- 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.