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مقایسه شاخص‌‌های پوشش گیاهی و مانگرو در تهیه نقشه جنگلهای مانگرو روی تصاویر سنتینل2 مبتنی‌بر سامانه Google Earth Engine | ||
| تحقیقات جنگل و صنوبر ایران | ||
| مقاله 3، دوره 30، شماره 3 - شماره پیاپی 89، آذر 1401، صفحه 224-240 اصل مقاله (548.88 K) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2022.359648.2063 | ||
| نویسندگان | ||
| یوسف عرفانی فرد* 1؛ محسن لطفی نصیرآباد2 | ||
| 1نویسنده مسئول، دانشیار، گروه سنجش از دور و GIS، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران | ||
| 2کارشناسی ارشد، گروه سنجش از دور و GIS، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران | ||
| چکیده | ||
| تهیه نقشه جنگلهای مانگرو، نیازمند دسترسی به شاخص‌‌های کارآمدی است که مانگروها را از پدیده‌‌های دیگر روی تصاویر سنجش از دور شناسایی کنند. امروزه، مجموعه‌‌ای متنوع از شاخص‌‌های پوشش گیاهی و مانگرو در دسترس است که ارزیابی مقایسه‌‌ای کارایی آن‌ها، ضروری به‌نظر می‌‌رسد. پژوهش پیش‌رو با هدف مقایسه کارایی شش شاخص پوشش گیاهی و هفت شاخص مانگرو مختص استفاده روی تصاویر سنتینل2 در خلیج نایبند (استان بوشهر)، سیریک (استان هرمزگان) و خلیج گواتر (استان سیستان و بلوچستان) انجام شد تا روشی کارآمد در نقشهبرداری از مانگروها در سامانه محاسبه ابری Google Earth Engine (GEE) به‌دست آید. تصاویر شاخص‌‌ها با استفاده از الگوریتم ماشین بردار پشتیبان طبقه‌بندی شدند. نقشه مانگروها علاوه‌بر معیارهای متداول صحت‌سنجی، با استفاده از سطح زیر منحنی (AUC) مشخصه نسبی عملکرد (ROC) نیز ارزیابی شدند. نتایج نشان داد که شاخص‌‌های مانگرو، عملکرد بهتری نسبت به شاخص‌‌های پوشش گیاهی در نقشهبرداری جنگلهای مانگرو داشتند. از بین شاخص‌‌های پوشش گیاهی، بیشترین AUC (91/0 تا 92/0) متعلق به MCARI (Modified Chlorophyll Absorption in Reflectance Index) بود، درحالی‌که بین شاخص‌‌های مانگرو، بیشینه AUC (93/0 تا 95/0) در MFI (Mangrove Forest Index) مشاهده شد. به‌طورکلی، نتایج این پژوهش نشان داد که کاربرد MFI روی تصاویر سنتینل2 در سامانه GEE، کارایی مناسبی برای نقشهبرداری از جنگلهای مانگرو در مناطق مورد پژوهش دارد. | ||
| کلیدواژه‌ها | ||
| حرا؛ شاخص مانگرو؛ ماشین بردار پشتیبان؛ منحنی مشخصه نسبی عملکرد | ||
| عنوان مقاله [English] | ||
| Comparison of vegetation and mangrove indices in mangrove mapping on Sentinel-2 imagery based on Google Earth Engine | ||
| نویسندگان [English] | ||
| Y. Erfanifard1؛ M. Lotfi Nasirabad2 | ||
| 1Corresponding author, Associate Prof., Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran | ||
| 2M.Sc., Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| Effective mangrove mapping needs reliable indices that can characterize mangroves from other land cover types on remote sensing data. Currently, a variety of vegetation- and mangrove indices are available, while a comparative assessment of their efficiency seems essential. The aim of this study was to evaluate six vegetation indices and seven mangrove indices developed for Sentinel-2 imagery to obtain a robust approach in mangrove mapping within Google Earth Engine (GEE) cloud computing platform. The rasterized indices were classified by support vector machine. The final maps were evaluated by area under curve (AUC) of receiver operating characteristic (ROC) in addition to common accuracy assessment criteria. Results showed that mangrove indices were more reliable than vegetation indices. Amongst the vegetation indices, Modified Chlorophyll Absorption in Reflectance Index (MCARI) (AUCmangrove from 0.91 to 0.92) achieved the highest AUC values, while MFI (Mangrove Forest Index) returned the highest values amongst the mangrove indices (AUCmangrove from 0.93 to 0.95). All in all, results revealed that MFI on Sentinel-2 imagery in GEE was efficient in mangrove mapping within the study sites. | ||
| کلیدواژه‌ها [English] | ||
| Avicennia marina, Google Earth, mangrove index, receiver operating characteristic, support vector machine | ||
| مراجع | ||
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