Calibrating SLEUTH with big data: Projecting California's land use to 2100

作者：Keith C. Clarke; J. Michael Johnson

来源：Computers, Environment and Urban Systems, 2020, 83.

DOI：10.1016/j.compenvurbsys.2020.101525

• Land use
• Land cover change
• SLEUTH
• California
• Calibration
• Simulation

This study investigated the spatial consistency of the SLEUTH urban growth and land use change model using a massive data set. The research asks whether SLEUTH can yield both a reliable forecast of land use in the state of California for the year 2100 CE, and an assessment of the forecast’s reliability. Data were prepared, and SLEUTH calibrated for 174 tiles made by partitioning the data within the 6 California State Plane Zones. A null hypothesis that all data divisions of California would give similar calibration outcomes so that a uniform simulated rate of growth would apply to statewide future simulations was proven false by mapping and Moran’s I values. Spatial autocorrelation was found to propagate forward into the SLEUTH forecasts, resulting in major differences within the state in land use change and change rates. We also explored the spatial distribution of the rules that changed pixels between land use classes, finding that almost 99% of forecast growth in California comes from outward spread from new and existing settlements. The paper concludes with an examination of the uncertainty inherent within, and displayed by the SLEUTH forecasts.