**5. Conclusions**

We report the use of four machine learning algorithms: ANN, DT, RF, and SVM for classifying mangrove ages. The estimated ages agreed well with the ground-truth field data, and the SVM was found to be the most accurate algorithm for mangrove species classification. Sentinel-1 backscatter mechanisms for different mangrove species are basically a function of tree structure, height, and density, which, combined with multispectral bands of SPOT-7, allowed us to discriminate between the Ban mangrove, Su, and Vet at an acceptable accuracy. Our assessment of multi-decadal mangrove changes in extent used the Landsat-X image series. We found a fluctuation in the first two decades, then a constant expansion of mangrove forest during the period 1998 to 2019. For the accuracy assessment, confusion matrices, producer–user accuracy, overall accuracy, and Kappa coefficients were used to measure

the extent of agreemen<sup>t</sup> between image-based extraction and ground-truth data. These accuracy indices showed that all the classifications were accurate, and generally greater than 75%. Further research should test SAR and optical image fusion on other mangrove species as we found supportive information of SAR backscatters for classifying di fferent mangrove species, and gained finer resolution of the panchromatic layer of optical images.

**Author Contributions:** Conceptualization, N.H.Q., C.R.H., L.C.S., and L.T.V.H.; Methodology, N.H.Q., L.C.S., R.C., and C.H.Q.; Field investigation, D.V.T., R.C., C.R.H., and N.H.Q.; Data analysis, N.H.Q., P.T.T.N., C.H.Q., D.V.T., and L.T.V.H.; Writing—original draft preparation, N.H.Q.; Writing—review and editing, N.H.Q., C.R.H., C.H.Q., L.C.S., P.T.T.N, L.T.V.H., R.C., and D.V.T; Visualization, N.H.Q., R.C., and C.R.H.; Supervision, C.H.Q., L.C.S., P.T.T.N., and L.T.V.H.; Project administration, C.H.Q. and L.T.V.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was financially supported by the Newton RCUK-SEAMED project "Harnessing multiple benefits from resilient mangrove systems" funded by NAFOSTED RCUK, ESRC reference: ES/R003300/1. Publication was supported by UKRI via the University of Leeds Open Access Fund.

**Acknowledgments:** The authors would like to thank all the anonymous reviewers and the assistant editor Ms. Milica Kovaˇcevi´c for their constructive comments and suggestions on this manuscript.

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