Mapping oyster habitat quality in Matagorda Bay through remote sensing-derived water quality datasets

Evaluating oyster habitat quality is gaining importance as populations face drastic global declines. Oyster Habitat Suitability Index (HSI) models evaluate habitat quality. Environmental and water quality indicators (WQIs) generate these oyster HSIs. In this study, we extracted WQIs from remote sensing data from 2014 to 2023 (Chapter II), then utilized these WQIs alongside other physical variables to produce five oyster HSI models for Matagorda Bay (Chapter III). These oyster HSIs generated used salinity, turbidity, temperature, depth, and water velocity to depict habitat quality. Remote sensing datasets offer a unique opportunity to observe spatial and temporal trends in WQIs, such as chlorophyll-a, salinity, and turbidity, across various aquatic ecosystems. In this study, we used available in-situ WQI measurements (chlorophyll-a: 17, salinity: 478, and turbidity: 173) along with Landsat-8 surface reflectance data to examine the capability of empirical and machine learning (ML) models in retrieving these indicators over Matagorda Bay, Texas, between 2014 and 2023. Models with greatest performance were applied to generate datasets for each WQI from 2018 to 2023. Five oyster HSI models were then generated over Matagorda Bay on both monthly and annual scales from 2018 to 2023. Each model utilized five physical parameters (e.g., model inputs), including salinity, turbidity, water temperature, depth, and water velocity. The developed approach provides a reference context, a structured framework, and valuable insights for utilizing empirical and ML models and Landsat-8 data to retrieve WQIs over aquatic ecosystems. Additionally, oyster HSI models generated from this study suggests locations of optimal, moderate, and unsuitable habitat based on long-term water quality in Matagorda Bay.