EVERGREEN

Joint Journal of Novel Carbon Resource Sciences and Green Asia Strategy

ISSN:2189-0420 (Print until Mar 2020)
ISSN:2432-5953 (Online)

SCImago Journal & Country Rank

Open Access
Scopus
Google Scholar
Crossref
SCImago Journal & Country Rank
4.3
2024CiteScore
 
69th percentile
Powered by Scopus
Metrics by SCOPUS 2024
CiteScore
4.3
SJR
0.391
SNIP
1.192

Automated Mangrove Detection Method using Combined Machine Learning and Mangrove Index over Indonesia

Randy Prima Brahmantara1, Kurnia Ulfa2,*, Ferman Setia Nugroho3, Danang Surya Candra1, Fanny Aditya Putri3, D. Heri Yuli Sulyantara1, Marendra Eko Budiono1, Wahid Akhsin Budi Nur Sidiq4
1Research Center for Geoinformatics, Research Organization for Electronics and Informatics, National Research and Innovation Agency, M.H. Thamrin No. 8, Jakarta Pusat 10340 Indonesia
2Deputy for Research and Innovation Infrastructure, National Research and Innovation Agency, M.H. Thamrin No. 8, Jakarta Pusat 10340 Indonesia
3Center for Data and Information, National Research and Innovation Agency, M.H. Thamrin No. 8, Jakarta Pusat 10340 Indonesia
4Faculty of Social and Political Sciences, Semarang State University, Sekaran, Gunung Pati, Semarang City, Central Java 50229 Indonesia
*Author to whom correspondence should be addressed:
E-mail: kurnia.ulfa.example@university.edu (KU)
Evergreen, Vol. 12, Iss. 02, pp. 885–894, 2025
DOI: 10.5109/7363482
Creative Commons Attribution 4.0 International Creative Commons Attribution 4.0 International
Received: September 27, 2024 | Revised: February 06, 2025 | Accepted: April 01, 2025 | Published: June 2025
Abstract
The carbon storage potential of mangrove forests is higher than highland tropical forests. It makes mapping mangroves essential to estimate a total carbon storage potential. Integration of satellite data which covers a wide area and machine learning can obtain mangrove detection to be faster and more accurate. The study proposes an automated mangrove detection method using a combined mangrove index and machine learning over Indonesia. Normalized Difference Moisture Index and Mangrove Vegetation Index from Landsat 8/9 images were used to obtain training datasets for the Random Forest process. The results show that the overall accuracy is 0.93, and the kappa accuracy is 0.91. It proves that the proposed method can be used for mapping mangroves and non-mangroves accurately.
Keywords
machine learning ; random forest ; Landsat 8 ; Normalized Difference Moisture Index ; Mangrove Vegetation Index ; Landsat 9 ; mangrove mapping
Available Repositories
Journal DOI Zenodo Archive
Share Article
Article Metrics
--
Views
--
Downloads
--
Citations
Export Citation
BibTeX RIS Semantic Scholar CrossRef Mendeley Google Scholar
Full Text
Download PDF
References
  1. 1) S.Y.Lee, J.H. Primavera, F. Dahdouh‐Guebas, K. McKee, J.O. Bosire, S. Cannicci, K. Diele, F. Fromard, N. Koedam, C. Marchand, I. Mendelssohn, N. Mukherjee, and S. Record, "Ecological role and services of tropical mangrove ecosystems: a reassessment," Glob. Ecol. Biogeogr., 23 (7) 726-743 (2014) doi:10.1111/geb.12155
