Monte Carlo-Based Spatial Optimization of Simulation Plots for Forest Growth Modeling

Accurate placement and geometry of simulation plots are essential for spatially explicit modeling of forest ecosystems. This study introduces a Monte Carlo-based approach for optimizing the spatial alignment of simulation plots with their source polygons, improving their ability to represent stand-level heterogeneity. The method is implemented in GenSimPlot, an open-source Python plugin for QGIS (version 3.30) that automates the generation, placement, and refinement of simulation plots using simple geometric shapes. Monte Carlo optimization iteratively adjusts translation, rotation, and scaling parameters to maximize spatial congruence, thereby enhancing the fidelity of forest growth simulations. A built-in hyperparameter tuning module based on random search enables users to explore optimal parameter settings systematically. In addition, GenSimPlot supports the extraction of qualitative and quantitative environmental variables and terrain from raster datasets, facilitating integration with forest growth models and broader ecological simulations. The proposed approach improves plot representativeness and enables robust scenario analysis across heterogeneous landscapes.