Beyond the canopy: How satellite data detection thresholds influence policy evaluation and deforestation behavior

Satellite data is essential for enforcing and evaluating environmental policy, but technological limitations of monitoring systems can create perverse incentives and bias impact assessment. This study examines how detection thresholds in satellite monitoring systems affect both the implementation and evaluation of forest conservation policies. We identify three key mechanisms: a measurement issue, where datasets with larger minimum detection thresholds systematically miss small-scale deforestation; a loophole effect, where policy only reduces detectable, large-scale deforestation; and strategic adaptation, where regulated agents adjust behavior to exploit known detection thresholds, substituting from large- to small-scale deforestation. Studying Brazil’s 2008 municipal Blacklisting policy, we find that the government’s primary monitoring system, which does not report patches below 6.25 hectares, overestimates policy effectiveness by a third compared to datasets with smaller minimum detection thresholds. When measured with those datasets, blacklisting reduced deforestation by 31.2 % from baseline—substantially less than the 47.6 % reduction suggested by government data. Average clearing size declined by 28.9 %, with significant increases in patches below detection thresholds, reflecting both undetected and strategically fragmented activity. Our analysis reveals a critical challenge for environmental governance: as monitoring systems improve, so too do evasion strategies, requiring close attention to how technology shapes observed outcomes and on-the-ground incentives.