Volume 12, Issue 1 And 45 (8-2025)
Journal of Spatial Analysis Environmental Hazards 2025, 12(1 And 45): 49-62 |
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shabani S, mohseni B, kornejady A, ahmadi A, faramarzi H, silakhori E. Spatial Prediction of Deforestation in Iran’s Hyrcanian Forests: Integrating Climatic, Topographic, and Anthropogenic Factors. Journal of Spatial Analysis Environmental Hazards 2025; 12 (1 and 45) : 4
URL: http://jsaeh.khu.ac.ir/article-1-3492-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3492-en.html
Saeid Shabani1 
, Behrooz Mohseni2 , Aiding Kornejady2 , Akram Ahmadi2 , Hassan Faramarzi3 , Esmaeil Silakhori4
, Behrooz Mohseni2 , Aiding Kornejady2 , Akram Ahmadi2 , Hassan Faramarzi3 , Esmaeil Silakhori4
1- AREEO , s.shabani@areeo.ac.ir
2- AREEO
3- Faculty of Natural Resources and Marine Sciences, Noor, Mazandaran
4- gorgan university
2- AREEO
3- Faculty of Natural Resources and Marine Sciences, Noor, Mazandaran
4- gorgan university
Abstract: (1514 Views)
Deforestation is one of the primary challenges and environmental threats facing forest ecosystems, including the Hyrcanian forests, and occurs under the influence of various natural and anthropogenic drivers. This study aimed to model the probability of deforestation occurrence within the Loveh forest management district located in northern Iran. The dataset comprised 104 documented deforestation points and 14 explanatory variables, derived through spatial analysis using GIS and environmental, topographic, and anthropogenic data. To assess the relationships among variables and predict the likelihood of deforestation, two statistical models were employed: logistic regression and the Generalized Additive Model (GAM). The results revealed that the GAM outperformed the logistic regression model, achieving a higher Kappa coefficient (0.84) and Area Under the Curve (AUC) value (0.956), and providing a more realistic spatial distribution of deforestation risk. The most influential variables included distance from roads, slope, wind effect, and elevation. Based on the GAM output, approximately 20% of the study area was categorized as high and very high risk. These findings underscore the pivotal role of access infrastructure, human pressure, and climatic factors in accelerating deforestation processes. The results of this study can serve as a scientific basis for prioritizing conservation interventions, reassessing road development policies, and enhancing spatial planning for sustainable forest management in northern Iran.
Article number: 4
Keywords: Probability of occurrence, Road development, Kappa coefficient, Human factors, Sustainable management
Type of Study: Research |
Subject:
Special
Received: 2025/05/2 | Accepted: 2025/06/10 | Published: 2025/08/10
Received: 2025/05/2 | Accepted: 2025/06/10 | Published: 2025/08/10
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