A modular terrain model for daily variations in machine-specific forest soil trafficability

Vega-Nieva, Daniel José; Murphy, Paul; Castonguay, Mark; Ogilvie, Jae; Arp, Paul A.

doi:10.4141/cjss06033

Cited by 41 publications

(40 citation statements)

References 19 publications

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“…The best-fitted scatter plot in figure 6 shows that the field-determined CI max values conform to CI (equation 2) quite well, but with an under-prediction bias, likely due to two reasons: (1) the manually produced CI max data level off as soil resistance to penetration increases with increasing soil depth (figure 5) and (2) equation 2 summarizes CI trends across several studies with interstudy biases removed (Vega-Nieva et al 2009). Such biases refer to study-to-study differences in cone angle and size, penetration velocity, extent of soil cementation, and other variations in site and study conditions.…”

Section: Severity Classmentioning

confidence: 99%

“…The weather-dependent component would in part be influenced by the extent of vegetation cover and related evapotranspirational water losses and upslope soil disturbances including soil compaction which affects runoff initiation following precipitation events and lower soil percolation rates thereafter (Rab et al 2005;Foltz 2006). Some of the vegetation-related, season-related, and weather-related complications can, at least in part, be accommodated by calibrating equation16 with hydrologically derived soil moisture levels from ridge tops to depressions, as outlined by Vega-Nieva et al (2009).…”

Section: Severity Classmentioning

confidence: 99%

“…These authors also also showed that the above soil properties conform more closely to the mapped DTW index than the widely used DEM-derived topographic wetness index (i.e., TWI = ln([low accumulation ÷ slope]). For that purpose, Vega-Nieva et al (2009) suggested using 1 ÷ (1 -CF) 2 and (1 -CF) 2 as CI and rut depth multipliers, respectively. Normally, small gravel-like particles embedded within the fine earth do not strongly affect CI as the penetrometer pushes these fragments to the side.…”

Section: Gfluz G7mentioning

confidence: 99%

“…For context of the monitoring of machine-induced soil disturbances and compaction (Miller et al 2010), one can use the CI-based rut-depth projections to estimate the extent of soil compaction and subsequent moisture content within the rut-impacted soil as follows (Vega-Nieva et al 2009):…”

Section: Gfluz G7mentioning

confidence: 99%

See 3 more Smart Citations

Modeling and mapping soil resistance to penetration and rutting using LiDAR-derived digital elevation data

Campbell¹,

White²,

Arp³

2013

Journal of Soil and Water Conservation

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Soil resistances to penetration were probed with a hand-held soil cone penetrometer across ridge-to-depression transects for two contrasting study areas in Alberta, Canada: one in the foothills west of Calgary, and one in the boreal plain north of Peace River. The resulting cone index values (CI) were related to plot-measured values for soil moisture, density, texture, organic matter content, soil depth, elevation, slope, and a cartographic depth-to-water index (DTW) by way of multiple regression analysis. Elevation, slope, DTW, and the associated flow accumulation pattern were all derived from a light detection and ranging (LiDAR)-generated bare ground digital elevation model (DEM), at 1 m (3 ft) resolution. The field-determined CI values conformed to a previous formulation that related CI to soil texture, density, and waterfilled pore space. In terms of topographic position, CI increased with increasing DTW, in parallel with decreasing soil moisture content and increasing soil density. The resulting best-fitted regression between CI, log 10 (DTW), and elevation (or study area) was used to map CI and expected all-terrain recreational vehicle-specific rutting depths across the landscape. The maps so generated were in general agreement with the results of a 40 km (25 mi) long soil disturbance survey along a recreational vehicle trail section within the foothill area.

