VedgeSat: An automated, open‐source toolkit for coastal change monitoring using satellite‐derived vegetation edges

• Published in: Earth surface processes and landforms 2024-06, Vol.49 (8), p.2405-2423
• Main Authors: Muir, Freya M. E., Hurst, Martin D., Richardson‐Foulger, Luke, Rennie, Alistair F., Naylor, Larissa A.
• Format: Article
• Language: English
• Subjects: Aerial surveys; Brackishwater environment; Climate change; coastal ecology; Coastal erosion; Coastal geomorphology; Coastal landforms; Coastal storms; Coastal zone management; Coasts; Data management; Environmental monitoring; Environmental risk; Estuaries; Estuarine environments; Geomorphology; Google Earth Engine; image classification; Image processing; Landforms; Landsat; Landsat satellites; Monitoring; neural network; Neural networks; Pixels; Planet; Remote sensing; Risk management; Root-mean-square errors; Satellite imagery; Satellite observation; Satellites; Sentinel‐2; Storm damage; Storms; Toolkits; Vegetation; vegetation edge
• ISSN: 0197-9337; 1096-9837
• DOI: 10.1002/esp.5835

Public satellite platforms offer regular observations for global coastal monitoring and climate change risk management strategies. Unfortunately, shoreline positions derived from satellite imagery, representing changes in intertidal topography, are noisy and subject to tidal bias that requires correction. The seaward‐most vegetation boundary reflects a change indicator which shifts on event–decadal timescales, and informs coastal practitioners of storm damage, sediment availability and coastal landform health. We present and validate a new open‐source tool VedgeSat for identifying vegetation edges (VEs) from high (3 m) and moderate (10–30 m) resolution satellite imagery. The methodology is based on the CoastSat toolkit, with streamlined image processing using cloud‐based data management via Google Earth Engine. Images are classified using a newly trained vegetation‐specific neural network, and VEs are extracted at subpixel level using dynamic Weighted Peaks thresholding. We performed validation against ground surveys and manual digitisation of aerial imagery across eroding and accreting open coasts and estuarine environments at a site in Scotland. Smaller‐than‐pixel vegetation boundary detection was achieved across 83% of Sentinel‐2 imagery (Root Mean Square Error of 9.3 m). An overall RMSE of 19.0 m was achieved across Landsat 5 & 8, Sentinel‐2 and PlanetScope images. Performance varied by coastal geomorphology, with highest accuracies across sandy open coasts owing to high spectral contrast and less false positives from intertidal vegetation. The VedgeSat tool can be readily applied in tandem with waterlines near‐globally, to support adaptation decisions with historic coastal trends across the whole shoreface, even in normally data‐scarce areas. We use open‐source satellite imagery via Google Earth Engine and an artificial neural network to automatically extract coastal vegetation edges. Using the open‐source Python software VedgeSat, coastal vegetation edges can be automatically extracted near globally for the last 40 years.