شبیه‌سازی حالت‌های ممکن کاربری زمین‌ در آبخیز تجن در سال 1420

نوع مقاله : پژوهشی

نویسندگان

1 دانشجوی دکترای علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی و علوم دریایی، دانشگاه تربیت مدرس

2 دانشیار گروه علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی و علوم دریایی، دانشگاه تربیت مدرس

3 دانشیار گروه مدیریت جهانگردی، دانشکده علوم انسانی و اجتماعی، دانشگاه مازندران

چکیده

این پژوهش با هدف شبیه­سازی و پیش­بینی‌کردن تغییر کاربری و پوشش زمین‌ با کاربرد مدل تغییر سرزمین (LCM) بر مبنای دو حالت‌ ممکن مدیریتی تداوم و افزایش کشاورزی در آبخیز تجن استان مازندران به انجام رسید. تصویرهای ماهواره‌ی لندست و سنجنده­های تی‌ام و اوال‌آی در 1370، 1389 و 1398 تجزیه‌وتحلیل شد. کاربری­ها در هر سه مقطع زمانی به پنج طبقه‌ی مسکونی، کشاورزی، جنگل، باغ و مرتع طبقه­بندی شد. پیش­بینی وضعیت کاربری زمین برای 1398 با نقشه‌ی کاربری­ سال‎های 1370 و 1389 در نرم­افزار ال‌سی‌ام انجام شد. این نرم­افزار شبکه‌ی عصبی پرسپترون چندلایه را برای ساخت زیرمدل­های دگرگونی، تهیه‌ی نقشه‌ی احتمال دگرگونی، و پیش­بینی تغییر کاربری زمین‌ به‌کار می‌برد. متغیرهای مکانی فاصله از جاده، فاصله از رودخانه، شیب، ارتفاع، فاصله از روستا، و فاصله از زمین کشاورزی در جایگاه عامل‌های موثر بر تغییر در شبکه‌ی عصبی به‌کار برده شد. برای اعتبارسنجی نرم‎افزار نقشه‌ی کاربری زمین 1398 به‌کار برده شد، و ضریب کاپا و ROC به‌ترتیب 0/84 و 0/89 به‌دست آمد. نتیجه‌های شبیه­سازی بر مبنای حالت‌ ممکن تداوم نشان­دهنده‌ی 5/74 % کاهش مساحت زمین‌های مرتعی، و 9/17 % افزایش در مساحت زمین‌های باغی در 20 سال آینده است، در حالی­که در حالت‌ ممکن دوم مساحت زمین‌های کشاورزی 8/61% افزایش، و زمین‌های مرتعی 5/59 % کاهش نشان می­دهد. در نتیجه، زمین‌های مرتعی و جنگلی در هر دو حالت‌ ممکن رو به کاهش، و زمین‌های کشاورزی و مسکونی رو به افزایش است.

کلیدواژه‌ها


عنوان مقاله [English]

The Consequences of Following Two Different Land Use Change Scenarios in 2040 in the Tajan Watershed

نویسندگان [English]

  • Mohammad Taghi Avand 1
  • Hamid Reza Moradi 2
  • Mehdi Ramazanzadeh Lasbuie 3
1 Ph.D. Student Watershed Management Science and Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University
2 Associate Professor of Watershed Management Science and Engineering, Faculty of Natural Resources and Marine Sciences, Tarbiat Modarres University
3 Associate Professor, Department of Tourism Management, Faculty of Humanities and Social Sciences, Mazandaran University
چکیده [English]

The purpose of this research was to model and predict land use/cover changes using the LCM model based on two management scenarios, namely the sustainability scenario and the agricultural increase scenario in the Tajan Watershed, the Province of Mazandaran. Landsat satellite images and the data collected by the TM and OLI sensors were analyzed for 1991, 2010 and 2019. Images from the three-time periods were categorized into five categories: residential, farmland agricultural, forest, orchard, and rangeland. Land use forecasting for 2019 was performed using the 1991 and 2010 land use maps in the LCM model. The LCM model uses the perceptron multilayer neural network to construct transmission sub-models, map the probability of transmission, and ultimately predict the land use changes. Spatial variables such as distance from a road, distance from a river, slope, altitude, distance from a village, and distance from farmland were used as the influencing factors on the neural network changes. The land use map of 2019 was used to validate the model, which resulted in the Kappa and ROC coefficients of 0.84 and 0.89, respectively. The modelling results based on the sustainability scenario show a 5.74 percent decrease in the rangeland area and a 9.17 percent growth in the orchard area over the next 20 years, while the second scenario showed an increase of 8.61% in farmlands and a decrease of 5.59% in rangeland areas. Therefore, both rangeland and forest areas are declining and farmland and residential areas are increasing. A better land use management will require conservation scenarios in different parts of the watershed.

کلیدواژه‌ها [English]

  • Artificial neural network
  • land use
  • LCM method
  • satellite images
  • Tajan Watershed
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