Ecological monitoring programs are fundamental to following natural-system populational trends. Drones are a new key to animal monitoring, presenting different benefits but two basic re-strictions First, the increase of information requires a high capacity of storage and, second, time invested in data analysis. We present a protocol to develop an automatic object recognizer to minimize analysis time and optimize data storage. We conducted this study at the Cruces River, Valdivia, Chile, using a Phantom 3 Advanced drone with an HD-standard camera. We used a Black-necked swan (Cygnus melancoryphus) as a model because it is abundant and has a contrasting color compared to the environment, making it easy detection. The drone flew 100 m from water surface (correcting AGL in relation to pilot landing altitude) obtaining georeferenced images with 75% overlap and developing approximately 0.69 km2 of orthomosaics images. We estimated the swans’ spectral signature to build the recognizer and adjusted nine criteria for object-oriented classification. We obtained 140 orthophotos classified into three brightness categories. We found that the Precision, Sensitivity, Specificity, and Accuracy indicator were higher than 0.93 and a calibration curve with R2= 0.991 for images without brightness. The recognizer prediction decreases with brightness but is corrected using ND8-16 filter lens. We discuss the importance of this recognizer to data analysis optimization and the advantage of using this recognition protocol for any object in ecological studies.
Automatic Recognition of Black-Necked Swan (Cygnus melancoryphus) from Drone Imagery
2023
Article (Journal)
Electronic Resource
Unknown
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