title: Prediction and uncertainty source analysis of the spatial and temporal disturbance from off-road vehicular traffic in a complex ecosystem.
abstract: Vehicle use during military training activities results in soil disturbance and vegetation loss. The capacity of lands to sustain training is a function of the sensitivity of lands to vehicle use and the pattern of land use. The sensitivity of land to vehicle use has been extensively studied. Less well understood are the spatial patterns of vehicle disturbance. Since disturbance from off-road vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) was used to predict the pattern of vehicle disturbance across a training facility. An uncertainty analysis of the model predictions assessed the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty. For the most part, this analysis showed that mapping and modeling process errors contributed more than 95% of the total uncertainty of predicted disturbance, while satellite imagery error contributed less than 5% of the uncertainty. When the total uncertainty was larger than a threshold, modeling error contributed 60% to 90% of the prediction uncertainty. Otherwise, mapping error contributed about 10% to 50% of the total uncertainty. These uncertainty sources were further partitioned spatially based on other sources of uncertainties associated with vehicle moment, landscape characterization, satellite imagery, etc.
authors: University of Alberta, Edmonton, Canada.
authors: Shoufan
authors: S
authors: Fang
authors: George Z
authors: GZ
authors: Gertner
authors: Alan B
authors: AB
authors: Anderson
authors: Heidi R
authors: HR
authors: Howard
authors: Patricia
authors: P
authors: Sullivan
authors: Chris
authors: C
authors: Otto
mesh_terms: D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods*; D005544:Forecasting / Q000379:methods; D005843:Geography; D008889:Military Personnel; D015233:Models, Statistical; D015541:Off-Road Motor Vehicles; D010944:Plants; D012987:Soil; D035501:Uncertainty
chemical_list: D012987:Soil