Please use this identifier to cite or link to this item:
http://hdl.handle.net/123456789/5267
DC Field | Value | Language |
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dc.contributor.author | James, J. | en_US |
dc.contributor.author | Daliman S. | en_US |
dc.contributor.author | Rendra, P. P. R. | en_US |
dc.contributor.author | Sukiyah, E. | en_US |
dc.contributor.author | Hadian, M. S. D. | en_US |
dc.contributor.author | Sulaksana, N. | en_US |
dc.date.accessioned | 2023-12-27T07:06:45Z | - |
dc.date.available | 2023-12-27T07:06:45Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 22731709 | - |
dc.identifier.uri | http://hdl.handle.net/123456789/5267 | - |
dc.description | Scopus | en_US |
dc.description.abstract | The research conducted in Kelantan focused on analysing the distribution of oil palm plantations using remote sensing data and ArcGIS, a Geographic Information System (GIS) platform. The demand for accurate and up-to-date information on oil palm plantations has been increasing due to advancements in technology and the need for effective management of the environment. The study aimed to compare the distribution of oil palm plantations in 2016 and 2021 by using vegetation analysis techniques such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Soil-Adjusted Vegetation Index (SAVI). Remote sensing data from Landsat 8, specifically Bands 5, 4, and 2, were utilized to derive these vegetation indices. Ground-truthing data, obtained through GPS coordinates, were employed to increase the accuracy of the analysis. The expansion of oil palm plantations and non-oil palm areas was assessed using the Supervised Classification Maximum Likelihood method. The distribution data of oil palm plantations is highly sought after by oil palm plantation companies and serves public and private purposes, contributing to environmental monitoring and promoting sustainable practices. | en_US |
dc.language.iso | en | en_US |
dc.publisher | EDP Sciences | en_US |
dc.subject | Oil palm plantation | en_US |
dc.subject | GIS | en_US |
dc.subject | remote sensing | en_US |
dc.title | Integrating Remote Sensing and GIS Techniques for Accurate Mapping and Analysis of Oil Palm Plantation Distribution in Kelantan: A Case Study | en_US |
dc.type | International | en_US |
dc.relation.conference | BIO Web of Conferences | en_US |
dc.identifier.doi | 10.1051/bioconf/20237305009 | - |
dc.description.page | 1-8 | en_US |
dc.volume | 73 | en_US |
dc.relation.seminar | 5th International Conference on Tropical Resources and Sustainable Sciences (CTReSS 5.0) | en_US |
dc.title.titleofbook | BIO Web of Conferences | en_US |
dc.description.articleno | 05009 | en_US |
dc.date.seminarstartdate | 2023-08-07 | - |
dc.date.seminarenddate | 2023-08-08 | - |
dc.description.placeofseminar | Faculty of Earth Science, Universiti Malaysia Kelantan | en_US |
dc.description.seminarorganizer | Faculty of Earth Science, Universiti Malaysia Kelantan | en_US |
dc.description.type | Indexed Proceedings | en_US |
dc.contributor.correspondingauthor | shaparas@umk.edu.my | en_US |
item.languageiso639-1 | en | - |
item.openairetype | International | - |
item.grantfulltext | open | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | Faculty of Earth Science - Proceedings |
Files in This Item:
File | Description | Size | Format | |
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bioconf_ctress2023_05009.pdf | Integrating Remote Sensing and GIS Techniques for Accurate Mapping and Analysis of Oil Palm Plantation Distribution in Kelantan: A Case Study | 2.34 MB | Adobe PDF | View/Open |
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