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Mapping Mangrove Species Distribution and Density Using Sentinel-2 Satellite Imagery and Spectral Analysis

Lalu Muhamad Jaelani, Dwi Sugma Safitri, Naomy Elliana Kristian, Aldea Noor Alina, Muhammad Aldila Syariz, Hartanto Sanjaya, Abdul Rauf Abdul Rasam

Abstract


This study aimed to conduct a comprehensive analysis and mapping of mangrove species distribution and density in conservation areas of Surabaya, Indonesia. The investigation focused on assessing the current state of mangrove ecosystems using Sentinel-2 satellite imagery and advanced spectral analysis methods, which were crucial for climate regulation, food security, and poverty reduction. Moreover, Linear Spectral Unmixing (LSU) was used to accurately classify mangrove species and individual densities. The methodology included the use of radiometrically corrected Sentinel-2A imagery and spectral library data obtained from various national agencies. The findings showed that the Pamurbaya protected area covered 7,965,971 m², with Avicennia Marina accounting for 74% of the mangrove, followed by Rhizophora Mucronata (24%) and Rhizophora Apiculata (2%). Additionally, this study showed significant density variations, with 83% of the area densely populated, and also provided novel insights by applying LSU, indicating a significant advancement in environmental monitoring. The outcome offered critical information for policymakers and stakeholders to develop effective conservation and management strategies to ensure the long-term sustainability of critical coastal ecosystems. Finally, the findings showed the urgency of systematic conservation efforts to address the impact of deforestation and land-use changes on mangrove habitats worldwide.

 

Doi: 10.28991/HEF-2025-06-01-01

Full Text: PDF


Keywords


Linear Spectral Unmixing; Mangrove; Pamurbaya; Remote Sensing; Sustainability.

References


Jusoff, K. (2008). Geospatial Information Technology for Conservation of Coastal Forest and Mangroves Environment in Malaysia. Computer and Information Science, 1(2), 129. doi:10.5539/cis.v1n2p129.

van Bochove, J. W., Sullivan, E., Nakamura, T., & Drakou, E. G. (2014). The importance of mangroves to people: a call to action. United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), Cambridge, United Kingdom.

Alappatt, J. P. (2018). Structure and species diversity of mangrove ecosystem. Biodiversity and Climate Change Adaptation in Tropical Islands, 127–144. doi:10.1016/B978-0-12-813064-3.00005-3.

Warfield, A. D., & Leon, J. X. (2019). Estimating mangrove forest volume using terrestrial laser scanning and UAV-derived structure-from-motion. Drones, 3(2), 1–17. doi:10.3390/drones3020032.

Friess, D. A., Rogers, K., Lovelock, C. E., Krauss, K. W., Hamilton, S. E., Lee, S. Y., Lucas, R., Primavera, J., Rajkaran, A., & Shi, S. (2019). The State of the World’s Mangrove Forests: Past, Present, and Future. Annual Review of Environment and Resources, 44(1), 89–115. doi:10.1146/annurev-environ-101718-033302.

Islam, K., Sarker, S., Morsad, G., Khan, M. F., Chowdhury, A., & Islam, S. L. U. (2024). Spatial–temporal changes of shoreline and Vegetation: Impacts on mangrove cover along the Sundarbans area, Bangladesh. Journal of Coastal Conservation, 28(1). doi:10.1007/s11852-023-01016-z.

Dong, H., Gao, Y., Chen, R., & Wei, L. (2024). MangroveSeg: Deep-Supervision-Guided Feature Aggregation Network for Mangrove Detection and Segmentation in Satellite Images. Forests, 15(1), 127. doi:10.3390/f15010127.

Ginting, D. N. B., Setiawan, K. T., Anggraini, N., Suardana, A. P., Nandika, M. R., Ulfa, A., Aziz, K., & Dewanti, R. (2024). Comparison between top and bottom of atmosphere Sentinel-2 image for mangrove mapping in Balikpapan Bay, East Kalimantan. BIO Web of Conferences, 89. doi:10.1051/bioconf/20248907003.

Alongi, D. M. (2014). Carbon cycling and storage in mangrove forests. Annual Review of Marine Science, 6, 195–219. doi:10.1146/annurev-marine-010213-135020.

Rovai, A. S., Twilley, R. R., Worthington, T. A., & Riul, P. (2022). Brazilian Mangroves: Blue Carbon Hotspots of National and Global Relevance to Natural Climate Solutions. Frontiers in Forests and Global Change, 4. doi:10.3389/ffgc.2021.787533.

