Assessment of Factors of Mangrove Extent Growth in Eastern Leyte, Philippines | IJLTEMAS
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Assessment of Factors of Mangrove Extent Growth in Eastern Leyte, Philippines

Authors

Sarah Jane Cabral

1College of Engineering, Eastern Visayas State University, Tacloban City, Leyte 6500, Philippines 2 DIT Candidate, La Consolacion University Philippines, Malolos, Bulacan 3000 Philippines (PH)

Jayson M. Victoriano

College of Information and Communications Technology, Bulacan State University, Malolos, Bulacan 3000, Philippines (PH)

Article Information

DOI: 10.51583/IJLTEMAS.2025.140500066

Subject Category: Eastern Leyte Philippines

Volume/Issue: 14/5 | Page No: 623-639

Publication Timeline

Submitted: 2025-06-16

Published: 2025-06-16

Abstract

Abstract: Recent satellite imagery analysis in the Eastern Leyte province indicates significant growth in mangrove forests following the destruction of approximately 86% of these forests during Typhoon Yolanda (Haiyan) in 2013. However, there are no studies that explore the process of mangrove expansion in the region and the factors influencing its growth. This study aims to investigate and analyze the climatological and anthropogenic factors contributing to the expansion of mangrove forests in the area. Using the mangrove index derived from Sentinel-2A satellite imagery, a percentile-based threshold segmentation was applied to classify and identify mangrove extent. Annual mangrove area data from 2017 to 2023 were calculated and correlated with climatological variables and anthropogenic factors. The result of the analysis shows a strong positive correlation between mangrove coverage and sea level with a variance of r2=0.90 and α<0.001 which is statistically significant. The relationship of the population to the mangrove coverage shows the model has a variance of r2=0.64 and α=0.030. In contrast, the rainfall shows a very weak and has no significant relationship with the mangrove coverage with a variance of r2= 0.022 and α=0.05. The findings reveal the significant role of sea level and population in the mangrove expansion. This study provides significant insights into accurate rehabilitation plans and strategies for mangrove preservation and conservation in the province.

Keywords

Mangrove, Remote Sensing, Mangrove Index, Linear Regression, Correlation Analysis

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References

1. Mitra, A. (2019). Mangroves: A Shield Against Storms and Wave Actions. Mangrove Forests in India. Https://doi.org/10.1007/978-3-030-20595-9_2 [Google Scholar] [Crossref]

2. Kathiresan, K. (2012). Importance of Mangrove Ecosystem. The international journal of marine science, 2. Https://doi.org/10.5376/IJMS.2012.02.0010 [Google Scholar] [Crossref]

3. Fry, B., and Cormier, N. (2011). Chemical ecology of red mangroves, Rhizophora mangle, in the Hawaiian Islands1. Pac. Sci. 65, 219–234. doi: 10.2984/65.2.219 [Google Scholar] [Crossref]

4. Murugan, S., & Anandhi, D. (2016). An Overview of Crustacean Diversity in Mangrove Ecosystem. Https://doi.org/10.1007/978-981-10-1518-2_5 [Google Scholar] [Crossref]

5. Cuenca, G.C., Macusi, E.D., Abreo, N.A., Ranara, C.T., Andam, M.B., Cardona, L.C., & Conserva, G.G. (2015). Mangrove Ecosystems and Associated Fauna with Special Reference to Mangrove Crabs in the Philippines: A Review. Https://doi.org/10.7718/IJEC.V15I1.998 [Google Scholar] [Crossref]

6. PAGASA. (n.d.). https://www.pagasa.dost.gov.ph/climate/tropical-cyclone-information#:~:text=More%20tropical%20cyclone%20(TCs)%20are,70%25%20of%20al%20typhoon%20develop. [Google Scholar] [Crossref]

7. Cabello, K. E., Germentil, M. Q., Blanco, A. C., Macatulad, E. G., & Salmo III, S. G. (2021). POST-DISASTER ASSESSMENT OF MANGROVE FOREST RECOVERY IN LAWAAN-BALANGIGA, EASTERN SAMAR USING NDVI TIME SERIES ANALYSIS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume V-3-2021. https://doi.org/10.5194/isprs-annals-V-3-2021-243-2021 [Google Scholar] [Crossref]

8. Ghosh, M. K., Kumar, L., & Roy, C. (2017). Climate Variability and Mangrove Cover Dynamics at Species Level in the Sundarbans, Bangladesh. Sustainability, 9(5), 805. https://doi.org/10.3390/su9050805 [Google Scholar] [Crossref]

9. 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 [Google Scholar] [Crossref]

10. Friess, D. A., Chua, S. C., Jaafar, Z., Krauss, K. W., & Yando, E. S. (2020). Mangroves and people: Impacts and interactions. Estuarine Coastal and Shelf Science, 248, 107155. https://doi.org/10.1016/j.ecss.2020.107155 [Google Scholar] [Crossref]

11. Cavanaugh, K. C., Kellner, J. R., Forde, A. J., Gruner, D. S., Parker, J. D., Rodriguez, W., ... & Feller, I. C. (2014). Poleward expansion of mangroves is a threshold response to decreased frequency of extreme cold events. Proceedings of the National Academy of Sciences, 111(2), 723-727. [Google Scholar] [Crossref]

12. Spalding, M., Kainuma, M., & Collins, L. (2016). World atlas of mangroves. Routledge. [Google Scholar] [Crossref]

13. Lovelock, C. E., Feller, I. C., McKee, K. L., Thompson, R., & Reef, R. (2015). Responses of mangrove root respiration to external nitrogen and phosphorus supplies. Biogeosciences, 12(16), 5073-5082. [Google Scholar] [Crossref]

