Accelerated Adoption of Google Earth Engine for Mangrove Monitoring: A Global Review
Mangrove forests support coastal resilience, biodiversity, and significant carbon sequestration, yet they face escalating threats from climate change, urban expansion, and land-use change. Traditional remote sensing workflows often struggle with large data volumes, complex preprocessing, and limited...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/13/2290 |
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| Summary: | Mangrove forests support coastal resilience, biodiversity, and significant carbon sequestration, yet they face escalating threats from climate change, urban expansion, and land-use change. Traditional remote sensing workflows often struggle with large data volumes, complex preprocessing, and limited computational resources. Google Earth Engine (GEE) addresses these challenges through scalable, cloud-based computation, extensive, preprocessed imagery catalogs, built-in algorithms for rapid feature engineering, and collaborative script sharing that improves reproducibility. To evaluate how the potential of GEE has been harnessed for mangrove research, we systematically reviewed peer-reviewed articles published between 2017 and 2022. We examined the spectrum of GEE-based tasks, the extent to which studies incorporated mangrove-specific preprocessing, and the challenges encountered. Our analysis reveals a noteworthy yearly increase in GEE-driven mangrove studies but also identifies geographic imbalances, with several high-mangrove-density countries remaining underrepresented. Although most studies leveraged streamlined preprocessing and basic classification workflows, relatively few employed advanced automated methods. Persistent barriers include limited coding expertise, platform quotas, and sparse high-resolution data in certain regions. We outline a generalized workflow that includes automated tidal filtering, dynamic image composite generation, and advanced classification pipelines to address these gaps. By synthesizing achievements and ongoing limitations, this review offers guidance for future GEE-based mangrove studies and conservation efforts and aims to improve methodological rigor and maximize the potential of GEE. |
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| ISSN: | 2072-4292 |