Misclassification error in satellite imagery data: Implications for empirical land-use models

Satellite-based land-use data sets are providing new opportunities for land-use research. However, care must be used when working with these datasets due to misclassification error, which causes inconsistent parameter estimates in typical land-use models. Results from satellite imagery data from the Northern Great Plains indicate that ignoring misclassification will lead to biased results. Even seemingly insignificant levels of misclassification error (e.g., 1%) result in biased parameter estimates, which alter marginal effects enough to affect policy inference. At the levels of misclassification typical in current satellite imagery datasets (e.g., 35%), ignoring misclassification can lead to systematically erroneous land-use policies.