Enhancing the predictive performance of remote sensing for ecological variables of tidal flats using encoded features from a deep learning model

Tidal flats are among the ecologically richest areas of the world where sediment composition (e.g. median grain size and silt content) and the macrozoobenthic presence play an important role in the health of the ecosystem. Regular monitoring of environmental and ecological variables is essential for sustainable management of the area. While monitoring based on field sampling is very time-consuming, the predictive performance of these variables using satellite images is low due to the spectral homogeneity over these regions. We tested a novel approach that uses features from a variational autoencoder (VAE) model to enhance the predictive performance of remote sensing images for environmental and ecological variables of tidal flats. The model was trained using the Sentinel-2 spectral bands to reproduce the input images, and during this process, the VAE model represents important information on the tidal flats within its layer structure. The information in the layers of the trained model was extracted to form features with identical spatial coverage to the spectral bands. The features and the spectral bands together form the input to random forest models to predict field observations of the sediment characteristics such as median grain size and silt content, as well as the macrozoobenthic biomass and species richness. The maximum prediction accuracy of feature-based maps was close to 62% for the sediment characteristics and 37% for benthic fauna indices. The encoded features improved the prediction accuracy of the random forest regressor model by 15% points on average in comparison to using just the spectral bands. Our method enhances the predictive performance of remote sensing, in particular the spatiotemporal dynamics in median grain size and silt content of the sediment thereby contributing to better-informed management of coastal ecosystems.</p

intertidal regions (geotif, epsg:3035)

Intertidal regions in the Wadden Sea: 1. Denmark; 2. Schleswig-Holstein; 3. Lower Saxony; 4. Netherland

bottom shear stress Q75 (geotif, epsg:3035)

75th percentile of bottom shear stress due to current

6_consensus: R script to produce the consensus predictions

This script produces the consensus predictions (weighted average and weighted prediction variance) on the basis of the ensemble of predictions. It also adds the presence only data

4_gop: R-script for computing goodness of prediction

Goodness of Prediction is computed for all models based on random and spatial cross-validation. The gop of the consensus model is also computed. The script outputs the predictions of the best (lowest RMSE) models and latex tables

distance to land (geotif, epsg:3035)

used for imputing sedimen

func_seagrass R script with libraries and helper functions

func_seagrass R script with libraries and helper function

3_train: R-script model training

This script trains the ML algorithms on the basis of spatial and random cross-validation. It applies the caret machinery

Spread in the habitat suitability scores (geotif; epsg:3035)

Spread in the habitat suitability scores for littoral seagrass habitat in the Wadden Sea

1_impute: R script for imputation of sediment data

1_impute: R script for imputation of sediment dat