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

Austin M. Sandler and Benjamin S. Rashford

Land Use Policy, 2018, vol. 75, issue C, 530-537

Abstract: 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.

Keywords: Land-use; Econometrics; Misclassification error; Satellite imagery data; Northern Great Plains (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:lauspo:v:75:y:2018:i:c:p:530-537

DOI: 10.1016/j.landusepol.2018.04.008

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