Alan B. Anderson

Spatial and temporal prediction and uncertainty of soil loss using the revised universal soil loss equation: a case study of the rainfall-runoff erosivity R factor

Abstract Soil loss is commonly predicted using the revised universal soil loss equation consisting of rainfall–runoff erosivity, soil erodibility, slope steepness and length, cover management, and support practice factors. Because of the multiple factors, their interactions, and spatial and temporal variability, soil erosion varies considerably over space and time. For these reasons, modeling soil loss is very complicated. Decision-makers need local and regional estimates of soil loss as well as their corresponding uncertainties. Neglecting the local and detailed information may lead to improper decision-making. This paper demonstrates a strategy based on a sample data set and a geostatistical method called sequential Gaussian simulation to derive local estimates and their uncertainties for the input factors of a soil erosion system. This strategy models the spatial and temporal variability of the factors and derives their estimates and variances at any unknown location and time. This strategy was applied to a case study at which the rainfall–runoff erosivity R factor was spatially and temporally estimated using a data set of rainfall. The results showed that the correlation between the observations and estimates by the strategy ranged from 0.89 to 0.97, and most of the mean estimates fell into their confidence intervals at a probability of 95%. Comparing the estimates of the R factor using a traditional isoerodent map to the observed values suggested that the R factor might have increased and a new map may be needed. The method developed in this study may also be useful for modeling other complex ecological systems.

SPATIAL UNCERTAINTY IN PREDICTION OF THE TOPOGRAPHICAL FACTOR FOR THE REVISED UNIVERSAL SOIL LOSS EQUATION (RUSLE)

The Revised Universal Soil Loss Equation (RUSLE) is a model widely used to predict soil loss. An important component of RUSLE is the combined topographical factor (LS), which is the product of the slope length factor (L) and the slope steepness factor (S). It is important to identify the main sources of uncertainty in the LS factor and reduce the uncertainty where practical. Moreover, the uncertainty of the LS factor may vary across space, and this spatial uncertainty may require error management. For this reason, the spatial effects of slope steepness and slope length should be quantified, and their uncertainty propagation should be modeled. This article presents a general methodology for spatial uncertainty assessment of the RUSLE and its application results to the uncertainty analysis of LS as an example. A sequential indicator simulation was used to develop spatial prediction maps of slope steepness and slope length based on collected field data. The uncertainty due to slope steepness, slope length, and model parameters were propagated through topographical factor LS using the Fourier Amplitude Sensitivity Test (FAST). Spatial variance partitioning was performed to generate error budgets, and uncertainty sources were identified. Slope steepness contributed the largest variance in predicting topographical factor LS, followed by slope length. The variance contributions from the model parameters and measurement errors were relatively small. The results provide modelers and decision–makers with spatial uncertainty information for the purpose of error management.

Combining stratification and up-scaling method-block cokriging with remote sensing imagery for sampling and mapping an erosion cover factor

Abstract When a ground and vegetation cover factor related to soil erosion is mapped with the aid of remotely sensed data, a cost-efficient sample design to collect ground data and to obtain an accurate map is required. However, the supports used to collect ground data are often smaller than the desirable pixels used for mapping, which leads to complexity in developing procedures for sample design and mapping. For these purposes, a sampling and mapping method was developed by integrating stratification and an up-scaling method in geostatistics — block cokriging with Landsat Thematic Mapper imagery. This method is based on spatial correlation and stratified sampling. It scales up not only the ground sample data but also the uncertainties associated with the data aggregation from smaller supports to larger pixels or blocks. This method uses the advantages of both stratification and block cokriging variance-based sample design, which leads to sample designs with variable grid spacing, and thus significantly increases the unit cost-efficiency of sample data in sampling and mapping. This outcome was verified by the results of this study.

