Research Article
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Estimating Soil Strength Using GIS-Based Maps - A case study in Sweden

Year 2023, Volume: 9 Issue: 2, 70 - 79, 26.12.2023
https://doi.org/10.33904/ejfe.1321075

Abstract

Soil strength is an important parameter for planning of forest roads and harvesting operations. Locating roads to areas with high soil strength reduce both build and maintenance costs. Locating logging trails to high strength areas minimise soil disturbances, e.g., rutting and compaction of forest soils. GIS-based maps of soil type and soil moisture can be valuable tools to estimate soil strength. The aim of this study was to evaluate the use of soil moisture map, i.e., depth-to-water (DTW), maps and soil type maps, to estimate soil strength expressed as California bearing ratio (CBR). CBR, volumetric water content, and ground penetration depth were measured in 120 sample points, separated on three soil classes (clay-silt sediments, sand sediments, glacial till) and two soil moisture classes (wet, dry). In each point, soil samples were collected for validation of the soil type maps. There was a high conformance between soil moisture predicted by DTW maps and field measurements, but conformance of the soil type between maps and field estimates varied between soil types. For sediment soils, dry soils were consistently stronger than wet soils. Soil strength of glacial till soils was more complicated with a binary CBR distribution depending on soil stoniness. Glacial till soils possible to penetrate to 20 cm depth with the dynamic cone penetrometer had CBR values close to those for sand sediments. There is a potential to estimate soil strength from DTW and soil type maps, but these variables should preferably be complemented with other data.

Thanks

The authors wants to thank Stiftelsen Nils och Dorthi Troedssons Forskningsfond for funding this project

