Improving Estimates of Transitions from Satellite Data: A Hidden Markov Model Approach
Adrian Torchiana, Ted Rosenbaum, Paul T. Scott, Eduardo Souza-Rodrigues
Abstract
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.