| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 2,846 |
| تعداد مقالات | 32,300 |
| تعداد مشاهده مقاله | 41,387,088 |
| تعداد دریافت فایل اصل مقاله | 18,562,167 |
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 | ||
|
- Alatorre, L.C., Sánchez-Andrés, R., Cirujano, S., Beguería, S. and Sánchez-Carrillo, S., 2011. Identification of mangrove areas by remote sensing: the ROC curve technique applied to the northwestern Mexico coastal zone using Landsat imagery. Remote Sensing, 3(8): 1568-1583. - Amiri, S.N., Sajadi, J. and Sadough Vanini, S.H., 2011. Application of vegetation indices derived from IRS data for detecting the Avicennia forest area near the south Pars Oil Apparatus. Environmental Sciences, 8(1): 69-84 (In Persian with English summary). - Baloloy, A.B., Blanco, A.C., Sharma, S. and Nadaoka, K., 2021. Development of a rapid mangrove zonation mapping workflow using Sentinel 2-derived indices and biophysical dataset. Frontiers in Remote Sensing, 2: 730238. - Baloloy, A.B., Blanco, A.C., Sta. Ana, R.R.C. and Nadaoka, K., 2020. Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 166: 95-117. - Binh, N.A., Hauser, L.T., Hoa, P.V., Thao, G.T.P., An, N.N., Nhut, H.S., ... and Verrelst, J., 2022. Quantifying mangrove leaf area index from Sentinel-2 imagery using hybrid models and active learning. International Journal of Remote Sensing, 43(15-16): 5636-5657. - Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R.M. and Thomas, N., 2022. Global Mangrove Watch: Updated 2010 mangrove forest extent (v2.5). Remote Sensing, 14(4): 1034. - Bunting, P., Rosenqvist, A., Lucas, R.M., Rebelo, L.M., Hilarides, L., Thomas, N., ... and Finlayson, C.M., 2018. The Global Mangrove Watch—A new 2010 global baseline of mangrove extent. Remote Sensing, 10(10): 1669. - Chen, N., 2020. Mapping mangrove in Dongzhaigang, China using Sentinel-2 imagery. Journal of Applied Remote Sensing, 14(1): 014508. - Congalton, R.G. and Green, K., 2019. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Third Edition. CRC Press, Boca Raton, Florida, 346p. - Danehkar, A., Mahmoudi, B., Sabaei, M., Ghadirian, T., Asadolahi, Z., Sharifi, N. and Petrosian, H., 2012. Sustainable mangrove management. Final Report of Iran’s national Research Project, Published by Forests, Range and Watershed Management Organization, Tehran, 624p (In Persian). - Daughtry, C.S.T., Walthall, C.L., Kim, M.S., de Colstoun, E.B. and McMurtrey, J.E., 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2): 229-239. - Diniz, C., Cortinhas, L., Nerino, G., Rodrigues, J., Sadeck, L., Adami, M. and Souza-Filho, P.W.M., 2019. Brazilian mangrove status: Three decades of satellite data analysis. Remote Sensing, 11(7): 808. - FAO, 2020. Global Forest Resources Assessment 2020: Main report. Rome, 186p. - Gupta, K., Mukhopadhyay, A., Giri, S., Chanda, A., Datta Majumdar, S., Samanta, S., ... and Hazra, S., 2018. An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery. MethodsX, 5: 1129-1139. - Heumann, B.W., 2011. An object-based classification of mangroves using a hybrid decision tree—support vector machine approach. Remote Sensing, 3(11): 2440-2460. - Jia, M., Wang, Z., Wang, C., Mao, D. and Zhang, Y., 2019. A new vegetation index to detect periodically submerged mangrove forest using single-tide Sentinel-2 imagery. Remote Sensing, 11(7): 2043. - Mafi Gholami, D., Baharlouii, M. and Mahmoudi, B., 2017. Mapping area changes of mangroves using RS and GIS (Case study: mangroves of Hormozgan province). Environmental Sciences, 15(2): 75-91 (In Persian with English summary). - Makowski, C. and Finkl, C.W., 2018. Threats to Mangrove Forests: Hazards, Vulnerability, and Management. Springer, Cham, Switzerland, 724p. - Mondal, P., Liu, X., Fatoyinbo, T.E. and Lagomasino, D., 2019. Evaluating combinations of Sentinel-2 data and machine-learning algorithms for mangrove mapping in West Africa. Remote Sensing, 11(24): 2928. - Muhsoni, F.F., Sambah, A.B., Mahmudi, M. and Wiadnya, D.G.R., 2018. Comparison of different vegetation indices for assessing mangrove density using sentinel-2 imagery. International Journal of Geomate, 14(45): 42-51. - Polo, T.C.F. and Miot, HA., 2020. Use of ROC curves in clinical and experimental studies. Jornal Vascular Brasileiro, 19: e20200186. - Safiari, Sh., 2017. Mangrove forests in Iran. Journal of Iran Nature, 2(2): 49-57 (In Persian with English summary). - Tabatabaie, T. and Amiri, F., 2019. Multi-temporal assessment of mangrove forests change in the coastal areas of Bushehr region based on Landsat satellite imagery. Iranian Journal of Applied Ecology, 8(3): 45-62 (In Persian with English summary). - Taghizadeh, A., Danehkar, A., Kamrani, E. and Mahmoudi, B., 2009. Investigation on the structure and dispersion of mangrove forest community in Sirik site in Hormozgan province. Iranian Journal of Forest, 1(1): 25-34 (In Persian with English summary). - Valderrama-Landeros, L., Flores-de-Santiago, F., Kovacs, J.M. and Flores-Verdugo, F., 2018. An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification Scheme. Environmental Monitoring and Assessment, 190: 23. - Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., Wang, R., … and Wu, X., 2018. Evaluating the performance of Sentinel-2, Landsat 8 and Pléiades-1 in mapping mangrove extent and species. Remote Sensing, 10(9): 1468. - Winarso, G., Purwanto, A.D. and Yuwono, D.M., 2014. New mangrove index as degradation/health indicator using Remote Sensing data : Segara Anakan and Alas Purwo case study. Proceedings of the 12th Biennial Conference of Pan Ocean Remote Sensing Conference. Bali, Indonesia, 4-7 Nov. 2014: 309-316. - Xia, Q., He, T.T., Qin, C.Z., Xing, X.M. and Xiao, W., 2022. An improved submerged mangrove recognition index-based method for mapping mangrove forests by removing the disturbance of tidal dynamics and S. alterniflora. Remote Sensing, 14(13): 3112. - Xia, Q., Qin, C.Z., Li, H., Huang, C., Su, F.Z. and Jia, M.M., 2020. Evaluation of submerged mangrove recognition index using multi-tidal remote sensing data. Ecological Indicators, 113: 106196. - Yang, G., Huang, K., Sun, W., Meng, X., Mao, D. and Ge, Y., 2022. Enhanced mangrove vegetation index based on hyperspectral images for mapping mangrove. ISPRS Journal of Photogrammetry and Remote Sensing, 189: 236-254. | ||
|
آمار تعداد مشاهده مقاله: 355 تعداد دریافت فایل اصل مقاله: 236 |
||