پهنه‌بندی گستره جنگل‌های مانگرو در ایران با استفاده از تصاویر سنتینل2

نوع مقاله : علمی- پژوهشی

نویسندگان

1 نویسنده مسئول، دانشیار، گروه سنجش از دور و GIS، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران

2 کارشناسی ارشد، گروه سنجش از دور و GIS، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران

چکیده

مانگروها، بوم‌‌سازگان‌‌های ساحلی هستند که خدمات متنوع بوم‌‌شناختی، اقتصادی و اجتماعی ارائه می‌کنند. در مدیریت پایدار مانگروها، دسترسی به نقشه دقیق گستره آن‌ها مبتنی‌بر روش‌های نوین، ضروری است. پژوهش پیش‌رو با هدف نقشه‌‌برداری از جنگل‌های مانگرو در سرتاسر نوار ساحلی جنوبی ایران از استان خوزستان تا سیستان و بلوچستان و نیز شناسایی رویشگاه‌‌های جدید با استفاده از سنجش از دور انجام شد. به این منظور، شاخص ویژه شناسایی مانگروها روی تصاویر سنتینل2 به‌نام MVI (Mangrove vegetation index) در سکوی محاسبه ابری GEE (Google Earth Engine) به‌کار گرفته شد. تصاویر شاخص MVI با استفاده از الگوریتم جنگل تصادفی طبقه‌بندی شدند. نقشه نهایی مانگروها علاوه‌بر معیارهای متداول صحت‌‌سنجی، با استفاده از صحت و F1-score نیز ارزیابی شد. نتایج نشان داد که مجموع مساحت رویشگاه‌‌های مانگرو در جنوب ایران در اکتبر 2021 بالغ‌بر 8/12471 هکتار (صحت 98/0 و F1-score برابر 97/0) است که در 40 رویشگاه در چهار استان خوزستان (7/286 هکتار)، بوشهر (9/296 هکتار)، هرمزگان (9/11281 هکتار) و سیستان و بلوچستان (3/606 هکتار) پراکنده شدند. همچنین، برخی از رویشگاه‌‌های جدید مانند مانگروهای بندر ماهشهر استان خوزستان با مساحت حدود 290 هکتار نیز در این پژوهش شناسایی شدند.

کلیدواژه‌ها


عنوان مقاله [English]

Mapping mangrove forest extent in Iran using Sentinel-2 imagery

نویسندگان [English]

  • Y. Erfanifard 1
  • M. Lotfi Nasirabad 2
1 Corresponding author, Associate Prof., Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
2 MSc., Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
چکیده [English]

Mangrove forests are coastal ecosystems that provide various benefits for ecology, economy and society. To manage these forests sustainably, accurate mapping of their distribution using modern techniques is crucial. This study aimed to map the mangrove forests along the southern coast of Iran from Khuzestan province to Sistan and Baluchestan province and identify new sites using remote sensing. The Mangrove vegetation index (MVI) was applied to Sentinel-2 images on the Google Earth Engine (GEE) cloud computing platform. The MVI images were classified by the random forest algorithm. The final maps were evaluated by accuracy, F1-score and other common indices. The results indicated that the total area of mangrove forests in southern Iran was about 12471.8 ha (accuracy 0.98, F1-score 0.97) in October 2021 in 40 sites across four provinces: Khuzestan (286.7 ha), Bushehr (296.9 ha), Hormozgan (11281.9 ha) and Sistan and Baluchestan (606.3 ha). Moreover, some new sites, such as the mangroves of Mahshahr Harbor with an area of around 290 ha in Khuzestan province, were discovered in this study.

کلیدواژه‌ها [English]

  • Mahshahr Harbor
  • MVI
  • Qeshm
  • random forest
- Baloloy, A.B., Blanco, A.C., Ana, R.R.C.S. 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.
- Bihamta Toosi, N., Soffianian, A.R., Fakheran, S., Pourmanafi, S., Ginzler, C. and Waser, L., 2019. Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran. Global Ecology and Conservation, 19: e00662.
- Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R.M., Thomas, N., Tadono, T., Worthington, T.A., Spalding, M., Murray, N.J. and Rebelo, L.M., 2022. Global mangrove extent change 1996–2020: Global mangrove watch version 3.0. Remote Sensing, 14: 3657.
- 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: 1669.
- Congalton, R.G. and Green, K., 2019. Assessing the Acuracy of Remotely Sensed Data: Principles and Practices, 3rd 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. Iran national plan, National Forests, Range and Watershed Management Organization, Tehran, Iran, 624p (In Persian with English summary).
- Elmahdy, S.I. and Ali, T.A., 2022. Monitoring changes and soil characterization in mangrove forests of the United Arab Emirates using the canonical correlation forest model by multitemporal of Landsat data. Frontiers in Remote Sensing, 3: 782869.
- Erfanifard, Y. and Lotfi Nasirabad, M., 2022. Comparison of vegetation and mangrove indices in mangrove mapping on Sentinel-2 imagery based on Google Earth Engine. Iranian Journal of Forest and Poplar Research, 3(3): 224-240 (In Persian with English summary).
- Erfanifard, Y., Lotfi Nasirabad, M. and Stereńczak, K., 2022. Assessment of Iran’s mangrove forest dynamics (1990–2020) using Landsat time series. Remote Sensing, 14: 4912.
- FAO, 2020. Global Forest Resources Assessment 2020: Main report. Rome, Italy, 186p.
- Giri, C., Ochieng, E., Tieszen, L.L., Zhu, Z., Singh, A., Loveland, T., Masek, J. and Duke, N., 2011. Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20: 154-159.
- 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: 2043.
- Mafi-Gholami, D., Zenner, E.K., Jaafari, A. and Bui, D.T., 2020. Spatially explicit predictions of changes in the extent of mangroves of Iran at the end of the 21st century. Estuarine, Coastal and Shelf Science, 237: 106644.
- Makowski, C. and Finkl, C.W., 2018. Threats to Mangrove Forests: Hazards, Vulnerability, and Management. Springer, Cham, Switzerland, 724p.
- Safiari, Sh., 2017. Mangrove forests in Iran. Journal of Iran Nature, 2(2): 49-57 (In Persian with English summary).
- Tran, T.V., Reef, R. and Zhu, X., 2022. A review of spectral indices for mangrove remote sensing. Remote Sensing, 14: 4868.
- 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: 1468.
- Winarso, G., Purwanto, A.D. and Yuwono, DM., 2014. New mangrove index as degradation/healthindicator using remote sensing data : Segaraanakan 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: 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.
- Yaghoubzadeh, M., Salman Mahiny, A., Mikaeili Tabrizi, A. and Danehkar, A., 2020. Mangrove forests and threats facing them (Emphasizing the effects of climate change and the response of mangroves to these changes). Iranian Journal of Marine Science and Technology, 23(92): 46-62 (In Persian with English summary).
- 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.
- Zahed, M.A., Rouhani, F., Mohajeri, S., Bateni, F. and Mohajeri, L., 2010. An overview of Iranian mangrove ecosystems, northern part of the Persian Gulf and Oman Sea. Acta Ecologica Sinica, 30: 240-244.