Tidal Marsh Elevation Forecasts from Remote Sensing and Process Model Fusion

The Marsh Equilibrium Model (MEM) integrates Landsat 8, WV2, AVIRIS, and PRISM remote sensing data to project tidal marsh elevation changes under sea-level rise scenarios. This research dataset, authored by Kristin B. Byrd of the United States Geological Survey, compares biomass and sediment concentration models to forecast habitat distributions up to 100 years into the future. Results focus on a brackish tidal marsh in San Francisco Bay.

Use Cases

• Projecting long-term marsh elevation changes based on integrated remote sensing and mechanistic model inputs.
• Comparing biomass estimation accuracy from different satellite sensors (Landsat 8, WV2, AVIRIS) for wetland monitoring.
• Modeling annual average suspended sediment concentration (SSC) using Landsat 8 time series data for coastal accretion studies.
• Forecasting habitat type distributions (mudflat, low marsh, high marsh) under sea-level rise scenarios for conservation planning.

Strengths

• Model inputs derived from multiple satellite platforms (Landsat 8, WV2, AVIRIS, PRISM) enable cross-sensor validation.
• Biomass models achieved percent RMSE between 15% and 17%, indicating reasonable accuracy.
• The integration accounts for organic and inorganic feedbacks in marsh accretion, a key mechanistic process.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file formats are unknown, which may limit suitability assessment.
• The geographic scope is focused on a specific brackish tidal marsh in San Francisco Bay.

Provenance

Source

United States Geological Survey

Collection Method

Integration of Earth observation data (satellite/airborne imagery) with the mechanistic Marsh Equilibrium Model (MEM).

Time Range

Projections extend up to 100 years into the future.

Geography

Brackish tidal marsh in San Francisco Bay, California, USA.