**4. Conclusions**

We present a new tool—the GEEMMM—for mapping and monitoring mangrove ecosystems. By leveraging GEE, this new tool circumvents many traditional barriers to conventional methods. In addition, it presents an internal, image-based approach for tidal calibration. The GEEMMM—including the well commented source code—is available online and is ready to be used by practitioners anywhere mangrove ecosystems exist; please see information in Supplementary Material Section on how to access the GEEMMM.

While operational, the GEEMMM is not without its limitations: the larger the area the more complex the mapping task, particularly when it comes to creating optimal imagery composites within defined seasonal windows. In addition, the upper limits of GEE and internet connectivity present a challenge in terms of the time associated with and reliability of running the GEEMMM; however, when compared to the conventional processing times associated with standalone workstations it remains much faster, and once a part of the GEEMMM starts running it will continue to run even if the internet connection is lost. In any application, the resulting maps and dynamics assessments will only ever be as good as the examples of target map classes provided. Coastal managers will normally have such information available to them and GEEMMM provides them with a framework through which to capitalizes on this local knowledge, rather than relying on external datasets, which allow little to no customization, to map and monitor their mangroves.

The GEEMMM makes a significant and ready-to-go contribution toward accessible mangrove mapping and monitoring. It also remains a living tool wherein non-profit users are encouraged by the authors to make useful suggestions for modifications or additions, or modify the tool directly themselves to meet their own customized needs. While piloting the GEEMMM for Myanmar is an important first step, additional applications and tests are required, particularly for smaller areas of interest, wherein the GEEMMM can help fill a critical sub-national mapping gap. The authors welcome the opportunity to receive feedback from and work with users to more comprehensively assess the tool and gauge areas for improvement. A series of in-person and online instructional materials will go a long way toward ensuring the maximum and optimal utility of the GEEMMM. This first iteration of the GEEMMM further sets the stage for a comparatively more automated and even more accessible version to be deployable completely on mobile devices.

#### **Supplementary Materials:** The GEEMMM tool is freely available within the GitHub repository: https://github. com/Blue-Ventures-Conservation/GEEMMM.

**Author Contributions:** The GEEMMM was conceived of by, T.G.J., S.R.G., L.G., C.F., and J.M.M.Y. Contributions to the methodology were made by T.G.J., S.R.G., and C.F., with J.M.M.Y. developing the key tidal detection methods. J.M.M.Y. wrote all of the code in GEE for the GEEMMM tool, with the work reviewed by C.F. The results of this paper were validated by J.M.M.Y., T.G.J., S.R.G., C.F., and A.L. Formal analysis was performed by J.M.M.Y., T.G.J., and S.R.G., using the analysis tools developed by J.M.M.Y. Investigation for this work was conducted by J.M.M.Y., T.G.J., S.R.G., C.F., and A.L., J.M.M.Y. performed all of the data curation for this paper. The original manuscript writing was conducted by T.G.J. and J.M.M.Y.; with T.G.J. writing the introduction, discussion points, and conclusion and J.M.M.Y. writing the bulk of the methods and results. All authors, T.G.J., J.M.M.Y., S.R.G., C.F., A.L., and, L.G. were involved in writing—review and editing. Visualizations were generated by J.M.M.Y., A.L., and S.R.G. The project was administrated and supervised by T.G.J. and L.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Blue Ventures Conservation, with support from the UK Government's International Climate Fund, part of the UK commitment to developing countries to help them address the challenges presented by climate change and benefit from the opportunities.

**Acknowledgments:** We thank the following authors of studies referenced in this paper: Ake Rosenqvist, of solo Earth Observation (soloEO), for provision of and support regarding GMW data; J. Ronald Eastman and James Toledano, of Clark Labs, for guidance regarding the Clark Labs Aquaculture dataset; Chandra Giri, of United States Environmental Protection Agency, for provision of data and associated guidance; Edward L. Webb, of National University Singapore, for data provision and guidance; and Ate Poortinga, of Spatial Informatics Group, for provision of and guidance regarding SERVIR-Mekong data.

**Conflicts of Interest:** The authors declare no conflict of interest.



**Appendix A**


