IDEAS home Printed from https://ideas.repec.org/a/caa/jnljfs/v61y2015i5id78-2014-jfs.html
   My bibliography  Save this article

Deforestation modelling using logistic regression and GIS

Author

Listed:
  • M. Pir Bavaghar

    (Faculty of Natural Resources, Center for Research & Development of Northern Zagros Forests, University of Kurdistan, Sanandaj, Iran)

Abstract

A methodology has been used by means of which modellers and planners can quantify the certainty in predicting the location of deforestation. Geographic information system and logistic regression analyses were employed to predict the spatial distribution of deforestation and detects factors influencing forest degradation of Hyrcanian forests of western Gilan, Iran. The logistic regression model proposed that deforestation is a function of slope, distance to roads and residential areas. The coefficients for the explanatory variables indicated that the probability of deforestation is negatively related to slope, distance from roads and residential areas. Although the distance factor was found to be a contributor to deforestation, its effect is lower than that of slope. The correlates of deforestation may change over time, and so the spatial model should be periodically updated to reflect these changes. Like in any model, the quality may be improved by introducing the new variables that may contribute to explaining the spatial distribution of deforestation.

Suggested Citation

  • M. Pir Bavaghar, 2015. "Deforestation modelling using logistic regression and GIS," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 61(5), pages 193-199.
  • Handle: RePEc:caa:jnljfs:v:61:y:2015:i:5:id:78-2014-jfs
    DOI: 10.17221/78/2014-JFS
    as

    Download full text from publisher

    File URL: http://jfs.agriculturejournals.cz/doi/10.17221/78/2014-JFS.html
    Download Restriction: free of charge

    File URL: http://jfs.agriculturejournals.cz/doi/10.17221/78/2014-JFS.pdf
    Download Restriction: free of charge

    File URL: https://libkey.io/10.17221/78/2014-JFS?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. Chris Chatfield, 1995. "Model Uncertainty, Data Mining and Statistical Inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(3), pages 419-444, May.
    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. C.C. Draghici & D. Peptenatu & A.G. Simion & R.D. Pintilii & D.C. Diaconu & C. Teodorescu & R.M. Papuc & A.M. Grigore & C.R. Dobrea, 2016. "Assessing economic pressure on the forest fund of Maramureș County - Romania," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 62(4), pages 175-185.
    2. Fansi Lang & Yutian Liang & Shangqian Li & Zhaofeng Cheng & Guanfeng Li & Zijing Guo, 2024. "Spatio-Temporal Patterns of Land Use and Cover Change in the Lancang–Mekong River Basin during 2000–2020," Land, MDPI, vol. 13(3), pages 1-20, February.

    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. Claudia García-García & Catalina B. García-García & Román Salmerón, 2021. "Confronting collinearity in environmental regression models: evidence from world data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 895-926, September.
    2. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    3. Sai Ding & John Knight, 2011. "Why has China Grown So Fast? The Role of Physical and Human Capital Formation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(2), pages 141-174, April.
    4. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    5. Buchholz, Anika & Hollander, Norbert & Sauerbrei, Willi, 2008. "On properties of predictors derived with a two-step bootstrap model averaging approach--A simulation study in the linear regression model," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2778-2793, January.
    6. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    7. Cassandra Crowe & Belinda Middleweek & Laura Ryan & Alicia Vidler & Bronwen Whiting, 2024. "The role of gender in promotion rates in the Australian Finance Industry," Papers 2409.14384, arXiv.org.
    8. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    9. Aris Spanos, 2009. "Statistical Misspecification and the Reliability of Inference: The Simple T-Test in the Presence of Markov Dependence," Korean Economic Review, Korean Economic Association, vol. 25, pages 165-213.
    10. W. Robert Reed, 2009. "The Determinants Of U.S. State Economic Growth: A Less Extreme Bounds Analysis," Economic Inquiry, Western Economic Association International, vol. 47(4), pages 685-700, October.
    11. Prost, Lorène & Makowski, David & Jeuffroy, Marie-Hélène, 2008. "Comparison of stepwise selection and Bayesian model averaging for yield gap analysis," Ecological Modelling, Elsevier, vol. 219(1), pages 66-76.
    12. Coleman, Stephen, 2005. "Testing Theories with Qualitative and Quantitative Predictions," MPRA Paper 105171, University Library of Munich, Germany.
    13. Cairns, Andrew J. G., 2000. "A discussion of parameter and model uncertainty in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 27(3), pages 313-330, December.
    14. Johannes Ziesmer, 2024. "Identifying key sectors of sustainable development: A Bayesian framework estimating policy‐impacts in a general equilibrium," Agribusiness, John Wiley & Sons, Ltd., vol. 40(2), pages 458-483, April.
    15. Lee, Yun Shin & Scholtes, Stefan, 2014. "Empirical prediction intervals revisited," International Journal of Forecasting, Elsevier, vol. 30(2), pages 217-234.
    16. Johan Verbeeck & Martin Geroldinger & Konstantin Thiel & Andrew Craig Hooker & Sebastian Ueckert & Mats Karlsson & Arne Cornelius Bathke & Johann Wolfgang Bauer & Geert Molenberghs & Georg Zimmermann, 2023. "How to analyze continuous and discrete repeated measures in small‐sample cross‐over trials?," Biometrics, The International Biometric Society, vol. 79(4), pages 3998-4011, December.
    17. Ewout W. Steyerberg, 2005. "Local Applicability of Clinical and Model-Based Probability Estimates," Medical Decision Making, , vol. 25(6), pages 678-680, November.
    18. Teplova, Tamara & Mikova, Evgeniya & Nazarov, Nikolai, 2017. "Stop losses momentum strategy: From profit maximization to risk control under White’s Bootstrap Reality Check," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 240-258.
    19. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    20. Song, Xiaodong & Bryan, Brett A. & Paul, Keryn I. & Zhao, Gang, 2012. "Variance-based sensitivity analysis of a forest growth model," Ecological Modelling, Elsevier, vol. 247(C), pages 135-143.

    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:caa:jnljfs:v:61:y:2015:i:5:id:78-2014-jfs. 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: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    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.