I.C. Burke | Geology Reports

USGS2009

Soil carbon flux following pulse precipitation events in the shortgrass steppe

Pulses of water availability characterize semiarid and arid ecosystems. Most precipitation events in these ecosystems are small (???10 mm), but can stimulate carbon flux. The large proportion of carbon stored belowground and small carbon inputs create the potential for these small precipitation events to have large effects on carbon cycling. Land-use change can modify these effects through alteration of the biota and soil resources. The goal of our research was to determine how small precipitation events (2, 5, and 10 mm) affected the dynamics of soil carbon flux and water loss in previously cultivated Conservation Reserve Program (CRP) fields and undisturbed shortgrass steppe. Total carbon loss and duration of elevated carbon flux increased as event size increased in all field types. Time since cultivation increased in importance for carbon flux as event size increased. A comparison of water loss rates to carbon flux suggests that water is limiting to carbon flux for the smallest events, but is less limiting for events above 5 mm. We also describe how water availability interacts with temperature in controlling carbon flux rate. We conclude that small precipitation events have the potential for large short-term losses of carbon in the shortgrass steppe. ?? 2009 The Ecological Society of Japan.

Ecological Research↗

USGS2005

Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests

The ponderosa pine forests of the Colorado Front Range, USA, have historically been subjected to wildfires. Recent large burns have increased public interest in fire behavior and effects, and scientific interest in the carbon consequences of wildfires. Remote sensing techniques can provide spatially explicit estimates of stand structural characteristics. Some of these characteristics can be used as inputs to fire behavior models, increasing our understanding of the effect of fuels on fire behavior. Others provide estimates of carbon stocks, allowing us to quantify the carbon consequences of fire. Our objective was to use discrete-return lidar to estimate such variables, including stand height, total aboveground biomass, foliage biomass, basal area, tree density, canopy base height and canopy bulk density. We developed 39 metrics from the lidar data, and used them in limited combinations in regression models, which we fit to field estimates of the stand structural variables. We used an information–theoretic approach to select the best model for each variable, and to select the subset of lidar metrics with most predictive potential. Observed versus predicted values of stand structure variables were highly correlated, with r 2 ranging from 57% to 87%. The most parsimonious linear models for the biomass structure variables, based on a restricted dataset, explained between 35% and 58% of the observed variability. Our results provide us with useful estimates of stand height, total aboveground biomass, foliage biomass and basal area. There is promise for using this sensor to estimate tree density, canopy base height and canopy bulk density, though more research is needed to generate robust relationships. We selected 14 lidar metrics that showed the most potential as predictors of stand structure. We suggest that the focus of future lidar studies should broaden to include low density forests, particularly systems where the vertical structure of the canopy is important, such as fire prone forests.

Colorado↗