Project Overview

Future resilience of sandy coasts in light of climate change is a growing concern, not only to the United Nations (Goal 13), to European agencies (e.g. ESA) and scientists, but also to coastal managers and local communities, highlighting the need for spatially extensive, consistent and reliable monitoring of coastal adaptation, to allow for appropriate and timely mitigation measures and strategies. At the same time, there is growing consensus that natural adaptation is complex, with multi-level feedbacks across scales (i.e. system units), non-linear responses and system tipping-points. On one hand, consistent, large scale, in situ monitoring of sandy coasts implies prohibitive costs in money and human resources, while satellite data already meet the criteria of broad spatial coverage and consistency (McAllister et al. 2022). On the other hand, individual morphological units are typically monitored disconnectedly, with no links to the evolution and adaptation of the system as a whole. The vision of CREST is to go beyond existing solutions that manage coastal systems for resistance (structural stability) rather than for resilience (functional dynamism) (Feagin et al. 2015), toward holistic approaches that address resilient coasts as complex adaptive systems.

Objectives

CREST aims to formulate a remote sensing methodology for coastal resilience that learns from past system pathways, identifying and accounting for related adaptation mechanisms, recovery timescales, feedbacks and potential thresholds. To this end, CREST will use open source satellite imagery, sacrificing some spatial detail but maximising temporal (e.g., Sentinel-2) and spatial coverage.

These issues pose two major challenges that CREST aims to address: a) Obtaining cost-effective morphological data at the highest possible frequency and longest possible time scale, over a range of sandy environments, representative of the natural variability across scales or units; and b) Formulating approaches using this data to assess and monitor the resilience (conservation status) of the coast.

To address each of these challenges, the project aims to combine and further explore recent advances in two scientific fields: 1) remote sensing and coastal feature identification using satellite imagery (Garzon et al. 2021, Vos et al. 2019, Caballero and Stumpf, 2019) and 2) resilience assessment methodologies for geomorphologic adaptation of coastal systems (Kombiadou et al. 2019, Kombiadou et al. 2020), developing an approach to monitor resilience indicators of sandy coasts from satellite imagery. In this manner, CREST aspires to make a critical jump in connecting the fields of remote sensing and non-linear geomorphic system adaptation, advancing state-of-the-art understanding and approaches in both fields. These are the two main pillars around which the project will be built, integrating the expertise and experience of the interdisciplinary team on coastal resilience assessment (CIMA, UL and UCA), monitoring coastal processes (CIMA, UL, UCA, APA and UNSW) and extracting coastal features from satellite images (UNSW, CSIC and CIMA).

Crest vision and interdisciplinary approach.

Project Tasks

The work within CREST is organised in 7 tasks: Tasks 1 to 3 involve satellite data processing (imagery retrieval and processing, detection of morphological indicators, error/uncertainty assessment and process automation), Tasks 4 to 5 transfer the remotely sensed indicators to resilience (system state identification, dimensions and compilation of trajectories, identification of system tipping points and critical dimensions), Task 6 unifies the entire workflow into an automated prototype monitoring tool (PMT), while Task 7 is an overarching task, charged with internal and external communication and dissemination.

Structure and workflow within the CREST project.

Bibliography

Caballero & Stumpf (2019) Retrieval of nearshore bathymetry from Sentinel-2A and 2B satellites in South Florida coastal waters. Estuarine, Coastal and Shelf Science. 226 106277. 10.1016/j.ecss.2019.106277

Feagin, Figlus, Zinnert, Sigren, Martínez, Silva, et al. (2015) Going with the flow or against the grain? The promise of vegetation for protecting beaches, dunes, and barrier islands from erosion. Frontiers in Ecology and the Environment. 13 (4), 203–210. 10.1890/140218

Garzon, Costas & Ferreira (2021) Biotic and abiotic factors governing dune response to storm events. Earth Surface Processes and Landforms. n/a. 10.1002/esp.5300

Kombiadou, Costas, Carrasco, Plomaritis, Ferreira & Matias (2019) Bridging the gap between resilience and geomorphology of complex coastal systems. Earth-Science Reviews. 198. 10.1016/j.earscirev.2019.102934

Kombiadou, Matias, Costas, Rita Carrasco, Plomaritis & Ferreira (2020) Barrier island resilience assessment: Applying the ecological principles to geomorphological data. CATENA. 10.1016/j.catena.2020.104755

McAllister, Payo, Novellino, Dolphin & Medina-Lopez (2022) Multispectral satellite imagery and machine learning for the extraction of shoreline indicators. Coastal Engineering. 104102. 10.1016/j.coastaleng.2022.104102

Vos, Splinter, Harley, Simmons & Turner (2019) CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery. Environmental Modelling and Software. 122 104528. 10.1016/j.envsoft.2019.104528

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