Spatiotemporal Distribution of Mangrove at Kuala Sepetang Forest Reserve, Malaysia, Using Remotely Sensed Data

Latif, ZA; Anuar, N; Zaki, NAM; Omar, H; Suratman, MN; Pradhan, B

Spatiotemporal Distribution of Mangrove at Kuala Sepetang Forest Reserve, Malaysia, Using Remotely Sensed Data

Latif, ZA Anuar, N Zaki, NAM Omar, H Suratman, MN Pradhan, B

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Concepts and Applications of Remote Sensing in Forestry, 2023, pp. 305-316
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Latif, ZA
dc.contributor.author	Anuar, N
dc.contributor.author	Zaki, NAM
dc.contributor.author	Omar, H
dc.contributor.author	Suratman, MN
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2023-11-15T01:56:25Z
dc.date.available	2023-11-15T01:56:25Z
dc.date.issued	2023-01-01
dc.identifier.citation	Concepts and Applications of Remote Sensing in Forestry, 2023, pp. 305-316
dc.identifier.isbn	9789811941993
dc.identifier.uri	http://hdl.handle.net/10453/173422
dc.description.abstract	Mangrove plays an important role in mitigating the impact of climate change by sequestering CO2 as the main greenhouse gas emission. They maintain water quality and clarity, filtering pollutants and trapping sediments originating from land. However, mangroves have been reported to be threatened by land conversion for other activities. The objectives of this chapter were: (1) to derive Normalized Difference Vegetation Index (NDVI) in the study area and (2) to map the status of the mangrove species distribution from year 2004 to 2019. Matang Mangrove Forest Reserves (MMFR) at Kuala Sepetang, Perak, Malaysia were determined using multi-temporal satellite imageries by Landsat TM and Sentinel-2 from year 2004 to 2019. The classification of land use land cover (LULC) was performed using the ISODATA Clustering method and Maximum Likelihood Classifier (MCL) method along with vegetation index differencing technique. The overall classification accuracy was 90% with Kappa statistic accuracy of 0.89. The results indicated changes in area of the mangrove forest to a cleared area occurred at 1340.01–1666.30 ha. (2004–2019). Combinations of these approaches were useful for change detection and an indication of the nature of these changes. The result also revealed that Rhizophora apiculata and Avicennia sonneratia are still being preserved and estimated to be 70% of the total species. Temporal changes of mangrove species for the 15–period showed that the mangrove species of R. apiculata was 58%, A. sonneratia was 6%, Bruguiera parviflora 8% and other mangrove species 28% in year 2019 as compared to 2004 in which R. apiculata was 71%, A. Sonneratia 6%, B. parviflora 10% and other mangrove species 14%. The findings indicated that the status and loss of mangroves due to direct impacts from surrounding land development activities.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Concepts and Applications of Remote Sensing in Forestry
dc.relation.isbasedon	10.1007/978-981-19-4200-6_15
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Spatiotemporal Distribution of Mangrove at Kuala Sepetang Forest Reserve, Malaysia, Using Remotely Sensed Data
dc.type	Chapter
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
dc.date.updated	2023-11-15T01:56:25Z
pubs.publication-status	Published

Abstract:

Mangrove plays an important role in mitigating the impact of climate change by sequestering CO2 as the main greenhouse gas emission. They maintain water quality and clarity, filtering pollutants and trapping sediments originating from land. However, mangroves have been reported to be threatened by land conversion for other activities. The objectives of this chapter were: (1) to derive Normalized Difference Vegetation Index (NDVI) in the study area and (2) to map the status of the mangrove species distribution from year 2004 to 2019. Matang Mangrove Forest Reserves (MMFR) at Kuala Sepetang, Perak, Malaysia were determined using multi-temporal satellite imageries by Landsat TM and Sentinel-2 from year 2004 to 2019. The classification of land use land cover (LULC) was performed using the ISODATA Clustering method and Maximum Likelihood Classifier (MCL) method along with vegetation index differencing technique. The overall classification accuracy was 90% with Kappa statistic accuracy of 0.89. The results indicated changes in area of the mangrove forest to a cleared area occurred at 1340.01–1666.30 ha. (2004–2019). Combinations of these approaches were useful for change detection and an indication of the nature of these changes. The result also revealed that Rhizophora apiculata and Avicennia sonneratia are still being preserved and estimated to be 70% of the total species. Temporal changes of mangrove species for the 15–period showed that the mangrove species of R. apiculata was 58%, A. sonneratia was 6%, Bruguiera parviflora 8% and other mangrove species 28% in year 2019 as compared to 2004 in which R. apiculata was 71%, A. Sonneratia 6%, B. parviflora 10% and other mangrove species 14%. The findings indicated that the status and loss of mangroves due to direct impacts from surrounding land development activities.

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