Estimating total length of partially submerged crocodylians from drone imagery
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.
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Format: | Texto biblioteca |
Language: | eng |
Subjects: | Cocodrilos, Población animal, Imágenes de drones, Alometría, Análisis demográfico, |
Online Access: | https://doi.org/10.3390/drones8030115 |
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KOHA-OAI-ECOSUR:645492024-05-03T17:54:57ZEstimating total length of partially submerged crocodylians from drone imagery Aubert, Clément autor Le Moguédec, Gilles autor Velasco, Alvaro autor Combrink, Xander autor Lang, Jeffrey W. autor Griffith, Phoebe autor/a Pacheco Sierra, Gualberto autor Pérez, Etiam autor/a Charruau, Pierre Alexandre Rémy Robert Doctor autor 13179 Villamarín, Francisco autor Roberto, Igor J. autor Marioni, Boris autor Colbert, Joseph E. autor Mobaraki, Asghar autor Woodward, Allan R. autor Somaweera, Ruchira autor Tellez, Marisa autora Brien, Matthew autor Shirley, Matthew H. autor textengUnderstanding 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.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.CocodrilosPoblación animalImágenes de dronesAlometríaAnálisis demográficoDroneshttps://doi.org/10.3390/drones8030115Acceso en línea sin restricciones |
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Cocodrilos Población animal Imágenes de drones Alometría Análisis demográfico Cocodrilos Población animal Imágenes de drones Alometría Análisis demográfico |
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Cocodrilos Población animal Imágenes de drones Alometría Análisis demográfico Cocodrilos Población animal Imágenes de drones Alometría Análisis demográfico Aubert, Clément autor Le Moguédec, Gilles autor Velasco, Alvaro autor Combrink, Xander autor Lang, Jeffrey W. autor Griffith, Phoebe autor/a Pacheco Sierra, Gualberto autor Pérez, Etiam autor/a Charruau, Pierre Alexandre Rémy Robert Doctor autor 13179 Villamarín, Francisco autor Roberto, Igor J. autor Marioni, Boris autor Colbert, Joseph E. autor Mobaraki, Asghar autor Woodward, Allan R. autor Somaweera, Ruchira autor Tellez, Marisa autora Brien, Matthew autor Shirley, Matthew H. autor Estimating total length of partially submerged crocodylians from drone imagery |
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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. |
format |
Texto |
topic_facet |
Cocodrilos Población animal Imágenes de drones Alometría Análisis demográfico |
author |
Aubert, Clément autor Le Moguédec, Gilles autor Velasco, Alvaro autor Combrink, Xander autor Lang, Jeffrey W. autor Griffith, Phoebe autor/a Pacheco Sierra, Gualberto autor Pérez, Etiam autor/a Charruau, Pierre Alexandre Rémy Robert Doctor autor 13179 Villamarín, Francisco autor Roberto, Igor J. autor Marioni, Boris autor Colbert, Joseph E. autor Mobaraki, Asghar autor Woodward, Allan R. autor Somaweera, Ruchira autor Tellez, Marisa autora Brien, Matthew autor Shirley, Matthew H. autor |
author_facet |
Aubert, Clément autor Le Moguédec, Gilles autor Velasco, Alvaro autor Combrink, Xander autor Lang, Jeffrey W. autor Griffith, Phoebe autor/a Pacheco Sierra, Gualberto autor Pérez, Etiam autor/a Charruau, Pierre Alexandre Rémy Robert Doctor autor 13179 Villamarín, Francisco autor Roberto, Igor J. autor Marioni, Boris autor Colbert, Joseph E. autor Mobaraki, Asghar autor Woodward, Allan R. autor Somaweera, Ruchira autor Tellez, Marisa autora Brien, Matthew autor Shirley, Matthew H. autor |
author_sort |
Aubert, Clément autor |
title |
Estimating total length of partially submerged crocodylians from drone imagery |
title_short |
Estimating total length of partially submerged crocodylians from drone imagery |
title_full |
Estimating total length of partially submerged crocodylians from drone imagery |
title_fullStr |
Estimating total length of partially submerged crocodylians from drone imagery |
title_full_unstemmed |
Estimating total length of partially submerged crocodylians from drone imagery |
title_sort |
estimating total length of partially submerged crocodylians from drone imagery |
url |
https://doi.org/10.3390/drones8030115 |
work_keys_str_mv |
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