近30年來我國遭受小花蔓澤蘭(Mikania micrantha)之入侵,為有效且快速了解其分佈狀況,本研究透過遙感探測(Remote Sensing, RS),以無人空中載具(Unmanned Aerial Vehicle, UAV)影像進行像元影像分析(Pixel-based Image Analysis)及物件導向影像分析(Object-based Image Analysis),以人工判釋建立小花蔓澤蘭UAV影像判釋準則,透過該準則進行監督性分類。由不同空間解析度分類結果可得知,最佳分類空間解析度約為10-15 cm,且須於花季進行拍攝。若空間解析度小於10 cm,易形成椒鹽效應。建議先經過物件導向處理再進行監督性分類,或使高解析度(<10 cm)影像融合為一新低解析度(10-15 cm)影像進行分類。綜合上述以RGB波段UAV調查小花蔓澤蘭分佈狀態是可行的。
Over the past 30 years, Taiwan has been invaded by Mikania micrantha. In order to effectively and quickly understand its distribution, this study conducted pixel-based image analysis and object-based image analysis with unmanned aerial vehicle images through remote sensing detection, and detected Mikania micrantha with artificial interpretation. Criteria for image interpretation, according to which the supervised classification is carried out. According to the classification results of different spatial resolutions, the best classification spatial resolution is about 10-15 cm and the images must be taken during the flowering season. If the spatial resolution is less than 10 cm, the salt and pepper effect is easy to form. It is recommended to perform supervised classification after object based image analysis or to fuse high resolution images (<10 cm) into a new low resolution image (10-15 cm) for classification. On this basis, the distribution of Mikania micrantha can be studied with RGB UAVs.