REMOTE SENSING FOR COASTAL ECOSYSTEM MONITORING: A PRACTICAL SENSOR SELECTION FRAMEWORK

Andik Isdianto; Abu Bakar Sambah; Eden Louis Sabrina; Intan Astritya Anggraini; Aulia Lanudia Fathah; Berlania Mahardika Putri; Supriyadi Supriyadi

doi:10.62567/micjo.v3i4.3419

Authors

Andik Isdianto Universitas Brawijaya https://orcid.org/0000-0002-4808-7868
Abu Bakar Sambah Universitas Brawijaya
Eden Louis Sabrina Universitas Brawijaya
Intan Astritya Anggraini Universitas Brawijaya
Aulia Lanudia Fathah Universitas Brawijaya
Berlania Mahardika Putri Universitas Brawijaya
Supriyadi National Taiwan Ocean University

DOI:

https://doi.org/10.62567/micjo.v3i4.3419

Keywords:

Mangrove Mapping, Seagrass Meadows, Coral Reef Habitats, Multisensor Integration, Field Validation

Abstract

Coastal habitats require monitoring strategies that can capture changes in their extent, ecological characteristics, and surrounding environmental conditions. Although satellite and unmanned aerial vehicle observations offer diverse capabilities, selecting an appropriate sensor remains challenging because monitoring objectives, observation scales, and environmental constraints differ among ecosystems. This study synthesizes published evidence to develop a practical framework for selecting observation technologies for mangroves, seagrass meadows, coral reefs, and coastal environments. A structured narrative literature review was conducted using a Scopus search in September 2026, supplemented by relevant publications and technical documentation. The initial search identified 1,969 records; 95 priority candidates were screened further, and 47 publications underwent abstract-level relevance assessment. Of these, 28 were retained for the main qualitative synthesis. Evidence was organized according to monitoring objectives, sensor characteristics, environmental and operational constraints, and validation requirements. The synthesis shows that sensor suitability depends primarily on the intended monitoring product rather than nominal spatial or spectral resolution alone. Historical satellite archives support long-term habitat-change assessment, while high-resolution optical and unmanned aerial vehicle observations can provide finer spatial detail for localized applications. Radar offers complementary information where optical observations are constrained, whereas submerged habitat mapping requires particular attention to water-column effects, depth, and reference data. These findings were translated into a qualitative Sensor Selection Matrix linking monitoring objectives with candidate observation strategies and their principal limitations. The proposed framework supports context-dependent planning and transparent interpretation of monitoring outputs without establishing universal sensor rankings or transferable accuracy guarantees.

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References

Abdel-Hamid, A., Dubovyk, O., El-Magd, I. A., & Menz, G. (2018). Mapping mangroves extents on the Red Sea coastline in Egypt using polarimetric SAR and high resolution optical remote sensing data. Sustainability, 10(3), 646. https://doi.org/10.3390/su10030646

Ahmadi, B., Gholamalifard, M., Ghasempouri, S. M., & Kutser, T. (2025). Comparative assessment of machine learning algorithms for retrieving colored dissolved organic matter (CDOM) from Sentinel-2/MSI images in the coastal waters of the Persian Gulf. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2025.103171

Anggraini, I. A., Atikawati, D., & Isdianto, A. (2026). Cross-resolution consistency of UAV thermal and Landsat 8 TIRS LST in a tropical coastal–industrial city. Engineering, Technology & Applied Science Research, 16(3), 36886–36892. https://doi.org/10.48084/etasr.18505

Carpenter, S., Byfield, V., Felgate, S. L., Price, D. M., Andrade, V., Cobb, E., Strong, J., Lichtschlag, A., Brittain, H., Barry, C., Fitch, A., Young, A., Sanders, R., & Evans, C. (2022). Using unoccupied aerial vehicles (UAVs) to map seagrass cover from Sentinel-2 imagery. Remote Sensing, 14(3), 477. https://doi.org/10.3390/rs14030477

Dong, W., Roslan, S. N. A., Jia, P., Yu, S., Zheng, Z., Shafri, H. Z. M., & Shariff, A. R. M. (2026). Sentinel-1/2 fusion and multiyear joint training for consistent cross-year mangrove mapping and blue carbon assessment in a tropical intertidal estuary. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2026.3719085

