Remote Sensing for Mangrove Mapping and Monitoring: Sensors, Indices, Algorithms, and Applications
DOI:
https://doi.org/10.67710/jomafish.v1i1.26Keywords:
mangrove mapping, remote sensing, Sentinel-2, synthetic aperture radar, machine learningAbstract
Mangrove forests are spatially dynamic intertidal ecosystems whose monitoring is constrained by difficult field access, tidal variation, cloud cover, spectral similarity with terrestrial vegetation, and rapid land-use change. Remote sensing has therefore become a central tool for mapping mangrove extent, condition, species composition, structural attributes, and temporal change. This review synthesizes recent developments in optical, synthetic aperture radar (SAR), hyperspectral, unmanned aerial vehicle (UAV), and LiDAR approaches for mangrove mapping, with emphasis on freely available Landsat and Sentinel data and applications relevant to tropical coasts. Studies published mainly during 2016–2026 were examined across four analytical dimensions: sensor characteristics, spectral or structural features, classification algorithms, and accuracy assessment. The evidence shows that Landsat remains valuable for long-term change analysis, whereas Sentinel-2 improves detection of narrow and fragmented stands through 10-m observations. Combining Sentinel-1 SAR and Sentinel-2 optical time series generally improves robustness in persistently cloudy regions. Mangrove-specific indices such as the Mangrove Vegetation Index can simplify extent mapping, while hyperspectral imagery, UAV data, and LiDAR are increasingly important for species discrimination, canopy height, biomass, and restoration assessment. However, no sensor or index removes the need for representative training data and independent validation. Tidal stage, mixed pixels, seasonal compositing, class imbalance, and spatially non-independent validation remain major sources of uncertainty. For Indonesia, an operational strategy should integrate multi-temporal Sentinel-1/2 data, machine learning, field observations, and standardized accuracy reporting to support repeatable mapping for conservation, restoration, blue-carbon accounting, and coastal management.Downloads
References
Akbar, M. R., Arisanto, P. A. A., Sukirno, B. A., Merdeka, P. H., Priadhi, M. M., & Zallesa, S. (2020). Mangrove vegetation health index analysis by implementing NDVI (normalized difference vegetation index) classification method on Sentinel-2 image data case study: Segara Anakan, Kabupaten Cilacap. IOP Conference Series: Earth and Environmental Science, 584(1), 012069. https://doi.org/10.1088/1755-1315/584/1/012069
Alongi, D. M., Murdiyarso, D., Fourqurean, J. W., Kauffman, J. B., Hutahaean, A., Crooks, S., Lovelock, C. E., Howard, J., Herr, D., Fortes, M., Pidgeon, E., & Wagey, T. (2016). Indonesia’s blue carbon: A globally significant and vulnerable sink for seagrass and mangrove carbon. Wetlands Ecology and Management, 24(1), 3–13. https://doi.org/10.1007/s11273-015-9446-y
As-syakur, A. R., Novanda, I. G. A., Sugiana, I. P., Dewi, I. G. A. I. P., Andiani, A. A. E., Aryunisha, P. E. P., Riskianisya, A., & Wijana, I. M. S. (2026). Validasi Mangrove Health Index berbasis Sentinel-2 di Bali dan Jawa Timur, Indonesia. Jurnal Kelautan Tropis, 29(1), 59–69. https://doi.org/10.14710/jkt.v29i1.30565
Baloloy, A. B., Blanco, A. C., Sta. Ana, R. R. C., & 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. https://doi.org/10.1016/j.isprsjprs.2020.06.001
Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R. M., Thomas, N., Tadono, T., Worthington, T. A., Spalding, M., Murray, N. J., & Rebelo, L.-M. (2022). Global mangrove extent change 1996–2020: Global Mangrove Watch version 3.0. Remote Sensing, 14(15), 3657. https://doi.org/10.3390/rs14153657
Bunting, P., Rosenqvist, A., Lucas, R. M., Rebelo, L.-M., Hilarides, L., Thomas, N., Hardy, A., Itoh, T., Shimada, M., & Finlayson, C. M. (2018). The Global Mangrove Watch—A new 2010 global baseline of mangrove extent. Remote Sensing, 10(10), 1669. https://doi.org/10.3390/rs10101669
Cao, J., Liu, K., Zhuo, L., Liu, L., Zhu, Y., & Peng, L. (2021). Combining UAV-based hyperspectral and LiDAR data for mangrove species classification using the rotation forest algorithm. International Journal of Applied Earth Observation and Geoinformation, 102, 102414. https://doi.org/10.1016/j.jag.2021.102414
Diniz, C., Cortinhas, L., Nerino, G., Rodrigues, J., Sadeck, L., Adami, M., & Souza-Filho, P. W. M. (2019). Brazilian mangrove status: Three decades of satellite data analysis. Remote Sensing, 11(7), 808. https://doi.org/10.3390/rs11070808
Febrianto, S., Rahman, A., Jati, O. E., Wirasatriya, A., Muskananfola, M. R., & Latifah, N. (2025). Machine learning for mangrove species distribution using Sentinel 2 satellite image in Segara Anakan, Cilacap Region, Indonesia. Regional Studies in Marine Science, 81, 103984. https://doi.org/10.1016/j.rsma.2024.103984