References

  • Ågren, A., Lidberg, W., Ring, E. 2015. Mapping Temporal Dynamics in a Forest Stream Network-Implications for Riparian Forest Management. Forests, 6(9):2982-3001. https://doi.org/10.3390/ f6092982
  • Ågren, A.M., Larson, J., Paul, S.S., Laudon, H., Lidberg, W. 2021. Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape. Geoderma, 404. https://doi.org/10.1016/j.geoderma.2021.115280
  • Ågren, A.M., Hasselquist, E.M., Stendahl, J., Nilsson, M.B., Paul, S.S. 2022. Delineating the distribution of mineral and peat soils at the landscape scale in northern boreal regions. Soil, 8(2):733-749. https://doi.org/10.5194/soil-8-733-2022
  • Anon. 1996. User Guide to the Dynamic Cone Penetrometer; Office of Minnesota Road Research. http://www.dot.state.mn.us/materials/researchdocs/User_Guide.pdf.
  • Anon. 2020. Markfuktighet - Produktbeskrivning (DTW maps - product description). Swedish forestry Agency. https://www.skogsstyrelsen.se/globalassets/sjalvservice/karttjanster/geodatatjanster/produktbeskrivningar/raster-markfuktighetskartaproduktbeskriv ning.pdf
  • Anon 2021a. Soil types 1:25 000-1:100 000. Geological survey of Sweden. https://www.sgu.se/en/products/ maps/map-viewer/jordkartvisare/soil-types-125-000-1100-000/
  • Anon. 2021b. Nya jordartsdata finns tillgängliga: (New soil type maps available). https://www.sgu.se/om-sgu/nyheter/2021/januari/nya-jordartsdata-finns-tillg angliga/
  • Anon. 2022. National Land Cover Database. https://www.naturvardsverket.se/en/services-and-per mits/maps-and-map-services/national-land-cover-database/
  • Cambi, M., Certini, G., Neri, F., Marchi, E. 2015. The impact of heavy traffic on forest soils: A review. Forest Ecology and Management, 338:124-138. https://doi.org/10.1016/j.foreco.2014.11.022
  • Friberg, G., Bergkvist, I. 2016. Så påverkar arbetsrutiner och markfuktighetskartor körskador i skogsbruket (How operational procedures and depth-to-water maps can reduce damage on soil and water and rutting in the Swedish forestry). Working paper, 904-2016. Uppsala: Skogforsk.
  • Fisher, R.F., Binkley, D. 2000. Ecology and Management of Forest Soils. New York: Wiley-Blackwell. Hansson, L., Andersson, M., Johannesson, T. 2022. Bättre brandriskbedömningar, Skogforsk rapport. Uppsala: Skogforsk.
  • Hillel, D. 1998. Environmental Soil Physics. Academic Press, San Diego, CA.
  • Hoffmann, S., Schönauer, M., Heppelmann, J., Asikainen, A., Cacot, E., Eberhard, B., Hasenauer, H., Ivanovs, J., Jaeger, D., Lazdins, A., Mohtashami, S., Moskalik, T., Nordfjell, T., Stereńczak, K., Talbot, B., Uusitalo, J., Vuillermoz, M., Astrup, R. 2022. Trafficability Prediction Using Depth-to-Water Maps: the Status of Application in Northern and Central European Forestry. Current Forestry Reports, 8:55-71.https://doi.org/10.1007/s40725-021-00153-8
  • Ilintsev, A.S., Nakvasina, E.N., Högbom, L. 2021. Methods of Protection Forest Soils during Logging Operations (Review). Lesnoy Zhurnal [Russian Forestry Journal], 5:92-116. https://doi.org/10.37482 /0536-1036-2021-5-92-116
  • Jones, M.-F. 2019. Mapping soil trafficability by way of temporal hydrology modeling and spatial wet-areas-mapping. PhD-thesis. The University of New Brunswick, Forestry and Environmental Management. Canada. 224 p.
  • Karlsson, C., Sohlenius, G., Peterson Becher, G. 2021. Handledning för jordartsgeologiska kartor och databaser över Sverige. (Tutorial for Quaternary map viewers and databases over Sweden) Nr. 2021:17, SGU, Geological survey of Sweden, Uppsala.
  • Labelle, E.R., Hansson, L., Högbom, L., Jourgholami, M., Laschi, A. 2022. Strategies to Mitigate the Effects of Soil Physical Disturbances Caused by Forest Machinery: a Comprehensive Review. Current Forestry Reports, 8(1):20-37. https://doi.org/10.1007 /s40725-021-00155-6
  • Larson, J., Lidberg, W., Ågren, A.M., Laudon, H. 2022. Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices. Hydrology and Earth System Sciences, 26(19):4837-4851. https://doi.org/10.5194/hess-26-4837-2022
  • Lidberg, W., Nilsson, M., Agren, A. 2020. Using machine learning to generate high-resolution wet area maps for planning forest management: A study in a boreal forest landscape. Ambio. https://doi.org /10.1007/s13280-019-01196-9
  • Mohtashami, S., Eliasson, L., Jansson, G., Sonesson, J. 2017. Influence of soil type, cartographic depth-to-water, road reinforcement and traffic intensity on rut formation in logging operations: a survey study in Sweden. Silva Fennica, 51(5). https://doi.org/ 10.14214/sf.2018
