پیش‌بینی احتمال خشکیدگی جنگل‌های استان لرستان با استفاده از رویکرد مدل‌سازی ترکیبی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد ارزیابی و آمایش سرزمین، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، اصفهان، ایران

2 دانشیار گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان

3 دانشیار، گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، اصفهان، ایران

4 استادیار، گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، اصفهان، ایران

5 استادیار، گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، اصفهان، ایران

چکیده

شناسایی مناطق جنگلی مستعد خشکیدگی به منظور انجام اقدامات پیشگیرانه می‌تواند نقش بسزایی در مبارزه با این پدیده ایفا کند. مطالعات قبلی حاکی از کارآمدی مدل‌سازی در شناسایی این‌گونه مناطق است. از این‏رو برای شناسایی مناطق جنگلی مستعد خشکیدگی در استان لرستان از 15 مدل به صورت ترکیبی استفاده شد. مناطقی از جنگل که دارای زوال بالای 50%  بودند به عنوان متغیر وابسته و عوامل محیطی میانگین بارندگی سالیانه، میانگین دمای سالیانه، میانگین رطوبت نسبی، شاخص خشکی، شاخص خشکسالی، تبخیر و تعرق، شاخص گرد و غبار، فاصله از اراضی کشاورزی و آبراهه‌ها، شیب، جهت و NDVI به عنوان متغیرهای مستقل وارد مدل‌ها شدند. میزان AUC هر مدل در نقشه خروجی حاصل از آن ضرب و میانگین 15 مدل به عنوان مدل ترکیبی در نظر گرفته شد. نقشه احتمال خشکیدگی حاصله بیانگر افزایش احتمال خشکیدگی از قسمت‌های مرکزی جنگل‌های استان به سمت قسمت‌های جنوب و جنوب غربی بود. مدل‌های جنگل تصادفی و ماشین بردار پشتیبان با AUC برابر 1 بالاترین و مدل Bioclim با AUC برابر با 75/0 کمترین کارایی را داشتند. طبق مدل ترکیبی حدود 7/23، 5/7 و 5/19 درصد از جنگل‌های استان لرستان به ترتیب احتمال خشکیدگی کم، متوسط و زیاد را دارا هستند. عوامل اقلیمی شاخص خشکی، بارش، دما و تبخیر و تعرق به ترتیب بیشترین تأثیرگذاری را در مدل‌ها داشتند. پژوهش پیش رو علاوه بر تأکید بر کارایی مدل‌سازی در شناسایی مناطق جنگلی دارای احتمال خشکیدگی، نشان داد که استفاده ترکیبی از مدل‌ها نتایج بهتری را نسبت به استفاده مجزا از آن‌ها به بار می‌آورد.
 
 

کلیدواژه‌ها

عنوان مقاله [English]

Probabilistic prediction of forest decline in Lorestan province using a combined modeling approach

نویسندگان [English]

  • O. Ghadirian 1
  • Mahmood reza Hemami 2
  • A. Soffianian 3
  • S. Pourmanaphi 4
  • M. Malekian 4
  • M. Tarkesh 5
2 Associate Professor of environment science, Department of Natural Resources, Isfahan University of Technology
چکیده [English]

Identifying forest areas susceptible to decline in order to take preventive measures can play a significant role in inhibiting this phenomenon. Previous studies suggest the high performance of modeling to identify such areas. Hence, we used 15 models to identify forest areas prone to decline in Lorestan province. For modeling, forest areas with over 50% tree mortality were used as dependent variable, and environmental factors including annual mean rainfall, annual mean temperature, relative humidity, aridity index, evapotranspiration, dust storm index, drought index, distance to surface waters and agricultural lands, slope, aspect and NDVI as independent variables were introduced into the models. AUC of each model was multiplied by its output and the mean of these models was considered as the combined model. The forest decline risk map resulted from the combined model indicated a decline trend from central parts of the Lorestan’s forests to the south and south-western parts. The Random forest and Support vector machine were recognized as the best models with AUC value of 1 and the Bioclim as the weakest model with AUC of 0.75. According to the combined model, approximately 23.7%, 7.5%, and 19.5% of the studied forests had low, medium, and high risk of decline respectively. The climatic factors including aridity index, rainfall, temperature, and evapotranspiration were the most influencing environmental factors, respectively. The present research, in addition to emphasizing the modeling efficiency in identification of forest areas susceptible to decline, indicated that the combination of models yields better result rather than their separate use.

کلیدواژه‌ها [English]

  • Zagros forests decline
  • Climate change
  • adaptive measures
  • random forest
  • Support Vector Machine

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  • تاریخ دریافت: 11 بهمن 1395
  • تاریخ بازنگری: 12 اسفند 1396
  • تاریخ پذیرش: 21 شهریور 1396

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