Richards, D. R., Thompson, B. S., & Wijedasa, L. (2020). Quantifying net loss of global mangrove carbon stocks from 20 years of land cover change. Nature Communications, 11(1), 4260. doi:10.1038/s41467-020-18118-z.

Chatting, M., Al-Maslamani, I., Walton, M., Skov, M. W., Kennedy, H., Husrevoglu, Y. S., & Le Vay, L. (2022). Future Mangrove Carbon Storage under Climate Change and Deforestation. Frontiers in Marine Science, 9. doi:10.3389/fmars.2022.781876.

Chatting, M., LeVay, L., Walton, M., Skov, M. W., Kennedy, H., Wilson, S., & Al-Maslamani, I. (2020). Mangrove carbon stocks and biomass partitioning in an extreme environment. Estuarine, Coastal and Shelf Science, 244. doi:10.1016/j.ecss.2020.106940.

Tengku Hashim, T. M. Z., & Suratman, M. N. (2021). Mangroves as a Carbon Sink/Stocks. In Mangroves: Ecology, Biodiversity and Management. Springer Singapore. doi:10.1007/978-981-16-2494-0_7.

Alongi, D. M. (2008). Mangrove forests: Resilience, protection from tsunamis, and responses to global climate change. Estuarine, Coastal and Shelf Science, 76(1), 1–13. doi:10.1016/j.ecss.2007.08.024.

Alongi, D. M. (2015). The Impact of Climate Change on Mangrove Forests. Current Climate Change Reports, 1(1), 30–39. doi:10.1007/s40641-015-0002-x.

Giri, C. (2016). Observation and monitoring of mangrove forests using remote sensing: Opportunities and challenges. Remote Sensing, 8(9), 783. doi:10.3390/rs8090783.

Gandhi, S., & Jones, T. G. (2019). Identifying mangrove deforestation hotspots in South Asia, Southeast Asia and Asia-Pacific. Remote Sensing, 11(6), 728. doi:10.3390/RS11060728.

Carter, H. N., Schmidt, S. W., & Hirons, A. C. (2015). An international assessment of mangrove management: Incorporation in integrated coastal zone management. Diversity, 7(2), 74–104. doi:10.3390/d7020074.

Dahdouh-Guebas, F. (2002). The use of remote sensing and GIS in the sustainable management of tropical coastal ecosystems. Environment, Development and Sustainability, 4(2), 93–112. doi:10.1023/A:1020887204285.

Maurya, K., Mahajan, S., & Chaube, N. (2021). Remote sensing techniques: mapping and monitoring of mangrove ecosystem—a review. Complex and Intelligent Systems, 7(6), 2797–2818. doi:10.1007/s40747-021-00457-z.

Pham, T. D., Yokoya, N., Bui, D. T., Yoshino, K., & Friess, D. A. (2019). Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges. Remote Sensing, 11(3), 230. doi:10.3390/rs11030230.

Yudha, G. D., & Purnama, S. M. (2018). Analysis of dissemination methods for remote sensing information in maritime field to support the local government. IOP Conference Series: Earth and Environmental Science, 162(1), 12046. doi:10.1088/1755-1315/162/1/012046.

Wang, L., Jia, M., Yin, D., & Tian, J. (2019). A review of remote sensing for mangrove forests: 1956–2018. Remote Sensing of Environment, 231. doi:10.1016/j.rse.2019.111223.

Melesse, A. M., & Jordan, J. D. (2002). A comparison of fuzzy vs. augmented-ISODATA classification algorithms for cloud-shadow discrimination from Landsat images. Photogrammetric Engineering and Remote Sensing, 68(9), 905–911.

Kantakumar, L. N., & Neelamsetti, P. (2015). Multi-temporal land use classification using hybrid approach. Egyptian Journal of Remote Sensing and Space Science, 18(2), 289–295. doi:10.1016/j.ejrs.2015.09.003.

Keshava, N., & Mustard, J. F. (2002). Spectral unmixing. IEEE Signal Processing Magazine, 19(1), 44–57. doi:10.1109/79.974727.

Kärdi, T. (2007). Remote sensing of urban areas: Linear spectral unmixing of Landsat Thematic Mapper images acquired over Tartu (Estonia). Proceedings of the Estonian Academy of Sciences: Biology, Ecology, 56(1), 19–32. doi:10.3176/eco.2007.1.02.

