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Abstract Increasing frequency of droughts and wildfire are sparking concerns that these compounded disturbance events are pushing forested ecosystems beyond recovery. An improved understanding of how compounded events affect tree physiology and mortality is needed given the reliance of fire management planning on accurate estimates of postfire tree mortality. In this study, we use a toxicological dose-response approach to quantify the impact of variable-intensity drought and fire on the physiology and mortality of Pinus monticola and Pseudotsuga menziesii saplings. We show that the dose-response relationship between fire intensity and mortality shifts toward increased vulnerability under drought, indicating higher mortality with increasing drought at any fire intensity. The trajectory we observed in postfire chlorophyll fluorescence, an indicator of photosynthetic efficiency and stress, was an effective early warning sign of impending tree death. Postfire mortality modeling shows that accurate mortality classification can be achieved using prefire physiology and morphology metrics combined with fire intensity. Variable importance measures indicate that physiological condition and fire intensity have greater influence on the classification accuracy than morphological metrics. The wide range in drought and fire responses observed between this study and others highlights the need for more research on compound disturbance effects. Study Implications: An improved understanding of how drought and fire affect tree physiology and mortality is needed by natural resource managers looking to predict postfire tree mortality. This study advances our compound disturbance understanding by subjecting conifer saplings to variable drought and fire intensities and quantifying and modeling moderate-term recovery and mortality. The results show reduced physiological recovery and amplified mortality in saplings exposed to greater drought and fire intensity. Overall, this study highlights the importance of physiological condition when modeling tree mortality and could potentially be used to inform current postfire tree mortality models. 

Abstract On-the-ground sample-based forest inventory methods have been the standard practice for more than a century, however, remote sensing technologies such as airborne laser scanning (ALS) are providing wall-to-wall inventories based on individual tree measurements. In this study, we assess the accuracy of individual tree height, diameter, and volume derived from field-cruising measurements and three ALS data-derived methods in a 1.1 ha stand using direct measurements acquired on felled trees and log-scale volume measurements. Results show that although height derived from indirect conventional field measurements and ALS were statistically equivalent to felled tree height measurements, ALS measured heights had lower root mean square error (RMSE) and bias. Individual tree diameters modeled using a height-to-diameter-at-breast-height model derived from local forest inventory data and the software ForestView had moderate RMSE (8.3–8.5 cm) and bias (-3.0 – -0.3 cm). The ALS-based methods underdetected trees but accounted for 78%–91% of the field reference harvested merchantable volume and 71%–99% of the merchantable volume scaled at the mill. The results also illustrate challenges of using mill-scaled volume estimates as validation data and highlight the need for more research in this area. Overall, the results provide key insights to forest managers on accuracies associated with conventional field-derived and ALS-derived individual tree inventories. Study Implications: Forest inventory data provide critical information for operational decisions and forest product supply chain planning. Traditionally, forest inventories have used field sampling of stand conditions, which is time-intensive and cost-prohibitive to conduct at large spatial scales. Remote sensing technologies such as airborne laser scanning (ALS) provide wall-to-wall inventories based on individual tree measurements. This study advances our understanding of the accuracy of conventional field-derived and ALS-derived individual tree inventories by evaluating these inventories with felled tree and log scaling data. The results provide key insights to forest managers on errors associated with conventional field and ALS-derived individual tree measurements.