Study of Model Object-Based Image Analysis (OBIA) For Data Interpretation Based Mangrove Vegetation Landsat 8 Operational Land Imager on the West Coast City of Bengkulu - Srifitriani, Abditama; Supriyono, Supriyono; Parwito, Parwito (Sumatra Journal of Disaster, Geography and Geography Education, 2019)
Mangrove identification by using the image has been done with the classification model by pixel in the image value. But in this study see the interpretation of Landsat image data to the analysis of the object in the mangrove. Mangrove forests as major ecosystems support life activities in the coastal area and play an important role in maintaining the balance of the biological cycle in the environment. The potential of natural resources needs to be managed and utilized optimally to support the implementation of national development and improving people's welfare. So as to develop the coastal economic continuity with the management of mangrove forests as ecotourism. Identification observation and extensive distribution of mangrove forests in the western coastal city of Bengkulu was conducted in April 2019 by boat. Digital data Landsat 8 OLI (Operational Land Imager) parth / raw 125/63 used to map the mangrove forest. The method used in this study is a controlled multispectral classification Object-Based Image Analysis (OBIA) with the segmentation algorithm. Segmentation is performed using an algorithm Multiresolution Segmentation Segmentation and Spectral Difference. The results of the data analysis of Landsat 8 OLI and validation of field observation data, shows that the accuracy and wide distribution of mangrove forests in the coastal areas west of the city of Bengkulu is 255.24 ha. This method can be made an alternative to identifying information in mapping mangrove vegetation. Mangroves in the coastal areas west of the city of Bengkulu dominated by Rhizophora apiculata, Rhizophora mucronata and relatively good. Segmentation is performed using an algorithm Multiresolution Segmentation Segmentation and Spectral Difference. The results of the data analysis of Landsat 8 OLI and validation of field observation data, shows that the accuracy and wide distribution of mangrove forests in the coastal areas west of the city of Bengkulu is 255.24 ha. This method can be made an alternative to identifying information in pemetaanya mangrove vegetation. Mangroves in the coastal areas west of the city of Bengkulu dominated by Rhizophora apiculata, Rhizophora mucronata and relatively good. Segmentation is performed using an algorithm Multiresolution Segmentation Segmentation and Spectral Difference. The results of the data analysis of Landsat 8 OLI and validation of field observation data, shows that the accuracy and wide distribution of mangrove forests in the coastal areas west of the city of Bengkulu is 255.24 ha. This method can be made an alternative to identifying information in pemetaanya mangrove vegetation. Mangroves in the coastal areas west of the city of Bengkulu dominated by Rhizophora apiculata, Rhizophora mucronata and relatively good. This method can be made an alternative to identifying information in pemetaanya mangrove vegetation. Mangroves in the coastal areas west of the city of Bengkulu dominated by Rhizophora apiculata, Rhizophora mucronata and relatively good. This method can be made an alternative to identifying information in pemetaanya mangrove vegetation. Mangroves in the coastal areas west of the city of Bengkulu dominated by Rhizophora apiculata, Rhizophora mucronata and relatively good.