Sandy beaches are highly dynamic systems which provide natural protection from the impact of waves to coastal communities. With coastal erosion hazards predicted to increase globally, data to inform decision making on erosion mitigation and adaptation strategies is becoming critical. However, multi-temporal topographic data over wide geographical areas is expensive and time consuming and often requires highly trained professionals. In this study we demonstrate a novel approach combining citizen science with low-cost unmanned aerial vehicles that reliably produces survey-grade morphological data able to model sediment dynamics from event to annual scales. The high-energy wave-dominated coast of south-eastern Australia, in Victoria, is used as a field laboratory to test the reliability of our protocol and develop a set of indices to study multi-scale erosional dynamics. We found that citizen scientists provide unbiased data as accurate as professional researchers. We then observed that open-ocean beaches mobilise three times as much sediment as embayed beaches and distinguished between slowed and accelerated erosional modes. The data was also able to assess the efficiency of sand nourishment for shore protection. Our citizen science protocol provides high quality monitoring capabilities, which although subject to important legislative preconditions, it is applicable in other parts of the world and transferable to other landscape systems where the understanding of sediment dynamics is critical for management of natural or anthropogenic processes.

Sandpyper is a Python package that automates profile-based volumetric and altimetric sandy beaches analysis from a large amount of digital surface models and orthophotos. It includes functionalities to facilitate the cleaning of the elevation data from unwanted non-sand points or swash areas (where waves run up on the beach slope and 3D reconstruction is inaccurate) and to model beachface behavioural regimes using the Beachface Cluster Dynamics indices.

Seagrass restoration requires information on a range of factors including site environmental conditions, appropriate planting techniques, and the identification of sites most likely to support seagrass. To address the question of where to focus restoration efforts, a key first step is to identify trends in the spatio‐temporal distribution of seagrasses to identify areas of persistence, loss, and recent gains. Areas of recent recovery (and adjacent areas), can then be targeted by practitioners for assisted recovery and restoration, whilst areas of persistent loss can be avoided. Here we identified the contemporary distribution, density, and species composition of seagrass ecosystems (using Sentinel 2 imagery and supervised object‐based imagery analysis) and integrated these data with historic extents to identify spatio‐temporal trends in seagrass distribution in Western Port, Victoria, Australia. Contemporary classifications demonstrated acceptable accuracies (Overall Accuracy 0.77–0.85, User Accuracy 0.76–0.97) and predicted a contemporary seagrass extent of 222 km2; with 48 km2 of low‐density recovery predicted to have occurred since 1999. Comparisons with historical seagrass extents indicated some seagrass recovery since large‐scale losses in 1983, although some areas of loss were also present. Recovery included a net gain of approximately 95 km2 in the past 20 years and an eastward range expansion; suggesting environmental conditions have improved and are now conducive for restoration efforts in some areas. Results demonstrate that accurate, low‐cost, remote sensing of seagrass ecosystems is possible and show how understanding spatio‐temporal trends can guide the spatial allocation of resources by prioritizing areas for restoration where recovery is beginning to occur.

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