  • Mohtashami, S., Eliasson, L., Willén, E. 2018. Effects of soil clay content on rut formation. In: FORMEC 2018, 51st International symposium of Forestry Mechanisation, Madrid, Spain, September 25th-27th, 2018. Technical University of Madrid.
  • Mohtashami, S. 2022. GIS-based decision support systems to minimise soil impacts in logging operations. Doctoral Thesis. Acta Universitatis Agriculturae Sueciae. No. 2022:67. Faculty of Forest Sciences: Swedish University of Agricultural Sciences (SLU). https://doi.org/https://doi.org/ 10.54612/a.qq3cqbcknd
  • Murphy, P.N.C., Ogilvie, J., Connor, K, Arp, P.A. 2007. Mapping wetlands: a comparison of two different approaches for New Brunswick, Canada. Wetlands, 27(4):846-854.
  • Murphy, P.N.C., Ogilvie, J., Arp, P. 2009. Topographic modelling of soil moisture conditions: a comparison and verification of two models. European Journal of Soil Science, 60(1):94-109. https://doi.org/10.1111 /j.1365-2389.2008.01094.x
  • Naghdi, R., Solgi, A., Labelle, E.R., Nikooy, M. 2020. Combined effects of soil texture and machine operating trail gradient on changes in forest soil physical properties during ground-based skidding. Pedosphere, 30(4):508-516. https://doi.org/10.1016 /s1002-0160(17)60428-4
  • Niemi, M.T., Vastaranta, M., Vauhkonen, J., Melkas, T., Holopainen, M. 2017. Airborne LiDAR-derived elevation data in terrain trafficability mapping. Scandinavian Journal of Forest Research, 32(8):762-773.https://doi.org/10.1080/02827581.2017.1296181
  • Nilsson, T., Stendahl, J., Löfgren, O. 2015. Markförhållanden i svensk skogsmark – data från Markinventeringen 1993-2002 (Soil conditions in Swedish forest soils – data from the Swedish Forest Soil Inventory 1993-2002). Rapport 19. Institutionen för mark och miljö, Sveriges lantbruksuniversitet, Uppsala.
  • Østby-Berntsen, Ø., Fjeld, D. 2018. Mulighetsstudie lassbærere på bæresvak mark [Feasibility study of forwarders for soils of low bearing capacity], Norskog & Nibio (Norwegian Institute for bioeconomy). (3). Lilleaker.
  • Piikki, K., Söderström, M. 2017. Digital soil mapping of arable land in Sweden – Validation of performance at multiple scales. Geoderma, 352:342-350. https://doi. org/10.1016/j.geoderma.2017.10.049
  • Salmivaara, A., Launiainen, S., Perttunen, J., Nevalainen, P., Pohjankukka, J., Ala-Ilomäki, J., Sirén, M., Laurén, A., Tuominen, S., Uusitalo, J., Pahikkala, T., Heikkonen, J., Finér, L. 2020. Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology. Forestry: An International Journal of Forest Research, 93(5):662-674. https://doi.org/ 10.1093/ forestry/cpaa010
  • Schönauer, M., Hoffmann, S., Maack, J., Jansen, M. &, Jaeger, D. 2021. Comparison of Selected Terramechanical Test Procedures and Cartographic Indices to Predict Rutting Caused by Machine Traffic during a Cut-to-Length Thinning Operation. Forests, 12(2). https://doi.org/10.3390/f12020113
  • Swift, L.W., Burns, R.G. 1999. The Three Rs of Roads: Redesign, Reconstruction, and Restoration. Journal of Forestry 97(8): 40-44.
  • Toivio, J., Helmisaari, H.-S., Palviainen, M., Lindeman, H., Ala-Ilomäki, J., Sirén, M., Uusitalo, J. 2017. Impacts of timber forwarding on physical properties of forest soils in southern Finland. Forest Ecology and Management, 405:22-30. https://doi.org/10.1016 /j.foreco.2017.09.022
  • Uusitalo, J., Ala-Ilomäki, J., Lindeman, H., Toivio, J., Siren, M. 2020. Predicting rut depth induced by an 8-wheeled forwarder in fine-grained boreal forest soils. Annals of Forest Science, 77(2). https://doi.org/ 10.1007/s13595-020-00948-y
  • Vepakomma, U., Cormier, D., Hansson, L., Talbot, B. 2023. Remote Sensing at Local Scales for Operational Forestry. In: Boreal Forests in the Face of Climate Change: Sustainable Management. (74): 657-682.
  • Webster, S.L., Grau R.H., Williams, T.P. 1992. Description and application of dual mass dynamic cone penetrometer. US Army Corps of Engineers, Waterways Experiment Station, Geo technical Laboratory. Instruction Report GL-92-3. Vicksburg, MS. USA.
  • Wronski, E.B., Murphy, G. 1994. Responses of forest crops to soil compaction. In: Soil Compaction in Crop Production. Elsevier Science, 317-342.
Year 2023, Volume: 9 Issue: 2, 70 - 79, 26.12.2023
https://doi.org/10.33904/ejfe.1321075

