Skip to main content
SpringerLink
Log in

Multidecadal mangrove forest change in Macajalar Bay, Northern Mindanao, Philippines (1950–2020) using remote sensing and geographic information systems

  • Research
  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

The mangrove forest in Macajalar Bay is regarded as an important coastal ecosystem since it provides numerous ecosystem services. Despite their importance, the clearing of mangroves has been rampant and has reached critical rates. Addressing this problem and further advancing its conservation require accurate mangrove mapping. However, current spatial information related to mangroves is sparse and insufficient to understand the historical change dynamics. In this study, the synergy of 1950 vegetation maps and Landsat images was explored to provide multidecadal monitoring of mangrove forest change dynamics in Macajalar Bay, Philippines. Vegetation maps containing the 1950 mangrove extent and Landsat images were used as input data to monitor the rates of loss over 70 years. In 2020, the mangrove forest cover was estimated to be 201.73 ha, equivalent to only 61.99% of the 325.43 ha that was estimated in 1950. Between 1950 and 2020, net mangrove loss in Macajalar Bay totaled 324.29 ha. The highest clearing rates occurred between 1950 and 1990 when it recorded a total of 258.51 ha, averaging 6.46 ha/year. The original mangrove forest that existed in 1950 only represents 8.56% of the 2020 extent, suggesting that much of the old-growth mangrove had been cleared before 2000 and the existing mangrove forest is mainly composed of secondary mangrove forest stands. Across Macajalar Bay, intensified clearing that happened between 1950 and 1990 has been driven by large-scale aquaculture developments. Mangrove gains on the other hand were evident and have increased the total extent by 79.84 ha since 2000 as a result of several afforestation programs. However, approximately half of these gains that were observed since 2010 exhibited low canopy cover. As of writing, approximately 85% of the 2020 mangrove forest stands fall outside the 1950 original mangrove extent. Examining the viability of the original mangrove forest for mangrove reforestation together with promoting site-species matching, and biophysical assessment are necessary undertakings to advance current mangrove conservation initiatives in Macajalar Bay.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The datasets generated in this study are available upon reasonable request from the corresponding author.

References

  • Aladano, A., Cimene, F. T., & Asuncion, J. J. (2016). Community sustainability through mangrove rehabilitation: The Taytay, El Salvador City, Philippines experience. International Journal of Ecology and Conservation., 19, 70–86.

    Google Scholar 

  • Alongi, D. M. (2002). Present state and future of the world’s mangrove forests. Environmental Conservation, 29(3), 331–349. https://doi.org/10.1017/S0376892902000231

    Article  Google Scholar 

  • Baloloy, A. B., Blanco, A. C., Sta. Ana, R. R. C., & Nadaoka, K. (2020). Development and application of a new Mangrove Vegetation Index (MVI) for rapid and accurate mangrove mapping. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 95–117. https://doi.org/10.1016/j.isprsjprs.2020.06.001

    Article  Google Scholar 

  • Brown, W. H., & Fischer, A. F. (1920). Philippine mangrove swamps. Department of Agriculture and Natural Resources. Bureau of Forestry Bulletin, (17).

  • Buitre, M., Zhang, H., & Lin, H. (2019). The mangrove forests change and impacts from tropical cyclones in the Philippines using time series satellite imagery. Remote Sensing, 11(6), 688. https://doi.org/10.3390/rs11060688

    Article  Google Scholar 

  • Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R. M., Thomas, N., Tadono, T., Worthington, T. A., Spalding, M., Murray, N. J., & Rebelo, L.-M. (2022). Global mangrove extent change 1996–2020: Global Mangrove Watch version 3.0. Remote Sensing, 14(15), 3657. https://doi.org/10.3390/rs14153657

    Article  Google Scholar 

  • Cabili, J. R., Torres, M. A., Ignacio, M. T., Dagoc, K. M., & Guihawan, J. (2018). Object-based image analysis for extraction of mangrove forests in rehabilitated areas of Bacolod, Lanao Del Norte, Philippines using LIDAR data and GIS. Journal of Applied Sciences Research, 14(4), 6–10. https://doi.org/10.22587/jasr.2018.14.4.2

    Article  Google Scholar 

  • Cailing, C. A. G., Caban, J. L. S., Cultura, R. K. M., & Relox, R. E. (2018). Relationship of avifauna and mangroves in Laguindingan, Misamis Oriental, Philippines. Journal of Biodiversity and Environmental Sciences, 13(1), 216–222.

