Identifying conifer mortality induced by Armillaria root disease using airborne lidar and orthoimagery in south central Oregon

Armillaria root disease causes tree stress and mortality worldwide, including in conifer forests of western North America. Armillaria-induced is slower than other disturbances, such as fire, but persistent over time, therefore difficult to detect across large areas. Hence methods Armillaria-affected trees identify infested stands are great value forest managers. We used high-density light detection ranging (lidar), high-resolution aerial orthoimagery, associated field observations map individual health an dominated by Abies south central Oregon. The lidar point cloud was segmented into objects (polygons representing crown extents viewed from nadir), for which metrics spectral derived orthoimagery were computed. Lidar-detected paired with 150 field-observed corresponding measurements, a random classifier developed that separated into: 1) asymptomatic; 2) live, Armillaria-infected; 3) recently killed (≥50% red needles remaining); 4) dead (<50% remaining) classes 83% accuracy using metrics. applied status 290,964 the 1257-ha study area. Approximately 20% classified unhealthy infected 4% or dead. created hotspot maps Getis-Ord Gi* statistic analyzed clustering spatial patterns Ripley’s L statistic. Hotspot effectively identified clusters live mortality; found be significantly spatially clustered at distances 100–2500 m. density grid, we coupled lidar-derived canopy gap grid sites affected disease. Canopy openings mapped height model minimum opening area ≥ 202 m2. Clusters (excluding trees) intersecting gaps disease-induced mortality. Twenty-seven containing long-term plots previous studies evaluated presence All 27 correctly induced This approach detecting can aid in: i) prioritizing subsequent data collection; ii) planning silvicultural prescriptions; iii) assessing management expectations snags wildlife habitat where altering stand structure.