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      Multi-Spatiotemporal Analysis of Changes in Mangrove Forests in Palawan, Philippines: Predicting Future Trends Using Support Vector Machine Algorithm and Markov Chain Model

      In review


            Multi-temporal remote sensing imagery can be used to explore how mangrove assemblages are changing over time and facilitate critical interventions for ecological sustainability and effective management. This study aims to explore the spatial dynamics of mangrove extents in Palawan, Philippines, specifically in Puerto Princesa City (PPC), Taytay, and Aborlan, and facilitate future prediction for Palawan using the Markov Chain model. The multi-date Landsat imageries during the period 1988–2020 were used for this research. The Support Vector Machine algorithm was sufficiently effective for mangrove feature extraction to generate satisfactory accuracy results (>70% Kappa coefficient values; 91% average overall accuracies). In Palawan, a 5.2% (2,693 ha) decrease was recorded during 1988–1998 and an 8.6% increase in 2013–2020 to 4,371 ha. In PPC, 95.9% (2,758 ha) increase was observed during 1988–1998 and 2.0% (136 ha) decrease during 2013–2020. The mangroves in Taytay and Aborlan both gained an additional 2,138 ha (55.3%) and 228 ha (16.8%) during 1988–1998 but also decreased from 2013 to 2020 by 3.4% (247 ha) and 0.2% (3 ha), respectively. However, projected results suggest that the mangrove areas in Palawan will likely increase in 2030 (to 64,946 ha) and 2050 (to 66,972 ha). This study demonstrated the capability of the Markov Chain model in the context of ecological sustainability involving policy intervention. However, since this research did not capture the environmental factors that may had influenced the changes in mangrove patterns, it is suggested the addition of Cellular Automata in future Markovian mangrove modelling.


            Author and article information

            UCL Open: Environment Preprint
            UCL Press
            4 July 2022
            [1 ] Western Philippines University, College of Fisheries and Aquatic Sciences
            [2 ] Remote Sensing Group, Plymouth Marine Laboratory
            Author notes

            This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY) 4.0 https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

            Global Challenges Research Fund, Blue Communities, under the United Kingdom Research and Innovation NE/P021107/1

            The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
            Earth & Environmental sciences,Environmental studies,General environmental science
            Change detection,Image classification, Landsat,Land use/land cover,Markov Chain Model,Spatial dynamics,Support Vector Machine,Environmental modelling,Environmental protection,Environmental science


            Date: July 20 2022

            Handling Editor: Dr Jesús Aguirre Gutiérrez

            This article is a preprint article and has not been peer-reviewed. It is under consideration following submission to UCL Open: Environment for open peer review.

            2022-07-20 16:29 UTC

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