Abstract
Forests worldwide face intensifying natural and anthropogenic pressures that progressively erode their resilience, bringing them closer to abrupt, potentially irreversible shifts. Such loss of resilience can manifest as critical slowing down (CSD), a phenomenon characterized by slower recovery rates. It is often measured through statistical indicators, known as early warning signals, which are expected to rise in advance of a critical transition such as a tipping point. Earth Observation (EO) is well suited to monitoring CSD indicators at global scales. However, the reliability of these indicators depends strongly on the quality and consistency of the underlying satellite record given their derivation from optical biophysical proxies. This is particularly true for what is arguably one of the most useful EO dataset – the Landsat Collection 2 archive, given its extensive temporal coverage (1984-present) and 30-m spatial resolution. Here, we present a scalable and reproducible workflow for generating high-quality, long-term, cross-calibrated Landsat datasets specifically designed for the computation of statistical indicators of resilience over large extents of forest ecosystems. The workflow integrates robust imagery processing, consistent radiometric cross-calibration, and a multiscale grid-based implementation to improve the reliability of the Landsat imagery used for resilience analysis. Using an undisturbed boreal forest region in Quebec, Canada, as a case study, we demonstrate that the proposed workflow reduces inter-sensor discontinuities, enhances spatial consistency and preserves ecologically meaningful temporal dynamics across the 40-year Landsat record. Applied across ecoregions and biomes, this framework seeks to enable robust and comparable assessments of forest resilience, supporting large-scale ecosystem monitoring, early-warning assessments, and evidence-based conservation prioritisation.