Cipta, I. M., Jaelani, L. M., & Sanjaya, H. (2022). Identification of Paddy Varieties from Landsat 8 Satellite Image Data Using Spectral Unmixing Method in Indramayu Regency, Indonesia. ISPRS International Journal of Geo-Information, 11(10). doi:10.3390/ijgi11100510.

Cipta, I. M., Jaelani, L. M., & Sanjaya, H. (2024). A phenology-based linear spectral unmixing method for rice varieties identification from Landsat 8 Satellite Image in Ngawi District, East Java, Indonesia. Biodiversitas, 25(4), 1691–1702. doi:10.13057/biodiv/d250439.

Kusoiry, M. R., Jaelani, L. M., & Sanjaya, H. (2024). Using satellite image data to identify rice varieties through linear spectral unmixing method (case study: Karangjati Sub District, Ngawi Regency). Advances in Modern Agriculture, 5(2), 2538. doi:10.54517/ama.v5i2.2538.

Xu, X., Tong, X., Plaza, A., Zhong, Y., Xie, H., & Zhang, L. (2017). Using Linear Spectral Unmixing for Subpixel Mapping of Hyperspectral Imagery: A Quantitative Assessment. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(4), 1589–1600. doi:10.1109/JSTARS.2016.2624560.

Yu, J., Chen, D., Lin, Y., & Ye, S. (2017). Comparison of linear and nonlinear spectral unmixing approaches: a case study with multispectral TM imagery. International Journal of Remote Sensing, 38(3), 773–795. doi:10.1080/01431161.2016.1271475.

Green, E. P., Clark, C. D., Mumby, P. J., Edwards, A. J., & Ellis, A. C. (1998). Remote sensing techniques for mangrove mapping. International Journal of Remote Sensing, 19(5), 935–956. doi:10.1080/014311698215801.

Jensen, J. R., Lin, H., Yang, X., Ramsey, E., Davis, B. A., & Davis, C. W. (1991). The measurement of mangrove characteristics in southwest florida using spot multispectral data. Geocarto International, 6(2), 13–21. doi:10.1080/10106049109354302.

Inoue, T., Kohzu, A., Akaji, Y., Miura, S., & Baba, S. (2024). Diazotrophic nitrogen fixation through aerial roots occurs in Avicennia marina: implications for adaptation of mangrove plant growth to low-nitrogen tidal flats. New Phytologist, 241(4), 1464–1475. doi:10.1111/nph.19442.

Ramesh, C., Muthukumar, C., Koushik, S., Shunmugaraj, T., & Mallavarapu Venkata Ramana, M. (2024). Hypersalinity and Low Nitrogen Levels Cause Dwarf Growth in Mangrove Avicennia marina (Forssk.) Vierh. Proceedings of the National Academy of Sciences India Section B - Biological Sciences, 94(2), 331–336. doi:10.1007/s40011-023-01532-w.

Friis, G., Smith, E. G., Lovelock, C. E., Ortega, A., Marshell, A., Duarte, C. M., & Burt, J. A. (2024). Rapid diversification of grey mangroves (Avicennia marina) driven by geographic isolation and extreme environmental conditions in the Arabian Peninsula. Molecular Ecology, 33(4), e17260. doi:10.1111/mec.17260.

Sitepu, B. S., Chasani, A. R., Mukhlisi, M., Atmoko, T., Adman, B., & Prihatini, I. (2024). Camptostemon philippinensis, a new record of endangered mangrove species in the Balikpapan Bay, East Kalimantan, Indonesia. F1000Research, 12, 1394. doi:10.12688/f1000research.140887.2.

Kinya, G., Kairo, J. G., Nyoike, R. N., Nguu, J. G., Githinji, B. K., & Githaiga, M. N. (2024). Eco-Engineering Mangrove Restoration at Gazi Bay, Kenya. Diversity, 16(3), 135. doi:10.3390/d16030135.

Nimsi, K. A., Arya, H., Manjusha, K., & Kathiresan, K. (2024). Multifarious plant growth-promoting traits of mangrove yeasts: growth enhancement in mangrove seedlings (Rhizophora mucronata) for conservation. Archives of Microbiology, 206(4), 192. doi:10.1007/s00203-024-03913-9.


Full Text: PDF

DOI: 10.28991/HEF-2025-06-01-01

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Copyright (c) 2025 Lalu Muhamad Jaelani, Dwi Sugma Safitri, Naomy Elliana Kristian, Aldea Noor Alina, Muhammad Aldila Syariz, Hartanto Sanjaya, Abdul Rauf Abdul Rasam