Open AccessCOMPARISON OF SEAGRASS COVER CLASSIFICATION BASED ON SVM AND FUZZY LOGIC ALGORITHMS USING MULTI-SCALE IMAGERY IN KODINGARENG LOMPO ISLAND, SOUTH SULAWESI
Author(s) -
Anisa Aulia Sabilah,
Vincentius P. Siregar,
Muhammad Anshar Amran
Publication year - 2021
Publication title -
jurnal ilmu dan teknologi kelautan tropis
Language(s) - English
Resource type - Journals
eISSN - 2620-309X
pISSN - 2087-9423
DOI - 10.29244/jitkt.v13i1.34765
Subject(s) - seagrass , support vector machine , remote sensing , satellite imagery , fuzzy logic , scale (ratio) , habitat , environmental science , satellite , biota , computer science , algorithm , geography , cartography , artificial intelligence , ecology , engineering , biology , aerospace engineering
Seagrass beds play an ecological role in the shallow marine environment, such as a habitat for biota, primary producers, and sediment traps. They also act as nutrient recyclers. Since they have such an important role, this natural resource needs to be preserved. Therefore, continuous monitoring and mapping of seagrass beds, especially by remote sensing methods, is paramount. The current rapid development of satellite sensor technology, especially its spatial and spectral resolutions, has improved the quality of the seagrass distribution map. The use of proper classification methods and schemes in the classification of seagrass distribution based on satellite imagery can affect the accuracy of the map, which is why various alternative algorithm studies are required. In this study, the support vector machine and fuzzy logic algorithms were used to classify the WorldView-2 and Sentinel-2 satellite imagery on Kodingareng Lompo Island with four classes of seagrass cover, sparse (0–25%), moderate (26–50%), dense (51–75%), and very dense (76–100%). The result showed that the fuzzy logic algorithm applied to WorldView-2 imagery has the best overall accuracy of 78.60% seagrass cover classification.