| dc.description.abstract | Taiwan′s oyster aquaculture industry has a long history, primarily in Yunlin County for oyster seed production. Oyster farming methods include floating and overturned frames, depending on water depths. However, declining oyster production challenges local farmers′ livelihoods. Limited by land division and data reliability issues, there is incomplete information about oyster aquaculture in Yunlin′s coastal areas. This study uses multi-temporal SPOT satellite imagery and object-based image analysis (OBIA) to identify oyster cultivation areas in Yunlin′s Taixi Township. The Support Vector Machine (SVM) was employed as the classifier in two experiments using SPOT images with spatial resolutions of 6 m and 1.5 m. Spectral features including Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), brightness, spectral mean, and standard deviation, and GLCM (Gray-Level Co-Occurrence Matrix) texture features, including energy, entropy, correlation, and inverse difference moment were used in the classification. In Experiment 1, the entire image was classified, while in Experiment 2, the image was divided into floating frames and overturned frames. Meanwhile, the SVM was conducted and compared when only spectral features were used and both spectral and texture features were used. The results of Experiment 1 show that the overall accuracies of classification results are all above 85%, indicating that the OBIA method accurately recognizes oyster beds in SPOT images. Furthermore, the inclusion of texture features of energy and inverse difference moment effectively improves classification accuracy. In Experiment 2, with combining spectral and texture features, the classification accuracies of floating frames are consistently higher than 90%, while the classification of overturned frames has overall accuracies of 65.49% and 82.3% when applying 6 m and 1.5 m images, respectively. This indicates that the classification accuracy of floating frames is higher, and images with higher spatial resolution result in better oyster frames classification. Additionally, the study mapps oyster cultivation areas and frame numbers from 2003 to 2020 in Taixi Township. Overturned frames were common in the past but gradually decreased after 2010, while floating frames increased. In the past 18 years, the number of oyster frames during the larvae period declined in Taixi Township. This study bridges the information gap in Yunlin′s coastal oyster aquaculture, providing essential references for development and management. | en_US |