Inverse distance weighted interpolation can be easily optimized with commercially available software by selecting distance exponent values that minimize cross-validation (V CROSS ) errors of prediction. The effectiveness of this approach has not been critically evaluated but is of concern because of known limitations of the V CROSS procedure. To evaluate this optimization procedure, it should be validated with an independent data set (V IDS ). The objectives of this study were (1) to develop and test an optimization procedure based on V CROSS and V IDS analyses, (2) to test the accuracy of optimization of the V CROSS procedure, and (3) to evaluate the quality of maps created with V CROSS . Soil fertility and bulk soil electrical conductivity data from two previously published studies were used for the analyses. These included prediction and validation data sets for multiple locations. Our optimization procedure compared well with those obtained with a commercially available software program. The use of V CROSS resulted in overprediction of optimal distance exponent values and a substantial reduction in map quality. In many cases, the maps produced with V CROSS optimization were blocky and unrealistic. The V CROSS procedure should not be used to optimize distance exponent values for data collected on regular grids or along transects. Instead, distance exponent values between 1.5 and 2.0 should be used. Software developers should consider creating a V IDS optimizing procedure. (Soil Science 2005;170:504-515)
Terrain analysis can be used to locate concentrated flow erosion e.g., ephemeral gully erosion across landscapes. For example, studies have found that ephemeral gullies were likely to occur when field specific thresholds were exceeded for the following terrain attributes the product of upslope area, slope, and plan curvatures [ ] topographic wetness index, upslope area, and slope [ ] and the topographic wetness index and the product of the upslope area and slope [ ]. "nother study used a cartographic classification and threshold procedure for erosion channel identification [ ]."n alternative approach utilized logistic regression and artificial neural network procedures to predict where erosion channels would appear in agricultural fields based on digital terrain attributes [ ]. With leave-one-field-out validation, it was determined that the more simple logistic regression was more appropriate because it performed as well as the non-linear neural network procedure. In a follow up study, erosion channels predicted from terrain attributes derived from -m US Geological Survey USGS digital elevation models DEMs were compared to those derived from DEMs created with survey-grade real-time kinematic RTK Global Positioning System GPS data [ ]. The USGS models identified most eroded features but the RTK analyses delineated them more clearly. The authors concluded that the USGS predictions were adequate for many agricultural applications because creating DEMs with RTK was relatively costly while USGS data was freely available on the Internet for most of the United States. " graphical representation illustrates how the logistic regression analysis [ , ] can be fit step and then applied step in Figure .
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