  2. 2) E.M.Eid, K.M. Khedher, H. Ayed, M. Arshad, A. Moatamed, and A. Mouldi, "Evaluation of carbon stock in the sediment of two mangrove species, avicennia marina and rhizophora mucronata, growing in the farasan islands, saudi arabia," Oceanologia, 62 (2) 200-213 (2020) doi:10.1016/j.oceano.2019.12.001
  3. 3) P.Bunting, A. Rosenqvist, R. Lucas, L.-M. Rebelo, L. Hilarides, N. Thomas, A. Hardy, T. Itoh, M. Shimada, and C. Finlayson, "The global mangrove watch—a new 2010 global baseline of mangrove extent," Remote Sens., 10 (10) 1669 (2018) doi:10.3390/rs10101669
  4. 4) A.S.Rovai, R.R. Twilley, E. Castañeda-Moya, P. Riul, M. Cifuentes-Jara, M. Manrow-Villalobos, P.A. Horta, J.C. Simonassi, A.L. Fonseca, and P.R. Pagliosa, "Global controls on carbon storage in mangrove soils," Nat. Clim. Chang., 8 (6) 534-538 (2018) doi:10.1038/s41558-018-0162-5
  5. 5) S.Narayan, M.W. Beck, B.G. Reguero, I.J. Losada, B. van Wesenbeeck, N. Pontee, J.N. Sanchirico, J.C. Ingram, G.-M. Lange, and K.A. Burks-Copes, "The effectiveness, costs and coastal protection benefits of natural and nature-based defences," PLoS One, 11 (5) e0154735–e0154735 (2016) doi:10.1371/journal.pone.0154735
  6. 6) J.H.Primavera, "Overcoming the impacts of aquaculture on the coastal zone," Ocean Coast. Manag., 49 (9-10) 531-545 (2006) doi:10.1016/J.OCECOAMAN.2006.06.018
  7. 7) D.C.Donato, J.B. Kauffman, R.A. Mackenzie, A. Ainsworth, and A.Z. Pfleeger, "Whole-island carbon stocks in the tropical pacific: implications for mangrove conservation and upland restoration," J. Environ. Manage., 97 89-96 (2012) doi:10.1016/j.jenvman.2011.12.004
  8. 8) J.Su, D.A. Friess, and A. Gasparatos, "A meta-analysis of the ecological and economic outcomes of mangrove restoration," Nat. Commun., 12 (1) 5050 (2021) doi:10.1038/s41467-021-25349-1
  9. 9) K.Maurya, S. Mahajan, and N. Chaube, "Remote sensing techniques: mapping and monitoring of mangrove ecosystem—a review," Complex Intell. Syst., 7 (6) 2797-2818 (2021) doi:10.1007/s40747-021-00457-z
  10. 10) L.Wang, M. Jia, D. Yin, and J. Tian, "A review of remote sensing for mangrove forests: 1956-2018," Remote Sens. Environ., 231 (2019) doi:10.1016/j.rse.2019.111223
  11. 11) A.Yussupov, and R.Z. Suleimenova, "Use of remote sensing data for environmental monitoring of desertification," Evergreen, 10 (1) 300-307 (2023) doi:10.5109/6781080
  12. 12) D.Sudiana, J. Nisa, M. Rizkinia, R.D. Dimyati, N. Anggraini, I. Riyanto, A.S. Prabuwono, and J.T.S. Sumantyo, "Monitoring the distribution of mangrove area using synthetic aperture radar (sar) and optic remote sensing data fusion based on deep learning in kotabaru regency, indonesia," Evergreen, 11 (1) 536-546 (2024) doi:10.5109/7172320
  13. 13) C.Giri, E. Ochieng, L.L. Tieszen, Z. Zhu, A. Singh, T. Loveland, J. Masek, and N. Duke, "Status and distribution of mangrove forests of the world using earth observation satellite data," Glob. Ecol. Biogeogr., 20 (1) 154-159 (2010) doi:10.1111/j.1466-8238.2010.00584.x
  14. 14) A.Hartoko, S. Chayaningrum, D.A. Febrianti, D. Ariyanto, and Suryanti, "Carbon biomass algorithms development for mangrove vegetation in kemujan, parang island karimunjawa national park and demak coastal area – indonesia," Procedia Environ. Sci., 23 39-47 (2015) doi:10.1016/j.proenv.2015.01.007
