Soil Respiration and Related Abiotic and Remotely Sensed Variables in Different Overstories and Understories in a High-Elevation Southern Appalachian Forest

Accurately predicting soil respiration (R-s) has received considerable attention recently due to its importance as a significant carbon flux back to the atmosphere. Even small changes in R(s )can have a significant impact on the net ecosystem productivity of forests. Variations in R-s have been related to both spatial and temporal variation due to changes in both abiotic and biotic factors. This study focused on soil temperature and moisture and changes in the species composition of the overstory and understory and how these variables impact R-s. Sample plots consisted of four vegetation types: eastern hemlock (Tsuga canadensis L. Carriere)-dominated overstory, mountain laurel (Kalmia latifolia L.)-dominated understory, hardwood-dominated overstory, and cinnamon fern (Osmundastrum cinnamomeum (L.) C. Presl)-dominated understory, with four replications of each. Remotely sensed data collected for each plot, light detection and ranging, and hyperspectral data, were compiled from the National Ecological Observatory Network (NEON) to determine if they could improve predictions of R-s. Soil temperature and soil moisture explained 82% of the variation in R-s. There were no statistically significant differences between the average annual Rs rates among the vegetation types. However, when looking at monthly R-s, cinnamon fern plots had statistically higher rates in the summer when it was abundant and hemlock had significantly higher rates in the dormant months. At the same soil temperature, the vegetation types' R-s rates were not statistically different. However, the cinnamon fern plots showed the most sensitivity to soil moisture changes and were the wettest sites. Normalized Difference Lignin Index (NDLI) was the only vegetation index (VI) to vary between the vegetation types. It also correlated with R-s for the months of August and September. Photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), and normalized difference nitrogen index (NDNI) also correlated with September's R-s. In the future, further research into the accuracy and the spatial scale of VIs could provide us with more information on the capability of VIs to estimate R-s at these fine scales. The differences we found in monthly R-s rates among the vegetation types might have been driven by varying litter quality and quantity, litter decomposition rates, and root respiration rates. Future efforts to understand carbon dynamics on a broader scale should consider the temporal and finer-scale differences we observed.