Figure 3. Total number of simulations from an agent-based model that either returned the correct number of animals (one animal; Correct), omitted the animal (Omissions), or had multiple counts (Multiple) among various drone flight patterns, animal movement speeds, and animal movement patterns (Random = Random Walk, CRW = Correlated Random Walk; BRW = Biased Random Walk).
Discussion
This research represents the first work investigating the interactions of multiple drone flight patterns and animal movement behaviors during simulated surveys. We demonstrate that drone flight patterns can greatly influence animal count accuracy even over relatively small areas (herein ~22-24 ha) and provide support for use of a rarely considered drone flight pattern (a lawnmower pattern with 0% image overlap) for animal monitoring. While lawnmower patterns with large overlaps allow for development of image mosaics for landscape mapping (Frazier and Singh, 2021), we found that these flight patterns increasingly lead to overestimated counts of mobile animals as percent overlap increased, even when accounting for mosaicking. Our results have important and often overlooked implications for drone surveys compared to more commonly applied practices.
Easily programmed drone lawnmower pattern surveys typically use 60-80% overlapping imagery (Lyons et al. , 2019; Aubert et al. , 2021), but our results indicate this may have major implications for multiple count concerns during drone surveys if the animal of interest is mobile. While it is acknowledged that animals move during surveys (Brack, Kindel and Oliveira, 2018), many drone surveys assume animals are stationary (Sudholz et al. , 2022) and create a mosaic image to more easily count animals and understand distributions (Ezat, Fritsch and Downs, 2018; De Kock et al. , 2021) without quantifying the effect of animal movement on counting accuracy. A few field drone studies have attempted to address animal movement issues post data collection (Linchant et al. , 2018) and with manual image searches for clones, partial, or blurred animals after mosaicking (Barbedo and Vieira Koenigkan, 2018; Lenzi et al. , 2023). Another approach reviews individual overlapping images, comparing animal shapes, sizes, and positions to reduce the number of multiple counted animals (Witczuket al. , 2018; Cleguer et al. , 2021; Sudholz et al. , 2022). This additional post-processing of imagery can be helpful, but uncertainty remains in their effectiveness considering most animals are unmarked or indistinguishable from other individuals. These image reviews are also very labor intensive, time-consuming, and do not address animal omissions due to their movements (Brack, Kindel and Oliveira, 2018). Automated image classification approaches are being developed (Gonzalez et al. , 2016; Dujon et al. , 2021; Chabot, Stapleton and Francis, 2022; Krishnan et al. , 2023), but the development of accurate algorithms for aerial animal imagery is still in its infancy and has many challenges to overcome (Corcoranet al. , 2021; Sudholz et al. , 2022). Addressing the issue of multiple counting during data collection, as opposed to during post processing could reduce the likelihood of the multiple-count problem. Incorporating lawnmower patterns with minimal image overlap may be key, as noted in the increase in count accuracy of our simulations as overlap percentage decreased.
Subsampling the landscape or spreading sampling intervals has been suggested as a means to avoid issues of multiple counts of the same animal (Witczuk et al. , 2018). However, we found that animal movement can still influence counts of an individual in these scenarios. The average count and variation of the transect flight pattern both doubled when the animal was moving, as opposed to when the animal was stationary on the landscape. Similarly, for the systematic points flight pattern, the average count increased 1.5 times, with a slight increase in count variation as well. However, we also found that during the systematic points flight (Fig. 1f), an animal moving with directional persistence resulted in a large percentage of accurate surveys, which was consistent with other drone flight patterns. Thus, surveys at systematic points for animals behaving this way may be accurate and would result in less imagery for post-processing, sequentially leading to additional time-savings during data preparation and image evaluation. Ultimately, subsampling the landscape, compared to a full census, will require correction of counts (Buckland et al. , 2001), but as we have shown, these corrections should vary depending on if the animals of interest are anticipated to be mobile during the survey period.
Our simulations confirm that animal movement patterns and speeds influence whether an animal is correctly counted in drone imagery. The random and biased random walking animal movement patterns often resulted in overestimates from multiple counts of an animal traveling back into the path of the drone after its initial “capture.” An increase in the animal speed lowered the precision for most flight patterns and animal walks, with an exception for the correlated walking animal. Therefore, researchers need to consider animal movement behaviors to avoid count bias and consequential incorrect management prescriptions (Guerrasioet al. , 2022). Overall, our results emphasize that knowledge of animal movement patterns can help identify the optimal survey periods and drone flight patterns to minimize sampling error. To minimize count error, one might survey using a systematic points flight pattern during crepuscular periods when certain species, such as white-tailed deer, are most active (Kammermeyer and Marchinton, 1977). Or depending on the research question, 0% overlap lawnmower pattern surveys during other times of day or year when individuals are more stationary, such as when juveniles have not yet dispersed from natal areas, may also be appropriate.