14. Cabral, S. J. L. (2025). Classification and mapping of mangrove forest area in Eastern Leyte using Percentile-Based threshold segmentation. Journal of Information Systems Engineering & Management, 10(45s), 42–54. https://doi.org/10.52783/jisem.v10i45s.8707 [Google Scholar] [Crossref]

15. Alatorre, Sánchez-Andrés, Cirujano, Beguería and Sánchez-Carrillo, "Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery," Remote Sensing, vol. 3, no. Issue 8, 2011. [Google Scholar] [Crossref]

16. Xue, J., & Su, B. (2017). Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors, 2017, 1–17. https://doi.org/10.1155/2017/1353691 [Google Scholar] [Crossref]

17. Baloloy, A. B., Martinez, K. P., Blanco, A. C., Neri, M. E. P., Di V Ticman, K., Burgos, D. F., Principe, J. A., Reyes, R. B., Salmo, S. G., & Nadaoka, K. (2023). Mapping Multi-decadal Mangrove Forest Change in the Philippines: Vegetation Extent and Impacts of Anthropogenic and Climate-Related Factors. In Climate change management (pp. 217–248). https://doi.org/10.1007/978-3-031-28728-2_12 [Google Scholar] [Crossref]

18. Tran, T. V., Reef, R., & Zhu, X. (2022). A Review of Spectral Indices for Mangrove Remote Sensing. Remote Sensing, 14(19), 4868. https://doi.org/10.3390/rs14194868 [Google Scholar] [Crossref]

19. Zablan, C. D. C., Blanco, A. C., Nadaoka, K., Martinez, K. P., & Baloloy, A. B. (2023). Assessment Of Mangrove Extent Extraction Accuracy of Threshold Segmentation-Based Indices Using Sentinel Imagery. the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences/International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-4/W6-2022, 391–401. https://doi.org/10.5194/isprs-archives-xlviii-4-w6-2022-391-2023 [Google Scholar] [Crossref]

20. Nasa, E. S. D. S. (2024, June 24). Sentinel-2 MSI | Earthdata. Earthdata. https://www.earthdata.nasa.gov/sensors/sentinel-2-msi [Google Scholar] [Crossref]

21. Prayudha, B., Ulumuddin, Y. I., Siregar, V., Suyarso, Agus, S. B., Prasetyo, L. B., Suyadi, Avianto, P., & Ramadhani, M. R. (2024). Enhanced mangrove index: A spectral index for discrimination understorey, nypa, and mangrove trees. MethodsX, 12(May), 102778. https://doi.org/10.1016/j.mex.2024.102778 [Google Scholar] [Crossref]

22. USGS. (n.d.) https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites [Google Scholar] [Crossref]

23. Wang, W., Qiu, G., Wang, S., & Wu, (2018). Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species, Remote Sensing, vol. 10, no. 9. [Google Scholar] [Crossref]

24. Maulud, D., & Abdulazeez, A. M. (2020). A review on Linear Regression comprehensive in Machine Learning. Journal of Applied Science and Technology Trends, 1(2), 140–147. https://doi.org/10.38094/jastt1457 [Google Scholar] [Crossref]

25. Franzese, M., & Iuliano, A. (2018). Correlation analysis. In Elsevier eBooks (pp. 706–721). https://doi.org/10.1016/b978-0-12-809633-8.20358-0 [Google Scholar] [Crossref]

26. Feng, Z., Tan, G., Xia, J., Shu, C., Chen, P., Wu, M., & Wu, X. (2020). Dynamics of mangrove forests in Shenzhen Bay in response to natural and anthropogenic factors from 1988 to 2017. Journal of Hydrology, 591, 125271. https://doi.org/10.1016/j.jhydrol.2020.125271 [Google Scholar] [Crossref]

27. Krauss K, McKee K, Lovelock C, Cahoon D, Saintilan N, Reef R, Chen L (2014) How mangrove forests adjust to rising sea level. New Phytol 202:19–34. https://doi.org/10.1111/nph.12605 [Google Scholar] [Crossref]

28. Bardou, R., Osland, M. J., Alemu, J. B., Feher, L. C., Harlan, D. P., Scyphers, S. B., Shepard, C. C., Swinea, S. H., Thorne, K., Andrew, J. E., & Hughes, A. R. (2024). Projected changes in mangrove distribution and vegetation structure under climate change in the southeastern United States. Journal of Biogeography, 51(11), 2285–2297. https://doi.org/10.1111/jbi.14985 [Google Scholar] [Crossref]

29. Devaney, J. L., Pullen, J., Feller, I. C., & Parker, J. D. (2020). Low humidity and hypersalinity reduce cold tolerance in mangroves. Estuarine Coastal and Shelf Science, 248, 107015. https://doi.org/10.1016/j.ecss.2020.107015 [Google Scholar] [Crossref]

30. Hamylton, S., Kelleway, J., Rogers, K., McLean, R., Tynan, Z. N., & Repina, O. (2023). Mangrove expansion on the low wooded islands of the Great Barrier Reef. Proceedings of the Royal Society B Biological Sciences, 290(2010). https://doi.org/10.1098/rspb.2023.1183 [Google Scholar] [Crossref]

31. Kang, Y., Kaplan, D. A., & Osland, M. J. (2024). Mangrove freeze resistance and resilience across a tropical‐temperate transitional zone. Journal of Ecology. https://doi.org/10.1111/1365-2745.14440 [Google Scholar] [Crossref]

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