Forest Degradation using GIS and Logistic Regression (Case Study: Forests of Sardasht)

Document Type : Research Article

Authors

• Saleh Arekhi ¹
• Abdolrahman Mahmoudian ²
• Somia Emadaddian ¹

¹ Assistant Professor in Geography, Department of Geography and GIS, Human Sciences College, University of Golestan, Gorgan, Iran

² MSc Student in Geography and Environmental Hazards, Department of Geography and GIS, Human Sciences College, University of Golestan, Gorgan, Iran

10.22067/geoeh.2021.70743.1070

Abstract

Spatial-temporal pattern modeling of forest cover changes provides valuable information for better understanding the change process and determining the effective factors of areas under change. In this study, it was tried to identify the factors affecting the reduction and destruction of forests in western Iran using the capabilities of modern technologies including remote sensing, and then implementing in the form of a suitable computational model as a mathematical model based on the behavior of nature. In order to investigate the reduction of forest cover in Sardasht city of West Azarbaijan province, satellite images of MSS, ETM⁺ and OLI for the years of 1977, 2000 and 2018 were used. The above images were  preprocessed, processed, and classified into two categories of forest and non-forest. The logistic regression method was used to study the relationship between forest cover reduction and physiographic and human factors. In order to obtain the lands suitability map, a logistic regression relationship was established between the forest cover reduction map from 1977 to 2000 and 2000 to 2018 and also factors affecting it. Finally, a simple spatial model was proposed that was able to predict the spatial distribution of forest degradation using logistic regression. The results showed that over the 41 years, about 33721 hectares of forests of Sardasht city have been damaged. According to the results, it was determined that from topographic variables, parameters of distance from the road and distance from the village had the most impact on the forest degradation rate.

Graphical Abstract

Keywords

• Remote Sensing
• Modeling
• Degradation of the Forest
• West Azarbaijan

20.1001.1.23221682.1400.10.4.3.4

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