Preprints
https://doi.org/10.5194/bg-2023-95
https://doi.org/10.5194/bg-2023-95
01 Aug 2023
 | 01 Aug 2023
Status: a revised version of this preprint was accepted for the journal BG and is expected to appear here in due course.

Synergistic use of Sentinel-2 and UAV-derived data for Plant Community Cover distribution mapping of coastal meadows with Digital Elevation Models

Ricardo Martinez Prentice, Miguel Villoslada Peciña, Raymond D. Ward, Thaisa F. Bergamo, Chris B. Joyce, and Kalev Sepp

Abstract. Coastal wetlands provide a range of ecosystem services, yet are currently under threat from global change impacts. Thus, monitoring and assessment is vital for evaluating their status, extent and distribution. Remote sensing provides an excellent tool for evaluating coastal ecosystems, whether with small scale studies using drones or national/regional/global scale studies using satellite derived data. This study used a fine-scale plant community classification of coastal meadows in Estonia derived from a multispectral camera on board Unoccupied Aerial Vehicles (UAV) to calculate the Plant Fractional Cover (PFC) in Sentinel-2 MultiSpectral Instrument sensor (MSI) grids. A Random Forest algorithm was trained and tested with vegetation indices (VI) calculated from the spectral bands extracted from the MSI sensor to predict the PFC. Additional RF models were trained and tested after adding a Digital Elevation Model (DEM). After comparing the models, results show that using DEM with VI can increase the prediction accuracy of PFC up to two times (R2 58–70 %). This suggests the use of ancillary data such as DEM to improve the prediction of empirical machine learning models, providing an appropriate approach to upscale local studies to wider areas for management and conservation purposes.

Ricardo Martinez Prentice, Miguel Villoslada Peciña, Raymond D. Ward, Thaisa F. Bergamo, Chris B. Joyce, and Kalev Sepp

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2023-95', Anonymous Referee #1, 24 Aug 2023
    • AC1: 'Reply on RC1', Ricardo Martinez Prentice, 18 Sep 2023
      • AC3: 'Reply on AC1', Ricardo Martinez Prentice, 07 Oct 2023
  • RC2: 'Comment on bg-2023-95', Anonymous Referee #2, 23 Sep 2023
    • AC2: 'Reply on RC2', Ricardo Martinez Prentice, 07 Oct 2023
    • AC4: 'Reply on RC2', Ricardo Martinez Prentice, 18 Oct 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2023-95', Anonymous Referee #1, 24 Aug 2023
    • AC1: 'Reply on RC1', Ricardo Martinez Prentice, 18 Sep 2023
      • AC3: 'Reply on AC1', Ricardo Martinez Prentice, 07 Oct 2023
  • RC2: 'Comment on bg-2023-95', Anonymous Referee #2, 23 Sep 2023
    • AC2: 'Reply on RC2', Ricardo Martinez Prentice, 07 Oct 2023
    • AC4: 'Reply on RC2', Ricardo Martinez Prentice, 18 Oct 2023
Ricardo Martinez Prentice, Miguel Villoslada Peciña, Raymond D. Ward, Thaisa F. Bergamo, Chris B. Joyce, and Kalev Sepp
Ricardo Martinez Prentice, Miguel Villoslada Peciña, Raymond D. Ward, Thaisa F. Bergamo, Chris B. Joyce, and Kalev Sepp

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Short summary
The use of remote sensing technology helps to evaluate the ecological status of coastal wetlands, affected by global change impacts. This study models the plant community cover of coastal meadows in Estonia with a synergistic use of drone, satellite imagery and Digital Elevation Models, to map their distribution. This approach allows to scale local studies to monitor changes in larger areas for a better coastal management and conservation in coastal meadows.
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