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Land Cover Classification Using Landsat 8 Operational Land Imager (OLI) in West Langsa District Langsa City
Abdullah Ahmad Nasution, Ashabul Anhar

Last modified: 2021-09-19

Abstract


Land cover information is needed by various parties as a consideration in controlling land cover changes. The latest land cover information can be obtained using remote sensing techniques in the form of image classification maps. This technique is very effective in monitoring land cover because of its ability to quickly, precisely, and easily provide spatial information on the earth's surface. The purpose of this study was to classify land cover in West Langsa District, Langsa City using Landsat 8 OLI (Operational Land Imager) imagery. The classification method used in this study is the maximum likelihood classification (MLC) method. There are several considerations of various factors in the MLC method, including the probability of a pixel to be classified into a certain type or class. The results of Landsat 8 OLI image classification in West Langsa District resulted in 6 land cover classes, namely mangrove forests, settlements, rice fields, shrubs, ponds and bodies of water. The largest land cover class is ponds with an area of ​​1981.54 ha (38.71%) and the smallest land cover is rice fields with an area of ​​115.58 ha (2.26%) of the total land cover class. Classification accuracy is indicated by the overall accuracy and kappa accuracy of 91.15% and 82.75%, respectively. These results meet the requirements set by the USGS (Overall Accuracy > 85%) and indicate that the Landsat 8 OLI image classification map can be used for various purposes.

Keywords


Land cover; Remote sensing; Mangrove forest