The combination of direct human influences and the effects of climate change are resulting in altered ecological disturbance regimes, and this is especially the case for wildfires. Many regions that historically experienced low–moderate severity fire regimes are seeing increased area burned at high severity as a result of interactions between high fuel loads and climate warming with a number of negative ecological effects. While ecosystem impacts of altered fire regimes have been examined in the literature, little is known of the effects of changing fire regimes on forest understory plant diversity even though understory taxa comprise the vast majority of forest plant species and play vital roles in overall ecosystem function. We examined understory plant diversity across gradients of wildfire severity in eight large wildfires in yellow pine and mixed conifer temperate forests of the Sierra Nevada, California, USA. We found a generally unimodal hump‐shaped relationship between local (alpha) plant diversity and fire severity. High‐severity burning resulted in lower local diversity as well as some homogenization of the flora at the regional scale. Fire severity class, post‐fire litter cover, and annual precipitation were the best predictors of understory species diversity. Our research suggests that increases in fire severity in systems historically characterized by low and moderate severity fire may lead to plant diversity losses. These findings indicate that global patterns of increasing fire size and severity may have important implications for biodiversity.

1. Recent advances in remotely piloted aerial systems ('drones') and imagery processing enable individual tree mapping in forests across broad areas with lowcost equipment and minimal ground-based data collection. One such method involves collecting many partially overlapping aerial photos, processing them using 'structure from motion' (SfM) photogrammetry to create a digital 3D representation and using the 3D model to detect individual trees. SfM-based forest mapping involves myriad decisions surrounding methods and parameters for imagery acquisition and processing, but it is unclear how these individual decisions or their combinations impact the quality of the resulting forest inventories.2. We collected and processed drone imagery of a moderate-density, structurally complex mixed-conifer stand. We tested 22 imagery collection methods (altering flight altitude, camera pitch and image overlap), 12 imagery processing parameterizations (image resolutions and depth map filtering intensities) and 286 tree detection methods (algorithms and their parameterizations) to create 7,568 tree maps. We compared these maps to a 3.23-ha ground reference map of 1,775 trees >5 m tall that we created using traditional field survey methods.3. The accuracy of individual tree detection (ITD) and the resulting tree maps was generally maximized by collecting imagery at high altitude (120 m) with at least 90% image-to-image overlap, photogrammetrically processing images into a canopy height model (CHM) with a twofold upscaling (coarsening) step and detecting trees from the CHM using a variable window filter after applying a moving window mean smooth to the CHM. Using this combination of methods, we mapped trees with an accuracy exceeding expectations for structurally complex forests (for canopy-dominant trees >10 m tall, sensitivity = 0.69 and precision = 0.90). Remotely measured tree heights corresponded to groundmeasured heights with R 2 = 0.95. Accuracy was higher for taller trees and lower for understorey trees and would likely be higher in less dense and less structurally complex stands.

Recent advances in remotely piloted aerial system (“drone”) and imagery processing technologies have enabled individual tree mapping in forest stands across broad areas with low-cost equipment and minimal ground-based data collection. One such method, “structure from motion” (SfM), involves collecting many partially overlapping aerial photos over a focal area and using photogrammetric analysis to infer 3D structure and detect individual trees. SfM-based forest mapping involves myriad decisions surrounding the selection of methods and parameters for imagery acquisition and processing, but no studies have comprehensively and quantitatively evaluated the influence of these parameters on the accuracy of the resulting tree maps.We collected and processed drone imagery of a moderate-density, structurally complex mixed-conifer stand. We tested 22 imagery collection methods (altering flight altitude, camera pitch, and image overlap), 12 imagery processing parameterizations, and 286 tree detection methods (algorithms and their parameterizations) to create 7,568 tree maps. We compared these maps to a 3.23-ha ground-truth map of 1,916 trees > 5 m tall that we created using traditional field survey methods.We found that the accuracy of individual tree detection (ITD) and the resulting tree maps was generally maximized by collecting imagery at high altitude (120 m) with at least 90% image-to-image overlap, photogrammetrically processing images into a canopy height model (CHM) with a 2-fold upscaling (coarsening) step, and detecting trees from the CHM using a variable window filter after first applying a moving-window mean smooth to the CHM. Using this combination of methods, we mapped trees with an accuracy that exceeds expectations for our structurally complex forest based on other recent results (for overstory trees > 10 m tall, sensitivity = 0.69 and precision = 0.90). Remotely-measured tree heights corresponded to ground-measured heights with R2 = 0.95. Accuracy was higher for taller trees and lower for understory trees, and it is likely to be higher in lower density and less structurally-complex stands.Our results may guide others wishing to efficiently produce individual-tree maps of conifer forests over broad extents without investing substantial time tailoring imagery acquisition and processing parameters. The resulting tree maps create opportunities for addressing previously intractable ecological questions and increasing the efficiency of forest management.