A Web-Based National-Scale Coastal Tidal Flat Extraction System Using Multi-Algorithm Integration on AI Earth Platform
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| Publicado en: | Remote Sensing vol. 17, no. 16 (2025), p. 2911-2932 |
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| Autor Principal: | |
| Outros autores: | , , , , , |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en liña: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| Resumo: | As coastal tidal flats—ecosystems of high ecological significance and socio-economic value—face accelerating degradation driven by climate change and intensified anthropogenic disturbances, there is an urgent need for efficient, automated, and scalable monitoring solutions. Traditional monitoring approaches are constrained by high implementation costs and limited spatial coverage, whereas remote sensing—particularly multispectral satellite imagery such as Sentinel-2—has emerged as a primary and widely adopted tool for large-scale environmental observation. Building upon recent advancements in cloud computing and WebGIS technologies, this study presents a web-based, interactive tidal flat extraction system implemented on Alibaba’s AI Earth platform. The system integrates multiple water indices (NDWI, mNDWI, and IWI) with a machine learning algorithm (Random Forest), and is deployed through a user-friendly interface developed using Vue.js and Leaflet, enabling flexible parameter configuration and real-time visualization of extraction results. Its front-end/back-end decoupled architecture enables non-programming users to conduct large-scale tidal flat mapping, thereby substantially lowering the technical barriers to coastal tidal flat monitoring and management in China. |
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| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs17162911 |
| Fonte: | Advanced Technologies & Aerospace Database |