Surf and Turf: Two-Way Spatial Extrapolation Utilizing Drone-Based Monitoring Phenology of Sea-Land Invasive Alien Species

• Main Authors: Hung, Hao-Yuan, Kuo, Chin-Jin, Shao, Bao-Hua, Lo, Nan-Chang, Huang, Kai-Yi
• Format: Conference Proceeding
• Language: English
• Subjects: 3S technologies; Deep learning; Extrapolation; invasive alien species; Pattern recognition; Phenology; spatial extrapolation; Support vector machines; Surveys; Training data; unmanned ariel vehicle
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS53475.2024.10641245

This study integrated 3S technology with artificial intelligence (AI), specifically utilizing Geospatial AI (Geo-AI), to spatially extrapolate the distribution of two invasive alien species (IAS), smooth cordgrass (SC) living in the intertidal zone and bitter vine (BV) emerging and growing inland in Formosa Island. The aim is to test whether three sampling schemes could improve extrapolation effectiveness and fill the gap in utilizing unmanned ariel vehicle (UAV) combined with deep learning in this field. Support vector machine, random forest, U-net, and DeepLabV3 were employed to recognize spatial patterns of SC and BV. Results indicate that the third scheme combining training data from two sampling sites in their own's region yields optimal outcome, with deep learning outperforming machine learning, DeepLabV3 achieving F1 scores of 0.95 and 0.93 for SC and BV, respectively. This underscores the need for surveys with larger number of smaller separate sampling areas. Future works will utilize Geo-AI to search for additional IAS beyond the two regions, further realizing spatial extrapolation.