Environmental Condition Assessment of US Military Installations Using GIS Based Spatial Multi-Criteria Decision Analysis

Environment functions in various aspects including soil and water conservation, biodiversity and habitats, and landscape aesthetics. Comprehensive assessment of environmental condition is thus a great challenge. The issues include how to assess individual environmental components such as landscape aesthetics and integrate them into an indicator that can comprehensively quantify environmental condition. In this study, a geographic information systems based spatial multi-criteria decision analysis was used to integrate environmental variables and create the indicator. This approach was applied to Fort Riley Military installation in which land condition and its dynamics due to military training activities were assessed. The indicator was derived by integrating soil erosion, water quality, landscape fragmentation, landscape aesthetics, and noise based on the weights from the experts by assessing and ranking the environmental variables in terms of their importance. The results showed that landscape level indicator well quantified the overall environmental condition and its dynamics, while the indicator at level of patch that is defined as a homogeneous area that is different from its surroundings detailed the spatiotemporal variability of environmental condition. The environmental condition was mostly determined by soil erosion, then landscape fragmentation, water quality, landscape aesthetics, and noise. Overall, environmental condition at both landscape and patch levels greatly varied depending on the degree of ground and canopy disturbance and their spatial patterns due to military training activities and being related to slope. It was also determined the environment itself could be recovered quickly once military training was halt or reduced. Thus, this study provided an effective tool for the army land managers to monitor environmental dynamics and plan military training activities. Its limitation lies at that the obtained values of the indicator vary and are subjective to the experts’ knowledge and experience. Thus, further advancing this approach is needed by developing a scientific method to derive the weights of environmental variables.

INFLUENCE OF TRAVEL DIRECTION ON GPS ACCURACY FOR VEHICLE TRACKING

The influence of travel direction on GPS dynamic accuracy for vehicle tracking is discussed in two sections. The first section investigates the influence of travel direction on GPS accuracy due to the GPS satellite sky distribution. GPS dilution of precision (DOP) was calculated based on GPS satellite geometry at a variety of locations and different mask angle settings. Results show a significant difference between north DOP and east DOP in a mid-latitude area. A clear trend of the 24 h average ratio of the north DOP to the east DOP was found related to latitudes and mask angle settings. Cross-track dilution of precision (XDOP) is defined as the GPS DOP perpendicular to the travel direction. The influence of the GPS satellite geometry on GPS accuracy was mapped into the vehicle platform frame to derive the XDOP, and accordingly to derive the influence of travel direction on the GPS dynamic accuracy. Results showed that the XDOP increased as the course over ground (COG) changed from 0° to 90°. Considering that a regression line fitting through GPS data may be referenced as the true path for calculating GPS errors, the second section reviews methods for fitting linear models. The most commonly used approach for linear fitting is least-square regression that minimizes the sum square of vertical offsets, rather than perpendicular offsets. This approach can result in a potential model fitting error, which was found to be dependent on the direction of travel and the dynamic accuracy of the tested GPS receiver when this approach was used to generate the referenced true path for calculating GPS cross-track errors. Our results showed that the fitting error reached its maximum when the tested vehicle was traveling in the N-S (or S-N) direction and decreased when the travel direction moved away from the N-S direction.

Spatial and Temporal Assessment of Cumulative Disturbance Impacts Due to Military Training, Burning, Haying, and Their Interactions on Land Condition of Fort Riley

The effects of military training activities on the land condition of Army installations vary spatially and temporally. Training activities observably degrade land condition while also increasing biodiversity and stabilizing ecosystems. Moreover, other anthropogenic activities regularly occur on military lands such as prescribed burns and agricultural haying—adding to the dynamics of land condition. Thus, spatially and temporally assessing the impacts of military training, prescribed burning, agricultural haying, and their interactions is critical to the management of military lands. In this study, the spatial distributions and patterns of military training-induced disturbance frequency were derived using plot observation and point observation-based method, at Fort Riley, Kansas from 1989 to 2001. Moreover, spatial and variance analysis of cumulative impacts due to military training, burning, haying, and their interactions on the land condition of Fort Riley were conducted. The results showed that: (1) low disturbance intensity dominated the majority of the study area with exception of concentrated training within centralized areas; (2) high and low values of disturbance frequency were spatially clustered and had spatial patterns that differed significantly from a random distribution; and (3) interactions between prescribed burning and agricultural haying were not significant in terms of either soil erosion or disturbance intensity although their means and variances differed significantly between the burned and non-burned areas and between the hayed and non-hayed areas.

