Seagrass habitats are ecologically valuable and play an important role in sequestering and storing carbon. There is, thus, a need to estimate seagrass percentage cover in diverse environments in support of climate change mitigation, marine spatial planning and coastal zone management. In situ approaches are accurate but time-consuming, expensive and may not represent the larger spatial units collected by satellite imaging. Hence, there is a need for a consistent methodology that uses accurate point-based field surveys to deliver high-quality mapping of percentage seagrass cover at large spatial scales. Here, we develop a three-step approach that combines in situ (quadrats), aerial (unoccupied aerial vehicle—UAV) and satellite data to map percentage seagrass cover at Turneffe Atoll, Belize, the largest atoll in the northern hemisphere. First, the optical bands of four UAV images were used to calculate seagrass cover, in combination with in situ data. The seagrass cover calculated from the UAV was then used to develop training and validation datasets to estimate seagrass cover in Sentinel-2 pixels. Next, non-seagrass areas were identified in the Sentinel-2 data and removed by object-based classification, followed by a pixel-based regression to calculate seagrass percentage cover. Using this approach, percentage seagrass cover was mapped using UAVs (R² = 0.91 between observed and mapped distributions) and using Sentinel-2 data (R² = 0.73). This work provides the first openly available and explorable map of seagrass percentage cover across Turneffe Atoll, where we estimate approximately 242 km² of seagrass above 10% cover is located. We estimate that this approach offers 30 times more data for training satellite data than traditional methods, therefore presenting a substantial reduction in cost-per-point for data. Furthermore, the increase in data helps deliver a high-quality seagrass cover map, suitable for resolving trends of deteriorating, stable or recovering seagrass environments at 10 m² resolution to underpin evidence-based management and conservation of seagrass.

Accurate knowledge of the spatial extent of seagrass habitats is essential for monitoring and management purposes given their ecological and economic significance. Extent data are typically presented in binary (presence/absence) or arbitrary, semi-quantitative density bands derived from low-resolution satellite imagery, which cannot resolve fine-scale features and intra-habitat variability. Recent advances in consumer-grade unoccupied aerial vehicles (UAVs) have advanced our ability to survey large areas at higher resolution and at lower cost. This has improved the accessibility of mapping technologies to developing coastal nations, where a large proportion of the world’s seagrass habitats are found. Here, we present the application of UAV-gathered imagery to determine seagrass habitat extent and percent of canopy cover. Four contrasting sites were surveyed in the Turneffe Atoll Marine Reserve, Belize, and seagrass canopy cover was ground truthed from in situ quadrats. Orthomosaic images were created for each site from the UAV-gathered imagery. Three modelling techniques were tested to extrapolate the findings from quadrats to spatial information, producing binary (random forest) and canopy cover (random forest regression and beta regression) habitat maps. The most robust model (random forest regression) had an average absolute error of 6.8–11.9% (SE of 8.2–14), building upon previous attempts at mapping seagrass density from satellite imagery, which achieved errors between 15–20% approximately. The resulting maps exhibited great intra-habitat heterogeneity and different levels of patchiness, which were attributed to site energetics and, possibly, species composition. The extra information in the canopy cover maps provides greater detail and information for key management decisions and provides the basis for future spatial studies and monitoring programmes.

Resilience is widely seen as an important attribute of coastal systems and, as a concept, is increasingly prominent in policy documents. However, there are conflicting ideas on what constitutes resilience and its operationalisation as an overarching principle of coastal management remains limited. In this paper, we show how resilience to coastal flood and erosion hazard could be measured and applied within policy processes, using England as a case study. We define resilience pragmatically, in economic, environmental and social terms, integrating what is presently a disparate set of policy objectives for coastal areas. Our definition includes several dimensions of resilience and we develop a set of composite indicators for each of these, grounded empirically with reference to national geospatial datasets. A prototype model has been developed, which generates a quantitative resilience index for a given geographical unit (England’s coastal hazard zone being represented at a high spatial resolution, about 8,000 areal units). A range of different stakeholder perspectives are captured using relative indicator weightings. The illustrative results presented here demonstrate the practicality of formalising and quantifying resilience, and the insights obtained mainly concern this process of operationalisation. To re-focus national policy around the stated desire of enhancing resilience to coastal flooding and erosion would require firm commitment from government to develop an approach to monitor progress towards resilience, extending the present risk-based approach. This requires a consensus methodology in which stakeholder values are explicitly considered, and also requires incentives for coastal managers to engage with and apply this new approach. Such a transition would challenge existing governance arrangements at national and local levels, requiring more integration and inter-agency cooperation. However, it could provide a robust evidence-based framework for achieving more sustainable, equitable and societally acceptable adaptive responses to climate change at the coast.