English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Along the Baltic coastline of Germany, drifting vegetation and beach cast create overlays at the otherwise sandy or stony beaches. These overlays influence the morphodynamics and structures of the beaches. To better understand the influence of these patchy habitats on coastal environments, regular monitoring is necessary. Most studies, however, have been conducted on spatially larger and temporally more stable occurrences of aquatic vegetation such as floating fields of Sargassum. Nevertheless, drifting vegetation and beach cast pose a particular challenge, as they exhibit high temporal dynamics and sometimes small spatial extent. Regular surveys and mappings are the traditional methods to record their habitats, but they are time-consuming and cost-intensive. Spaceborne remote sensing can provide frequent recordings of the coastal zone at lower cost. Our study therefore aims at the monitoring of drifting vegetation and beach cast on spatial scales between 3 and 10 m. We developed an automated coastline masking algorithm and tested six supervised classification methods and various classification ensembles for their suitability to detect small-scale assemblages of drifting vegetation and beach cast in a study area at the coastline of the Western Baltic Sea using multispectral data of the sensors Sentinel-2 MSI and PlanetScope. The shoreline masking algorithm shows high accuracies in masking the land area while preserving the sand-covered shoreline. We could achieve best classification results using PlanetScope data with an ensemble of a random forest classifier, cart classifier, support vector machine classifier, naïve bayes classifier and stochastic gradient boosting classifier. This ensemble accomplished a combined f1-score of 0.95. The accuracy of the Sentinel-2 classifications was lower but still achieved a combined f1-score of 0.86 for the same ensemble. The results of this study can be considered as a starting point for the development of time series analysis of the vegetation dynamics along Baltic beaches.

Classification Ensembles for Beach Cast and Drifting Vegetation Mapping with Sentinel-2 and PlanetScope