Multi-spatiotemporal analysis of changes in mangrove forests in Palawan, Philippines: predicting future trends using
Cristobal B Cayetano1, Lota A Creencia1, Emma Sullivan2
1College of Fisheries and Aquatic Sciences, Western Philippines University, Sta. Monica, Puerto Princesa City, Palawan, Philippines.
UCL Open. Environment
|May 25, 2023
Summary
Remote sensing reveals changing mangrove areas in Palawan, Philippines. Projections indicate mangrove expansion by 2050, aiding ecological sustainability and management strategies.
Area of Science:
- Environmental Science
- Remote Sensing
- Ecology
Background:
- Mangrove ecosystems are vital for coastal protection and biodiversity.
- Understanding mangrove dynamics is crucial for effective conservation and management.
- Remote sensing offers a powerful tool for monitoring large-scale environmental changes.
Purpose of the Study:
- To analyze the spatial dynamics of mangrove extents in Palawan, Philippines (Puerto Princesa City, Taytay, Aborlan) from 1988-2020.
- To predict future mangrove cover changes using the Markov Chain model.
- To inform ecological sustainability and management interventions.
Main Methods:
- Utilized multi-temporal Landsat imagery from 1988-2020.
- Employed the Support Vector Machine algorithm for accurate mangrove feature extraction.
- Applied the Markov Chain model for spatial dynamics analysis and future projections.
Main Results:
- Palawan experienced a 5.2% mangrove decrease (1988-1998) and an 8.6% increase (2013-2020).
- Puerto Princesa City showed a significant increase (95.9%) from 1988-1998, followed by a slight decrease (2.0%) from 2013-2020.
- Taytay and Aborlan exhibited gains (1988-1998) but also experienced decreases (2013-2020).
- Projected mangrove areas indicate an increase by 2030 and 2050.
Conclusions:
- The Markov Chain model effectively demonstrated mangrove dynamics and potential for future predictions.
- Findings support the integration of remote sensing and modeling for ecological sustainability.
- Future research should incorporate environmental factors and cellular automata for enhanced mangrove modeling.
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