    Google Scholar 

  • Camacho, L. D., Gevaña, D. T., Sabino, L. L., Ruzol, C. D., Garcia, J. E., Camacho, A. C. D., Oo, T. N., Maung, A. C., Saxena, K. G., Liang, L., Yiu, E., & Takeuchi, K. (2020). Sustainable mangrove rehabilitation: Lessons and insights from community-based management in the Philippines and Myanmar. APN Science Bulletin, 10(1), 18–25. https://doi.org/10.30852/sb.2020.983

    Article  Google Scholar 

  • Canoy, M. E., & Roa-Quiaoit, H. A. (2011). Ridge to reef in the Philippines: A showcase of nine emerging and merging initiatives. Xavier University Press.

    Google Scholar 

  • Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893–903. https://doi.org/10.1016/j.rse.2009.01.007

    Article  Google Scholar 

  • Consolacion, F., Teves, M. R., Suson, P., Tiu-Tatil, W., Dagoc, F. L., & Ruben, A., Jr. (2021). Comparative economic analysis between commercial aquaculture and selected mangrove ecosystems along Macajalar Bay, Philippines. Journal of Biodiversity and Environmental Sciences, 19(1), 80–87.

    Google Scholar 

  • Corcino, R. C. B., Gerona-Daga, M. E. B., Samoza, S. C., Fraga, J. K. R., & Salmo III, S. G. (2023). Status, limitations, and challenges of blue carbon studies in the Philippines: A bibliographic analysis. Regional Studies in Marine Science, 102916. https://doi.org/10.1016/j.rsma.2023.102916

  • Duke, N. C., Meynecke, J.-O., Dittmann, S., Ellison, A. M., Anger, K., Berger, U., Cannicci, S., Diele, K., Ewel, K. C., Field, C. D., Koedam, N., Lee, S. Y., Marchand, C., Nordhaus, I., & Dahdouh-Guebas, F. (2007). A world without mangroves? Science, 317(5834), 41–42. https://doi.org/10.1126/science.317.5834.41b

    Article  CAS  Google Scholar 

  • Gevaña, D. T., Pulhin, J. M., & Tapia, M. A. (2019). Fostering climate change mitigation through a community-based approach: Carbon stock potential of community-managed mangroves in the Philippines. Coastal Management, 271–282. https://doi.org/10.1016/b978-0-12-810473-6.00014-5

  • Goldberg, L., Lagomasino, D., Thomas, N., & Fatoyinbo, T. (2020). Global declines in human-driven mangrove loss. Global Change Biology, 26(10), 5844–5855. https://doi.org/10.1111/gcb.15275

    Article  Google Scholar 

  • Green, G., Schweik, C., & Randolph, J. (2005). Retrieving land-cover change information from Landsat satellite images by minimizing other sources of reflectance variability. In E. Moran & E. Ostrom (Eds.), Seeing the forest and the trees: Human-environment interactions in forest ecosystems (pp. 130–160). MIT Press.

    Google Scholar 

  • Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021). A commentary review on the use of Normalized Difference Vegetation Index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6. https://doi.org/10.1007/s11676-020-01155-1

    Article  Google Scholar 

  • Hutchison, J., Manica, A., Swetnam, R., Balmford, A., & Spalding, M. (2014). Predicting global patterns in mangrove forest biomass. Conservation Letters, 7(3), 233–240. https://doi.org/10.1111/conl.12060

    Article  Google Scholar 

  • Jhonnerie, R., Siregar, V. P., Nababan, B., Prasetyo, L. B., & Wouthuyzen, S. (2015). Random Forest classification for mangrove land cover mapping using Landsat 5 TM and ALOS PALSAR imageries. Procedia Environmental Sciences, 24, 215–221. https://doi.org/10.1016/j.proenv.2015.03.028

