based on the IUCN Global Ecosystem Typology (GET), offering a scalable, dynamic and efficient alternative to current manual methods. Our method leveraged remote sensing data and thresholding algorithms to classify ecosystems into distinct ecosystem functional groups, accounting for challenges such as the temporal and spatial complexities of dynamic freshwater ecosystems and inconsistencies in manual classification. Unlike traditional approaches, which rely on manual cross-referencing to adapt existing maps and contain subjective biases, our system is repeatable, transparent and adaptable to new incoming satellite data. We demonstrate the applicability of this method in the Paroo–Warrego region of Australia (~14,000,000 ha), highlighting the automated classification’s capacity to process large areas with diverse ecosystems. Although some functional groups require static datasets due to current limitations in satellite data, the overall approach had high accuracy (84%). This work provides a foundation for future applications to other freshwater ecosystems around the world, underpinning biodiversity management, monitoring and reporting worldwide.

Remote SensingVol. 17(9)
Bush Heritage Australia (AU), UNSW Sydney (AU)
Ian Potter Foundation, Australian Research Council
Openalex Percentile: Top 92%
Soil Geostatistics and Mapping
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