Studying How Machine Learning Maps Mangroves in Moderate-Resolution Satellite Images

Agus Ambarwari, Emir Mauludi Husni


Intertidal mangrove forests are ecosystems that are extremely productive offering diverse socio-economic advantages. Preserving and appropriately using these ecosystems is crucial. However, safeguarding and restoring mangroves present challenges due to their extensive and hard-to-reach areas. Leveraging remote sensing technology and diverse image classification methods has shown promise in accurately mapping and monitoring mangroves. This study reviews the use of machine learning methods in mapping and monitoring mangroves, particularly using moderate-resolution multispectral satellite images. The literature study was conducted by systematically searching and analyzing articles published in Scopus-indexed journals from 2018 and 2023. The primary goals are to uncover methodologies for mapping mangroves with moderate-resolution imagery, identify advancements in machine learning algorithms, and assist researchers in staying updated in this field. The findings reveal that various machine-learning algorithms can be employed to map mangroves. Mangrove mapping with machine learning typically involves stages such as inputting multispectral images, image preprocessing, image classification, and assessing accuracy. Among the techniques, in the case of remote sensing data, ensemble tree-based approaches such as random forest outperform single classifiers. Potential and emerging issues for future research encompass automating the generation of training datasets for specific land cover classification, developing methods to transfer the classification model to different study areas, and making use of cloud-based technologies for processing remote sensing data.


machine learning; mapping; mangrove; multispectral; remote sensing

Full Text:



K. Maurya, S. Mahajan, and N. Chaube, “Remote sensing techniques: mapping and monitoring of mangrove ecosystem—a review,” Complex Intell. Syst., vol. 7, no. 6, pp. 2797–2818, Dec. 2021, doi: 10.1007/s40747-021-00457-z.

T. D. Pham, J. Xia, N. T. Ha, D. T. Bui, N. N. Le, and W. Tekeuchi, “A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018,” Sensors, vol. 19, no. 8, p. 1933, Apr. 2019, doi: 10.3390/s19081933.

P. Bunting et al., “The Global Mangrove Watch—A New 2010 Global Baseline of Mangrove Extent,” Remote Sensing, vol. 10, no. 10, p. 1669, Oct. 2018, doi: 10.3390/rs10101669.

P. Bunting et al., “Global Mangrove Extent Change 1996–2020: Global Mangrove Watch Version 3.0,” Remote Sensing, vol. 14, no. 15, p. 3657, Jul. 2022, doi: 10.3390/rs14153657.

C. Kuenzer, A. Bluemel, S. Gebhardt, T. V. Quoc, and S. Dech, “Remote Sensing of Mangrove Ecosystems: A Review,” Remote Sensing, vol. 3, no. 5, pp. 878–928, Apr. 2011, doi: 10.3390/rs3050878.

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., vol. 28, no. 1, pp. 39–51, Feb. 2020, doi: 10.1007/s41324-019-00268-y.

Z. Xue and S. Qian, “Generalized Composite Mangrove Index for Mapping Mangroves Using Sentinel-2 Time Series Data,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, vol. 15, pp. 5131–5146, 2022, doi: 10.1109/JSTARS.2022.3185078.

L. Wang, M. Jia, D. Yin, and J. Tian, “A review of remote sensing for mangrove forests: 1956–2018,” Remote Sensing of Environment, vol. 231, p. 111223, Sep. 2019, doi: 10.1016/j.rse.2019.111223.

Q. Zhao et al., “An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends,” Remote Sensing, vol. 14, no. 8, p. 1863, Apr. 2022, doi: 10.3390/rs14081863.

A. E. Maxwell, T. A. Warner, and F. Fang, “Implementation of machine-learning classification in remote sensing: an applied review,” International Journal of Remote Sensing, vol. 39, no. 9, pp. 2784–2817, May 2018, doi: 10.1080/01431161.2018.1433343.

A. Vali, S. Comai, and M. Matteucci, “Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review,” Remote Sensing, vol. 12, no. 15, p. 2495, Aug. 2020, doi: 10.3390/rs12152495.

J. Wang, M. Bretz, M. A. A. Dewan, and M. A. Delavar, “Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects,” Science of The Total Environment, vol. 822, p. 153559, May 2022, doi: 10.1016/j.scitotenv.2022.153559.

