| Abstrak/Abstract |
Seagrass meadows have many ecosystem services to coastal areas and adjacent ecosystems, these services include nursery area for marine organisms, sea turtle feeding ground, and blue carbon sequestration. Therefore, it is important to protect seagrass in order to preserve their functions. Seagrass percent cover is one of the parameters to asses seagrass condition. Several approaches have been developed to map seagrass in optically shallow waters and one of them is by using remote sensing. This approach is more effective and efficient compared to field survey alone. The aim of this study is to produce seagrass spatial distribution and percent cover map using high resolution image. In this research, Support Vector Machine (SVM) classification and regression, one of the machine learning algorithms, was used to classify PlanetScope image using field data as training area to map seagrass spatial distribution and percent cover. The result show that SVM produced 73.98% overall accuracy for benthic mapping, with seagrass class producer’s accuracy and user’s accuracy is 93.71% and 85.35% respectively. Meanwhile, for seagrass percent cover, the SVM algorithm produced map with 26.48% standard error. |
