A Unified Framework for Minimum-Information Reporting and Uncertainty-Aware Synthesis in Remote-Sensing-Based Boreal Forest Disturbance Mapping

Forest disturbance is a major driver of landscape structure, carbon exchange and ecological succession across the North American boreal zone. Mapping disturbance in this biome presents distinct observational and methodological challenges. Short growing seasons, low solar elevation, seasonal snow and persistent cloud reduce the availability of comparable optical observations, while intermittent freeze–thaw conditions and moisture variability complicate radar interpretation. We assess how optical, synthetic aperture radar (SAR), light detection and ranging (LiDAR), and ancillary data support the measurement and reporting of wildfire, insect outbreaks, harvesting, windthrow and ice damage. This review synthesises remote-sensing approaches according to the minimum information a disturbance product should report, including uncertainty in detection, spatial extent, timing, severity, recovery and causal-agent attribution. Published comparisons show that disturbance products may agree on the approximate year of occurrence while differing markedly in spatial overlap and causal-agent assignment. Performance depends on disturbance size, temporal tolerance and reference-data availability. We distinguish operational, nationally implemented, and emerging products and identify limitations not resolved by multi-sensor fusion, including weak detection of early insect disturbance, surface and sub-canopy fire, delayed mortality, and sparse structural sampling. We therefore propose a two-tier information standard and benchmark framework comprising core minimum and enhanced tiers. This framework would help ensure that products report sufficient information for users to assess fitness for use and interpret mapped events, associated uncertainties, and observational limitations. It does not assume that uncertainty reporting alone increases map accuracy. Instead, it makes reliability measurable, supports calibrated confidence estimates and enables fitness-for-purpose assessment for ecological synthesis, Earth-system model evaluation, carbon accounting and forest management.