Dumpati, S. M. (2026). Multi-sensor fusion of Sentinel-2 imagery and ICESat-2 satellite laser bathymetry for benthic habitat classification in Key Largo, Florida Keys. Remote Sensing Applications: Society and Environment. https://doi.org/10.1016/j.rsase.2026.102102

Elma, E., Gaulton, R., Chudley, T. R., Scott, C. L., East, H. K., Westoby, H., & Fitzsimmons, C. (2024). Evaluating UAV-based multispectral imagery for mapping an intertidal seagrass environment. quatic Conservation: Marine and Freshwater Ecosystems, 34(8), e4230. https://doi.org/10.1002/aqc.4230

Gao, E., & Zhou, G. (2023). Spatio-temporal changes of mangrove-covered tidal flats over 35 years using satellite remote sensing imageries: A case study of Beibu Gulf, China. Remote Sensing, 15(7), 1928. https://doi.org/10.3390/rs15071928

Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x

Harahap, Z. A., Bengen, D. G., Nurjaya, I W., Agus, S. B., & Naulita, Y. (2026). Comparing remote sensing approaches for precision benthic habitat classification in marine protected areas. Regional Studies in Marine Science. https://doi.org/10.1016/j.rsma.2026.104748

Hedley, J. D., Roelfsema, C. M., Chollett, I., Harborne, A. R., Heron, S. F., Weeks, S., Skirving, W. J., Strong, A. E., Eakin, C. M., Christensen, T. R. L., Ticzon, V., Bejarano, S., & Mumby, P. J. (2016). Remote sensing of coral reefs for monitoring and management: A review. Remote Sensing, 8(2), 118. https://doi.org/10.3390/rs8020118

Hossain, M. S., Bujang, J. S., Zakaria, M. H., & Hashim, M. (2015). The application of remote sensing to seagrass ecosystems: An overview and future research prospects. International Journal of Remote Sensing, 36(1), 61–114. https://doi.org/10.1080/01431161.2014.990649

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

Isdianto, A., Fathah, A. L., Putra, M. N. E., & Putri, B. M. (2026). Geospatial change detection of mangrove loss, persistence, and regeneration in Lombok Island, Indonesia (2019–2024) using geomatic technologies. Engineering, Technology & Applied Science Research, 16(2), 34270–34275. https://doi.org/10.48084/etasr.13896

Jia, M., Wang, Z., Mao, D., Ren, C., Wang, C., & Wang, Y. (2021). Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine. Remote Sensing of Environment, 255, 112285. https://doi.org/10.1016/j.rse.2021.112285

Kuenzer, C., Bluemel, A., Gebhardt, S., Vo Quoc, T., & Dech, S. (2011). Remote sensing of mangrove ecosystems: A review. Remote Sensing, 3(5), 878–928. https://doi.org/10.3390/rs3050878

Lachs, L., Donner, S., Edwards, A. J., Golbuu, Y., & Guest, J. (2025). Higher spatial resolution is not always better: Evaluating satellite-sensed sea surface temperature products for a west Pacific coral reef system. Scientific Reports, 15, 1321. https://doi.org/10.1038/s41598-024-84289-0

Lyons, M. B., Murray, N. J., Kennedy, E. V., Kovacs, E. M., Castro-Sanguino, C., Phinn, S. R., et al. (2024). New global area estimates for coral reefs from high-resolution mapping. Cell Reports Sustainability, 1(2), 100015. https://doi.org/10.1016/j.crsus.2024.100015

Makumbura, R. K., Gibney, E., Nash, R., Dooly, G., & Duraibabu, D. B. (2026). A systematic review of unmanned aerial vehicles (UAVs) for coastal ecosystem monitoring. Science of Remote Sensing, 13, 100438. https://doi.org/10.1016/j.srs.2026.100438

Maurya, K., Mahajan, S., & Chaube, N. (2021). Remote sensing techniques: Mapping and monitoring of mangrove ecosystem—a review. Complex & Intelligent Systems, 7, 2797–2818. https://doi.org/10.1007/s40747-021-00457-z

