FORMEC 2017

Determination of forest road surface roughness by Kinect depth imaging

Francesco Marinello, Andrea Rosario Proto, Giuseppe Zimbalatti, Andrea Pezzuolo, Raffaele Cavalli, Stefano Grigolato

Francesco Marinello
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy
Andrea Rosario Proto
Department of Agriculture, Mediterranean University of Reggio Calabria, Feo di Vito 89122, Reggio Calabria, Italy
Giuseppe Zimbalatti
Department of Agriculture, Mediterranean University of Reggio Calabria, Feo di Vito 89122, Reggio Calabria, Italy
Andrea Pezzuolo
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy
Raffaele Cavalli
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy
Stefano Grigolato
Department of Land, Environment, Agriculture and Forestry, University of Padova, Legnaro, Italy. Email: stefano.grigolato@unipd.it

Online First: November 16, 2017
Marinello, F., Rosario Proto, A., Zimbalatti, G., Pezzuolo, A., Cavalli, R., Grigolato, S. 2017. Determination of forest road surface roughness by Kinect depth imaging. Annals of Forest Research DOI:10.15287/afr.2017.893


Roughness is a dynamic property of the gravel road surface that affects safety, ride comfort as well as vehicle tyre life and maintenance costs. A rapid survey of gravel road condition is fundamental for an effective maintenance planning and definition of the intervention priorities.
Different non-contact techniques such as laser scanning, ultrasonic sensors and photogrammetry have recently been proposed to reconstruct three-dimensional topography of road surface and allow extraction of roughness metrics. The application of Microsoft Kinect™ depth camera is proposed and discussed here for collection of 3D data sets from gravel roads, to be implemented in order to allow quantification of surface roughness.
The objectives are to: i) verify the applicability of the Kinect sensor for characterization of different forest roads, ii) identify the appropriateness and potential of different roughness parameters and iii) analyse the correlation with vibrations recoded by 3-axis accelerometers installed on different vehicles. The test took advantage of the implementation of the Kinect depth camera for surface roughness determination of 4 different forest gravel roads and one well-maintained asphalt road as reference. Different vehicles (mountain bike, off-road motorcycle, ATV vehicle, 4WD car and compact crossover) were included in the experiment in order to verify the vibration intensity when travelling on different road surface conditions. Correlations between the extracted roughness parameters and vibration levels of the tested vehicles were then verified. Coefficients of determination of between 0.76 and 0.97 were detected between average surface roughness and standard deviation of relative accelerations, with higher values in the case of lighter vehicles.


Abulizi N., Kawamura A., Tomiyama K., Fujita, S. 2016. Measuring and evaluating of road roughness conditions with a compact road profiler and ArcGIS. Journal of Traffic and Transportation Engineering 3(5): 398–411. DOI: 10.1016/j.jtte.2016.09.004

Akay A.E., Sessions J., 2004. Roading and transport operations. Encyclopedia of Forest Sciences (193): 259-269.

Aursand P.O., Horvli I., 2009. Effect of a changed climate on gravel roads. In 8th International Conference on the Bearing Capacity of Roads, Railways and Airfields BCR2A 2009: 1091-1100.

Chambon S., Moliard J.M., 2011. Automatic road pavement assessment with image processing: Review and comparison, International Journal of Geophysics 2011: 1-20. DOI: 10.1155/2011/989354

Ciesa M., Grigolato S., Cavalli, R., 2014. Analysis on vehicle and walking speeds of search and rescue ground crews in mountainous areas. Journal of Outdoor Recreation and Tourism 5-6: 48-57. DOI:10.1016/j.jort.2014.03.004

Cigada A., Mancosu F., Manzoni S., Zappa E., 2010. Laser-triangulation device for in-line measurement of road texture at medium and high speed, Mechanical Systems and Signal Processing 24(7): 2225-2234. DOI: 10.1016/j.ymssp.2010.05.002

Dubbini M., Pezzuolo A., De Giglio M., Gattelli M., Curzio L., Covi D., Yezekyan T., Marinello F., 2017. Last generation instrument for agriculture multispectral data collection. CIGR Journal 19(1): 87-93.

Forslöf L., Jones H., 2015. Roadroid: Continuous road condition monitoring with smart phones. Journal of Civil Engineering and Architecture 9(4): 485-496. DOI:10.17265/1934-7359/2015.04.012

Grigolato S., Pellegrini M., Cavalli R., 2013. Temporal analysis of the traffic loads on forest road networks. iForest - Biogeosciences and Forestry 6(4): 255-261. DOI:10.3832/ifor0773-006

Hämmerle M., Höfle B., Fuchs J., Schröder-Ritzrau A., Voll-weiler N., Frank N., 2014. Comparison of Kinect and terrestrial Lidar capturing natural karst cave 3-D objects, IEEE Geoscience and Remote Sensing Letters 11(11): 1896-1900. DOI: 10.1109/LGRS.2014.2313599

Huntington G., Ksaibati K., 2007. Gravel roads surface performance modelling. Transportation Research Record 2016: 56-64. DOI:10.3141/2016-07

ISO 25178-2: 2012 Geometrical Product Specifications (GPS) - Surface texture: Areal - Part 2: Terms, definitions and surface texture parameters. International Standards Organization, Switzerland.

