The Mangrove Forests Change and Impacts from Tropical Cyclones in the Philippines Using Time Series Satellite Imagery
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Areas and Data Set
2.1.1. Study Areas
2.1.2. Satellite Data and Preprocessing
2.1.3. Tropical Cyclone Data and Preprocessing
2.2. Mangroves Classification
2.3. Spatial and Temporal Analysis of Mangrove Change
3. Results
3.1. Classification and Accuracy Assessment
3.2. Temporal and Spatial Changes of Mangroves
3.2.1. Areal Extent Changes
3.2.2. Decadal Change Detection
3.2.3. Landscape Pattern Changes
3.3. Qualitative Impacts of Tropical Cyclones
4. Discussion
4.1. Temporal Changes of Mangroves in the Philippines
4.2. Tropical Cyclone Influence on Mangroves in the Philippines
4.3. Anthropogenic Influences on Mangroves in the Philippines
4.3.1. Policies Implementation for Coron Mangroves
4.3.2. Policies Implementation on Balangiga-Lawaan Mangroves
4.4. Suggestion for the Sustainable Mangroves Management in the Philippines
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Heumann, B.W. Satellite remote sensing of mangrove forests: Recent advances and future opportunities. Prog. Phys. Geogr. 2011, 35, 87–108. [Google Scholar] [CrossRef]
- Donato, D.C.; Kauffman, J.B.; Murdiyarso, D.; Kurnianto, S.; Stidham, M.; Kanninen, M. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci. 2011, 4, 293–297. [Google Scholar] [CrossRef]
- Cinco, T.A.; de Guzman, R.G.; Ortiz, A.D.; Delfino, R.P.; Lasco, R.D.; Hilario, F.D.; Ares, E.D. Observed trends and impacts of tropical cyclones in the philippines. Int. J. Climatol. 2016, 36, 4638–4650. [Google Scholar] [CrossRef]
- Villamayor, B.R.; Rollon, R.N.; Samson, M.S.; Albano, G.G.; Primavera, J.H. Impact of haiyan on philippine mangroves: Implications to the fate of the widespread monospecific rhizophora plantations against strong typhoons. Ocean Coast. Manag. 2016, 132, 1–14. [Google Scholar] [CrossRef]
- Primavera, J.H. Mangroves and brackishwater pond culture in the philippines. Hydrobiologica 1995, 295, 303–309. [Google Scholar] [CrossRef]
- Garcia, K.B.; Malabrigo, P.L.; Gevaña, D.T. Philippines’ Mangrove Ecosystem: Status, Threats and Conservation; Springer Science+Business Media: New York, NY, USA, 2014; pp. 81–94. [Google Scholar]
- Bambalan, G.C. The Philippines Trajectory in Mangrove Development; Ministry of Foresty: Jakarta, Indonesia, 2013; pp. 30–35.
- Giri, C.; Ochieng, E.; Tieszen, L.L.; Zhu, Z.; Singh, A.; Loveland, T.; Masek, J.; Duke, N. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 2011, 20, 154–159. [Google Scholar] [CrossRef]
- Liu, M.F.; Zhang, H.S.; Lin, G.H.; Lin, H.; Tang, D.L. Zonation and directional dynamics of mangrove forests derived from time-series satellite imagery in Mai Po, Hong Kong. Sustainability 2018, 10, 1913. [Google Scholar] [CrossRef]
- Zhang, H.; Wang, T.; Liu, M.; Jia, M.; Lin, H.; Chu, L.; Devlin, A.T. Potential of combining optical and dual polarimetric sar data for improving mangrove species discrimination using rotation forest. Remote Sens. 2018, 10, 467. [Google Scholar] [CrossRef]
- Wan, L.; Zhang, H.; Wang, T.; Li, G.; Lin, H. Mangrove species discrimination from very high resolution imagery using gaussian markov random field model. Wetlands 2018. [Google Scholar] [CrossRef]
- Wang, T.; Zhang, H.S.; Lin, H.; Fang, C.Y. Textural-spectral feature-based species classification of mangroves in mai po nature reserve from worldview-3 imagery. Remote Sens. 2016, 8, 24. [Google Scholar] [CrossRef]
- Paknia, O.; Sh, H.R.; Koch, A. Lack of well-maintained natural history collections and taxonomists in megadiverse developing countries hampers global biodiversity exploration. Org. Divers. Evol. 2015, 15, 619–629. [Google Scholar] [CrossRef]
- Long, J.B.; Giri, C. Mapping the philippines’ mangrove forests using landsat imagery. Sensors 2011, 11, 2972–2981. [Google Scholar] [CrossRef]
- Primavera, J.H. Development and conservation of philippine mangroves: Institutional issues. Ecol. Econ. 2000, 35, 91–106. [Google Scholar] [CrossRef]
- Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA). Climate Map of the Philippines Based on the Modified Coronas Classification. Available online: http://bagong.Pagasa.Dost.Gov.Ph./information/climate-philippines (accessed on 20 March 2019).
- The Palawan Council for Sustainable Development (PCSD). In-Depth Coastal/Marine Resources Survey Report for Busuanga Municipality; Palawan Council for Sustainable Development: Puerto Princesa City, Philippines, 2006.