X. Zhang, P. M. Treitz, D. Chen, C. Quan, L. Shi, and X. Li, “Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure,” International Journal of Applied Earth Observation and Geoinformation, vol. 62, pp. 201–214, Oct. 2017, doi: 10.1016/j.jag.2017.06.010.

P. Mondal, X. Liu, T. E. Fatoyinbo, and D. Lagomasino, “Evaluating Combinations of Sentinel-2 Data and Machine-Learning Algorithms for Mangrove Mapping in West Africa,” Remote Sensing, vol. 11, no. 24, p. 2928, Dec. 2019, doi: 10.3390/rs11242928.

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,” Global Ecology and Conservation, vol. 19, p. e00662, Jul. 2019, doi: 10.1016/j.gecco.2019.e00662.

S. M. Hickey and B. Radford, “Turning the Tide on Mapping Marginal Mangroves with Multi-Dimensional Space–Time Remote Sensing,” Remote Sensing, vol. 14, no. 14, p. 3365, Jul. 2022, doi: 10.3390/rs14143365.

A. D. Purwanto, K. Wikantika, A. Deliar, and S. Darmawan, “Decision Tree and Random Forest Classification Algorithms for Mangrove Forest Mapping in Sembilang National Park, Indonesia,” Remote Sensing, vol. 15, no. 1, p. 16, Dec. 2022, doi: 10.3390/rs15010016.

F. Lombard and J. Andrieu, “Mapping Mangrove Zonation Changes in Senegal with Landsat Imagery Using an OBIA Approach Combined with Linear Spectral Unmixing,” Remote Sensing, vol. 13, no. 10, p. 1961, May 2021, doi: 10.3390/rs13101961.

M. H. Phan and M. J. F. Stive, “Managing mangroves and coastal land cover in the Mekong Delta,” Ocean & Coastal Management, vol. 219, p. 106013, Mar. 2022, doi: 10.1016/j.ocecoaman.2021.106013.

M. Guo, Z. Yu, Y. Xu, Y. Huang, and C. Li, “ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data,” Remote Sensing, vol. 13, no. 7, p. 1292, Mar. 2021, doi: 10.3390/rs13071292.

D. Lomeo and M. Singh, “Cloud-Based Monitoring and Evaluation of the Spatial-Temporal Distribution of Southeast Asia’s Mangroves Using Deep Learning,” Remote Sensing, vol. 14, no. 10, p. 2291, May 2022, doi: 10.3390/rs14102291.

T. Pham, N. Yokoya, D. Bui, K. Yoshino, and D. Friess, “Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges,” Remote Sensing, vol. 11, no. 3, p. 230, Jan. 2019, doi: 10.3390/rs11030230.

J. R. Jensen, Introductory Digital Image Processing: A Remote Sensing Perspective, 4th ed. in Pearson series in geographic information science. United States of America: Pearson, 2015.

G. Camps-Valls, D. Tuia, L. Gómez-Chova, S. Jiménez, and J. Malo, Remote Sensing Image Processing. in Synthesis Lectures on Image, Video, and Multimedia Processing. Cham: Springer International Publishing, 2012. doi: 10.1007/978-3-031-02247-0.

S. Borra, R. Thanki, and N. Dey, Satellite Image Analysis: Clustering and Classification. in SpringerBriefs in Applied Sciences and Technology. Singapore: Springer Singapore, 2019. doi: 10.1007/978-981-13-6424-2.

M. Kamal, S. Phinn, and K. Johansen, “Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets,” Remote Sensing, vol. 7, no. 4, pp. 4753–4783, Apr. 2015, doi: 10.3390/rs70404753.

T. M. Lillesand, R. W. Kiefer, and J. W. Chipman, Remote Sensing and Image Interpretation, 7th ed. Hoboken, NJ: Wiley, 2015.

T. T. P. Vu et al., “Mapping Multi-Decadal Mangrove Extent in the Northern Coast of Vietnam Using Landsat Time-Series Data on Google Earth Engine Platform,” Remote Sensing, vol. 14, no. 18, p. 4664, Sep. 2022, doi: 10.3390/rs14184664.

Z. Zhang, N. Xu, Y. Li, and Y. Li, “Sub-continental-scale mapping of tidal wetland composition for East Asia: A novel algorithm integrating satellite tide-level and phenological features,” Remote Sensing of Environment, vol. 269, p. 112799, Feb. 2022, doi: 10.1016/j.rse.2021.112799.

