Presentation Information
[hIaLijy4-05]Building Blocks for a Philippine Coastal-Ocean Digital Twin: Insights from Remote Sensing, Modeling, and Machine Learning
*Ayin M. Tamondong1 (1. University of the Philippines - Diliman (Philippines))
The Philippines is an archipelagic country whose coastal waters and marginal seas are closely tied to food security, livelihoods, biodiversity, disaster resilience, maritime activities, and the blue economy. These areas support fisheries and aquaculture, host important ecosystems such as coral reefs, seagrass beds, and mangroves, and help protect coastal communities. At the same time, they are increasingly affected by climate change, coastal development, pollution, extreme weather events, harmful algal blooms, habitat degradation, and competing uses of marine space.
Despite their importance, many coastal and ocean areas in the Philippines remain difficult to monitor on a regular basis. In-situ observations are often limited in coverage, while coastal and ocean datasets are often produced for specific projects, locations, and objectives. This makes it challenging to build a broader and more consistent picture of ocean conditions, environmental changes, and emerging risks across the country.
This presentation shares selected ocean-related research undertaken in different study areas in the Philippines. These include applications in coastal habitat mapping, coral bleaching detection, seagrass assessment, water quality monitoring, red tide prediction, storm surge simulation, oil spill and marine litter tracking, vessel monitoring, and fisheries-related oceanographic analysis. The studies use remote sensing, field data, geospatial analysis, physical models, and machine learning models to describe coastal and marine environments, examine environmental changes, and explore possible support for monitoring and decision-making.
While these studies were developed as separate research efforts, they offer useful examples of the kinds of data, methods, and applications that may be relevant to future coastal-ocean digital twin initiatives. The presentation therefore reflects on lessons from these Philippine case studies and discusses how such experiences may contribute to broader conversations on an Asia Hub for Ocean Digital Twins, especially for archipelagic and data-limited coastal regions.
Despite their importance, many coastal and ocean areas in the Philippines remain difficult to monitor on a regular basis. In-situ observations are often limited in coverage, while coastal and ocean datasets are often produced for specific projects, locations, and objectives. This makes it challenging to build a broader and more consistent picture of ocean conditions, environmental changes, and emerging risks across the country.
This presentation shares selected ocean-related research undertaken in different study areas in the Philippines. These include applications in coastal habitat mapping, coral bleaching detection, seagrass assessment, water quality monitoring, red tide prediction, storm surge simulation, oil spill and marine litter tracking, vessel monitoring, and fisheries-related oceanographic analysis. The studies use remote sensing, field data, geospatial analysis, physical models, and machine learning models to describe coastal and marine environments, examine environmental changes, and explore possible support for monitoring and decision-making.
While these studies were developed as separate research efforts, they offer useful examples of the kinds of data, methods, and applications that may be relevant to future coastal-ocean digital twin initiatives. The presentation therefore reflects on lessons from these Philippine case studies and discusses how such experiences may contribute to broader conversations on an Asia Hub for Ocean Digital Twins, especially for archipelagic and data-limited coastal regions.
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