Pele Island Coral Reef and Shoreline Defence Information Dashboard

Pele Island's reef, seagrass, and shoreline systems support food security, community identity, restoration work, and shoreline protection. The interim project report confirms that the foundational field campaign is complete, the Google Earth Engine processing environment has been set up, and the project is now focused on producing maps and models for community management plans.

Conservation activities are centred on community-led monitoring and practical planning. Training delivered through the August 2025 campaign covered marine survey methods, GPS use, drone survey foundations, data processing, GIS fundamentals, and QGIS workflows. Survey equipment, connectivity infrastructure, and ongoing weekly coordination meetings are intended to support long-term PIELSN capacity rather than one-off data collection.

Restoration and adaptation work will use the mapping products to identify priority coral restoration and coastal rehabilitation sites, support community conservation area mapping, and inform management planning across all four villages. The project is also exploring biodiversity finance mechanisms that use transparent spatial datasets and ocean accounting methods to support community-led conservation activities.

Future updates should replace placeholder values with validated field, drone, satellite, and wave-modelling outputs; add confirmed village social indicators; and extend the shoreline change component when coastal-risk scenarios are ready.

The Pele Island coastal and reef mapping integrates multiple data sources collected during the August 2025 field campaign and ongoing satellite monitoring. The interim report records comprehensive coverage across target survey areas, with only part of the northeastern island section missed because of bad weather. Primary data sources include:

Field Survey Data: geo-located underwater photographs from SCUBA and snorkel photo-transect surveys conducted by trained community members following University of Queensland standardized protocols, classified using ReefCloud AI to identify benthic cover (coral, algae, sand, seagrass, consolidated substrate). The field campaign also collected wave energy measurements and seafloor sediment samples.

Drone Data: very-high-resolution multispectral imagery (RGB and 14 multispectral bands), RGB imagery, and LiDAR point clouds captured through the UQ Drone Collaborative Research Platform. These data are processed to generate orthomosaics, digital elevation models (DEMs), and digital surface models (DSMs) of coastal topography, reef structure, vegetation, and restoration areas.

Satellite Imagery: high-resolution multispectral data from Sentinel-2 and commercial providers, including satellite-derived bathymetry, processed in Google Earth Engine using machine learning classifiers trained on community-collected field data. The Google Earth Engine cloud processing environment is set up for ongoing mapping and publishing workflows.

Wave and Oceanographic Data: in-situ wave energy measurements, seafloor sediment samples, and global wave climate models transformed to nearshore conditions. The modelling workflow uses SWAN for wave transformation, XBeach for wave-driven transport on exposed coastlines, and Delft3D where tidal currents are important, incorporating high-resolution bathymetry, LiDAR topography, wind, tide, and IPCC sea-level rise scenarios.

All methodologies follow open-source, reproducible workflows enabling ongoing community-led monitoring and are being published to publicly accessible repositories. Local expert input from Salome and Willie should be added when confirmed.