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The Review of Economics and Statistics Vol. 107 No. 2 2025

Improving Estimates of Transitions from Satellite Data: A Hidden Markov Model Approach

Adrian L. Torchiana1; Ted Rosenbaum2; Paul T. Scott3; Eduardo Souza-Rodrigues4

1 Granular, Inc. · 2 Federal Trade Commission · 3 New York University · 4 University of Toronto

open access

Abstract

Satellite-based image classification facilitates low-cost measurement of the Earth’s surface composition. However, misclassified imagery can lead to misleading conclusions about transition processes. We propose a correction for transition rate estimates based on the econometric measurement error literature to extract the signal (truth) from its noisy measurement (satellite-based classifications). No ground-truth data are required in the implementation. Our proposed correction produces consistent estimates of transition rates, confirmed by longitudinal validation data, while transition rates without correction are severely biased. Using our approach, we show how eliminating deforestation in Brazil’s Atlantic forest region through 2040 could save $100 billion in CO2 emissions.

DOI
10.1162/rest_a_01301
Volume
107
Issue
2
Pages
426-441
Language
en
Sources
bibtex:phds-export.bib openalex crossref

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