A. Waśniewski, A. Hościło, B. Zagajewski, and D. Moukétou-Tarazewicz, “Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon,” Forests, vol. 11, no. 9, p. 941, Aug. 2020, doi: 10.3390/f11090941.

R. Zhang et al., “Tracking annual dynamics of mangrove forests in mangrove National Nature Reserves of China based on time series Sentinel-2 imagery during 2016–2020,” International Journal of Applied Earth Observation and Geoinformation, vol. 112, p. 102918, Aug. 2022, doi: 10.1016/j.jag.2022.102918.

B. Ferreira, R. G. Silva, and M. Iten, “Earth Observation Satellite Imagery Information Based Decision Support Using Machine Learning,” Remote Sensing, vol. 14, no. 15, p. 3776, Aug. 2022, doi: 10.3390/rs14153776.

R. Zhang et al., “A Comparison of Gaofen-2 and Sentinel-2 Imagery for Mapping Mangrove Forests Using Object-Oriented Analysis and Random Forest,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, vol. 14, pp. 4185–4193, 2021, doi: 10.1109/JSTARS.2021.3070810.

J. Silva, F. Bacao, and M. Caetano, “Specific Land Cover Class Mapping by Semi-Supervised Weighted Support Vector Machines,” Remote Sensing, vol. 9, no. 2, p. 181, Feb. 2017, doi: 10.3390/rs9020181.

M. Belgiu and L. Drăguţ, “Random forest in remote sensing: A review of applications and future directions,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 114, pp. 24–31, Apr. 2016, doi: 10.1016/j.isprsjprs.2016.01.011.

M. Sheykhmousa, M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi, and S. Homayouni, “Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, vol. 13, pp. 6308–6325, 2020, doi: 10.1109/JSTARS.2020.3026724.

B. K. Kenduiywo, F. N. Mutua, T. G. Ngigi, and E. H. Waithaka, “Mapping mangrove forest using Landsat 8 to support estimation of land-based emissions in Kenya,” Model. Earth Syst. Environ., vol. 6, no. 3, pp. 1619–1632, Sep. 2020, doi: 10.1007/s40808-020-00778-x.

N. J. Murray et al., “The global distribution and trajectory of tidal flats,” Nature, vol. 565, no. 7738, pp. 222–225, Jan. 2019, doi: 10.1038/s41586-018-0805-8.

Y. Lu and L. Wang, “How to automate timely large-scale mangrove mapping with remote sensing,” Remote Sensing of Environment, vol. 264, p. 112584, Oct. 2021, doi: 10.1016/j.rse.2021.112584.

X. Yan and Z. Niu, “Reliability Evaluation and Migration of Wetland Samples,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, vol. 14, pp. 8089–8099, 2021, doi: 10.1109/JSTARS.2021.3102866.

N. J. Murray et al., “coastTrain: A Global Reference Library for Coastal Ecosystems,” Remote Sensing, vol. 14, no. 22, p. 5766, Nov. 2022, doi: 10.3390/rs14225766.

P. Bunting, A. Rosenqvist, L. Hilarides, R. M. Lucas, and N. Thomas, “Global Mangrove Watch: Updated 2010 Mangrove Forest Extent (v2.5),” Remote Sensing, vol. 14, no. 4, p. 1034, Feb. 2022, doi: 10.3390/rs14041034.

C. Giri et al., “Status and distribution of mangrove forests of the world using earth observation satellite data: Status and distributions of global mangroves,” Global Ecology and Biogeography, vol. 20, no. 1, pp. 154–159, Jan. 2011, doi: 10.1111/j.1466-8238.2010.00584.x.



  • There are currently no refbacks.

Office and Secretariat:

Big Data Research Centre
Puzzle Research Data Technology (Predatech)
Laboratory Building 1st Floor of Faculty of Science and Technology
UIN Sultan Syarif Kasim Riau

Jl. HR. Soebrantas KM. 18.5 No. 155 Pekanbaru Riau – 28293
Phone: 085275359942

Click Here for Information

Journal Indexing:

Google Scholar | ROAD | PKP Index | BASE | ESJI | General Impact Factor | Garuda | Moraref | One Search | Cite Factor | Crossref | WorldCat | Neliti  | SINTA | Dimensions | ICI Index Copernicus