    Article  Google Scholar 

  • Jones, T., Glass, L., Gandhi, S., Ravaoarinorotsihoarana, L., Carro, A., Benson, L., Ratsimba, H., Giri, C., Randriamanatena, D., & Cripps, G. (2016). Madagascar’s mangroves: Quantifying nation-wide and ecosystem specific dynamics, and detailed contemporary mapping of distinct ecosystems. Remote Sensing, 8(2), 106. https://doi.org/10.3390/rs8020106

    Article  Google Scholar 

  • Lee, S. Y., Primavera, J. H., Dahdouh-Guebas, F., McKee, K., Bosire, J. O., Cannicci, S., Diele, K., Fromard, F., Koedam, N., Marchand, C., Mendelssohn, I., Mukherjee, N., & Record, S. (2014). Ecological role and services of tropical mangrove ecosystems: A reassessment. Global Ecology and Biogeography, 23(7), 726–743. https://doi.org/10.1111/geb.12155

    Article  Google Scholar 

  • Long, J., Napton, D., Giri, C., & Graesser, J. (2014). A mapping and monitoring assessment of the Philippines’ mangrove forests from 1990 to 2010. Journal of Coastal Research, 30(2), 260–271. https://doi.org/10.2112/JCOASTRES-D-13-00057.1

    Article  Google Scholar 

  • Luna, D. A., Ocampo, D. V., Quiñones, S. G. L., Rivera, E. C., Sejalbo, C. G., Zara, P. R., Hizon, K. B., Delos Reyes, J. A., Ticzon, V., & Macandog, D. M. (2017). Coastal objects: Mangrove area extraction using remote sensing and aerial LiDAR data in Roxas Oriental Mindoro. Environment and Ecology Research, 5(4), 282–288. https://doi.org/10.13189/eer.2017.050405

    Article  Google Scholar 

  • Menéndez, P., Losada, I. J., Beck, M. W., Torres-Ortega, S., Espejo, A., Narayan, S., Díaz-Simal, P., & Lange, G.-M. (2018). Valuing the protection services of mangroves at national scale: The Philippines. Ecosystem Services, 34, 24–36. https://doi.org/10.1016/j.ecoser.2018.09.005

    Article  Google Scholar 

  • Nguyen, C. T., Chidthaisong, A., Kieu Diem, P., & Huo, L.-Z. (2021). A modified bare soil index to identify bare land features during agricultural fallow-period in Southeast Asia using Landsat 8. Land, 10(3), 231. https://doi.org/10.3390/land10030231

    Article  Google Scholar 

  • Olila, J. B., & Relox, R. (2021). Factors affecting avifauna diversity in selected mangrove areas of Misamis Oriental, Philippines: Basis for conservation and management. Journal of Biodiversity and Environmental Sciences, 19(2), 32–42.

    Google Scholar 

  • Perez, G. J. P., & Comiso, J. C. (2014). Seasonal and interannual variabilities of Philippine vegetation as seen from space. Philippine Journal of Science, 143(2), 147–155.

    Google Scholar 

  • Polidoro, B. A., Carpenter, K. E., Collins, L., Duke, N. C., Ellison, A. M., Ellison, J. C., Farnsworth, E. J., Fernando, E. S., Kathiresan, K., Koedam, N. E., Livingstone, S. R., Miyagi, T., Moore, G. E., Nam, V. N., Ong, J. E., Primavera, J. H., Salmo, S. G., III., Sanciangco, J. C., Sukardjo, S., … Yong, J. W. H. (2010). The loss of species: Mangrove extinction risk and geographic areas of global concern. PLoS ONE, 5(4), e10095. https://doi.org/10.1371/journal.pone.0010095

    Article  CAS  Google Scholar 

  • Potapov, P. V., Turubanova, S. A., Hansen, M. C., Adusei, B., Broich, M., Altstatt, A., Mane, L., & Justice, C. O. (2012). Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM+ data. Remote Sensing of Environment, 122, 106–116. https://doi.org/10.1016/j.rse.2011.08.027

    Article  Google Scholar 

  • Primavera, J., Sadaba, R., Lebata, M., Hazel, J., & Altamirano, J. (2004). Handbook of mangroves in the Philippines-Panay. Tigbauan, Iloilo, Philippines: Aquaculture Department, Southeast Asian Fisheries Development Center.