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Section: Severity Classmentioning

confidence: 99%

Section: Severity Classmentioning

confidence: 99%

Section: Gfluz G7mentioning

confidence: 99%

Section: Gfluz G7mentioning

confidence: 99%

See 2 more Smart Citations

Modeling and mapping soil resistance to penetration and rutting using LiDAR-derived digital elevation data

Campbell¹,

White²,

Arp³

2013

Journal of Soil and Water Conservation

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“…Mechanized wood harvesting operations can cause rut formation, which deteriorates soil quality, decreases forest productivity and affects hydrological balance and water quality through changed sediment discharge [1][2][3][4][5][6][7][8]. Thus, the rut depth distribution is one of the central measures of the environmental and economic impact of harvesting operations.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Rut Depth by UAV Photogrammetry

et al. 2017

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Abstract:The rut formation during forest operations is an undesirable phenomenon. A methodology is being proposed to measure the rut depth distribution of a logging site by photogrammetric point clouds produced by unmanned aerial vehicles (UAV). The methodology includes five processing steps that aim at reducing the noise from the surrounding trees and undergrowth for identifying the trails. A canopy height model is produced to focus the point cloud on the open pathway around the forest machine trail. A triangularized ground model is formed by a point cloud filtering method. The ground model is vectorized using the histogram of directed curvatures (HOC) method to produce an overall ground visualization. Finally, a manual selection of the trails leads to an automated rut depth profile analysis. The bivariate correlation (Pearson's r) between rut depths measured manually and by UAV photogrammetry is r = 0.67. The two-class accuracy a of detecting the rut depth exceeding 20 cm is a = 0.65. There is potential for enabling automated large-scale evaluation of the forestry areas by using autonomous drones and the process described.

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High-resolution harvester data for estimating rolling resistance and forest trafficability

Salmivaara,

Holmström,

Kulju

et al. 2024

Eur J Forest Res

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Information on terrain conditions is a prerequisite for planning environmentally and economically sustainable forest harvesting operations that avoid negative impact on soils. Current soil data are coarse, and collecting such data with traditional methods is expensive. Forest harvesters can be harnessed to estimate the rolling resistance coefficient ($$\mu _{RR}$$ μ RR ), which is a proxy for forest trafficability. Using spatio-temporal data on engine power used, speed travelled, and machine inclination, $$\mu _{RR}$$ μ RR can be computed for harvest areas. This study describes an extensive, high-resolution data on $$\mu _{RR}$$ μ RR collected in a boreal forest landscape in Southern Finland during the non-frost period of 2021, covering roughly 50 km of harvester routes. We report improvements in removing some of the previous restrictions on calculating $$\mu _{RR}$$ μ RR on steeper slopes, enabling the calculation within a $$-10^{\circ }$$ - 10 ∘ to $$+10^{\circ }$$ + 10 ∘ slope range with a speed range of 0.6–1.2 ms$$^{-1}$$ - 1 . We characterise the variation in $$\mu _{RR}$$ μ RR both between and within 11 test sites harvested during the April-August period. The site mean $$\mu _{RR}$$ μ RR varies from $$\sim$$ ∼ 0.14 to 0.19 and shows significant differences between the sites. Using simulations of the hydrological state of the soil and open spatial data on forest and topography, we identify features that best explain the extremes of $$\mu _{RR}$$ μ RR within the sites. Several wetness-related indices, such as the depth-to-water index with varying thresholds, explain the $$\mu _{RR}$$ μ RR extremes, while biomass-related stand attributes indirectly explain these through their linkage to site and soil characteristics. Obtaining $$\mu _{RR}$$ μ RR from actual operational data extends the capabilities of large-scale harvester-based data collection and paves the way for building data-driven models for trafficability prediction.

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A modular terrain model for daily variations in machine-specific forest soil trafficability

Cited by 41 publications

References 19 publications

Modeling and mapping soil resistance to penetration and rutting using LiDAR-derived digital elevation data

Modeling and mapping soil resistance to penetration and rutting using LiDAR-derived digital elevation data

Estimating the Rut Depth by UAV Photogrammetry

High-resolution harvester data for estimating rolling resistance and forest trafficability

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