| dc.description.abstract | Taoyuan algal reef is an important coastal ecological habitat, and the area of the reef is easily changed by the coverage of drifting sand. Recently, due to the construction of the third liquefied natural gas (LNG), the algal reefs in Taoyuan are at high-risk of being threatened. Therefore, monitoring the variation of algal reef areas is necessary and an important research topic. This study uses supervised classification to identify the area of algal reefs. Besides, we evaluate the accuracy of the classification methods by comparing the consistent areas between methods to the manual identification results.
Specifically, to identify the reef area and compare it with the manual results, we applied three methods using the orthophotograph and numerical surface model (DSM) data to classify images. Three methods were applied in this study are called as: (1)
Color intensity: using the intensity of color composition (RGB) to classify reefs and sand ; ( 2) Image gradient: the threshold value of detection classification through the variation of the grayscale caused by the transition of heterogeneous regions; (3) Rugosity: Calculating the ratio of the surface area of the undulating terrain to the plane after orthogonal projection. In addition, the results of the three classified methods above are intersected to verify whether the compilation of multiple analysis methods can improve the accuracy of image classification or not.
The results show that the color intensity accuracy is 0.49. Compared to other methods, this accuracy value is not as well as other methods for classification. Thereby, the color intensity is not the perfect method to distinguish the reef and sand areas. Whereas, the grayscale method gives the highest accuracy of about 0.8. The accuracy of the rugosity method is range between 0.34-0.71. Although rugosity can identify some parts of reef areas, but the accuracy varied with the mixing areas between reef and sand. Moreover, the accuracy of the intersection of the three classified methods ranges between 0.54-0.71. Even can not be higher than the accuracy of the gray-scale gradient method but better than the color intensity and rugosity method. | en_US |