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Modeling the Propagation of Forest Insect Infestation Using Machine Learning Techniques

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Computational Science and Its Applications -- ICCSA 2015 (ICCSA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9157))

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Abstract

Infestations caused by the mountain pine beetle (MPB) can be seen as complex spatio-temporal process with severe ecological impacts on the forest environment. In order to manage and prevent the insect infestation and reduce significant forest loss it is necessary to improve knowledge about the infestation process. The main objective of this research study is to design and implement a model based on decision trees (DT) mashie learning (ML) technique to forecast the spatial propagation of MPB infestation. The study is implemented in the Bulkley-Nechako region of British Columbia, Canada using data sets for the three time points 2004, 2008 and 2012. The results indicate that the derived DT can accurately characterize the relationships between the considered factors and MPB propagation. The developed DT method can be used to estimate future spread patterns of MPB infestations.

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References

  1. Bone, C., Altaweel, M.: Modeling micro-scale ecological processes and emergent patterns of mountain pine beetle epidemics. Ecological Modelling 289, 45–58 (2014)

    Article  Google Scholar 

  2. Bone, C., White, J.C., Wulder, M.A., Robertson, C., Nelson, T.A.: Impact of forest fragmentation on patterns of mountain pine beetle-caused tree mortality. Forests 4(2), 279–295 (2013)

    Article  Google Scholar 

  3. Bone, C., Dragicevic, S., Roberts, A.: A fuzzy-constrained cellular automata model of forest insect infestations. Ecological Modelling 192(1–2), 107–125 (2006)

    Article  Google Scholar 

  4. Bradley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145–1159 (1997)

    Article  Google Scholar 

  5. Chen, H., Ott, P., Wang, J., Ebata, T.: A positive response of mountain pine beetle to pine forest-clearcut edges at the landscape scale in British Columbia, Canada. Landscape Ecology 29(9), 1625–1639 (2014)

    Article  Google Scholar 

  6. Cohen, J.: A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1), 37–46 (1960)

    Article  Google Scholar 

  7. Coops, N.C., Wulder, M.A., White, J.C.: Integrating remotely sensed and ancillary data sources to characterize a mountain pine beetle infestation. Remote Sensing of Envi-ronment 105(2), 83–97 (2006)

    Article  Google Scholar 

  8. Coops, N.C., Wulder, M.A., Waring, R.H.: Modeling lodgepole and jack pine vulnerability to mountain pine beetle expansion into the western Canadian boreal forest. Forest Ecology and Management 274, 161–171 (2012)

    Article  Google Scholar 

  9. Fahse, L., Heurich, M.: Simulation and analysis of outbreaks of bark beetle infestations and their management at the stand level. Ecological Modelling 222(11), 1833–1846 (2014)

    Article  Google Scholar 

  10. Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

  11. Hall, M.A., Smith, L.A.: Practical feature subset selection for machine learning. In: Proceedings of the 21st Australasian Computer Science Conference, Acsc 1998, pp. 181–191. Springer-Verlag Singapore Pte Ltd, Singapore (1998)

    Google Scholar 

  12. Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A., Egorov, A., Chini, L., Justice, C.O., Townshend, J.R.G.: High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342(6160), 850–853 (2013)

    Article  Google Scholar 

  13. Hastie, T., Tibshirani, R., Friedman, J., Franklin, J.: The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer 27(2), 83–85 (2005)

    Google Scholar 

  14. Karvemo, S., Van Boeckel, T.P., Gilbert, M., Gregoire, J.C., Schroeder, M.: Large-scale risk mapping of an eruptive bark beetle - Importance of forest susceptibility and beetle pressure. Forest Ecology and Management 318, 158–166 (2014)

    Article  Google Scholar 

  15. Kim, Y., Street, W.N., Menczer, F.: Feature selection in data mining. In: Data mining: Opportunities and Challenges. Idea Group Publishing, pp. 80–105 (2003)

    Google Scholar 

  16. Kurz, W.A., Dymond, C.C., Stinson, G., Rampley, G.J., Neilson, E.T., Carroll, A.L., Ebata, T., Safranyik, L.: Mountain pine beetle and forest carbon feedback to climate change. Nature 452(7190), 987–990 (2008)

    Article  Google Scholar 

  17. Lamers, P., Junginger, M., Dymond, C.C., Faaij, A.: Damaged forests provide an opportunity to mitigate climate change. Global Change Biology Bioenergy 6(1), 44–60 (2014)

    Article  Google Scholar 

  18. Landis, J.R., Koch, G.G.: The Measurement of Observer Agreement for Categorical Data. Biometrics 33(1), 159–174 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  19. Latifi, H., Fassnacht, F.E., Schumann, B., Dech, S.: Object-based extraction of bark beetle (Ips typographus L.) infestations using multi-date LANDSAT and SPOT satellite imagery. Progress in Physical Geography 38(6), 755–785 (2014)

    Article  Google Scholar 

  20. Liang, L., Chen, Y.L., Hawbaker, T.J., Zhu, Z.L., Gong, P.: Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data. Remote Sensing 6(6), 5696–5716 (2014)

