Structural Forest Disturbances Revealed by LiDAR: Limits of Landsat Detection

Understanding when and where satellite disturbance products miss forest change is critical for reliable large-scale monitoring. Landsat-based products such as the National Land Cover Database (NLCD) provide long-term coverage, but their disturbance signals are primarily driven by spectral change and can fail to capture canopy structural losses, especially when changes are subtle, fragmented, or occur in short/transitional vegetation. Here we use airborne LiDAR canopy height models (CHMs) from 32 NEON sites across the continent of United States as an independent structural benchmark to quantify Landsat detection limits.

We compared multi-year LiDAR-derived canopy height change with temporally matched NLCD disturbance layers. From CHMs we derived pre-disturbance canopy height, canopy-height loss (severity), and patch size. We quantified Landsat recall at pixel scale, and evaluated how recall varies with height, severity, forest type, and disturbance patch size.

LiDAR revealed systematic detection biases in Landsat disturbance detection. Recall increased with canopy height and remained low for low-to-moderate structural losses, rising sharply only for the most severe canopy-height reductions. At the patch scale, detection fraction increased with disturbance size: small patches were rarely detected, whereas larger patches showed substantially higher detection. Detection agreement varied across forest types and was weakest in open-canopy woodlands and transitional vegetation. In conclusion, Landsat disturbance products preferentially capture large, high-severity canopy-loss events while frequently omitting smaller and lower-severity structural changes evident in LiDAR.