    Google Scholar 

  • Primavera, J. H. (1995). Mangroves and brackishwater pond culture in the Philippines. Hydrobiologia, 295(1–3), 303–309. https://doi.org/10.1007/bf00029137

    Article  Google Scholar 

  • Primavera, J. H. (2000). Development and conservation of Philippine mangroves: Institutional issues. Ecological Economics, 35(1), 91–106. https://doi.org/10.1016/s0921-8009(00)00170-1

    Article  Google Scholar 

  • Primavera, J. H. (2006). Overcoming the impacts of aquaculture on the coastal zone. Ocean & Coastal Management, 49(9–10), 531–545. https://doi.org/10.1016/j.ocecoaman.2006.06.018

    Article  Google Scholar 

  • Primavera, J. H., & Esteban, J. M. A. (2008). A review of mangrove rehabilitation in the Philippines: Successes, failures and future prospects. Wetlands Ecology and Management, 16(5), 345–358. https://doi.org/10.1007/s11273-008-9101-y

    Article  Google Scholar 

  • Primavera, J. H., Savaris, J. P., Bajoyo, B. E., Coching, J. D., Curnick, D. J., Golbeque, R. L., Guzman, A. T., Henderin, J. Q., Joven, R. V., Loma, R. A., & Koldewey, H. J. (2012). Manual on community-based mangrove rehabilitation. Zoological Society of London, United Kingdom.

    Google Scholar 

  • Rikimaru, A., Roy, P. S., & Miyatake, S. (2002). Tropical forest cover density mapping. Tropical Ecology, 43(1), 39–47.

    Google Scholar 

  • Rogers, K., Lymburner, L., Salum, R., Brooke, B. P., & Woodroffe, C. D. (2017). Mapping of mangrove extent and zonation using high and low tide composites of Landsat data. Hydrobiologia, 803(1), 49–68. https://doi.org/10.1007/s10750-017-3257-5

    Article  Google Scholar 

  • Sahana, M., Sajjad, H., & Ahmed, R. (2015). Assessing spatio-temporal health of forest cover using forest canopy density model and forest fragmentation approach in Sundarban Reserve Forest, India. Modeling Earth Systems and Environment, 1(4). https://doi.org/10.1007/s40808-015-0043-0

  • Samson, M. S., & Rollon, R. N. (2008). Growth performance of planted mangroves in the Philippines: Revisiting forest management strategies. AMBIO: A Journal of the Human Environment, 37(4), 234–240. https://doi.org/10.1579/0044-7447(2008)37[234:gpopmi]2.0.co;2

    Article  Google Scholar 

  • Santillan, J. R., Amora, A. M., Makinano-Santillan, M., Gingo, A. L., & Marqueso, J. T. (2019). Analyzing the impacts of land cover change to the hydrologic and hydraulic behaviours of the Philippines’ third largest river basin. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 4. 10.5194/isprs-annals-iv-3-w1-41-2019

  • Sharma, S., Nadaoka, K., Nakaoka, M., Uy, W. H., MacKenzie, R. A., Friess, D. A., & Fortes, M. D. (2017). Growth performance and structure of a mangrove afforestation project on a former seagrass bed, Mindanao Island Philippines. Hydrobiologia, 803(1), 359–371. https://doi.org/10.1007/s10750-017-3252-x

    Article  Google Scholar 

  • Sussman, R. W., Sweeney, S., Green, G. M., Porton, I., Andrianasolondraibe, O. L., & Ratsirarson, J. (2006). A preliminary estimate of Lemur catta population density using satellite imagery. Ringtailed lemur biology: Lemur catta in Madagascar, 16–31.