Influence of Travel Directions on the GPS Dynamic Accuracy for Vehicle Tracking

This paper discusses the influence of travel directions on GPS dynamic accuracy for vehicle tracking in two sections. The first section investigates the influence of travel directions on GPS accuracy due to the GPS satellite sky distribution. GPS dilutions of positions (DOP) were calculated based on GPS satellites geometry at a variety of locations and different mask angle settings. Results show a significant difference between North DOP and East DOP in mid-latitude area. A clear trend of the 24 hour average ratio of the North DOP to the East DOP was found related to latitudes and mask angle settings. A cross-track dilution of position (XDOP) is defined as the GPS dilution of position perpendicular to the travel direction. The influence of the GPS satellite geometry on GPS accuracy was mapped into the vehicle platform frame to derive the XDOP, and accordingly to derive the influence of travel directions on the GPS dynamic accuracy. Results show that the XDOP increases as the Course over Ground (COG) changes from o 0 to o 90. Considering that the regression line fitting through GPS data may be used as the referenced true path for calculating GPS errors, the second section reviewed methods for fitting linear models. The most commonly used approach of least square regression generates the line by minimizing the sum square of vertical offsets, instead of perpendicular offsets, which can result in a potential model fitting error. This fitting error depends on the travel directions and the tested GPS receivers dynamic accuracy. Results show that the fitting error reaches its maximum when the tested vehicle is traveling in the N-S (or S-N) direction and decreases when the travel direction moves away from the N-S direction.

Prediction and uncertainty source analysis of the spatial and temporal disturbance from off-road vehicular traffic in a complex ecosystem.

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.

Tire Force and Terrain Disturbance Measurements During Turning Maneuvers

A proof-of-concept study to link vehicle performance measures to associated terrain disturbance was performed with the intent to improve terrain impact prediction methods. Field tests to assess terrain disturbance during turning maneuvers were completed on dry and wet, grassy fields and slopes in northern Vermont. The vehicle tests consisted of a series of spiral maneuvers at two speeds, and serpentine maneuvers on slopes, to comprise a range of turning radii, upslope and downslope turns, and velocity. The CRREL instrumented vehicle (CIV) and a military high-mobility multipurpose wheeled vehicle (HMMWV) were instrumented to measure linear accelerations, angular and linear velocity, wheel forces, speeds, and location. The terrain was fully characterized for soil type, wetness, shear strength, and vegetation cover prior to testing. The disturbance created by the vehicle was measured using the cumulative impact width method based on disturbed width and impact severity. Results show increased disturbance for the wetter soil, but generally the impacts were low because of sufficient terrain strength for these two vehicles. Nonetheless, correlations were found between the measured horizontal forces, accelerations and yaw rates, and the terrain disturbance; most significantly, increased lateral tire-terrain interface forces resulted in increased cumulative impact width. Additionally, the vehicle lateral force, accerlation, and yaw rate that were directly measured are comparable with those calculated from the global positioning system (GPS) data, illustrating the potential of the much simpler measurements for this purpose.

Multi-Pass Rutting Study for Turning Wheeled and Tracked Vehicles

In this article, the effects of multiple vehicle passes and turning maneuvers by wheeled and tracked vehicles on rutting are discussed. Field tests were conducted at Fort Riley in August 2008 using an M1A1 combat tank, an armored personnel carrier (APC), a heavy expanded mobility tactical truck (HEMTT), and a high-mobility multi-purpose wheeled vehicle (HMMWV). These vehicles were operated in spiral patterns to evaluate the effect of different turning radii. Along each spiral in the same rut, each vehicle was driven up to eight passes. A vehicle tracking system (VTS) mounted on each vehicle utilized a Global Positioning System (GPS) to determine the vehicle dynamics (velocity and turning radius). As expected, compared to a single pass, results show that soil deformation and compaction increased with the increase in the number of passes. Multiple passes and turning maneuvers by vehicles resulted in rut depth increases in the range of 65% to 548%. The results of this study also confirmed that the multi-pass coefficient (α = 2) is appropriate to predict multi-pass rut depth for turning vehicles in loose soils.