  15. 15) J.Sanderman, T. Hengl, G. Fiske, K. Solvik, M.F. Adame, L. Benson, J.J. Bukoski, P. Carnell, M. Cifuentes-Jara, D. Donato, C. Duncan, E.M. Eid, P. zu Ermgassen, C.J.E. Lewis, P.I. Macreadie, L. Glass, S. Gress, S.L. Jardine, T.G. Jones, E.N. Nsombo, M.M. Rahman, C.J. Sanders, M. Spalding, and E. Landis, "A global map of mangrove forest soil carbon at 30 m spatial resolution," Environ. Res. Lett., 13 (5) 55002 (2018) doi:10.1088/1748-9326/aabe1c
  16. 16) F.Flores-Cárdenas, O. Millán-Aguilar, L. Díaz-Lara, L. Rodríguez-Arredondo, M.Á. Hurtado-Oliva, and M. Manzano-Sarabia, "Trends in the normalized difference vegetation index for mangrove areas in northwestern mexico," J. Coast. Res., 344 877-882 (2018) doi:10.2112/jcoastres-d-17-00022.1
  17. 17) M.I.Brown, T. Pearce, J. Leon, R. Sidle, and R. Wilson, "Using remote sensing and traditional ecological knowledge (tek) to understand mangrove change on the maroochy river, queensland, australia," Appl. Geogr., 94 71-83 (2018) doi:10.1016/j.apgeog.2018.03.006
  18. 18) C.Kuenzer, A. Bluemel, S. Gebhardt, T. V Quoc, and S. Dech, "Remote sensing of mangrove ecosystems: a review," Remote Sens., 3 (5) 878-928 (2011) doi:10.3390/rs3050878
  19. 19) A.W.Zulfa, K. Norizah, O. Hamdan, I. Faridah-Hanum, P.P. Rhyma, and A. Fitrianto, "Spectral signature analysis to determine mangrove species delineation structured by anthropogenic effects," Ecol. Indic., 130 108148 (2021) doi:10.1016/j.ecolind.2021.108148
  20. 20) T.D.Pham, N. Yokoya, D.T. Bui, K. Yoshino, and D.A. Friess, "Remote sensing approaches for monitoring mangrove species, structure, and biomass: opportunities and challenges," Remote Sens., 11 (3) (2019) doi:10.3390/rs11030230
  21. 21) P.Rhyma Purnamasayangsukasih, K. Norizah, A.A.M. Ismail, and I. Shamsudin, "A review of uses of satellite imagery in monitoring mangrove forests," in: IOP Conf. Ser. Earth Environ. Sci., 2016 doi:10.1088/1755-1315/37/1/012034
  22. 22) J.Xue, and B. Su, "Significant remote sensing vegetation indices: a review of developments and applications," J. Sensors, 2017 (2017) doi:10.1155/2017/1353691
  23. 23) N.Younes Cárdenas, K.E. Joyce, and S.W. Maier, "Monitoring mangrove forests: are we taking full advantage of technology?," Int. J. Appl. Earth Obs. Geoinf., 63 1-14 (2017) doi:10.1016/j.jag.2017.07.004
  24. 24) S.Thakur, I. Mondal, P.B. Ghosh, P. Das, and T.K. De, "A review of the application of multispectral remote sensing in the study of mangrove ecosystems with special emphasis on image processing techniques," Spat. Inf. Res., 28 (1) 39-51 (2020) doi:10.1007/s41324-019-00268-y
  25. 25) Y.Deng, C. Wu, M. Li, and R. Chen, "RNDSI: a ratio normalized difference soil index for remote sensing of urban/suburban environments," Int. J. Appl. Earth Obs. Geoinf., 39 40-48 (2015) doi:10.1016/J.JAG.2015.02.010
  26. 26) M.Jia, Z. Wang, C. Wang, D. Mao, and Y. Zhang, "A new vegetation index to detect periodically submerged mangrove forest using single-tide sentinel-2 imagery," Remote Sens., 11 (17) (2019) doi:10.3390/rs11172043
  27. 27) M.P.Neri, A.B. Baloloy, and A.C. Blanco, "LIMITATION assessment and workflow refinement of the mangrove vegetation index (mvi)-based mapping methodology using sentinel-2 imagery," Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XLVI-4/W6- 235-242 (2021) doi:10.5194/isprs-archives-xlvi-4-w6-2021-235-2021
