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ارزیابی مدل‌های پارامتریک و ناپارامتریک در تخمین تراکم تاج‌پوشش جنگل‌های زاگرس با استفاده از سنجش از دور و یادگیری ماشین | ||
| مجله اکوهیدرولوژی | ||
| دوره 12، شماره 2، تیر 1404، صفحه 749-761 اصل مقاله (1.04 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ije.2025.397227.1873 | ||
| نویسندگان | ||
| سجاد عالی محمودی سرآب* 1؛ محمدهادی معیری2؛ محمد میرزاوند3؛ شعبان شتایی جویباری4؛ علیرضا راشکی5 | ||
| 1بخش تحقیقات جنگل‌ها و مراتع، مرکز تحقیقات کشاورزی و منابع طبیعی استان خوزستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، اهواز، | ||
| 2دانشیار، دانشکده علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، ایران | ||
| 3استادیار گروه علوم و فناوری‌های محیطی، دانشکده مهندسی انرژی و منابع پایدار، دانشگاه تهران | ||
| 4استاد، دانشکده علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، ایران | ||
| 5دانشکده منابع طبیعی و محیطزیست،دانشگاه فردوسی مشهد، مشهد، ایران | ||
| چکیده | ||
| موضوع: ارزیابی مدل‌های پارامتریک و ناپارامتریک در تخمین تراکم تاج‌پوشش جنگل‌های زاگرس با استفاده از سنجش از دور و یادگیری ماشین. هدف: هدف از انجام این تحقیق مقایسۀ روش‌های پارامتریک و ناپارامتریک در برآورد درصد تاج‌پوشش جنگل در بخشی از اکوسیستم زاگرس بود. روش تحقیق: برای رسیدن به هدف تحقیق از نمونه برداری میدانی جهت تعیین درصد تاج پوشش و تصویر ماهواره‌‌ای با اندازه تفکیک مکانی بالا پلی‌آدیساستفاده شد و شاخص‌های TSAVI، NDVI و WDVI تهیه گردید. سپس ارزش‌های حاصل از تهیۀ شاخص‌های پوشش گیاهی در محل قطعات نمونه با استفاده از تابع Zonal statistical در Arc GIS استخراج و از رگرسیون خطی چندگانه و شبکۀ عصبی مصنوعی برای برآورد تراکم پوشش گیاهی استفاده شد. برای مقایسۀ این دو مدل از متغیرهای RMSE، RMSE% و R2 استفاده شد. یافته‌ها: نتایج رگرسیون خطی چندگانه نشان داد که میزان R2 و RMSE% در سطح اعتماد 0/05 به‌ترتیب برابر 0/54 و 10/4 بود. همچنین نتایج شبکۀ عصبی مصنوعی نشان داد که میزان R2 و RMSE% به‌ترتیب برابر 0/82 و 4/5 به دست آمد. نتیجه‌گیری: نتایج مقایسۀ مدل‌های رگرسیونی خطی چندگانه و شبکۀ عصبی مصنوعی نشان داد که شبکۀ عصبی ضمن خطای کمتر، برآورد بهتری نسبت به رگرسیون خطی چندگانه در برآورد تراکم پوشش گیاهی دارد. | ||
| کلیدواژه‌ها | ||
| پلی آدیس؛ درصد تاج پوشش؛ رگرسیون خطی چندگانه؛ سنجش از دور؛ شبکه عصبی مصنوعی | ||
| عنوان مقاله [English] | ||
| Evaluation of Parametric and Non-Parametric Models in Estimating Canopy Cover Density of Zagros Forests Using Remote Sensing and Machine Learning | ||
| نویسندگان [English] | ||
| Sajad Alimahmoodi Sarab1؛ MohamadHadi Moayery2؛ MOhammad Mirzavand3؛ Shaban Shataee Jouibary4؛ Alireza Rashki5 | ||
| 1Assistant Prof., Research Institute of Forests and Rangelands, Agricultural Research Education and Extension Organization (AREEO), Ahvaz, Iran. | ||
| 2Associate Prof., Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 3Assistant Prof., School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran | ||
| 4Professor., Dept. of Forestry, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. | ||
| 5Department of Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran | ||
| چکیده [English] | ||
| Research Topic: Evaluation of Parametric and Non-Parametric Models in Estimating Canopy Cover Density of Zagros Forests Using Remote Sensing and Machine Learning Objective: This study aims to compare parametric and non-parametric methods for estimating the percentage of forest canopy cover in a section of the Zagros ecosystem. Method: In order to achieve the research objective, field sampling was conducted to determine the percentage of canopy cover, and high-resolution satellite imagery was utilized. The vegetation indices TSAVI, NDVI, and WDVI were calculated. Subsequently, the values derived from the vegetation cover indices at the sample plots were extracted using the Zonal Statistics function in ArcGIS. Multiple linear regression and artificial neural networks were employed to estimate vegetation density. To compare the performance of these two models, the metrics RMSE, RMSE%, and R² were utilized. Results: The results indicated that the MLR model achieved an R² value of 0.54 and an RMSE% of 10.4 at a 0.05 confidence level, while the MLP model yielded an R² of 0.82 and an RMSE% of 4.5. Conclusions: The comparative analysis demonstrated that the artificial neural network (MLP) provided more accurate estimates with lower error rates than the multiple linear regression (MLR) method in predicting vegetation density. | ||
| کلیدواژه‌ها [English] | ||
| Poliades, Canopy Cover Percentage, Multiple Linear Regression, Remote Sensing, Artificial Neural Network | ||
| مراجع | ||
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