Assessing extraction trail trafficability using harvester CAN-bus data

Ala‐Ilomäki, Jari; Salmivaara, Aura; Launiainen, Samuli; Lindeman, Harri; Kulju, Sampo; Finér, Leena; Heikkonen, Jukka; Uusitalo, Jori

doi:10.1080/14942119.2020.1748958

Cited by 13 publications

(11 citation statements)

References 9 publications

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“…Currently, the development is moving towards dynamic terrain trafficability predictions where daily weather data is used to model up-to-date soil moisture conditions (Salmivaara et al 2020). In addition, Controlled Area Network (CAN)-bus data, which measures the rolling resistance of forest machines, may be used as teaching data to predict terrain trafficability (Ala-Ilomäki et al 2020).…”

Section: Logistic Regression Of Terrain Trafficabilitymentioning

confidence: 99%

Producing information from airborne LiDAR data for peatland forest management

Niemi¹

2022

Diss. For.

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In Finland, peatland forests are significant for wood supply, although simultaneously, they are also important for biodiversity, carbon sequestration, water conservation, and recreation. In the 1960s and 1970s, peatland forests in Finland were extensively drained to increase tree growth and fulfil the needs of the forest sector. However, this extensive drainage has negatively impacted on the biodiversity of peatland ecosystems, and substantially increased nutrient and sediment emissions to lakes and rivers resulting in eutrophication, turbidity, and brownification of these water bodies. This dissertation presents a number of approaches to move peatland forest management in a more environmentally sound direction, which may increase the general acceptability of peatland forestry. Airborne LiDAR (Light detection and ranging, i.e., laser scanning) derived 3D point cloud provides useful data, for example, to estimate forest biomass, to identify low-productive peatland forests, to model overland water flows, and to identify wet areas. The strength of airborne LiDAR is the ability of laser pulses to pass through tree canopies and obtain accurate observations from the ground level. The information derived from airborne LiDAR can enhance the planning of peatland forest management, as much of the planning can be done remotely, and supplementary field work can be implemented in areas of strategic need. This study presented the novel idea of applying local binary patterns for the prediction of terrain trafficability, which should be considered in further studies and practice. The moisture index derived from the local neighborhood can reveal the small-scale variations in terrain moisture. This study also presented the novel idea to create spatial models to identify suitable locations for water protection structures, which may help forest managers to plan water protection of ditch network maintenance or peatland restoration operations. Overall, the utilization of airborne LiDAR-derived information for the development of peatland forestry practices shows great potential.

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Section: Logistic Regression Of Terrain Trafficabilitymentioning

confidence: 99%

Producing information from airborne LiDAR data for peatland forest management

Niemi¹

2022

Diss. For.

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“…However, they have several needs for decision support (HarvPlanOptim). The maps of soil properties, depth to ground water, weather-dependent dynamic water content, and their further processed map layers for the prediction of site trafficability provide opportunities to improve the productivity and sustainability of timber and energy wood procurement [40][41][42][43]. The management of timber supply chains requires software tools, which may simulate and optimize the activities according to the local circumstances.…”

Section: Forest Property Management and Timber Procurement Servicesmentioning

confidence: 99%

Digital Transformation of Forest Services in Finland—A Case Study for Improving Business Processes

Kankaanhuhta

Packalen

Väätäinen

2021

Forests

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This case study introduces an innovation and development concept for agile software tools for the improvement of the productivity and customer experience of forest services. This need was recognized in the context of the opening of forest data and the development of service platforms for a forest-based bioeconomy in Finland. The forest services that were studied covered a continuum from a single type of work, e.g., soil preparation and young stand management through timber procurement, to comprehensive forest property management services. The study concentrated on the needs of micro-, small, and medium-sized enterprises (SMEs), which provide either retail- or business to business (B2B) services as sub-contractors. In addition, the challenges and bottlenecks in service processes detected by other stakeholders were considered. The prevailing service processes were conceptually modelled in order to search for opportunities for improvements in business and ecosystem services, i.e., agile software concepts. For example, we examined whether it would be possible to create opportunities for flexible operational models for precision, resilience, and protection of valuable microsites in forests. These software concepts were developed and evaluated in co-operation with the stakeholders in a co-creative workshop. The technological feasibility and commercial viability of the concepts, as well as the desirability for the customer were considered. The results of this business development process—i.e., agile software concepts and their anticipated benefits—were provided for further evaluation. In addition to the practical implications of this kind of innovation process tested, the potential of these kinds of agile tools for the further development of knowledge-intensive service processes was further discussed.

