Wheel rut measurements by forest machine-mounted LiDAR sensors – accuracy and potential for operational applications?

Salmivaara, Aura; Miettinen, Mikko; Finér, Leena; Launiainen, Samuli; Korpunen, Heikki; Tuominen, Sakari; Heikkonen, Jukka; Nevalainen, Paavo; Sirén, Matti; Ala‐Ilomäki, Jari; Uusitalo, Jori

doi:10.1080/14942119.2018.1419677

Cited by 31 publications

(43 citation statements)

References 35 publications

(46 reference statements)

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“…This data-driven approach, also referred to as precision forestry, gathers detailed information about the forest in order to improve decision-making and enable more efficient forest operations (Fardusi et al 2017;Holopainen et al 2014). Measurement data about either the characteristics of individual trees or the prevailing environmental conditions of the forest can be collected (Melander and Ritala 2018;Salmivaara et al 2018;Lindroos et al 2015). In-situ data can be automatically gathered during various phases of the forest management process, in particular, during operations where heavy machinery offers a platform for the data collection equipment.…”

Section: Introductionmentioning

confidence: 99%

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

Melander¹,

Ritala²,

Strandström³

2019

Silva Fenn.

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Highlights• An excavator was equipped with an inertial measurement unit for taking automatic measurements of soil stoniness during mounding work. • Supervised machine-learning classifiers were trained utilizing both the automatically measured data and manual stoniness measurements. • The class prediction for the soil stoniness achieved an accuracy of 70% when assigned to constant grid cells. AbstractThe stoniness index of forest soil describes the stone content in the upper soil layer at depths of 20-30 centimeters. This index is not available in any existing map databases, and traditional measurements for the stoniness of the soil have always necessitated laborious soil-penetration methods. Knowledge of the stone content of a forest site could be of use in a variety of forestry operations. This paper presents a novel approach to obtaining automatic measurements of soil stoniness during an excavator-based mounding operation. The excavator was equipped with only a low-cost inertial measurement unit and a satellite navigation receiver. Using the data from these sensors and manually conducted soil stoniness measurements, supervised machine learning methods were utilized to build a model that is capable of predicting the stoniness class of a given mounding location. This study compares different classifiers and feature selection methods to find the most promising solution for this learning problem. The discussion includes a proposition for a meaningful measurement resolution of the soil's stoniness, and a practical method for evaluating the variability of the stone content of the soil. The results indicate that it is possible to predict the soil stoniness class with 70% accuracy using only the inertial and location measurements.

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Section: Introductionmentioning

confidence: 99%

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

Melander¹,

Ritala²,

Strandström³

2019

Silva Fenn.

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“…As [16] states, the reference ground level (namely the ground level before the trail was formed) remains difficult to define by any means, including the best possible ground-based laser scanning and human assessment. The rut depth evaluation has to have some categorical element, addressing mainly two or three rut depth classes.…”

Section: Discussionmentioning

confidence: 99%

“…The general setting of the forest operation, the machinery and their chose weights and the routes used closely resemble a standard forest harvesting operation. As mentioned in several publications [4,16,22], the rut depth has high variability dependent on a multitude of factors, and currently, only case studies are practical. The ruts were measured manually using a horizontal hurdle and a measuring rod.…”

Section: Study Areamentioning

confidence: 99%

“…Other potential methods include light detection and ranging (LiDAR) scanning [16,19] and ultrasonic distance ranging accompanied by proxy measurements such as controller area network (CAN-bus) data [20]. Compared with other methods, UAV photogrammetry provides a cost-effective option for collecting high-resolution 3D point clouds documenting the operation site in full extent [4].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, both the spatial and temporal variation in trafficability is extremely large [14,15]. An easily collected and extensive dataset on rut depths that covers field measurements over several test sites and various weather conditions is thus needed for any attempts to model forest terrain trafficability [16].…”

Section: Introductionmentioning

confidence: 99%

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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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Assessment of soil compaction and rutting in managed forests through an airborne LiDAR technique

et al. 2022

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To ensure sustainable forest management, the assessment and monitoring of soil compaction and rutting are essential. Here, we used airborne light detection and ranging‐derived digital terrain model (LiDAR‐derived DTM), available for the forest of Compiègne in northern France, to compute a spatial index of soil rutting. Following an environmental systematic sampling design, we selected 45 plots representative of the forest stand conditions where we subsequently extracted information from the DTM to compute the cumulative length of ruts (CLR). To assess the quality of this LiDAR‐derived index, we related the CLR index to in‐situ soil and vegetation parameters such as soil texture, soil pH, and understory plant species composition. Floristic surveys were carried out across all 45 plots to generate plant species response curves along the CLR gradient. We found soil texture, soil type, and soil pH to be important determinants of CLR. For instance, CLR was the highest in soils with the highest clay content. A total of 22 out of the 94 understory plant species we analyzed showed a significant response curve along the CLR gradient. Most important, the occurrence probability of species associated with wet soils and stagnant waters (e.g., Juncus effusus), like those found in ruts, increased with CLR. Other species associated with dry soils (e.g., Hedera helix) showed a negative response curve along the CLR gradient. In conclusion, the proposed index (CLR) has proven useful to remotely assess soil compaction and rutting after logging operations.

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Wheel rut measurements by forest machine-mounted LiDAR sensors – accuracy and potential for operational applications?

Cited by 31 publications

References 35 publications

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

Classifying soil stoniness based on the excavator boom vibration data in mounding operations

Estimating the Rut Depth by UAV Photogrammetry

Assessment of soil compaction and rutting in managed forests through an airborne LiDAR technique

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