Permafrost thaw can affect decomposition rates by changing environmental conditions and litter quality. As permafrost thaws, soils warm and thermokarst (ground subsidence) features form, causing some areas to become wetter while other areas become drier. We used a common substrate to measure how permafrost thaw affects decomposition rates in the surface soil in a natural permafrost thaw gradient and a warming experiment in Healy, Alaska. Permafrost thaw also changes plant community composition. We decomposed 12 plant litters in a common garden to test how changing plant litter inputs would affect decomposition. We combined species' tissue‐specific decomposition rates with species and tissue‐level estimates of aboveground net primary productivity to calculate community‐weighted decomposition constants at both the thaw gradient and warming experiment. Moisture, specifically growing season precipitation and water table depth, was the most significant driver of decomposition. At the gradient, an increase in growing season precipitation from 200 to 300 mm increased mass loss of the common substrate by 100%. At the warming experiment, a decrease in the depth to the water table from 30 to 15 cm increased mass loss by 100%. At the gradient, community‐weighted decomposition was 21% faster in extensive than in minimal thaw, but was similar when moss production was included. Overall, the effect of climate change and permafrost thaw on surface soil decomposition are driven more by precipitation and soil environment than by changes to plant communities. Increasing soil moisture is thereby another mechanism by which permafrost thaw can become a positive feedback to climate change.

Unmanned aircraft systems (UAS) have advanced rapidly enabling low-cost capture of high-resolution images with cameras, from which three-dimensional photogrammetric point clouds can be derived. More recently UAS equipped with laser scanners, or lidar, have been employed to create similar 3D datasets. While airborne lidar (originally from conventional aircraft) has been used effectively in forest systems for many years, the ability to obtain important tree features such as height, diameter at breast height, and crown dimensions is now becoming feasible for individual trees at reasonable costs thanks to UAS lidar. Getting to individual tree resolution is crucial for detailed phenotyping and genetic analyses. This study evaluates the quality of three three-dimensional datasets from three sensors—two cameras of different quality and one lidar sensor—collected over a managed, closed-canopy pine stand with different planting densities. For reference, a ground-based timber cruise of the same pine stand is also collected. This study then conducted three straightforward experiments to determine the quality of the three sensors’ datasets for use in automated forest inventory: manual mensuration of the point clouds to (1) detect trees and (2) measure tree heights, and (3) automated individual tree detection. The results demonstrate that, while both photogrammetric and lidar data are well-suited for single-tree forest inventory, the photogrammetric data from the higher-quality camera is sufficient for individual tree detection and height determination, but that lidar data is best. The automated tree detection algorithm used in the study performed well with the lidar data, detecting 98% of the 2199 trees in the pine stand, but fell short of manual mensuration within the lidar point cloud, where 100% of the trees were detected. The manually-mensurated heights in the lidar dataset correlated with field measurements at r = 0.95 with a bias of −0.25 m, where the photogrammetric datasets were again less accurate and precise.