Abstract

References

  • Ågren, A., Lidberg, W., Ring, E. 2015. Mapping Temporal Dynamics in a Forest Stream Network-Implications for Riparian Forest Management. Forests, 6(9):2982-3001. https://doi.org/10.3390/ f6092982
  • Ågren, A.M., Larson, J., Paul, S.S., Laudon, H., Lidberg, W. 2021. Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape. Geoderma, 404. https://doi.org/10.1016/j.geoderma.2021.115280
  • Ågren, A.M., Hasselquist, E.M., Stendahl, J., Nilsson, M.B., Paul, S.S. 2022. Delineating the distribution of mineral and peat soils at the landscape scale in northern boreal regions. Soil, 8(2):733-749. https://doi.org/10.5194/soil-8-733-2022
  • Anon. 1996. User Guide to the Dynamic Cone Penetrometer; Office of Minnesota Road Research. http://www.dot.state.mn.us/materials/researchdocs/User_Guide.pdf.
  • Anon. 2020. Markfuktighet - Produktbeskrivning (DTW maps - product description). Swedish forestry Agency. https://www.skogsstyrelsen.se/globalassets/sjalvservice/karttjanster/geodatatjanster/produktbeskrivningar/raster-markfuktighetskartaproduktbeskriv ning.pdf
  • Anon 2021a. Soil types 1:25 000-1:100 000. Geological survey of Sweden. https://www.sgu.se/en/products/ maps/map-viewer/jordkartvisare/soil-types-125-000-1100-000/
  • Anon. 2021b. Nya jordartsdata finns tillgängliga: (New soil type maps available). https://www.sgu.se/om-sgu/nyheter/2021/januari/nya-jordartsdata-finns-tillg angliga/
  • Anon. 2022. National Land Cover Database. https://www.naturvardsverket.se/en/services-and-per mits/maps-and-map-services/national-land-cover-database/
  • Cambi, M., Certini, G., Neri, F., Marchi, E. 2015. The impact of heavy traffic on forest soils: A review. Forest Ecology and Management, 338:124-138. https://doi.org/10.1016/j.foreco.2014.11.022
  • Friberg, G., Bergkvist, I. 2016. Så påverkar arbetsrutiner och markfuktighetskartor körskador i skogsbruket (How operational procedures and depth-to-water maps can reduce damage on soil and water and rutting in the Swedish forestry). Working paper, 904-2016. Uppsala: Skogforsk.
  • Fisher, R.F., Binkley, D. 2000. Ecology and Management of Forest Soils. New York: Wiley-Blackwell. Hansson, L., Andersson, M., Johannesson, T. 2022. Bättre brandriskbedömningar, Skogforsk rapport. Uppsala: Skogforsk.
  • Hillel, D. 1998. Environmental Soil Physics. Academic Press, San Diego, CA.
  • Hoffmann, S., Schönauer, M., Heppelmann, J., Asikainen, A., Cacot, E., Eberhard, B., Hasenauer, H., Ivanovs, J., Jaeger, D., Lazdins, A., Mohtashami, S., Moskalik, T., Nordfjell, T., Stereńczak, K., Talbot, B., Uusitalo, J., Vuillermoz, M., Astrup, R. 2022. Trafficability Prediction Using Depth-to-Water Maps: the Status of Application in Northern and Central European Forestry. Current Forestry Reports, 8:55-71.https://doi.org/10.1007/s40725-021-00153-8
  • Ilintsev, A.S., Nakvasina, E.N., Högbom, L. 2021. Methods of Protection Forest Soils during Logging Operations (Review). Lesnoy Zhurnal [Russian Forestry Journal], 5:92-116. https://doi.org/10.37482 /0536-1036-2021-5-92-116
  • Jones, M.-F. 2019. Mapping soil trafficability by way of temporal hydrology modeling and spatial wet-areas-mapping. PhD-thesis. The University of New Brunswick, Forestry and Environmental Management. Canada. 224 p.
