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

Adrian L. Torchiana, Ted Rosenbaum, Paul T. Scott and Eduardo Souza-Rodrigues
Additional contact information
Adrian L. Torchiana: Granular, Inc.
Ted Rosenbaum: Federal Trade Commission
Paul T. Scott: New York University
Eduardo Souza-Rodrigues: University of Toronto

The Review of Economics and Statistics, 2025, vol. 107, issue 2, 426-441

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.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1162/rest_a_01301
Access to PDF is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tpr:restat:v:107:y:2025:i:2:p:426-441

Ordering information: This journal article can be ordered from
https://mitpressjour ... rnal/?issn=0034-6535

Access Statistics for this article

The Review of Economics and Statistics is currently edited by Pierre Azoulay, Olivier Coibion, Will Dobbie, Raymond Fisman, Benjamin R. Handel, Brian A. Jacob, Kareen Rozen, Xiaoxia Shi, Tavneet Suri and Yi Xu

More articles in The Review of Economics and Statistics from MIT Press
Bibliographic data for series maintained by The MIT Press ().