Evaluating measurement error in remote-sensing deforestation data

Remote sensing methods are transformative tools used to study deforestation, but they are by no means perfect. In the new paper "Remotely Incorrect? Accounting for Nonclassical Measurement Error in Satellite Data on Deforestation," published in the Journal of the Association of Environmental and Resource Economists, authors Jennifer Alix-Garcia and Daniel L. Millimet discuss how errors in remote sensing deforestation data occur, the implications these errors may have for research using remotely sensed data, and how to account for the errors.