A Logistic Regression Model for the Prediction of Vegetation Recruitment in the Kinu River, Japan

Vegetation overgrowth in rivers worldwide is a considerable problem because it can potentially reduce the flood-flowing capacity and cause biodiversity loss. In this study, we developed a model to predict vegetation recruitment during the initial stages of secondary succession, which leads to vegetation overgrowth. This study chose a logistic regression model to predict vegetation recruitment because of its simplicity and lower computational load than machine learning. The model was designed for the Kinu River in Japan which is associated with extensive vegetation overgrowth. Data for the model development were obtained from unmanned aerial vehicle (UAV) aerial surveys and public databases. To ensure the model's applicability beyond the training rivers, we trained the logistic regression model across different river flows and geomorphic characteristics, including normal and flood times and gravel and sand beds. The results indicated that the logistic regression model with three explanatory variables, namely distance from the river stream, relative height, and vegetation existence history, was optimal for all rivers, with F-measures in the range of 0.79-0.85. In addition, using UAV imagery allows for high-spatial resolution in predicting vegetation recruitment. The best model prediction map of vegetation recruitment demonstrated that it could accurately predict the vegetation distribution along the main river channel for gravel and sand beds. The simplicity of the present model would be advantageous when applied to other rivers with similar topographic and biological characteristics within the same river segment without hydrodynamic calculations.