Even on our simplified landscape, we observed large amounts of bias among animal counts during scenario simulations with one mobile animal. While our assumptions of 100% availability and detectability are highly unlikely in real-world applications (Gilbert et al. , 2021), for example, due to visual obstructions above the animals or the ability of the animal to dive underwater or move under cover (Hodgson, Peel and Kelly, 2017; Brunton, Leon and Burnett, 2020), this assumption allowed us to simplify our scenarios and better understand how flight patterns and animal movements may create counting errors. Typically, surveyors are concerned with omission rates associated with conventional animal survey methods (i.e., occupied aircraft and ground surveys) due to detectability issues, and there are means of addressing some of these problems (Steinhorst and Samuel, 1989; Samuel et al. , 1992; Hamilton et al. , 2018; Brack, Kindel, de Oliveira, et al. , 2023). For example, the inclusion of detection probabilities in statistical models has greatly improved our ability to estimate animal populations (Martin et al. , 2012; Corcoran, Denman and Hamilton, 2021), and incorporating detection probabilities into drone-based estimates would be a helpful advancement (Hodgson, Peel and Kelly, 2017; Brack, Kindel, de Oliveira, et al. , 2023; Hodgson, Kelly and Peel, 2023). It is also notable that false positives (i.e., multiple counts) are less frequent during ground-based and occupied aircraft surveys, something that researchers using drones need to carefully consider moving forward (Brack, Kindel and Oliveira, 2018).
We acknowledge other tradeoffs must be considered for drone surveys such as balancing battery life and line-of-sight limitations during survey planning (Linchant et al. , 2015; Baxter and Hamilton, 2018). Hence, trade-offs between the area sampled and survey accuracy may need to be considered for larger sampling areas. There may also be potential for increased accuracy with alternative flight patterns that we did not consider. For example, sea turtle density estimates were calculated using a modified strip-transect approach with 35-45% frontal overlap and sequential images used in counts to reduce multiple counting potential (Sykora-Bodie et al. , 2017; Brack, Kindel, Berto,et al. , 2023), and point count drone surveys with 360-degree rotations were found to be a promising approach for mesocarnivore abundance estimates (Bushaw, Ringelman and Rohwer, 2019). In either case, the method was not thoroughly vetted for accuracy, although this could be explored in future work. Barbedo and Vieira Koenigkan (2018) suggest flying multiple drones in formation to collect accurate counts, acknowledging that animals could otherwise move between survey efforts. However, they also note that this would greatly increase survey cost, and that formation flights have many technical challenges. In any case, researchers should not assume mosaics composed of overlapped images can be used for both vegetation mapping and animal surveying simultaneously. Instead, careful thought is needed for drone flight patterns with objectives related to animal monitoring.
Conclusions
As the use of drones in animal monitoring continues to grow, consideration of how these survey platforms can be appropriately incorporated into animal survey techniques is vital. Based on our results, when using a drone to survey areas similar to our simulations (~22-24 ha), we recommend that, researchers interested in animal counts should consider a lawnmower flight pattern with 0% overlap as an alternative to other more easily programmed, overlapping patterns. We also recommend that animal life history knowledge be incorporated in survey design, aligning with stages and times of day when animals may exhibit more sedentary or more directional movements. This will allow for the most accurate counts as well as maximize overall ground coverage area when accounting for limited battery capabilities (Linchant et al. , 2015). The simulated approach we utilized also allows for robust inference to investigate a myriad of animal behaviors and population processes that can be broadly applied across many taxa and provide guidance of drone applications in a variety of wildlife management applications. Future efforts with our agent-based modeling approach can help assess the influence of animal density, distributions, and detection probabilities to better simulate real-world environments.
Author Contributions
Emma A. Schultz: Conceptualization, Data curation, Formal analysis, Methodology, Writing- Original draft and EditingNatasha Ellison: Conceptualization, Coding design, Visualization, Validation, Methodology, Writing- Review and EditingMelanie R. Boudreau: Conceptualization, Methodology, Writing- Review and Editing Garrett M. Street: Conceptualization, Methodology, Project administration, Writing- Review and EditingLandon R. Jones: Conceptualization, Methodology, Writing- Review and Editing Kristine O. Evans: Conceptualization, Methodology, Writing- Review and Editing Raymond B. Iglay:Conceptualization, Funding acquisition, Methodology, Project administration, Writing- Review and Editing.
Acknowledgements
We thank Dr. Sathish Samiappan for his review of the manuscript. We also thank Dr. Pat Clark for his sponsorship and use of high-performance computing resources. This publication is a contribution of the Mississippi State University Forest and Wildlife Research Center [FWRC; McIntire-Stennis Project MISZ-085160] and Mississippi Agricultural and Forestry Experiment Station [MAFES; HATCH Project MIS-085180].
Funding
This work was funded by FWRC and MAFES of Mississippi State University and United States Department of Agriculture Agricultural Research Service Non-Assistance Cooperative Agreement #58-0200-0-002 via the Graduate Summer Research Experience in High-Performance Computing and Agriculture fellowship program in the Geosystems Research Institute at Mississippi State University.
Data Availability Statement
Data will be available in Supporting Information, Appendix 2, upon acceptance.