Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis

In spite of their importance, mangroves are still threatened by a significant reduction in global forest cover due to conversion to non-forest land uses. To implement robust policies and actions in mangrove conservation, quantitative methods in monitoring mangrove attributes are vital. This study in...

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Main Authors: Fejaycris Pillodar, Peter Suson, Maricar Aguilos, Ruben Amparado
Format: Article
Language:English
Published: MDPI AG 2023-05-01
Series:Forests
Subjects:
Online Access:https://www.mdpi.com/1999-4907/14/6/1080
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author Fejaycris Pillodar
Peter Suson
Maricar Aguilos
Ruben Amparado
author_facet Fejaycris Pillodar
Peter Suson
Maricar Aguilos
Ruben Amparado
author_sort Fejaycris Pillodar
collection DOAJ
description In spite of their importance, mangroves are still threatened by a significant reduction in global forest cover due to conversion to non-forest land uses. To implement robust policies and actions in mangrove conservation, quantitative methods in monitoring mangrove attributes are vital. This study intends to study the trend in mangrove resource mapping using remote sensing (RS) to determine the appropriate methods and datasets to be used in monitoring the distribution, aboveground biomass (AGB), and carbon stock (CS) in mangroves. A meta-analysis of several research publications related to mangrove resource mapping using RS in the Philippines has been conducted. A database was constructed containing 59 peer-reviewed articles selected using the protocol search, appraisal, synthesis, analysis, report (PSALSAR) framework and preferred reporting items for systematic reviews and meta-analysis (PRISMA). The study clarified that support vector machine (SVM) has shown to be more effective (99%) in discriminating mangroves from other land cover. Light detection and ranging (LiDAR) data also has proven to give a promising result in overall accuracy in mangrove-extent mapping (99%), AGB, and CS estimates (99%), and even species-level mapping (77%). Medium to low-resolution datasets can still achieve high overall accuracy by using appropriate algorithms or predictive models such as the mangrove vegetation index (MVI). The study has also found out that there are still few reports on the usage of high-spatial-resolution datasets, most probably due to their commercial restrictions.
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spelling doaj.art-e0dc18a37347417db1d01308aaa4fa9a2023-11-18T10:26:06ZengMDPI AGForests1999-49072023-05-01146108010.3390/f14061080Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-AnalysisFejaycris Pillodar0Peter Suson1Maricar Aguilos2Ruben Amparado3Environmental Science Graduate Program, Department of Biological Sciences, College of Science and Mathematics, Mindanao State University-Iligan Institute of Technology, Iligan City 9200, PhilippinesEnvironmental Science Graduate Program, Department of Biological Sciences, College of Science and Mathematics, Mindanao State University-Iligan Institute of Technology, Iligan City 9200, PhilippinesDepartment of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USAEnvironmental Science Graduate Program, Department of Biological Sciences, College of Science and Mathematics, Mindanao State University-Iligan Institute of Technology, Iligan City 9200, PhilippinesIn spite of their importance, mangroves are still threatened by a significant reduction in global forest cover due to conversion to non-forest land uses. To implement robust policies and actions in mangrove conservation, quantitative methods in monitoring mangrove attributes are vital. This study intends to study the trend in mangrove resource mapping using remote sensing (RS) to determine the appropriate methods and datasets to be used in monitoring the distribution, aboveground biomass (AGB), and carbon stock (CS) in mangroves. A meta-analysis of several research publications related to mangrove resource mapping using RS in the Philippines has been conducted. A database was constructed containing 59 peer-reviewed articles selected using the protocol search, appraisal, synthesis, analysis, report (PSALSAR) framework and preferred reporting items for systematic reviews and meta-analysis (PRISMA). The study clarified that support vector machine (SVM) has shown to be more effective (99%) in discriminating mangroves from other land cover. Light detection and ranging (LiDAR) data also has proven to give a promising result in overall accuracy in mangrove-extent mapping (99%), AGB, and CS estimates (99%), and even species-level mapping (77%). Medium to low-resolution datasets can still achieve high overall accuracy by using appropriate algorithms or predictive models such as the mangrove vegetation index (MVI). The study has also found out that there are still few reports on the usage of high-spatial-resolution datasets, most probably due to their commercial restrictions.https://www.mdpi.com/1999-4907/14/6/1080mangrovesmeta-analysisremote sensingPhilippines
spellingShingle Fejaycris Pillodar
Peter Suson
Maricar Aguilos
Ruben Amparado
Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis
Forests
mangroves
meta-analysis
remote sensing
Philippines
title Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis
title_full Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis
title_fullStr Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis
title_full_unstemmed Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis
title_short Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis
title_sort mangrove resource mapping using remote sensing in the philippines a systematic review and meta analysis
topic mangroves
meta-analysis
remote sensing
Philippines
url https://www.mdpi.com/1999-4907/14/6/1080
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AT rubenamparado mangroveresourcemappingusingremotesensinginthephilippinesasystematicreviewandmetaanalysis