  28. 28) M.Kamal, S. Phinn, and K. Johansen, "Assessment of multi-resolution image data for mangrove leaf area index mapping," Remote Sens. Environ., 176 242-254 (2016) doi:10.1016/j.rse.2016.02.013
  29. 29) F.Rodríguez-Puerta, R.A. Ponce, F. Pérez-Rodríguez, B. Águeda, S. Martín-García, R. Martínez-Rodrigo, and I. Lizarralde, "Comparison of machine learning algorithms for wildland-urban interface fuelbreak planning integrating als and uav-borne lidar data and multispectral images," Drones, 4 (2) 1-18 (2020) doi:10.3390/drones4020021
  30. 30) N.G.Pricope, K.L. Mapes, and K.D. Woodward, "Remote sensing of human-environment interactions in global change research: a review of advances, challenges and future directions," Remote Sens., 11 (23) (2019) doi:10.3390/rs11232783
  31. 31) N.B.Toosi, A.R. Soffianian, S. Fakheran, S. Pourmanafi, C. Ginzler, and L.T. Waser, "Comparing different classification algorithms for monitoring mangrove cover changes in southern iran," Glob. Ecol. Conserv., 19 e00662 (2019) doi:10.1016/j.gecco.2019.e00662
  32. 32) R.Huerta-Soto, F. Francis, M. Asís-López, and J. Panduro-Ramirez, "Implementation of machine learning in supply chain management process for sustainable development by multiple regression analysis approach (mraa)," Evergreen, 10 (2) 1113-1119 (2023) doi:10.5109/6793671
  33. 33) S.Maryati, H. Shimada, A. Hamanaka, T. Sasaoka, and K. Matsui, "Determine appropriate post mining land use in indonesia coal mining using land suitability evaluation," J. Nov. Carbon Resour. Sci., 5 (78) 33-38 (2012)
  34. 34) B.Pranoto, I. Adilla, H. Soekarno, N.K. Supriatna, Adrian, L. Efiyanti, D.A. Indrawan, N.W. Hesty, and S.R. Fithri, "Using satellite data of palm oil area for potential utilization in calculating palm oil trunk waste as cofiring fuel biomass," Evergreen, 10 (3) 1784-1791 (2023) doi:10.5109/7151728
  35. 35) H.Kausarian, Lady Redyafry, J.T.S. Sumantyo, A. Suryadi, and M.Z. Lubis, "Structural analysis of the central sumatra basin using geological mapping and landsat 8 oli/tirs c2 l1 data," Evergreen, 10 (2) 792-804 (2023) doi:10.5109/6792830
  36. 36) N.J.Murray, T.A. Worthington, P. Bunting, S. Duce, V. Hagger, C.E. Lovelock, R. Lucas, M.I. Saunders, M. Sheaves, M. Spalding, N.J. Waltham, and M.B. Lyons, "High-resolution mapping of losses and gains of earth’s tidal wetlands," Science (80-. )., 376 (6594) 744-749 (2022) doi:10.1126/science.abm9583
  37. 37) K.Maurya, S. Mahajan, and N. Chaube, "Remote sensing techniques: mapping and monitoring of mangrove ecosystem—a review," Complex Intell. Syst., 7 (6) 2797-2818 (2021) doi:10.1007/s40747-021-00457-z
  38. 38) D.Wang, B. Wan, P. Qiu, Y. Su, Q. Guo, R. Wang, F. Sun, and X. Wu, "Evaluating the performance of sentinel-2, landsat 8 and pléiades-1 in mapping mangrove extent and species," Remote Sens., 10 (9) (2018) doi:10.3390/rs10091468
  39. 39) A.B.Baloloy, A.C. Blanco, R.R.C. Raymund Rhommel, and K. Nadaoka, "Development and application of a new mangrove vegetation index (mvi) for rapid and accurate mangrove mapping," ISPRS J. Photogramm. Remote Sens., 166 95-117 (2020) doi:10.1016/j.isprsjprs.2020.06.001
Other Papers in This Issue