In-Depth Mining Spatial Pattern Of Invasive Alien Species By Deep Learning From Phenological-Based Drone Images

Spartina alterniflora (smooth cordgrass, SC) has invaded Formosa Island in recent years and is considered an invasive alien species (IAS). Careful assessment of IAS's impact on the island’s ecology is a top priority. Drones can improve the lack of autonomy and maneuverability of spaceborne and airborne systems, and better grasp plant’s phenology. We used drones with multispectral camera to acquire two phenological-phase orthoimages. Support vector machines (SVM), random forests (RF), and DeepLabV3 were used to extract SC from background, aiming to find out and map the spatial pattern of SC accurately. The results show that the two-date images can improve the accuracy; DeepLabV3 is far better than SVM and RF. The revalidation results show that the DeepLabV3 estimate was the closest to the real situation. The process done with the combination of drones and DeepLabV3 herein is effective and efficient, which can extract SC more accurately from background.