- Primavera, J.H.; dela Cruz, M.; Montilijao, C.; Consunji, H.; dela Paz, M.; Rollon, R.N.; Maranan, K.; Samson, M.S.; Blanco, A. Preliminary assessment of post-haiyan mangrove damage and short-term recovery in eastern samar, central philippines. Mar. Pollut. Bull. 2016, 109, 744–750. [Google Scholar] [CrossRef] [PubMed]
- Alura, D.P.; Alura, R.P.C. Regeneration of mangrove forest devastated by typhoon haiyan in eastern samar, philippines. Int. J. Curr. Res. 2016, 8, 32373–32377. [Google Scholar]
- Zhang, H.S.; Wang, T.; Zhang, Y.H.; Dai, Y.R.; Jia, J.J.; Yu, C.; Li, G.; Lin, Y.Y.; Lin, H.; Cao, Y. Quantifying short-term urban land cover change with time series landsat data: A comparison of four different cities. Sensors 2018, 18, 4319. [Google Scholar] [CrossRef]
- Lee, T.M.; Yeh, H.C. Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities, Taiwan. Ecol. Eng. 2009, 35, 487–496. [Google Scholar] [CrossRef]
- Zhu, Z.; Wang, S.X.; Woodcock, C.E. Improvement and expansion of the fmask algorithm: Cloud, cloud shadow, and snow detection for landsats 4–7, 8, and sentinel 2 images. Remote Sens. Environ. 2015, 159, 269–277. [Google Scholar] [CrossRef]
- Mountrakis, G.; Im, J.; Ogole, C. Support vector machines in remote sensing: A review. ISPRS J. Photogramm. 2011, 66, 247–259. [Google Scholar] [CrossRef]
- Kuenzer, C.; Bluemel, A.; Gebhardt, S.; Quoc, T.V.; Dech, S. Remote sensing of mangrove ecosystems: A review. Remote Sens. 2011, 3, 878–928. [Google Scholar] [CrossRef]
- Green, E.P.; Clark, C.D.; Mumby, P.J.; Edwards, A.J.; Ellis, A.C. Remote sensing techniques for mangrove mapping. Int. J. Remote Sens. 1998, 19, 935–956. [Google Scholar] [CrossRef] [Green Version]
- Zhang, H.S.; Zhang, Y.Z.; Lin, H. A comparison study of impervious surfaces estimation using optical and sar remote sensing images. Int. J. Appl. Earth Obs. 2012, 18, 148–156. [Google Scholar] [CrossRef]
- Vaz, E. Managing urban coastal areas through landscape metrics: An assessment of mumbai’s mangrove system. Ocean Coast. Manag. 2014, 98, 27–37. [Google Scholar] [CrossRef]
- McGarigal, K.; Cushman, S.; Ene, E. Fragstats v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps; University of Massachusetts: Amherst, MA, USA, 2012. [Google Scholar]
- Conchedda, G.; Durieux, L.; Mayaux, P. An object-based method for mapping and change analysis in mangrove ecosystems. ISPRS J. Photogramm. 2008, 63, 578–589. [Google Scholar] [CrossRef]
- Murdiyarso, D.; Purbopuspito, J.; Kauffman, J.B.; Warren, M.W.; Sasmito, S.D.; Donato, D.C.; Manuri, S.; Krisnawati, H.; Taberima, S.; Kurnianto, S. The potential of indonesian mangrove forests for global climate change mitigation. Nat. Clim. Chang. 2015, 5, 1089–1092. [Google Scholar] [CrossRef]
- Cavanaugh, K.C.; Kellner, J.R.; Forde, A.J.; Gruner, D.S.; Parker, J.D.; Rodriguez, W.; Feller, I.C. Poleward expansion of mangroves is a threshold response to decreased frequency of extreme cold events. Proc. Natl. Acad. Sci. USA 2014, 111, 723–727. [Google Scholar] [CrossRef]
- Curnick, D.J.; Pettorelli, N.; Amir, A.A.; Balke, T.; Barbier, E.B.; Crooks, S.; Dahdouh-Guebas, F.; Duncan, C.; Endsor, C.; Friess, D.A.; et al. The value of small mangrove patches. Science 2019, 363, 239. [Google Scholar] [PubMed]
- The Palawan Council for Sustainable Development (PCSD). Baseline Report on Coastal Resources for Coron, Municipality; Palawan Council for Sustainable Development: Puerto Princesa City, Philippines, 2006.
- Alura, D.P.; Alura, N.C.; Alura, R.P.C. Mangrove forest and seagrass bed of eastern samar, philippines: Extent of damage by typhoon yolanda. Int. J. Nov. Res. Life Sci. 2015, 2, 30–35. [Google Scholar]
- Abrenica, M.; Ilagan, G.; Liuag, H.; Napeñas, A.; Tabion, R.; Tamina, R. Municipality of Coron Ecan Resource Management Plan 2017–2022; Che-dcerp; University of the Philippines: Los Baños, Philippines, 2017. [Google Scholar]
- Alcala, A.C.; Bucol, A.A.; Nillos-Kleiven, P. Directory of Marine Reserves in the Visayas, Philippines; Foundation for the Philippine Environment and Silliman University-Angelo King Center for Research and Environmental Management (SUAKCREM): Dumaguete City, Philippines, 2008. [Google Scholar]
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Buitre, M.J.C.; Zhang, H.; Lin, H. The Mangrove Forests Change and Impacts from Tropical Cyclones in the Philippines Using Time Series Satellite Imagery. Remote Sens. 2019, 11, 688. https://doi.org/10.3390/rs11060688
Buitre MJC, Zhang H, Lin H. The Mangrove Forests Change and Impacts from Tropical Cyclones in the Philippines Using Time Series Satellite Imagery. Remote Sensing. 2019; 11(6):688. https://doi.org/10.3390/rs11060688
Chicago/Turabian StyleBuitre, Mary Joy C., Hongsheng Zhang, and Hui Lin. 2019. "The Mangrove Forests Change and Impacts from Tropical Cyclones in the Philippines Using Time Series Satellite Imagery" Remote Sensing 11, no. 6: 688. https://doi.org/10.3390/rs11060688
APA StyleBuitre, M. J. C., 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