Enhancing the predictive performance of remote sensing for ecological variables of tidal flats using encoded features from a deep learning model. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Madhuanand, Logambal; Philippart, Catharina J. M.; Wang, Jiong; Nijland, Wiebe; de Jong, Steven M.; Bijleveld, Allert I.; Addink, Elisabeth A.
• Is Part Of: GIScience & remote sensing. Volume 60:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Convolutional neural network -- autoencoder model -- sediment characteristics -- random forest regression -- Macrozoobenthos -- Wadden Sea; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2022.2163048
• Languages: English
• ISSNs: 1548-1603

Abstract

ABSTRACT: 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.