Predicted dynamics of saltmarsh vegetation

Published: 3 October 2025| Version 1 | DOI: 10.17632/wntbry8cx7.1
Contributor:
Tao Li

Description

This dataset encompasses hydro-geomorphological factors and data on saltmarsh vegetation dynamics. These inputs were used in an XGBoost model to simulate the predicted distribution and area changes of saltmarsh vegetation expansion and contraction. The resulting predictions facilitate dynamic modeling of saltmarsh vegetation based on hydro-geomorphological processes.

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Steps to reproduce

First, hydro-geomorphological factors were derived by integrating a coupled hydro-sedimentary numerical model with remote sensing techniques. Concurrently, data on saltmarsh vegetation dynamics were generated using a random forest classification method. Subsequently, these two datasets were integrated and harnessed to train an XGBoost model, which produced predictions for the distribution and area changes of the saltmarsh vegetation.

Institutions

  • China University of Geosciences Beijing

Categories

Hydrology, Vegetation Succession, Geomorphology, Machine Learning, Coastal Vegetation, Salt Marsh, Vegetation

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