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

Author

Listed:
  • Eduardo Souza-Rodrigues
  • Adrian L. Torchiana
  • Ted Rosenbaum
  • Paul T. Scott

Abstract

Satellite-based image classification facilitates low-cost measurement of the Earth's surface composition. However, image classification techniques can lead to misleading conclusions about transition processes (e.g., deforestation, urbanization, and industrialization). 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-level truth data is required to implement the correction. Our proposed correction produces consistent estimates of transition rates, confirmed by Monte Carlo simulations and panel validation data. In contrast, transition rates without correction for misclassifications are severely biased.

Suggested Citation

  • Eduardo Souza-Rodrigues & Adrian L. Torchiana & Ted Rosenbaum & Paul T. Scott, 2020. "Improving Estimates of Transitions from Satellite Data: A Hidden Markov Model Approach," Working Papers tecipa-672, University of Toronto, Department of Economics.
    • Handle: RePEc:tor:tecipa:tecipa-672
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    More about this item

    Keywords

    Measurement Error; Remote-Sensing Data; Land Cover; Hidden Markov Model;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns

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