ارزیابی روش شبکۀ عصبی مصنوعی در پهنه‌بندی مکانی پتانسیل رویشگاه گونه‌ها (مطالعۀ موردی: مراتع سیاه بیشه، مازندران)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار، دانشگاه علوم کشاورزی و منابع طبیعی ساری

2 دانش آموختۀ کارشناسی ارشد مرتعداری، دانشگاه علوم کشاورزی و منابع طبیعی ساری

3 استاد، دانشگاه علوم کشاورزی و منابع طبیعی ساری

چکیده

هدف از تحقیق حاضر، پیش­بینی پراکنش مکانی گونه­های Festuca Ovina و ­Bromus briziformis در مراتع سیاه بیشه با استفاده از روش شبکۀعصبی مصنوعی است. نمونه­برداری از پوشش گیاهی به روش طبقه­بندی تصادفی در 29 واحد ­همگن انجام شد. 290 پلات 1 مترمربعی در منطقه مستقر و درصد پوشش تاجی گیاهان ثبت گردید. در هر واحد، 3 نمونه خاک از عمق 30-0 برداشت شد. در این مطالعه، داده­های محیطی 20 عامل (شیب، جهت شیب، ارتفاع از سطح دریا، فاصله از جاده، فاصله از رودخانه، فاصله از دامداری، همباران، سنگ شناسی، سیلت، رس، شن، رطوبت، کربن، مادۀآلی، اسیدیته خاک، هدایت الکتریکی، آهک، ازت، فسفر و پتاسیم) به عنوان متغیر مستقل و داده­های مربوط به حضور گونه­های گیاهی Festuca Ovina و ­Bromus briziformis به عنوان متغیر وابسته استفاده گردید. لایه­های اطلاعاتی هر کدام از این عوامل در نرم افزار Arc GIS تهیه و با استفاده از روش نسبت فراوانی هر کدام از این عوامل کلاسه­بندی شدند. نتایج حاصله نشان داد که مهم­ترین متغیرهای محیطی اثرگذار در پراکنش گونه­های مطالعه شده، خصوصیات ارتفاع، بافت خاک و عناصر غذایی بودند.سپس به ترتیب 70 و 30 درصد داده­ها جهت آموزش و آزمون شبکه استفاده شد. در این تحقیق ساختار شبکۀعصبی­ مصنوعی با ساختار 20 نرون در لایۀ ورودی و لایۀ پنهان و یک نرون در لایۀ خروجی، مقایر MSE برای فستوکا 75/0و بروموس 72/0 محاسبه شد. سپس نقشه­های پهنه­بندی گونه­های گیاهی با 4 پهنۀ عدم حضور، حضورکم، متوسط و زیاد تهیه شد. نقشۀ پهنه­بندی حاصل با منحنی ROC و ضریب کاپا ارزیابی شدند که صحت آن­ها با روش منحنی  ROC برابر 10/97، 10/84 درصد و با ضریب کاپا برابر 78/0 و 66/0 به ترتیب برای گونۀ Festuca ovina،  و گونۀ Bromus briziformis بودند که نشان دهندة ارزیابی خوب مدل است.

کلیدواژه‌ها

عنوان مقاله [English]

Evaluation artificial neural network method for spatial mapping of species potential habitat (Case study: Rangeland Siah‌bisheh, Mazandaran)

نویسندگان [English]

  • Zeinab Jafarian 1
  • zeinab bahreini 2
  • maryam shokri 3

1 q

2 q

3 q

چکیده [English]

Prediction of the spatial distribution of Festuca Ovina and Bromus briziformis in Siahbisheh Rangelands using artificial neural network was the purpose of this study. Random classification sampling was done for vegetation in 29 homogenous units. 290 plot 1 m² were established in the area and was recorded percent of canopy cover. 3 soil samples were collected from a depth of 0-30 in any homogenous unit. In this study, 20 Environmental factors (Slope, aspect, elevation, distance from road, distance from river, precipitation, distance from livestock, geology, percent of silt, clay, sand, moisture, carbon, organic matter, ph, EC and N.P.K) were independent variables and species presence data of Festuca Ovina and Bromus briziformis was dependent variable. The information layers of each these factors prepared in Arc GIS and were classified using the frequency of each these factors. The results showed that the most important environmental variables affecting the distribution of the studied species were elevation, soil texture and nutrients. Then 70 and 30 percent of the data were used for training and test network respectively. In this study, artificial neural network structure with the 20 neurons in the input layer and the hidden layer and one neuron in the output layer, values of MSE were calculated for festuca 0.75 and Bromus 0.72. Then zoning maps of plant species were prepared with 4 zones including absence and presence of low, medium, high. Zoning maps were evaluated using ROC curves and Kappa coefficient that accuracy with ROC curves were 97.10, 84.10 and with kappa coefficient were 0.78, 0.66 percent for Festuca ovina, and Bromus briziformis respectively that represents a good evaluation of model.

کلیدواژه‌ها [English]

  • Spatial Distribution
  • Soil properties
  • ROC Curve
  • Kappa Coefficient
  • frequency ratio
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