Forests serve as a large terrestrial carbon sink. However, land use change is a major threat to forested areas. Land use change is a critical component when studying carbon fluxes across the landscape, but the likelihood of change is still unknown. Furthermore, landscape simulation models have been extensively used for forecasting ecosystem dynamics. However, there is an existing need to connect empirical with simulated data. This research focuses on forest as a land use, and how forest conditions and population growth affect the carbon stocks in six U.S. states. The goals of the study are (1) to model the likelihood of land use change and carbon stock’s dynamics and (2) to evaluate the LANDIS-II landscape simulation model, using USDA Forest Inventory and Analysis (FIA) data. We used two modeling approaches: a machine learning algorithm (random forest), and generalized mixed-effects models. Explanatory variables included ecological attributes, topography, census data, forest disturbances and forest conditions. Model predictions and Landsat spectral information were used to produce wall-to-wall probability maps of land use change using Google Earth Engine. LANDIS-II was evaluated using FIA data for the initial conditions and compared with empirical data for the study period.
Results/Conclusions
During the study period (2000-2017), 3.4% of the analyzed FIA plots transitioned from forest to other land uses. Results indicate that land use change from forests is more likely with increasing population and housing growth rates, and non-public areas have a higher probability of forest change. Areas such as the Menominee reservation presented a lower risk of converting to non-forest. On the other hand, areas closer to cities presented a higher risk of transition, as well as coastal areas. Out of the six states analyzed, Colorado had the highest risk of conversion. Interestingly, forest disturbances were not a major predictor of land use change. Other results will show the outcomes of an 18-year simulation with LANDIS-II using FIA data at a plot/cell scale. Our results reinforce that land use change is a critical topic with global impacts. Its study, together with carbon fluxes, provides information for decision-makers and land managers for designing policies and practices aimed at improving ecosystem dynamics and mitigating climate change.