Corn Yield Expectations

published or submitted for publicationnot peer reviewe

Modeling un-authorized land use sprawl with integrated remote sensing-GIS technique and cellular automata

We have used cellular automata integrated with GIS and remote sensing to analyze urban sprawl aiming at analyzing expansion of potential un-authorized land uses for residential, commercial and industrial based on spatial factor deriving from remote sensing high resolution data. The spatial factors considered are used as parameter to measure either land use in these expansion process develop as urban legal sprawl or sprawl with declined the development planned (un-authorized). Results of the study indicated that residential area is most probable risk to un-authorized land use sprawl, given set of spatial factors considering the vicinity of highway strip, land use segregation and leapfrog development

A meta-modeling approach for spatio-temporal uncertainty and sensitivity analysis: an application for a cellular automata-based Urban growth and land-use change model

The paper presents a computationally efficient meta-modeling approach to spatially explicit uncertainty and sensitivity analysis in a cellular automata (CA) urban growth and land-use simulation model. The uncertainty and sensitivity of the model parameters are approximated using a meta-modeling method called polynomial chaos expansion (PCE). The parameter uncertainty and sensitivity measures obtained with PCE are compared with traditional Monte Carlo simulation results. The meta-modeling approach was found to reduce the number of model simulations necessary to arrive at stable sensitivity estimates. The quality of the results is comparable to the full-order modeling approach, which is computationally costly. The study shows that the meta-modeling approach can significantly reduce the computational effort of carrying out spatially explicit uncertainty and sensitivity analysis in the application of spatio-temporal models