Accurate mapping of mangrove is necessary for effective planning and management of ecosystem and resources, due to the function of mangrove as a provider of natural products The use of satellite remote sensing to map mangrove has become widespread as it can provide accurate, effecient, and repeatable assessments. The type of remote sensing that is based on imaging using the pixel method sometimes results in the misclassification of the imaging due to the “salt and pepper effects”. The aim of this study to use approach support vector machine (SVM) algorithm to classification mangrove land cover using sentinel-2B and Landsat 8 OLI imagery based on object-based classification method (OBIA). The field observation was done using Unmanned Aerial Vehicle (UAV) at Liong River, Bengkalis, Riau Province. The result by show overall accuracy classification using Sentinel-2B was better than Landsat 8 OLI imagery the value of 78.7% versus 62.7% and them were different significantly 7.23%.

Research on mangrove mapping at the Liong River Bengkalis Riau Province was very limited, therefore the spatial data availability of mangrove in Liong River is also very limited. The use of satellite remote sensing to map mangrove has become widespread as it can provide accurate, effecient, and repeatable assessments. The purposed of this study was to map mangrove at the community level using sentinel 2B imagery based on object-based classification method (OBIA) and it compared pixel-based classification at Liong River, Bengkalis, Riau Provinc. This study was used support vector machine (SVM) algorithm. The scheme classification use is that land cover and mangrove community. The classification data of land cover was collected using unmanned aerial vehicle (UAV) and community mangrove was using transect data of 2013. The result of land cover classification and community mangrove indicated that object-based classification technique was better than pixel-based classification. The highest an overall accuracy of land cover is 78.7% versus 70.9%, whereas mangrove community is 76.6 versus 75.0%. Approximately 7.8% increase in accuracy can be achieved by object-based method of classification for land cover and 1.6% for mangrove community. ABSTRAKPenelitian pemetaan mangrove di Sungai Liong, Bengkalis Provinsi Riau sangat terbatas, sehingga ketersediaan data spasial di wilayah ini masih sangat terbatas. Pemanfaatan citra satelit dapat dijadikan alternatif dalam menyediakan data spasial secara efektif dan efesien. Penelitian ini bertujuan untuk memetakan mangrove sampai tingkat komunitas menggunakan citra sentinel 2B dengan metode klasifikasi berbasis objek/OBIA dan membandingkannya dengan teknik klasifikasi berbasis piksel. Algoritma yang digunakan pada penelitian ini adalah support vector machine (SVM). Pengembangan skema klasifikasi mangrove pada penelitian ini di bagi menjadi 2 level, yaitu kelas penutup lahan di sekitar mangrove dan kelas komunitas mangrove. Data yang digunakan untuk klasifikasi kelas penutup lahan adalah data foto udara yang diperoleh dengan menggunakan pesawat tanpa awak (unmanned aerial vehicle/UAV) dan untuk klasifikasi komunitas menggunakan data transek tahun 2013. Akurasi keseluruhan (OA) yang diperoleh untuk klafikasi penutup lahan mangrove dengan kedua teknik klasifikasi berbasis objek dan piksel berturut-turut adalah 78,7% dan 70,9%. Sedangkan akurasi keseluruhan (OA) untuk klasifikasi komunitas mangrove berbasis objek dan piksel berutruturut yaitu 76,6% dan 75,0%. Sekitar 7,8% peningkatan akurasi pemetaan penutup lahan dan sekitar 1,6% peningkatan akurasi pemetaan komunitas mangrove yang diperoleh dengan metode klasifikasi berbasis objek.Kata kunci : klasifikasi piksel-objek, mangrove, sentinel-2b, sungai liong, support vector machine, unmanned aerial vehicle Klasifikasi Mangrove Berbasis Objek dan Piksel Menggunakan . . .

Lake Pandan is one of the lakes located in Tapanuli Tengah district. Morphometry can explain the biological and chemical processes of the lake, regulate nutrient load, productivity and the influence of input loads from the surrounding area. Therefore, the morphometry of lake was needed as a basis for lake utilization and management. This is what underlies the morphometric research of Lake Pandan, where there has been no research on this topic. The purpose of this study was to determine the morphometry of Lake Pandan. This research was conducted on April 2019. The results of measurements of lake surface dimensions obtained that Lake Pandan has an area of ± 103 Ha with a maximum length of 2,034.60 m, a maximum width of 1,033.38 m, and a length of around the lake of 5,395 m. With an edge (Sl) of 5.395 m so that the Lake Development Index (LDI) was obtained at 0.302. The LDI value of 0.302 illustrates that the lake has an irregular shape. LDI can be used to describe the level of productivity of water if the value is greater, the waters are more fertile. The subsurface dimension found that the average depth (Z) of Lake Pandan was 0.21 m with maximum depth (Zmax) 1.7 m. The morphometric parameters of lake influence the physical, chemical and biological processes in the waters of Lake Pandan, such as depth. Based on depth, Lake Pandan has low stability and easy to experience stirring.