An analysis of anthropogenic deforestation using logistic regression and GIS

Ludeke, Aaron Kim; Maggio, Robert C.; Reid, Leslie M.

doi:10.1016/s0301-4797(05)80038-6

Cited by 155 publications

(123 citation statements)

References 4 publications

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“…It was found that 83% of deforestation occurred within a 2-km distance from roads. Similarly, Ludeke et al (1990) also found that deforestation decreased rapidly with a distance from roads and there was a steep drop in the percentage area deforested beyond 2 km from access routes. Wilson et al (2005) also mentioned that 90% of the deforested area is within 2.5 kilometres from roads.…”

Section: Discussionmentioning

confidence: 75%

“…To investigate the correlations that exist between a dichotomous dependent variable (deforested/non-deforested) and independent variables which cannot be assumed to satisfy the required assumptions of discriminant analysis (normality assumption), a logistic regression model has been used (Ludeke et al 1990). In logistic regression, a dependent variable transforms into a logit variable (the natural log of the odds of the dependent variable occurring or not), and then based on the independent variables, maximum likelihood estimation is applied to estimate the probability of occurrence of a certain event (deforestation) (Rueda 2010).…”

Section: Methodsmentioning

confidence: 99%

“…In this method, R 2 is called pseudo R 2 because it is not computed in the same way as the regular regression R 2 (Ludeke et al 1990). This R square indicates the fitness of the model, but does not give as much information as the regular regression R 2 about the scatter of the data around the fitted line (Ludeke et al 1990). The value of R 2 is low in logistic regression models because of the binary response variable (Bio et al 1998).…”

Section: Methodsmentioning

confidence: 99%

“…Knowledge of the rate and extent of deforestation and its driving factors is necessary for environmental planners and managers (Ludeke et al 1990). To understand the deforestation process, determination and knowledge of the relationship between natural and manmade variables and deforestation are an essential step (Linkie et al 2004).…”

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confidence: 99%

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Deforestation modelling using logistic regression and GIS

Bavaghar¹

2015

J. For. Sci.

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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.

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Section: Discussionmentioning

confidence: 75%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Deforestation modelling using logistic regression and GIS

Bavaghar¹

2015

J. For. Sci.

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“…In Indonesia, a study at Pelepat -a sub-watershed of Batanghari watershed indicated that the important predictors influencing the change of land use pattern are distances of land to road, river, settlements, and logging area, slope, soil organic matter, population density, and profitability (net present value) of agroforestry (Murdiyarso et al, 2000). Other studies indicate that population density is strongly correlated with deforestation rate, with the correlation increasing with the number of rural landless families (Ludeke et al, 1990;Reis andMargulis (op cit), 1991, Adger andBrown, 1994;Harrington, 1996;Sisk et al, 1994;Kaimowitz, 1997;Ochoa-Gaona and Gonzales-Espinosa, 2000). It was also found that agricultural prices, regional per capita income, access to markets, better quality of soil and flatter lands were in general associated with higher deforestation rates (Adger and Brown, 1994).…”

Section: Methodsmentioning

confidence: 97%

Assessment of carbon leakage in multiple carbon-sink projects: a case study in Jambi Province, Indonesia

Boer

Wasrin

Hendri

et al. 2007

Mitig Adapt Strat Glob Change

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Abstract. Rehabilitation of degraded forest land through implementation of carbon sink projects can increase terrestrial carbon stock. However, carbon emissions outside the project boundary, which is commonly referred to as leakage, may reduce or negate the sequestration benefits. This study assessed leakage from carbon sink projects that could potentially be implemented in the study area comprised of eleven sub-districts in the Batanghari District, Jambi Province, Sumatra, Indonesia.The study estimates the probability of a given land use/cover being converted into other uses/cover, by applying a logit model. The predictor variables were: proximity to the center of the land use area, distance to transportation channel (road or river), area of agricultural land, unemployment (number of job seekers), job opportunities, population density and income. Leakage was estimated by analyzing with and without carbon sink projects scenarios. Most of the predictors were estimated as being significant in their contribution to land use cover change.The results of the analysis show that leakage in the study area can be large enough to more than offset the project's carbon sequestration benefits during the period 2002-2012. However, leakage results are very sensitive to changes of carbon density of the land uses in the study area. By reducing C-density of lowland and hill forest by about 10% for the baseline scenario, the leakage becomes positive. Further data collection and refinement is therefore required. Nevertheless, this study has demonstrated that regional analysis is a useful approach to assess leakage.

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