Understanding patterns of aboveground carbon storage across forest types is increasingly important as managers adapt to threats of global change. We combined ﬁeld measures of aboveground biomass with lidar to model ﬁne-scale biomass in deciduous forests located in two watersheds; one watershed was underlain by sandstone and the other by shale. We measured tree and shrub biomass across three topographic positions for both watersheds and analyzed biomass using mixed models. The watershed underlain by shale had 60% more aboveground biomass than the sandstone watershed. Although spatial patterns of biomass were different across watersheds, both had higher (between about 40% and 55%) biomass values at the toe-slope position than at the ridge-top position. To model ﬁne-scale spatial patterns of biomass, we tested the effectiveness of leaf-on and leaf-off lidar combined with topographic metrics to develop a spatially explicit random forest model of tree and shrub biomass across both watersheds. Leaf-on variables were more important for modeling shrub biomass, while leaf-off variables were more effective at modeling tree biomass. Our model of tree and shrub biomass reﬂects the distribution of biomass across both watersheds at a ﬁne scale and highlights the potential of abiotic factors such as topography and bedrock to affect carbon storage.
Brubaker Kristen M., Johnson Quincey K., and Kaye Margot W. (2018): Spatial patterns of tree and shrub biomass in a deciduous forest using leaf-off and leaf-on lidar. Canadian Journal of Forest Research, 48: 1–14 . DOI: 10.1139/cjfr-2018-0033
This Paper/Book acknowledges NSF CZO grant support.