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

Document Type : Research Article


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


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

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


باقری، رضا؛ شتایی، شعبان؛ 1389. مدل­سازی کاهش گستره جنگل با استفاده از رگرسیون لجستیک (مطالعه موردی: حوزه آبخیز چهل چای استان گلستان). مجله جنگل ایران. 2(3): ۲۴۳-۲۵۲.
پیر­باوقار، مهتاب؛ 1383. بررسی تغییرات گستره جنگل در ارتباط با عوامل توپوگرافی و مناطق انسان‌ساخت (مطالعه موردی: جنگل­های شرق استان گیلان). پایان­نامه کارشناسی ارشد جنگلداری. دانشگاه تهران. 136ص.
ثاقب­طالبی، خسرو؛ ساجدی، تکتم؛ یزدیان، فرشاد؛ 1383. نگاهی به جنگل‌های ایران. انتشارات موسسه تحقیقات جنگل‌ها و مراتع. 56 ص.
جزیره­ای، محمدحسین؛ ابراهیمی­رستاقی، مرتضی؛ 1382. جنگل­شناسی زاگرس. انتشارات دانشگاه تهران. 560 صفحه
رفیعیان، امید؛ درویش­صفت، علی­اصغر؛ نمیرانیان، منوچهر؛ 1385. تعیین تغییرات گستره جنگل­های شمال کشور بین سال­های 73 تا 80 با استفاده از تصاویر سنجنده ETM+. مجله علوم و فنون کشاورزی و منابع طبیعی. 10(3): 277- 286.
شادمانی، سعدالله؛ قدس­خواه ­دریایی، مهرداد؛ قجر، اسماعیل؛ حیدری­صفری کوچی، ابوذر؛ 1399. مدل­سازی درجات تخریب جنگل‌های حوضه 12 ماسال استان گیلان با استفاده از رگرسیون لجستیک. مجله محیط‌زیست طبیعی. منابع طبیعی ایران. دوره 73. شماره 1. 1-49.
مسگری، سعید؛ 1381. بررسی تغییرات سطوح جنگل­ها با استفاده از GIS و سنجش‌ازدور. تهران. طرح پژوهشی دانشکده فنی. دانشگاه خواجه‌نصیرالدین طوسی.
مهدوی، علی؛ رنگین، سمیه؛ مهدی­زاده، حسین؛ میرزایی­زاده، وحید؛ 1397. مد­­ل­سازی تخریب جنگل‌های زاگرس با استفاده از رگرسیون لجستیک (مطالعه موردی: جنگل‌های چرداول استان ایلام). مجله جغرافیا و پایداری محیط. 8(27). 1-13.
Adhikari S, Fik T, Dwivedi P., 2017. Proximate causes of land use and land cover change in Bannerghatta national park: a spatial statistical model, pp 1-23
Chavez PSJ., 1996. Image-Based Atmospheric Corrections -Revisited and Improved. Photogrammetric Engineering and Remote Sensing (PE&RS). 62: 1025–1036.
Cheng J, Masser I., 2003. Urban growth pattern modeling: a case study of Wuhan city, PR China. Landscape and urban planning, 62, 199-217.
Clark WAV, Peter LH., 1986. Statistical Methods for Geographers. 1st Edition. Chichester: Wiley.
Dendoncker N, Patrick B, Mark R., 2006. A Statistical Method to Downscale Aggregated Land Use Data and Scenarios. Journal of Land Use Science 1(2–4): 63–82.
Eastman JR., 2012. Idrisi Production, Clark Labs-Clark University IDRISI Selva Tutorial.
Ghajar I, Najafi A, Torabi SA, Boston K., 2012. Rock share estimation in forest road excavation using the Ordinal Logistic Regression (OLR) and the Analytical Hierarchy Process (AHP). Iranian Journal of Forest and Poplar Research 20(2): 313-323. (In Persian).
Gruenberg WD, Curtin P, Shaw W., 2000. Deforestation Risk for the Maya Biosphere Reserve, Guatemala. School of Renewable Natural Resources, The University of Arizona, Tucson, Arizona, USA, 266 pp.
Lee S, Sampath T., 2006. Landslide Susceptibility Mapping in the Damrei Romel Area, Cambodia Using Frequency Ratio and Logistic Regression Models. Environmental Geology, volume 50, pages 847–855.
Lin YP, Hong, NM, Wu PJ, Verburg PH., 2007. Impacts of land use change scenarios on hydrology and land use patterns in the Wu-Tu watershed in northern Taiwan. Landscape and Urban Planning, 80, 111-126.
Matthew L, Robert J, Smith RJ, Nigel LW., 2004. Mapping and predicting deforestation patterns in the lowlands of Sumatra. Biodiversity and Conservation, 13: 1809–1818.
Millington J, Perry DA, George L, Romero-Calcerrada R., 2007. Regression techniques for examining land use/cover change: A case study of a Mediterranean landscape. Ecosystems, 10, 562-578.
Miriam SW, Taylor VS., 2010. Modeling social and land-use/land-cover change data to assess drivers of smallholder deforestation in Belize. Applied Geography 30: 329–342.
Phompila C, Lewis M, Ostendorf B, Clarke K., 2017. Forest cover changes in Lao tropical forests: physical and socio-economic factors are the most important drivers. Land, 6(23), 1-14.
Pirbavaghar M., 2015. Deforestation modelling using logistic regression and GIS. Journal of Forest Science, 61(5), 193-199. (In Persian).
Salman Mahiny A, Turner BJ., 2003. Modeling past vegetation change through remote sensing and GIS: a comparison of neural networks and logistic regression methods. School of Resources, Environment and Society, the Australian National University, Canberra 0200, Australia.
Schneider LC, Pontius JRG., 2001. Modeling land-use change in the Ipswich watershed, Massachusetts, USA. Agriculture Ecosystems & Environment, 85, 83-94.
Sumon N, Mizoue N, Zawhtum N, Kajisa T, Yoshida S., 2012. Factors affecting deforestation and forest degradation in selectively logged production forest: A case study in Myanmar. Article in Forest Ecology and Management, 267, 190-198.
Vu QM, Le QB, Frossard E, Vlek PLG., 2014. Socio-economic and biophysical determinants of land degradation in Vietnam: An integrated causal analysis at the national level. Land Use Policy, 36, 605-617.
  • Receive Date: 01 June 2021
  • Revise Date: 23 July 2021
  • Accept Date: 10 August 2021
  • First Publish Date: 10 August 2021