    Article  Google Scholar 

  21. Liu, K., Liu, L., Liu, H.X., Li, X., Wang, S.G.: Exploring the effects of biophysical parameters on the spatial pattern of rare cold damage to mangrove forests. Remote Sensing of Environment 150, 20–33 (2014)

    Article  Google Scholar 

  22. Marx, A.: Detection and Classification of Bark Beetle Infestation in Pure Norway Spruce Stands with Multi-temporal RapidEye Imagery and Data Mining Techniques. Photogrametrie Fernerkundung Geoinformation 4, 243–252 (2010)

    Article  Google Scholar 

  23. N.R.C.: The threat of mountain pine beetle to Canada’s boreal forest. Government of Canada, Natural Resources Canada (2015)

    Google Scholar 

  24. Negron, J.F., Allen, K., Cook, B., Withrow, J.R.: Susceptibility of ponderosa pine, Pinus ponderosa (Dougl. ex Laws.), to mountain pine beetle, Dendroctonus ponderosae Hopkins, attack in uneven-aged stands in the Black Hills of South Dakota and Wyoming USA. Forest Ecology and Management 254(2), 327–334 (2008)

    Article  Google Scholar 

  25. Perez, L., Dragicevic, S.: Modeling mountain pine beetle infestation with an agent-based approach at two spatial scales. Environmental Modelling & Software 25(2), 223–236 (2010)

    Article  Google Scholar 

  26. Perez, L., Dragicevic, S.: Landscape-level simulation of forest insect disturbance: Coupling swarm intelligent agents with GIS-based cellular automata model. Ecological Modelling 231, 53–64 (2012)

    Article  Google Scholar 

  27. Pouliot, D., Latifovic, R., Fernandes, R., Olthof, I.: Evaluation of annual forest dis-turbance monitoring using a static decision tree approach and 250 m MODIS data. Remote Sensing of Environment 113(8), 1749–1759 (2009)

    Article  Google Scholar 

  28. Preisler, H.K., Hicke, J.A., Ager, A.A., Hayes, J.L.: Climate and weather influences on spatial temporal patterns of mountain pine beetle populations in Washington and Oregon. Ecology 93(11), 2421–2434 (2012)

    Article  Google Scholar 

  29. Pukkala, T., Moykkynen, T., Robinet, C.: Comparison of the potential spread of pinewood nematode (Bursaphelenchus xylophilus) in Finland and Iberia simulated with a cellular automaton model. Forest Pathology 44(5), 341–352 (2014)

    Article  Google Scholar 

  30. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers (1993)

    Google Scholar 

  31. Robertson, C., Wulder, M.A., Nelson, T.A., White, J.C.: Risk rating for mountain pine beetle infestation of lodgepole pine forests over large areas with ordinal regression modelling. Forest Ecology and Management 256(5), 900–912 (2008)

    Article  Google Scholar 

  32. Robinet, C., Van Opstal, N., Baker, R., Roques, A.: Applying a spread model to identify the entry points from which the pine wood nematode, the vector of pine wilt disease, would spread most rapidly across Europe. Biological Invasions 13(12), 2981–2995 (2011)

    Article  Google Scholar 

  33. Safranyik, L., Carroll, A.L., Régnière, J., Langor, D.W., Riel, W.G., Shore, T.L., Peter, B., Cooke, B.J., Nealis, V.G., Taylor, S.W.: Potential for range expansion of mountain pine beetle into the boreal forest of North America. The Canadian Entomologist 142(05), 415–442 (2010)

    Article  Google Scholar 

  34. British Columbia Ministry of Forests: Forest Health Aerial Overview Survey Standards for British Columbia: The BC Ministry of Forests Adaptation of the Canadian Forest Service’s FHN Report 97–1 “Overview Aerial Survey Standards for British Columbia and the Yukon”. Resources Inventory Committee, Victoria (2000)

    Google Scholar 

  35. Shannon, C.E.: A Mathematical Theory of Communication. Bell System Technical Journal 27(3), 379–423 (1948)

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Spatial Analysis and Modeling Laboratory, Department of Geography, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada

    Mileva Samardžić-Petrović & Suzana Dragićević

Authors
  1. Mileva Samardžić-Petrović
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  2. Suzana Dragićević
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Corresponding author

Correspondence to Mileva Samardžić-Petrović .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Canaanland, Nigeria

    Sanjay Misra

  4. University of Calgary, Calgary, Alberta, Canada

    Marina L. Gavrilova

  5. University of Minho, Braga, Portugal

    Ana Maria Alves Coutinho Rocha

  6. Polytechnic University, Bari, Italy

    Carmelo Torre

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

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Samardžić-Petrović, M., Dragićević, S. (2015). Modeling the Propagation of Forest Insect Infestation Using Machine Learning Techniques. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science(), vol 9157. Springer, Cham. https://doi.org/10.1007/978-3-319-21470-2_47

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  • DOI: https://doi.org/10.1007/978-3-319-21470-2_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21469-6

  • Online ISBN: 978-3-319-21470-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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