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Integration of normalised different vegetation index and Soil-Adjusted Vegetation Index for mangrove vegetation delineation


Parman, Rhyma Purnamasayangsukasih and Kamarudin, Norizah and Omar, Hamdan and Ibrahim, Faridah Hanum and Abdul Whab @ Abdul Wahab, Zulfa (2020) Integration of normalised different vegetation index and Soil-Adjusted Vegetation Index for mangrove vegetation delineation. Remote Sensing Applications: Society and Environment, 17. pp. 1-14. ISSN 2352-9385


Discriminating between vegetation and non-vegetation areas is of crucial importance in the study of mangroves. This will help mangrove monitoring and management since losses and degradation of the mangroves are reported to be substantial in recent years. This study investigates the integration of Normalised Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) as a tool to discriminate vegetation covers in the mangrove forest. High resolution remotely sensed images from Satellite Pour l’Observation de la Terre (SPOT-6 and SPOT-7) satellite, with 1.5 m spatial resolution over the Matang Mangrove Forest Reserve (MMFR) was acquired. A complete ground-truthing was conducted at the preliminary stages of land cover classification. A Moran's I analysis shows a random pattern of ground-truthing; (Kuala Sepetang; p-value = 0.219; Kuala Trong, p-value = 0.163; Sungai Kerang, p-value = 0.159). Since SAVI requires a suitable L-factor to be used to distinguish the vegetation areas, four different L-factors viz. 0.1, 0.25, 0.5 and 0.75 were tested, and the multiple linear regressions, using the stepwise regression method of backward elimination, found that the L-factor 0.75 was significant to be used for MMFR. A correlation analysis conducted between the results of NDVI, SAVI and supervised classification shows a high significant relationship, especially between NDVI and SAVI (0.991) at 99.99% level. This shows that NDVI and SAVI are useful analyses that can be employed to improve the accuracy of classification in the mangroves.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Forestry
Institute of Tropical Forestry and Forest Products
DOI Number: https://doi.org/10.1016/j.rsase.2019.100280
Publisher: Elsevier
Keywords: Vegetation indices; Mangrove; Supervised classification; Geospatial technology; Matang mangrove forest
Depositing User: Ms. Nuraida Ibrahim
Date Deposited: 18 Aug 2021 09:33
Last Modified: 18 Aug 2021 09:33
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1016/j.rsase.2019.100280
URI: http://psasir.upm.edu.my/id/eprint/89417
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