TY - JOUR
T1 - Estimating Total Length of Partially Submerged Crocodylians from Drone Imagery
AU - Aubert, Clément
AU - Le Moguédec, Gilles
AU - Velasco, Alvaro
AU - Combrink, Xander
AU - Lang, Jeffrey W.
AU - Griffith, Phoebe
AU - Pacheco-Sierra, Gualberto
AU - Pérez, Etiam
AU - Charruau, Pierre
AU - Villamarín, Francisco
AU - Roberto, Igor J.
AU - Marioni, Boris
AU - Colbert, Joseph E.
AU - Mobaraki, Asghar
AU - Woodward, Allan R.
AU - Somaweera, Ruchira
AU - Tellez, Marisa
AU - Brien, Matthew
AU - Shirley, Matthew H.
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/3
Y1 - 2024/3
N2 - Understanding the demographic structure is vital for wildlife research and conservation. For crocodylians, accurately estimating total length and demographic class usually necessitates close observation or capture, often of partially immersed individuals, leading to potential imprecision and risk. Drone technology offers a bias-free, safer alternative for classification. We evaluated the effectiveness of drone photos combined with head length allometric relationships to estimate total length, and propose a standardized method for drone-based crocodylian demographic classification. We evaluated error sources related to drone flight parameters using standardized targets. An allometric framework correlating head to total length for 17 crocodylian species was developed, incorporating confidence intervals to account for imprecision sources (e.g., allometric accuracy, head inclination, observer bias, terrain variability). This method was applied to wild crocodylians through drone photography. Target measurements from drone imagery, across various resolutions and sizes, were consistent with their actual dimensions. Terrain effects were less impactful than Ground-Sample Distance (GSD) errors from photogrammetric software. The allometric framework predicted lengths within ≃11–18% accuracy across species, with natural allometric variation among individuals explaining much of this range. Compared to traditional methods that can be subjective and risky, our drone-based approach is objective, efficient, fast, cheap, non-invasive, and safe. Nonetheless, further refinements are needed to extend survey times and better include smaller size classes.
AB - Understanding the demographic structure is vital for wildlife research and conservation. For crocodylians, accurately estimating total length and demographic class usually necessitates close observation or capture, often of partially immersed individuals, leading to potential imprecision and risk. Drone technology offers a bias-free, safer alternative for classification. We evaluated the effectiveness of drone photos combined with head length allometric relationships to estimate total length, and propose a standardized method for drone-based crocodylian demographic classification. We evaluated error sources related to drone flight parameters using standardized targets. An allometric framework correlating head to total length for 17 crocodylian species was developed, incorporating confidence intervals to account for imprecision sources (e.g., allometric accuracy, head inclination, observer bias, terrain variability). This method was applied to wild crocodylians through drone photography. Target measurements from drone imagery, across various resolutions and sizes, were consistent with their actual dimensions. Terrain effects were less impactful than Ground-Sample Distance (GSD) errors from photogrammetric software. The allometric framework predicted lengths within ≃11–18% accuracy across species, with natural allometric variation among individuals explaining much of this range. Compared to traditional methods that can be subjective and risky, our drone-based approach is objective, efficient, fast, cheap, non-invasive, and safe. Nonetheless, further refinements are needed to extend survey times and better include smaller size classes.
KW - UAV
KW - allometry
KW - alternative methods
KW - crocodiles survey
KW - ecology
KW - non-invasive survey
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U2 - 10.3390/drones8030115
DO - 10.3390/drones8030115
M3 - Article
AN - SCOPUS:85188753325
SN - 2504-446X
VL - 8
JO - Drones
JF - Drones
IS - 3
M1 - 115
ER -