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

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  • Sandler, Austin M.
  • Rashford, Benjamin S.

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.

Suggested Citation

  • Sandler, Austin M. & Rashford, Benjamin S., 2018. "Misclassification error in satellite imagery data: Implications for empirical land-use models," Land Use Policy, Elsevier, vol. 75(C), pages 530-537.
    • Handle: RePEc:eee:lauspo:v:75:y:2018:i:c:p:530-537
    DOI: 10.1016/j.landusepol.2018.04.008
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