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Deforestation modelling using logistic regression and GIS

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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.
    2. 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.

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