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“…Making it useful for operational purposes, Lidar-based sensors could detect actual rutting, created by an operating harvester, evolving the real-time generation of trafficability maps as a part of normal forestry operations [26]. Another sensor-based solution aims at rolling resistance, which has been recently assumed to be able to assess machine-operating trail trafficability [27]. In agreement, Salmivaara et al [78] reported the high potential of CAN-bus-derived rolling resistance to be used for predictions of trafficability across complex landscapes and changing conditions.…”

Section: Prediction Of Ruttingmentioning

confidence: 99%

“…A prediction of such damages can be facilitated by terramechanical test procedures (e.g., [24]) or by trafficability maps [25]. Recent approaches show the possibility to incorporate information on trafficability, measured during the first machine pass, in such maps [18,26,27]. Both terramechanical tests and trafficability maps can sufficiently support the operational flexibility of company management and entrepreneurs as well as machine operators, in order to avoid and reduce disturbances to forest soils, with associated high costs.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Selected Terramechanical Test Procedures and Cartographic Indices to Predict Rutting Caused by Machine Traffic during a Cut-to-Length Thinning Operation

Schönauer

Hoffmann

Maack

et al. 2021

Forests

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Timber harvesting operations using heavy forest machinery frequently results in severe soil compaction and displacement, threatening sustainable forest management. An accurate prediction of trafficability, considering actual operating conditions, minimizes these impacts and can be facilitated by various predictive tools. Within this study, we validated the accuracy of four terramechanical parameters, including Cone Index (MPa, Penetrologger), penetration depth (cm, Penetrologger), cone penetration (cm blow−1, dual-mass dynamic cone penetrometer) and shear strength (kPa, vane meter), and additionally two cartographic indices (topographic wetness index and depth-to-water). Measurements applying the four terramechanical approaches were performed at 47 transects along newly assigned machine operating trails in two broadleaved dominated mixed stands. After the CTL thinning operation was completed, measurement results and cartographic indices were correlated against rut depth. Under the rather dry soil conditions (29 ± 9 vol%), total rut depth ranged between 2.2 and 11.6 cm, and was clearly predicted by rut depth after a single pass of the harvester, which was used for further validations. The results indicated the easy-to-measure penetration depth as the most accurate approach to predict rut depth, considering coefficients of correlation (rP = 0.44). Moreover, cone penetration (rP = 0.34) provided reliable results. Surprisingly, no response between rut depth and Cone Index was observed, although it is commonly used to assess trafficability. The relatively low moisture conditions probably inhibited a correlation between rutting and moisture content. Consistently, cartographic indices could not be used to predict rutting. Rut depth after the harvester pass was a reliable predictor for total rut depth after 2–5 passes (rP = 0.50). Rarely used parameters, such as cone penetration or shear strength, outcompeted the highly reputed Cone Index, emphasizing further investigations of applied tools.

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Assessing extraction trail trafficability using harvester CAN-bus data

Cited by 13 publications

References 9 publications

Producing information from airborne LiDAR data for peatland forest management

Producing information from airborne LiDAR data for peatland forest management

Digital Transformation of Forest Services in Finland—A Case Study for Improving Business Processes

Comparison of Selected Terramechanical Test Procedures and Cartographic Indices to Predict Rutting Caused by Machine Traffic during a Cut-to-Length Thinning Operation

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