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Fig 1.

Map of the stand locations on the UIEF and illustration of the experimental setup.

(A) The 21 stands are delineated according to total basal area (m2/ha) and the UIEF unit boundaries are shown in blue. Background map is 1-m NAIP imagery. (B) Illustration of global navigation satellite system (GNSS) technology paired with radio frequency (RF) transmission (GNSS-RF). GNSS-RF transponders (Atlas PTs) receive positional information from GNSS satellites and relay this information to one another using radio frequency transmission. Atlas PT X is located at the geofence intersection point, while Atlas PTs A, B, and C are located at the triangle points A, B, and C, respectively. The manual faller carried a PT attached at the hip (Atlas PT F).

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Fig 1 Expand

Fig 2.

LOS path sections.

Each LOS path was divided into 20 5-m sections and three sections were randomly selected for each LOS path. This figure shows the 20 sections and their locations along the LOS path. Sections highlighted in green represent the three randomly selected sections for which all vegetative obstructions were measured using the key in Table 1.

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Fig 2 Expand

Table 1.

Obstacle list and key.

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Table 1 Expand

Fig 3.

Illustration of slope classification as concave, convex, or both.

Blue dots represent the higher of the two LOS endpoints while red dots represent the lower of the two LOS endpoints. Green lines represent the ground surface along the LOS path.

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Fig 3 Expand

Table 2.

Model parameters.

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Table 2 Expand

Table 3.

Summary of mixed-effects logistic regression model using stand as a random effect and the odds of missed position updates as the response.

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Table 3 Expand

Fig 4.

Mixed-effects model predictions for PT RMSE.

Predicted RMSE as a function of the two model variables (Ht and QMD). Predictions for each variable were made using the mean of the other predictor. 95% confidence intervals computed using the bootstrap are shown as colored bands. Points on each plot represent partial residuals.

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Fig 4 Expand

Table 4.

Summary of mixed-effects linear regression model using day as a random effect and RMSE as the response.

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Table 4 Expand

Fig 5.

Mixed-effects model predictions for geofence intersection alert delay.

Predicted delay as a function of the three model variables (TBA, concave, and aspect). Predictions for each variable were made using the mean of the other predictors. 95% confidence intervals computed using the bootstrap are shown as colored bands. Points on each plot represent partial residuals.

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Fig 5 Expand

Table 5.

Summary of mixed-effects linear regression model using day as a random effect and geofence intersection alert delay as the response.

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Table 5 Expand