Assessing the accuracy of sensitivity analysis: an application for a cellular automata model of Bogota's urban wetland changes

This study analyzes the outcomes of Cellular Automata (CA) with different neighborhood sizes and spatial resolution configurations on the performance of the Future Land Use Simulation (FLUS) model. The analysis is executed using three analogic images to extract the land use/land cover in Bogota, Colombia, for three years: 1998, 2004, and 2010. The FLUS model has an Artificial Neuronal Network model, which was used for calculating the relationships between the land uses and the associated drivers and to estimate the probability of occurrence of each land use. Whenever a CA is used to model and simulate, sensitivity analysis (SA) becomes a crucial step in CA modeling to understand better the influence of parameters' changes in the simulation outcomes. Therefore, the SA is conducted by varying the neighborhood sizes between 3 x 3, 5 x 5, and 7 x 7 for 5 and 30 meters. In addition, cross-classification maps, Area Under the Curve (AUC) of the Total Operating Characteristic, landscape metrics, the figure of merit, Fuzzy Kappa, and disagreement metrics were calculated to assess how well the model performed. High AUC values and low disagreement results show that, in general, the model performed well, and the accuracy of the outputs improves with a 3 x 3 neighborhood size and 5 meters spatial resolution. This study provides a broad assessment approach to the different methods that must be considered to evaluate the sensitivity of CA models in the simulation of urban wetlands' spatial-temporal evolution.

Automated summary

This study examines the effect of different neighborhood sizes and spatial resolutions on the performance of the Future Land Use Simulation (FLUS) model using Cellular Automata. The FLUS model employs an Artificial Neuronal Network to calculate the relationship between land uses and drivers and estimate the probability of each land use. Sensitivity analysis is conducted by varying the neighborhood sizes and spatial resolutions, and various metrics are used to assess the model's performance. The results show that a 3 x 3 neighborhood size and 5 meters spatial resolution yield the best accuracy.