Rawa Singkil Wildlife Reserve plays an important role in protecting the environmental services. It contains, particularly as a protective balance of water systems and natural warehouses for carbon storage to mitigate the effects of global warming. However, the existence of Rawa Singkil Wildlife Reserve is disturbed by human activities without paying attention to the impact of natural balance. The research objective is to identify plant diversity and vegetation cover classification. This study was carried out using a rectangular path method for various stages including seedlings, saplings, poles, and trees. The results provide information on the condition of the vegetation structure and composition of various stages of tree growth. There are 25 types of vegetation found in Rawa Singkil Wildlife Reserve, which consists of three stages, namely saplings, poles, and trees. Based on the Importance Value Index analysis that Nyamplung (Calophyllum inophyllum) has the highest Importance Value Index, the highest Importance Value Index in the pole stage is Beras-Beras (Syzygium zeilanicum), and In the tree stage the highest index of importance was Ubar Susu (Glutta renghas) and Pucuk Merah (Syzigium myrtifolium).
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 Sub 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 Sub 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.
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