Mills, M. S., Ungermann, M., Rigot, G., den Haan, J., Leon, J. X., & Schils, T. (2024). Coral reefs in transition: Temporal photoquadrat analyses and validation of underwater hyperspectral imaging for resource-efficient monitoring in Guam. PLOS ONE, 19(3), e0299523. https://doi.org/10.1371/journal.pone.0299523

Muller-Karger, F. E., et al. (2018). Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems. Ecological Applications. https://doi.org/10.1002/eap.1682

Murray, N. J., Phinn, S. R., DeWitt, M., Ferrari, R., Johnston, R., Lyons, M. B., Clinton, N., Thau, D., & Fuller, R. A. (2019). The global distribution and trajectory of tidal flats. Nature, 565, 222–225. https://doi.org/10.1038/s41586-018-0805-8

Nieuwenhuis, B. O., Marchese, F., Casartelli, M., Sabino, A., van der Meij, S. E. T., & Benzoni, F. (2022). Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs. Remote Sensing, 14(19), 5017. https://doi.org/10.3390/rs14195017

Oiry, S., Davies, B. F. R., Sousa, A. I., Rosa, P., Zoffoli, M. L., Brunier, G., Gernez, P., & Barillé, L. (2024). Discriminating seagrasses from green macroalgae in European intertidal areas using high-resolution multispectral drone imagery. Remote Sensing, 16(23), 4383. https://doi.org/10.3390/rs16234383

Shabani, F., Raie, M., & Kabiri, K. (2025). Studying the impact of sea surface temperature (SST) changes and El Niño phenomenon on coral reefs bleaching around Kish Island in the northern Persian Gulf: A remote sensing approach. Deep-Sea Research Part II: Topical Studies in Oceanography. https://doi.org/10.1016/j.dsr2.2025.105550

Smart, J. N., Cowley, D., Carrasco Rivera, D. E., Hafizt, M., Lyons, M., Phinn, S., & Roelfsema, C. (2026). Hyperspectral remote sensing for monitoring coral reef and seagrass ecosystems: Capabilities, limitations and future directions. Marine Environmental Research, 217, 107928. https://doi.org/10.1016/j.marenvres.2026.107928

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039

Sun, Z., Jiang, W., Ling, Z., Zhong, S., Zhang, Z., Song, J., & Xiao, Z. (2023). Using multisource high-resolution remote sensing data (2 m) with a habitat–tide–semantic segmentation approach for mangrove mapping. Remote Sensing, 15(22), 5271. https://doi.org/10.3390/rs15225271

Traganos, D., Aggarwal, B., Poursanidis, D., Topouzelis, K., Chrysoulakis, N., & Reinartz, P. (2018). Towards global-scale seagrass mapping and monitoring using Sentinel-2 on Google Earth Engine: The case study of the Aegean and Ionian Seas. Remote Sensing, 10(8), 1227. https://doi.org/10.3390/rs10081227

Trinh, X. T., Nguyen, L. D., & Takeuchi, W. (2023). Sentinel-2 mapping of a turbid intertidal seagrass meadow in Southern Vietnam. Geocarto International. https://doi.org/10.1080/10106049.2023.2186490

Wang, L., Zhang, Z., Li, Z., Li, Y., Zhang, Q., & Jiang, Y. (2025). A rule-based automatic approach for mapping intertidal seagrass meadows using optical and synthetic aperture radar images. Journal of Remote Sensing. https://doi.org/10.34133/remotesensing.0506

Wei, J., Lee, Z., Garcia, R., Zoffoli, L., Armstrong, R. A., Shang, Z., Sheldon, P., & Chen, R. F. (2018). An assessment of Landsat-8 atmospheric correction schemes and remote sensing reflectance products in coral reefs and coastal turbid waters. Remote Sensing of Environment, 215, 18–32. https://doi.org/10.1016/j.rse.2018.05.033

Wen, K., Liao, P., & Jiang, A. (2024). Integrating time series Sentinel-2 images and tide height to mapping tidal flats in the Chinese mainland. Journal of Hydrology, 645(B), 132264. https://doi.org/10.1016/j.jhydrol.2024.132264

Zhao, Y., Yan, C., Lu, S., Wang, P., Qiu, G. Y., & Li, R. (2019). Estimation of chlorophyll content in intertidal mangrove leaves with different thicknesses using hyperspectral data. Ecological Indicators. https://doi.org/10.1016/j.ecolind.2019.105511