Earth observation and causal inference in assessing protected area effectiveness

Saija Papunen , Eduardo Eiji Maeda, Johanna Eklund

Area-based conservation is a fundamental component of global conservation strategies, yet robust causal evidence on the long-term effectiveness of protected areas is still emerging. Alongside traditional field data collection methods, Earth observation offers cost-effective means of generating high-resolution, spatially extensive information, and its broader integration into conservation science is increasingly important. Equally essential are causal inference methods that support rigorous evaluation of conservation effectiveness. We review space- and airborne Earth observation data and causal inference frameworks used to evaluate the impacts of protected areas worldwide. Our review shows that the existing literature mainly uses matching methods for confounder control and avoided deforestation as the primary outcome. This emphasis provides only a partial view of protected areas, not fully capturing their broader ecological roles and social impacts, including effects on ecosystem functioning, biodiversity, and local community livelihoods. We discuss the study findings along with characteristics and limitations of commonly used outcomes and statistical approaches for controlling confounding. We further highlight emerging Earth observation technologies—such as high-resolution sensors, LiDAR, and machine learning approaches applied to remotely sensed data—that can extend assessments beyond traditional land cover metrics. Through practical examples across four thematic areas, we offer a forward-looking synthesis to guide the next generation of conservation impact evaluations and support a more comprehensive understanding of protected area outcomes.