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This research aims to enhance the monitoring of Przewalski's gazelle using UAV and AI technologies while minimizing behavioral disturbance.

May 11, 2026Ecological InformaticsOpen Access

Low-disturbance UAV-AI monitoring of the endangered Przewalski's gazelle on the Qinghai-Tibetan plateau

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This research aims to enhance the monitoring of Przewalski's gazelle using UAV and AI technologies while minimizing behavioral disturbance.
Developed a non-invasive UAV monitoring framework combining VTOL UAV and deep learning techniques.
Conducted disturbance-response experiments in the field to evaluate behavioral reactions of gazelles to UAV operations.
Established a UAV imagery dataset and assessed multiple object-detection models under varying training-data conditions.
YOLOv11 achieved a mean Average Precision of 0.872 under low training data conditions (20%).
UAV counts exhibited a mean relative agreement of 94.52% with ground surveys, confirming reliability.
Identified significant positive spatial autocorrelation (Moran's I = 0.238, p < 0.001) and high-density clusters of gazelles.

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Randomized trial evaluates low-disturbance UAV monitoring effectiveness for Przewalski's gazelle, suggesting viable conservation strategies.

Han et al. (2026) studied this question.

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Randomized trial evaluates low-disturbance UAV monitoring effectiveness for Przewalski's gazelle, suggesting viable conservation strategies.