  • Taneo, L. (2021). Inventory of mangrove in Molugan, El Salvador City, Misamis Oriental. International Journal of Advanced Multidisciplinary Studies, 1(3), 26(47).

  • Thomas, N., Lucas, R., Bunting, P., Hardy, A., Rosenqvist, A., & Simard, M. (2017). Distribution and drivers of global mangrove forest change, 1996–2010. PLoS ONE, 12(6), e0179302. https://doi.org/10.1371/journal.pone.0179302

    Article  CAS  Google Scholar 

  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0

    Article  Google Scholar 

  • Tyukavina, A., Hansen, M. C., Potapov, P., Parker, D., Okpa, C., Stehman, S. V., ... & Turubanova, S. (2018). Congo Basin forest loss dominated by increasing smallholder clearing. Science advances, 4(11), eaat2993. https://doi.org/10.1126/sciadv.aat2993

  • UNEP (2014). The importance of mangroves to people: A call to action. van Bochove, J., Sullivan, E., Nakamura, T. (Eds). United Nations Environment Programme World Conservation Monitoring Centre, Cambridge. 128 pp.

  • Valderrama-Landeros, L., Flores-de-Santiago, F., Kovacs, J. M., & Flores-Verdugo, F. (2018). An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme. Environmental Monitoring and Assessment, 190, 1–13. https://doi.org/10.1007/s10661-017-6399-z

    Article  Google Scholar 

  • Walters, B. B. (2003). People and mangroves in the Philippines: Fifty years of coastal environmental change. Environmental Conservation, 30(3), 293–303. https://doi.org/10.1017/S0376892903000298

    Article  Google Scholar 

  • Yancho, J., Jones, T., Gandhi, S., Ferster, C., Lin, A., & Glass, L. (2020). The Google Earth Engine Mangrove Mapping Methodology (GEEMMM). Remote Sensing, 12(22), 3758. https://doi.org/10.3390/rs12223758

    Article  Google Scholar 

Download references

Acknowledgements

The technical and logistical assistance provided by the Department of Environmental Science and Technology is acknowledged. Additional technical assistance provided by the Department of Science and Technology – Accelerated Science and Technology Human Resource Development Program (DOST-ASTHRDP) is similarly acknowledged.

Funding

This study was funded by the Department of Science and Technology – Accelerated Science and Technology Human Resource Development Program (DOST-ASTHRDP).

Author information

Authors and Affiliations

  1. School of Environmental Science and Management (SESAM), University of the Philippines Los Baños, Los Baños, Laguna, Philippines

    Mary Jean D. Salvaña

  2. Department of Environmental Science and Technology, College of Science and Mathematics, University of Science and Technology of Southern Philippines – Cagayan de Oro Campus, Cagayan de Oro City, Misamis Oriental, Philippines

    Mary Jean D. Salvaña, Justin Rhea F. Osa & Gifford Jay L. Agudo

Authors
  1. Mary Jean D. Salvaña
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Justin Rhea F. Osa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gifford Jay L. Agudo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Mary Jean Salvaña: research conceptualization, methodology conceptualization, formal analysis, formal writing—original draft, map validation, data visualization. Justin Rhea Osa: methodology conceptualization, data curation, formal writing—comments and reviews. Gifford Jay Agudo: research conceptualization, supervision, methodology conceptualization, GEE JavaScript environment code writing, formal analysis, formal writing—original draft, formal writing—original draft. All authors reviewed and agreed to submit the published version of the manuscript.

Corresponding author

Correspondence to Gifford Jay L. Agudo.

Ethics declarations

Declarations

All authors have read, have understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salvaña, M.J.D., Osa, J.R.F. & Agudo, G.J.L. Multidecadal mangrove forest change in Macajalar Bay, Northern Mindanao, Philippines (1950–2020) using remote sensing and geographic information systems. Environ Monit Assess 196, 507 (2024). https://doi.org/10.1007/s10661-024-12622-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-024-12622-1

Keywords

Search

Navigation