IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v354y2017icp1-10.html
   My bibliography  Save this article

Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance

Author

Listed:
  • Liang, Lu
  • Li, Xuecao
  • Huang, Yanbo
  • Qin, Yuchu
  • Huang, Huabing

Abstract

Cellular automata (CA) is a powerful tool for modeling the evolution of macroscopic scale phenomena as it couples time, space, and variables together while remaining in a simplified form. However, such application has remained challenging in forest insect epidemics due to the highly dynamic nature of insect behavior. Recent advances in temporal trajectory-based image analysis offer an alternative way to obtain high-frequency model calibration data. In this study, we propose an insect-CA modeling framework that integrates cellular automata, remote sensing, and Geographic Information System to understand the insect ecological processes, and tested it with measured data of mountain pine beetle (MPB) in the Rocky Mountains. The overall accuracy of the predicted MPB mortality pattern in the test years ranged from 88% to 94%, which illuminates its effectiveness in modeling forest insect dynamics. We further conducted sensitivity analysis to examine responses of model performance to various parameter settings. In our case, the ensemble random forest algorithm outperforms the traditional linear regression in constructing the suitability surface. Small neighborhood size is more effective in simulating the MPB movement behavior, indicating that short-distance is the dominating dispersal mode of MPB. The introduction of a stochastic perturbation component did not improve the model performance after testing a broad range of randomness degree, reflecting a relative compact dispersal pattern rather than isolated outbreaks. We conclude that CA with remote sensing observation is useful for landscape insect movement analyses; however, consideration of several key parameters is critical in the modeling process and should be more thoroughly investigated in future work.

Suggested Citation

  • Liang, Lu & Li, Xuecao & Huang, Yanbo & Qin, Yuchu & Huang, Huabing, 2017. "Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance," Ecological Modelling, Elsevier, vol. 354(C), pages 1-10.
  • Handle: RePEc:eee:ecomod:v:354:y:2017:i:c:p:1-10
    DOI: 10.1016/j.ecolmodel.2017.03.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380017302053
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2017.03.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. H Couclelis, 1985. "Cellular Worlds: A Framework for Modeling Micro—Macro Dynamics," Environment and Planning A, , vol. 17(5), pages 585-596, May.
    2. Yassemi, S. & Dragićević, S. & Schmidt, M., 2008. "Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour," Ecological Modelling, Elsevier, vol. 210(1), pages 71-84.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Orozco-Fuentes, S. & Griffiths, G. & Holmes, M.J. & Ettelaie, R. & Smith, J. & Baggaley, A.W. & Parker, N.G., 2019. "Early warning signals in plant disease outbreaks," Ecological Modelling, Elsevier, vol. 393(C), pages 12-19.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gong, Jian-zhou & Liu, Yan-sui & Xia, Bei-cheng & Zhao, Guan-wei, 2009. "Urban ecological security assessment and forecasting, based on a cellular automata model: A case study of Guangzhou, China," Ecological Modelling, Elsevier, vol. 220(24), pages 3612-3620.
    2. Jing Yang & Feng Shi & Yizhong Sun & Jie Zhu, 2019. "A Cellular Automata Model Constrained by Spatiotemporal Heterogeneity of the Urban Development Strategy for Simulating Land-use Change: A Case Study in Nanjing City, China," Sustainability, MDPI, vol. 11(15), pages 1-19, July.
    3. A’kif AL-FUGARA & Abdel Rahman AL-SHABEEB & Yahya AL-SHAWABKEH & Hani AL-AMOUSH & Rida AL-ADAMAT, 2018. "Simulation And Prediction Of Urban Spatial Expansion In Highly Vibrant Cities Using The Sleuth Model: A Case Study Of Amman Metropolitan, Jordan," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 13(1), pages 37-56, February.
    4. Xiaoli Hu & Xin Li & Ling Lu, 2018. "Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    5. Bosch, Martí & Chenal, Jérôme & Joost, Stéphane, 2019. "Addressing urban sprawl from the complexity sciences," MPRA Paper 93489, University Library of Munich, Germany.
    6. Jonathan Corcoran & Gary Higgs & David Rohde & Prem Chhetri, 2011. "Investigating the association between weather conditions, calendar events and socio-economic patterns with trends in fire incidence: an Australian case study," Journal of Geographical Systems, Springer, vol. 13(2), pages 193-226, June.
    7. Carlos Díaz‐Avalos & Pablo Juan, 2022. "Modeling the spatial evolution wildfires using random spread process," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
    8. Patterson, Zachary & Kryvobokov, Marko & Marchal, Fabrice & Bierlaire, Michel, 2010. "Disaggregate models with aggregate data: Two UrbanSim applications," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(2), pages 5-37.
    9. Morales, Juan Manuel & Mermoz, Mónica & Gowda, Juan Haridas & Kitzberger, Thomas, 2015. "A stochastic fire spread model for north Patagonia based on fire occurrence maps," Ecological Modelling, Elsevier, vol. 300(C), pages 73-80.
    10. Muzy, A. & Nutaro, J.J. & Zeigler, B.P. & Coquillard, P., 2008. "Modeling and simulation of fire spreading through the activity tracking paradigm," Ecological Modelling, Elsevier, vol. 219(1), pages 212-225.
    11. Liu, Xiaoping & Li, Xia & Shi, Xun & Wu, Shaokun & Liu, Tao, 2008. "Simulating complex urban development using kernel-based non-linear cellular automata," Ecological Modelling, Elsevier, vol. 211(1), pages 169-181.
    12. Susete Marques & Marco Marto & Vladimir Bushenkov & Marc McDill & JoséG. Borges, 2017. "Addressing Wildfire Risk in Forest Management Planning with Multiple Criteria Decision Making Methods," Sustainability, MDPI, vol. 9(2), pages 1-17, February.
    13. Basse, Reine Maria, 2013. "A constrained cellular automata model to simulate the potential effects of high-speed train stations on land-use dynamics in trans-border regions," Journal of Transport Geography, Elsevier, vol. 32(C), pages 23-37.
    14. Naderpour, Mohsen & Rizeei, Hossein Mojaddadi & Khakzad, Nima & Pradhan, Biswajeet, 2019. "Forest fire induced Natech risk assessment: A survey of geospatial technologies," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    15. Perez, Liliana & Dragicevic, Suzana, 2012. "Landscape-level simulation of forest insect disturbance: Coupling swarm intelligent agents with GIS-based cellular automata model," Ecological Modelling, Elsevier, vol. 231(C), pages 53-64.
    16. Wu, Daqian & Liu, Jian & Zhang, Gaosheng & Ding, Wenjuan & Wang, Wei & Wang, Renqing, 2009. "Incorporating spatial autocorrelation into cellular automata model: An application to the dynamics of Chinese tamarisk (Tamarix chinensis Lour.)," Ecological Modelling, Elsevier, vol. 220(24), pages 3490-3498.
    17. Federico Amato & Biagio Antonio Maimone & Federico Martellozzo & Gabriele Nolè & Beniamino Murgante, 2016. "The Effects of Urban Policies on the Development of Urban Areas," Sustainability, MDPI, vol. 8(4), pages 1-22, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:354:y:2017:i:c:p:1-10. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.