Deforestation modelling using logistic regression and GIS - M. Pir Bavaghar (Journal of Forest Science, 2015)
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.