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

Abstract

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