  • Karlsson, C., Sohlenius, G., Peterson Becher, G. 2021. Handledning för jordartsgeologiska kartor och databaser över Sverige. (Tutorial for Quaternary map viewers and databases over Sweden) Nr. 2021:17, SGU, Geological survey of Sweden, Uppsala.
  • Labelle, E.R., Hansson, L., Högbom, L., Jourgholami, M., Laschi, A. 2022. Strategies to Mitigate the Effects of Soil Physical Disturbances Caused by Forest Machinery: a Comprehensive Review. Current Forestry Reports, 8(1):20-37. https://doi.org/10.1007 /s40725-021-00155-6
  • Larson, J., Lidberg, W., Ågren, A.M., Laudon, H. 2022. Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices. Hydrology and Earth System Sciences, 26(19):4837-4851. https://doi.org/10.5194/hess-26-4837-2022
  • Lidberg, W., Nilsson, M., Agren, A. 2020. Using machine learning to generate high-resolution wet area maps for planning forest management: A study in a boreal forest landscape. Ambio. https://doi.org /10.1007/s13280-019-01196-9
  • Mohtashami, S., Eliasson, L., Jansson, G., Sonesson, J. 2017. Influence of soil type, cartographic depth-to-water, road reinforcement and traffic intensity on rut formation in logging operations: a survey study in Sweden. Silva Fennica, 51(5). https://doi.org/ 10.14214/sf.2018
  • Mohtashami, S., Eliasson, L., Willén, E. 2018. Effects of soil clay content on rut formation. In: FORMEC 2018, 51st International symposium of Forestry Mechanisation, Madrid, Spain, September 25th-27th, 2018. Technical University of Madrid.
  • Mohtashami, S. 2022. GIS-based decision support systems to minimise soil impacts in logging operations. Doctoral Thesis. Acta Universitatis Agriculturae Sueciae. No. 2022:67. Faculty of Forest Sciences: Swedish University of Agricultural Sciences (SLU). https://doi.org/https://doi.org/ 10.54612/a.qq3cqbcknd
  • Murphy, P.N.C., Ogilvie, J., Connor, K, Arp, P.A. 2007. Mapping wetlands: a comparison of two different approaches for New Brunswick, Canada. Wetlands, 27(4):846-854.
  • Murphy, P.N.C., Ogilvie, J., Arp, P. 2009. Topographic modelling of soil moisture conditions: a comparison and verification of two models. European Journal of Soil Science, 60(1):94-109. https://doi.org/10.1111 /j.1365-2389.2008.01094.x
  • Naghdi, R., Solgi, A., Labelle, E.R., Nikooy, M. 2020. Combined effects of soil texture and machine operating trail gradient on changes in forest soil physical properties during ground-based skidding. Pedosphere, 30(4):508-516. https://doi.org/10.1016 /s1002-0160(17)60428-4
  • Niemi, M.T., Vastaranta, M., Vauhkonen, J., Melkas, T., Holopainen, M. 2017. Airborne LiDAR-derived elevation data in terrain trafficability mapping. Scandinavian Journal of Forest Research, 32(8):762-773.https://doi.org/10.1080/02827581.2017.1296181
  • Nilsson, T., Stendahl, J., Löfgren, O. 2015. Markförhållanden i svensk skogsmark – data från Markinventeringen 1993-2002 (Soil conditions in Swedish forest soils – data from the Swedish Forest Soil Inventory 1993-2002). Rapport 19. Institutionen för mark och miljö, Sveriges lantbruksuniversitet, Uppsala.
