If coral reefs continue to degrade, waves on coastlines may substantially increase, leading to greater coastal erosion.

Very high resolution (VHR) airborne data enable detection and physical measurements of individual coral reef colonies. The bathymetric LiDAR system, as an active remote sensing technique, accurately computes the coral reef ecosystem’s surface and reflectance using a single green wavelength at the decimetre scale over 1-to-100 km² areas. A passive multispectral camera mounted on an airborne drone can build a blue-green-red (BGR) orthorectified mosaic at the centimetre scale over 0.01-to-0.1 km² areas. A combination of these technologies is used for the first time here to map coral reef ecological state at the submeter scale. Airborne drone BGR values (0.03 m pixel size) serve to calibrate airborne bathymetric LiDAR surface and intensity data (0.5 m pixel size). A classification of five ecological states is then mapped through an artificial neural network (ANN). The classification was developed over a small area (0.01 km²) in the lagoon of Moorea Island (French Polynesia) at VHR (0.5 m pixel size) and then extended to the whole lagoon (46.83 km²). The ANN was first calibrated with 275 samples to determine the class of coral state through LiDAR-based predictors; then, the classification was validated through 135 samples, reaching a satisfactory performance (overall accuracy = 0.75).

Mapping the spatial allocation of fishing effort while including key stakeholders in the decision making process is essential for effective fisheries management but is difficult to implement in complex small-scale fisheries that are diffuse, informal and multifaceted. Here we present a standardized but flexible approach that combines participatory mapping approaches (fishers’ spatial preference for fishing grounds, or fishing suitability) with socioeconomic approaches (spatial extrapolation of social surrogates, or fishing capacity) to generate a comprehensive map of predicted fishing effort. Using a real world case study, in Moorea, French Polynesia, we showed that high predicted fishing effort is not simply located in front of, or close to, main fishing villages with high dependence on marine resources; it also occurs where resource dependency is moderate and generally in near-shore areas and reef passages. The integrated approach we developed can contribute to addressing the recurrent lack of fishing effort spatial data through key stakeholders' (i.e., resource users) participation. It can be tailored to a wide range of social, ecological and data availability contexts, and should help improve place-based management of natural resources.