Prediction of spatial land use changes based on LCM in a GIS environment (A case study of Sarabeleh (Ilam), Iran

Document Type : Research Paper

Author

Assisstant Professor, Geography Department, Human Sciences College, Golestan University, Gorgan, Iran

Abstract

   This study aims at monitoring and predicting land use changes using LCM module in the Sarabeleh area. Land sat images of year 1988, 2001 and 2011 were employed to produce digital land use maps. The images were classified into five classes including forest, rangeland, barren land, agriculture and residential areas. LCM module in Idrisi GIS software was used to analyze the land use changes and predict the land uses status in 2011, based on artificial neural network (ANN) and Markov Chain analysis. ANN was trained with various influencing factors include distance from road, distance from residential areas, distance from forest edge, fragmentation index, elevation, slope and aspect. The results indicated that 14691 hectares of the forest cover have been degraded during the period 1988-2011. Furthermore, the barren land areas have been increased 9874 ha in comparison to initial situation. The results of transition power modeling using artificial neural network showed high accuracy in most of the sub modules (60-86%). The total error in modeling for the year 1390 image obtained as12/84% which represents conformity between the predicting of mode land ground truth image and acceptability of the model. Also, the result shows that forest area will decrease in the year 1400 compare to 1390 while, barren lands increase. لندست 7 سال 1380 و TM لندست 7 سال 1390 تجزیه­وتحلیل شد. تصاویر هر سه مقطع زمانی به پنج طبقه جنگل، مرتع، اراضی بایر، اراضی کشاورزی و مناطق مسکونی طبقه‌بندی شد. پیش‌بینی وضعیت کاربری اراضی برای سال 1390، با استفاده از نقشه‌های کاربری سال‌های 1367 و 1380 و به کمک مدل LCM و بر پایه شبکه‌های عصبی مصنوعی و تحلیل زنجیره مارکوف انجام گردید. به این منظور، از متغیر‌های مکانی فاصله از جاده‌، فاصله از مناطق مسکونی، فاصله از حاشیه جنگل، شاخص گسستگی جنگل، ارتفاع، شیب و جهت به‌عنوان عوامل مؤثر بر تغییرات در شبکه عصبی مصنوعی استفاده شد. بنا بر نتایج، در طول دوره 1367-1390، 14691 هکتار جنگل تخریب شده است. همچنین اراضی بایر به مقدار 9874 هکتار نسبت به سطح اولیه خود توسعه یافته است. نتایج مدل‌سازی نیروی انتقال با استفاده از شبکه عصبی مصنوعی در بیشتر زیر مدل­ها صحت بالایی را (60 تا 86 درصد) نشان داد. خطای کل در مدل­سازی برای سال1390، 84/12% به‎دست آمد که نشان‌دهنده انطباق زیاد تصویر پیش‌بینی شده مدل با تصویر واقعیت زمینی و قابل قبول بودن مدل است. همچنین، نتایج پیش­بینی نشان داد که مساحت اراضی جنگلی در سال 1400 در مقایسه با 1390 کاهش و اراضی بایر افزایش خواهند یافت.
 

Keywords

References

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History

  • Receive Date: 08 April 2014
  • Revise Date: 05 January 2015
  • Accept Date: 11 July 2014

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