Baloloy, B., Blanco, A., Raymund Rhommel, 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, pp. 95–117.
https://doi.org/10.1016/j.isprsjprs.2020.06.001.
Bihamta, N., Soffianian, A., Fakheran, S., and Pourmanafi, S., 2019. Integration of CART algorithm and vegetation indices in preparing mangrove forest land map using Landsat 8 image.
Forest Research and Development,
5(4), pp. 557-569. doi:
10.30466/JFRD.2019.120794.
Cissell, R., Canty, W., Steinberg, K., and Simpson T., 2021. Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery. Applied Sciences, 11(9), p. 4258. https://doi.org/10.3390/APP11094258.
Daryaei, A., Sohrabi, H., Atzberger, C., and Immitzer, M., 2020. Fine-scale detection of vegetation in semi-arid mountainous areas with focus on riparian landscapes using Sentinel-2 and UAV data.
Computers and Electronics in Agriculture,
177, pp. 105686.
https://doi.org/10. 10 16/j.compag.2020.105686.
Diniz, C., Cortinhas, L., Nerino, G., Rodrigues, J., Sadeck, L., Adami, M., and Souza-Filho, M., 2019. Brazilian mangrove status: Three decades of satellite data analysis.
Remote Sensing, 11(7), p. 808.
https://doi.org/10.3390/RS11070808.
Ghorbanian, A., Zaghian, S., Asiyabi, M., Amani, M., Mohammadzadeh, A., and Jamali, S., 2021. Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine. Remote Sensing, 13(13), pp. 2565.
https://doi. org/10.3390/RS13132565.
Giri, C., 2016. Observation and Monitoring of Mangrove Forests Using Remote Sensing: Opportunities and Challenges.
Remote Sensing,
8, pp. 783.
https://doi.org/10.3390/RS8090783.
Hu, L., Xu, N., Liang, Z., Chen, L., and Zhao, F., 2020. Advancing the mapping of mangrove forests at national-scale using Sentinel-1 and Sentinel-2 time-series data with Google Earth Engine: A case study in China.
Remote Sensing, 12(19), p. 3120.
https://doi.org/10.3390/RS 12193120.
Jones, R., Raja Segaran, R., Clarke, D., Waycott, M., Goh, S., and Gillanders, M., 2020. Estimating mangrove tree biomass and carbon content: a comparison of forest inventory techniques and drone imagery.
Frontiers in Marine Science,
6, p. 784.
https://doi.org/10. 3389/FMARS.2019.00784/BIBTEX.
Li, H., Jia, M., Zhang, R., Ren, Y., and Wen, X., 2019a. Incorporating the plant phonological trajectory into mangrove species mapping with dense time series Sentinel-2 imagery and the Google Earth Engine platform. Remote Sensing. 11(21), pp. 2479. https://doi.org/ 10.3390/ rs11212479.
Li, Z., Zan, Q., Yang, Q., Zhu, D., Chen, Y., and Yu, S., 2019b. Remote estimation of mangrove aboveground carbon stock at the species level using a low-cost unmanned aerial vehicle system.
Remote Sensing.
11(9), 1018.
https:// doi.org/10.3390/RS11091018.
Miraki, M., Sohrabi, H., Fatehi, P., and Kneubuehler, M., 2020. Comparison of Machine Learning Algorithms for Broad Leaf Species Classification Using UAV-RGB Images.
Journal of Geomatics Science and Technology, 10(2), pp. 1-10.
http://jgst.issge.ir/ article-1-926-fa.html. (In Persian).
Näsi, R., Honkavaara, E., Lyytikäinen-Saarenmaa, P., Blomqvist, M., Litkey, P., Hakala, T., and Holopainen, M., 2015. Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level.
Remote Sensing, 7(11), pp. 15467-15493.
https://doi.org /10.33 90/RS71115467.
Nevalainen, A., Nilton, N., Antonio, G., 2017. Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging.
Remote Sens,
9(3), p. 185.
https://doi.org/10.3390/rs 9030185.
Nilmini Wijeyaratne, W.D. and Liyanage, P.M., 2020. Allometric modelling of the stem carbon content of Rhizophora mucronata in a Tropical Mangrove Ecosystem.
International Journal of Forestry Research,
2020, pp.1-6.
https://doi. org/10.1155/2020/8849413.
Safiari, Sh., 2018. Mangrove forests in Iran. Iran's nature. pp. 49-57 (In Persian). https://doi.org /10. 22092/IRN.2017.111425.
Sheikhi, H., Darvish Sefat, A., Fatehi, P., Rajabpour Rahmati, M., and Etemad, V., 2020. Evaluation of data capability of Landsat 8 and Sentinel 2 satellites to prepare a map of Hyrcanian forest type in Kojoor watershed.
Wood and Forest Science and Technology Research,
27 (2), pp. 79-98.
10.22069/JWF ST.2020.17881.1866.
Wang, D., Wan, B., Qiu, P., Zuo, Z., Wang, R., and Wu, X., 2019. Mapping height and aboveground biomass of mangrove forests on Hainan Island using UAV-LiDAR sampling.
Remote Sensing,
11(18), p. 2156.
https://doi. org/10.3390/RS11182156.
Wang, D., Wan, B., Liu, J., Su, Y., Guo, Q., Qiu, P., and Wu, X., 2020. Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery.
International Journal of Applied Earth Observation and Geoinformation,
85, p. 101986.
https://doi.org/ 10.1016/J.JAG.2019.101986.
Yaghoubzadeh, M., Salmanmahiny, A., Mikaeili Tabrizi, A., Danehkar, A., 2020. Forecasting inundation zone caused by climate change in mangrove forests.
Journal of Marine Science and Technology, in press.
https://doi.org/10. 22113/jmst.2020.202372.2312. (In Persian).
Zuhair, M., Hussin, A., and Weir, C., 2001. Monitoring mangrove forests using remote sensing and GIS. In: The balance between biodiversity conservation and sustainable use of tropical rain forests: Proceedings of a workshop. held 6-8 December. pp. 251-257.