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کاربرد منحنی ROC در ارزیابی روش‌های طبقه‌بندی پیکسل- پایه روی تصاویر هوایی UltraCam-D برای تفکیک تاج درختان در توده‌های خالص بلوط ایرانی در جنگل‌های زاگرس | ||
| تحقیقات جنگل و صنوبر ایران | ||
| مقاله 9، دوره 22، شماره 4، دی 1393، صفحه 649-663 اصل مقاله (2 M) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2014.13179 | ||
| نویسنده | ||
| سیدیوسف عرفانی فرد* | ||
| هیات علمی دانشگاه شیراز | ||
| چکیده | ||
| مدیریت پایدار جنگل در زاگرس نیازمند برآورد تراکم تاج‌پوشش درختان جنگلی است و صحت نقشه‌های مربوط به این ویژگی که از سنجش از دور به‌دست آمده است، باید با روش‌های مناسب مورد ارزیابی قرار گیرد. این پژوهش با هدف ارزیابی نتایج سه طبقه‌بندی‌کننده پیکسل- پایه روی تصاویر هوایی UltraCam-D برای تفکیک تاج درختان بلوط ایرانی در جنگل‌های زاگرس به‌وسیله منحنی عملیاتی دریافت‌کننده (ROC) انجام شد. یک قطعه‌نمونه با مساحت 30 هکتار در بخشی از توده‌های خالص بلوط ایرانی این جنگل‌ها انتخاب شد و موقعیت مکانی و محدوده تاج همه درختان آن به‌طور کامل مساحی شدند. تصویر هوایی UltraCam-D منطقه موردنظر بااستفاده از طبقه‌بندی‌کننده‌های حداکثر احتمال (ML)، شبکه عصبی مصنوعی (ANN) و ماشین بردار پشتیبان (SVM) طبقه‌بندی شد. سپس نتایج حاصل از طبقه‌بندی بااستفاده از منحنی ROC و شاخص‌های صحت کلی و ضریب کاپا و نقشه واقعیت زمینی مورد ارزیابی قرار گرفت. نتایج نشان داد بیشترین سطح زیر منحنی ROC مربوط به «تاج درختان» در طبقه‌بندی‌کننده ML بوده است (894/0) و در مقابل، کمترین سطح زیر منحنی مربوط به طبقه‌بندی‌کننده SVM بود (819/0). حساسیت و ویژگی «تاج درختان» در طبقه‌بندی‌کننده ML (به‌ترتیب 999/0 و 999/0) بیشتر از دو طبقه‌بندی‌کننده دیگر بود. اگرچه دقت طبقه‌بندی‌کننده SVM در تفکیک «تاج درختان» حداکثر مقدار ممکن بود (000/1)، اما صحت این طبقه در طبقه‌بندی‌کننده ML (999/0) بیشتر بود. به‌طور کلی این پژوهش نشان داد که منحنی ROC قادر به ارزیابی صحت و دقت روش‌های طبقه‌بندی پیکسل- پایه موردبررسی روی تصاویر هوایی UltraCam-D به‌منظور تفکیک «تاج درختان» بوده است. | ||
| کلیدواژه‌ها | ||
| تاج درختان؛ زاگرس؛ طبقه‌بندی‌کننده‌های پیکسل- پایه؛ منحنی ROC؛ UltraCam-D | ||
| عنوان مقاله [English] | ||
| Application of ROC curve to assess pixel-based classification methods on UltraCam-D aerial imagery to discriminate tree crowns in pure stands of Brant`s oak in Zagros forests | ||
| نویسندگان [English] | ||
| Seyyed Yousef Erfanifard | ||
| Assistant Professor, Department of Natural Resources and Environment, College of Agriculture, Shiraz University, Shiraz, I.R. | ||
| چکیده [English] | ||
| Sustainable forest management in Zagros Mountains entails accurate information on tree crown density, which could be possibly derived from remote sensing data. Moreover, those remote sensing products need to be objectively evaluated. In this study, the results of three pixel-based classifiers of UltraCam-D aerial imagery were evaluated for classifying Brant`s oak (Quercus brantii Lindl.) crowns in Zagros forests in western Iran. This was carried out by means of receiver operating characteristic (ROC) curve. Therefore, a 30 ha plot was selected in pure Brant`s oak stand, in which the location and crown area of all trees were mapped. The UltraCam-D aerial imagery was classified by maximum likelihood (ML), artificial neural networks (ANNs) and support vector machines (SVMs) classifiers. The classification results were then evaluated by ROC curve and were presented by overall accuracy and Kappa coefficient. Results showed that the ML classified returned the largest area under ROC curve of "tree crowns" (0.894), whereas the lowest rate was found for SVM classifier (0.819). Sensitivity and specificity of "tree crowns" in ML classifier (0.999 and 0.999, respectively) were higher than those in two other classifiers. Although the precision of SVM classifier was the highest in discriminating "tree crowns" (1.000), the achieved accuracy of tree “crown class” was higher for ML classifier (0.999). This study concluded that using ROC curve enables an evaluation accuracy and precision of common pixel-based classifiers of such aerial imagery to discriminate tree crowns. | ||
| کلیدواژه‌ها [English] | ||
| Tree crown, Zagros, pixel-based classifiers, ROC curve, UltraCam-D | ||
| مراجع | ||
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