Kalantari Z., Folkeson L. 2013. Road drainage in Sweden: Current practice and suggestions for adaptation to climate change. Journal of Infrastructure Systems 19(2): 147-156. DOI:10.1061/(ASCE)IS.1943-555X.0000119

Laschi A., Neri F., Montorselli N.B., Marchi, E., 2016. A methodological approach exploiting modern techniques for forest road network planning. Croatian Journal of Forest Engineering 37(2): 319-331.

Lischke V., Byhahn C., Westphal K., Kessler P. 2001. Mountaineering accidents in the European Alps: Have the numbers increased in recent years? Wilderness and Environmental Medicine 12(2): 74-80.

Marinello F., Pezzuolo A., Gasparini F., Arvidsson J., Sartori L., 2015. Application of the Kinect sensor for dynamic soil surface characterization. Precision Agriculture 16(6): 601-612. DOI: 10.1007/s11119-015-9398-5

Mathavan S., Kamal K., Rahman M., 2015. A review of three-dimensional imaging technologies for pavement distress detection and measurements, IEEE Transactions On Intelligent Transportation Systems 16(5): 2353-2362. DOI: 10.1109/TITS.2015.2428655

Mills K., Pyles M., Thoreson R. 2007. Aggregate surfacing design and management for low-volume roads in temperate, mountainous areas. Transportation Research Record. Journal of the Transportation Research Board 1989: 154-160. DOI:10.3141/1989-59

Nitsche P., Stütz R., Kammer M., Maurer P. 2014. Comparison of Machine Learning Methods for Evaluating Pavement Roughness Based on Vehicle Response. Journal of Computing in Civil Engineering 28(4): 04014015. DOI:10.1061/(ASCE)CP.1943-5487.0000285

Sanda N.S.R. 2013. Aspects regarding 3D laser scanning surveys for road design. Agricultura 85(1-2): 140-144. DOI: 10.15835/arspa.v85i1-2.10014

Pellegrini M., Grigolato S., Cavalli R., 2013. Spatial multi-criteria decision process to define maintenance priorities of forest road network: an application in the Italian Alpine region. Croatian Journal of Forest Engineering 34(1): 31-42.

Reid L.M., Dunne T. 1984. Sediment production from forest road surfaces. Water Resources Research 20(11): 1753-1761.

Roman A., Ursu T.-M., Fărcaş S., Lăzărescu V.-A., Opreanu C.H., 2017. An integrated airborne laser scanning approach to forest management and cultural heritage issues: A case study at Porolissum, Romania. Annals of Forest Research 60(1): 171-187. DOI: 10.15287/afr.2016.755

Romano A., Costa A., Basile M., Raimondi R., Posillico M., Scinti Roger D., Crisci A., Piraccini R., Raia P., Matteucci G., 2017. Conservation of salamanders in managed forests: Methods and costs of monitoring abundance and habitat selection. Forest Ecology and Management 400: 12-18. DOI:10.1016/j.foreco.2017.05.048

Sadale R., Kolhe R., Wathore S., Aghav J., Warade S., Udayagiri S., 2013. Steer-by-wire implementation using Kinect, Advances in Intelligent Systems and Computing. In: Kumar M. A., R. S., Kumar T. (eds) Proceedings of International Conference on Advances in Computing. Advances in Intelligent Systems and Computing, vol 174. pp 163-170. Springer, New Delhi. DOI: 10.1007/978-81-322-0740-5_21

Sitzia T., Campagnaro T., Grigolato S. 2016. Ecological risk and accessibility analysis to assess the impact of roads under Habitats Directive. Journal of Environmental Planning and Management 59(12): 2251-2271. DOI: 10.1080/09640568.2016.1140023

Talbot B., Pierzchała M., Astrup, R. 2017. Applications of remote and proximal sensing for improved precision in forest operations. Croatian Journal of Forest Engineering 38(2): 327-336.

Thompson M., Sessions J., Boston K., Skaugset A., Tomberlin D. 2010. Forest road erosion control using multiobjective optimization. JAWRA Journal of the American Water Resources Association 46(4): 712-723. DOI:10.1111/j.1752-1688.2010.00443.x

Tiong L.T.Y., Mustaffar M., Hainin M.R., 2012. Road surface assessment of pothole severity by close range digital photogrammetry method. World Applied Sciences Journal 19(6): 867-873. DOI: 0.5829/idosi.wasj.2012.19.06.3353

Torresan C., Corona P., Scrinzi G., Marsal J.V., 2016. Using classification trees to predict forest structure types from LiDAR data. Annals of Forest Research 59(2): 281-298. DOI: 10.15287/afr.2016.423

Van der Gryp A., Van Zyl G., 2007. Variability and control of gravel road visual assessments. Transportation Research Record: Journal of the Transportation Research Board, 1989(1989): 247-253. DOI:10.3141/1989-70


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