Ghorbanian, A., Zaghian, S., Asiyabi, R. M., Amani, M., Mohammadzadeh, A., & 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), 2565. https://doi.org/10.3390/rs13132565
Giri, C., Ochieng, E., Tieszen, L. L., Zhu, Z., Singh, A., Loveland, T., Masek, J., & Duke, N. (2011). Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecology and Biogeography, 20(1), 154–159. https://doi.org/10.1111/j.1466-8238.2010.00584.x
Goldberg, L., Lagomasino, D., Thomas, N., & Fatoyinbo, T. (2020). Global declines in human-driven mangrove loss. Global Change Biology, 26(10), 5844–5855. https://doi.org/10.1111/gcb.15275
Hamilton, S. E., & Casey, D. (2016). Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Global Ecology and Biogeography, 25(6), 729–738. https://doi.org/10.1111/geb.12449
Hidayah, Z., Rachman, H. A., & As-Syakur, A. R. (2023). Pemetaan kondisi hutan mangrove di kawasan pesisir Selat Madura dengan pendekatan Mangrove Health Index memanfaatkan citra satelit Sentinel-2. Majalah Geografi Indonesia, 37(1), 84–91. https://doi.org/10.22146/mgi.78136
Hu, L., Xu, N., Liang, J., Li, Z., Chen, L., & 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), 3120. https://doi.org/10.3390/rs12193120
Lassalle, G., Ferreira, M. P., Cué La Rosa, L. E., Del'Papa Moreira Scafutto, R., & de Souza Filho, C. R. (2023). Advances in multi- and hyperspectral remote sensing of mangrove species: A synthesis and study case on airborne and multisource spaceborne imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 195, 298–312. https://doi.org/10.1016/j.isprsjprs.2022.12.003
Pinkaew, S., Boonrat, P., Koedsin, W., & Huete, A. (2024). Semi-automated mangrove mapping at national-scale using Sentinel-2, Sentinel-1, and SRTM data with Google Earth Engine: A case study in Thailand. The Egyptian Journal of Remote Sensing and Space Sciences, 27(3), 555–564. https://doi.org/10.1016/j.ejrs.2024.07.001
Pradisty, N. A., Schlund, M., Horstman, E. M., & Willemen, L. (2025). Estimating canopy height and aboveground biomass in tropical mangrove restoration areas through multisource remote sensing. Ecological Informatics, 92, 103522. https://doi.org/10.1016/j.ecoinf.2025.103522
Rahmandhana, A. D., Kamal, M., & Wicaksono, P. (2022). Spectral reflectance-based mangrove species mapping from WorldView-2 imagery of Karimunjawa and Kemujan Island, Central Java Province, Indonesia. Remote Sensing, 14(1), 183. https://doi.org/10.3390/rs14010183
Richards, D. R., & Friess, D. A. (2016). Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proceedings of the National Academy of Sciences of the United States of America, 113(2), 344–349. https://doi.org/10.1073/pnas.1510272113
Sharifi, A., Felegari, S., & Tariq, A. (2022). Mangrove forests mapping using Sentinel-1 and Sentinel-2 satellite images. Arabian Journal of Geosciences, 15, 1593. https://doi.org/10.1007/s12517-022-10867-z
Simard, M., Fatoyinbo, L., Smetanka, C., Rivera-Monroy, V. H., Castañeda-Moya, E., Thomas, N., & Van der Stocken, T. (2019). Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nature Geoscience, 12, 40–45. https://doi.org/10.1038/s41561-018-0279-1
Thomas, N., Lucas, R., Bunting, P., Hardy, A., Rosenqvist, A., & Simard, M. (2017). Distribution and drivers of global mangrove forest change, 1996–2010. PLOS ONE, 12(6), e0179302. https://doi.org/10.1371/journal.pone.0179302
Wang, D., Qiu, P., Wan, B., Cao, Z., & Zhang, Q. (2022). Mapping α- and β-diversity of mangrove forests with multispectral and hyperspectral images. Remote Sensing of Environment, 275, 113021. https://doi.org/10.1016/j.rse.2022.113021
Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., Wang, R., Sun, F., & 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. https://doi.org/10.3390/rs10091468
Yancho, J. M. M., Jones, T. G., Gandhi, S. R., Ferster, C., Lin, A., & Glass, L. (2020). The Google Earth Engine Mangrove Mapping Methodology (GEEMMM). Remote Sensing, 12(22), 3758. https://doi.org/10.3390/rs12223758
Yang, G., Huang, K., Sun, W., Meng, X., Mao, D., & Ge, Y. (2022). Enhanced mangrove vegetation index based on hyperspectral images for mapping mangrove. ISPRS Journal of Photogrammetry and Remote Sensing, 189, 236–254. https://doi.org/10.1016/j.isprsjprs.2022.05.003
Yu, H., Wu, J., Aslam, R. W., Naz, I., Tariq, A., Ullah, S., Elmannai, H., & Said, Y. (2026). Integrating multi-index remote sensing and machine learning for mangrove dynamics assessment using Sentinel-2 imagery. iScience, 29(8), 116796. https://doi.org/10.1016/j.isci.2026.116796
Yu, J., Nie, S., Liu, W., Zhu, X., Sun, Z., Li, J., Wang, C., Xi, X., & Fan, H. (2024). Mapping global mangrove canopy height by integrating Ice, Cloud, and Land Elevation Satellite-2 photon-counting LiDAR data with multi-source images. Science of the Total Environment, 939, 173487. https://doi.org/10.1016/j.scitotenv.2024.173487