  • Østby-Berntsen, Ø., Fjeld, D. 2018. Mulighetsstudie lassbærere på bæresvak mark [Feasibility study of forwarders for soils of low bearing capacity], Norskog & Nibio (Norwegian Institute for bioeconomy). (3). Lilleaker.
  • Piikki, K., Söderström, M. 2017. Digital soil mapping of arable land in Sweden – Validation of performance at multiple scales. Geoderma, 352:342-350. https://doi. org/10.1016/j.geoderma.2017.10.049
  • Salmivaara, A., Launiainen, S., Perttunen, J., Nevalainen, P., Pohjankukka, J., Ala-Ilomäki, J., Sirén, M., Laurén, A., Tuominen, S., Uusitalo, J., Pahikkala, T., Heikkonen, J., Finér, L. 2020. Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology. Forestry: An International Journal of Forest Research, 93(5):662-674. https://doi.org/ 10.1093/ forestry/cpaa010
  • Schönauer, M., Hoffmann, S., Maack, J., Jansen, M. &, Jaeger, D. 2021. Comparison of Selected Terramechanical Test Procedures and Cartographic Indices to Predict Rutting Caused by Machine Traffic during a Cut-to-Length Thinning Operation. Forests, 12(2). https://doi.org/10.3390/f12020113
  • Swift, L.W., Burns, R.G. 1999. The Three Rs of Roads: Redesign, Reconstruction, and Restoration. Journal of Forestry 97(8): 40-44.
  • Toivio, J., Helmisaari, H.-S., Palviainen, M., Lindeman, H., Ala-Ilomäki, J., Sirén, M., Uusitalo, J. 2017. Impacts of timber forwarding on physical properties of forest soils in southern Finland. Forest Ecology and Management, 405:22-30. https://doi.org/10.1016 /j.foreco.2017.09.022
  • Uusitalo, J., Ala-Ilomäki, J., Lindeman, H., Toivio, J., Siren, M. 2020. Predicting rut depth induced by an 8-wheeled forwarder in fine-grained boreal forest soils. Annals of Forest Science, 77(2). https://doi.org/ 10.1007/s13595-020-00948-y
  • Vepakomma, U., Cormier, D., Hansson, L., Talbot, B. 2023. Remote Sensing at Local Scales for Operational Forestry. In: Boreal Forests in the Face of Climate Change: Sustainable Management. (74): 657-682.
  • Webster, S.L., Grau R.H., Williams, T.P. 1992. Description and application of dual mass dynamic cone penetrometer. US Army Corps of Engineers, Waterways Experiment Station, Geo technical Laboratory. Instruction Report GL-92-3. Vicksburg, MS. USA.
  • Wronski, E.B., Murphy, G. 1994. Responses of forest crops to soil compaction. In: Soil Compaction in Crop Production. Elsevier Science, 317-342.
There are 37 citations in total.

Details

Primary Language English
Subjects Forest Products Transport and Evaluation Information, Forestry Sciences (Other)
Journal Section Research Articles
Authors

Sima Mohtashami 0000-0002-3127-064X

Linnea Hansson 0000-0002-9788-1734

Lars Eliasson 0000-0002-2038-9864

Early Pub Date December 13, 2023
Publication Date December 26, 2023
Published in Issue Year 2023 Volume: 9 Issue: 2

Cite

APA Mohtashami, S., Hansson, L., & Eliasson, L. (2023). Estimating Soil Strength Using GIS-Based Maps - A case study in Sweden. European Journal of Forest Engineering, 9(2), 70-79. https://doi.org/10.33904/ejfe.1321075

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The works published in European Journal of Forest Engineering (EJFE) are licensed under a  Creative Commons Attribution-NonCommercial 4.0 International License.