Scaling-up ecological understanding with remote sensing and causal inference

Decades of empirical ecological research have focused on understanding ecological dynamics at local scales. Remote sensing products can help to scale-up ecological understanding to support management actions that need to be implemented across large spatial extents. This new avenue for remote sensing applications requires careful consideration of sources of potential bias that can lead to spurious causal relationships. We propose that causal inference techniques can help to mitigate biases arising from confounding variables and measurement errors that are inherent in remote sensing products. Adopting these statistical techniques will require interdisciplinary collaborations between local ecologists, remote sensing specialists, and experts in causal inference. The insights from integrating 'big' observational data from remote sensing with causal inference could be essential for bridging biodiversity science and conservation.