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Jones LR, Mensah C, Elmore JA, Evans KO, Pfeiffer MB, Blackwell BF, Iglay RB. Heating decoys to mimic thermal signatures of live animals for drones. MethodsX 2024; 13:102933. [PMID: 39286441 PMCID: PMC11404203 DOI: 10.1016/j.mex.2024.102933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 08/26/2024] [Indexed: 09/19/2024] Open
Abstract
Thermal sensors mounted on drones (unoccupied aircraft systems) are popular and effective tools for monitoring cryptic animal species, although few studies have quantified sampling error of animal counts from thermal images. Using decoys is one effective strategy to quantify bias and count accuracy; however, plastic decoys do not mimic thermal signatures of representative species. Our objective was to produce heat signatures in animal decoys to realistically match thermal images of live animals obtained from a drone-based sensor. We tested commercially available methods to heat plastic decoys of three different size classes, including chemical foot warmers, manually heated water, electric socks, pad, or blanket, and mini and small electric space heaters. We used criteria in two categories, 1) external temperature differences from ambient temperatures (ambient difference) and 2) color bins from a palette in thermal images obtained from a drone near the ground and in the air, to determine if heated decoys adequately matched respective live animals in four body regions. Three methods achieved similar thermal signatures to live animals for three to four body regions in external temperatures and predominantly matched the corresponding yellow color bins in thermal drone images from the ground and in the air. Pigeon decoys were best and most consistently heated with three-foot warmers. Goose and deer decoys were best heated by mini and small space heaters, respectively, in their body cavities, with a heated sock in the head of the goose decoy. The materials and equipment for our best heating methods were relatively inexpensive, commercially available items that provide sustained heat and could be adapted to various shapes and sizes for a wide range of avian and mammalian species. Our heating methods could be used in future studies to quantify bias and validate methodologies for drone surveys of animals with thermal sensors.•We determined optimal heating methods for plastic animal decoys with inexpensive and commercially available equipment to mimic thermal signatures of live animals.•Methods could be used to quantify bias and improve thermal surveys of animals with drones in future studies.
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Affiliation(s)
- Landon R Jones
- Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Mississippi State, MS 39762, USA
| | - Cerise Mensah
- Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Mississippi State, MS 39762, USA
- Museum of Natural Science, Mississippi Department of Wildlife, Fisheries, and Parks, Jackson, MS 39202, USA
| | - Jared A Elmore
- Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Mississippi State, MS 39762, USA
- Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634, USA
| | - Kristine O Evans
- Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Mississippi State, MS 39762, USA
| | - Morgan B Pfeiffer
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Ohio Field Station, OH 44870, USA
| | - Bradley F Blackwell
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Ohio Field Station, OH 44870, USA
| | - Raymond B Iglay
- Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Mississippi State, MS 39762, USA
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2
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Pinel-Ramos EJ, Aureli F, Wich S, Longmore S, Spaan D. Evaluating Thermal Infrared Drone Flight Parameters on Spider Monkey Detection in Tropical Forests. SENSORS (BASEL, SWITZERLAND) 2024; 24:5659. [PMID: 39275572 PMCID: PMC11397880 DOI: 10.3390/s24175659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 08/23/2024] [Accepted: 08/28/2024] [Indexed: 09/16/2024]
Abstract
Geoffroy's spider monkeys, an endangered, fast-moving arboreal primate species with a large home range and a high degree of fission-fusion dynamics, are challenging to survey in their natural habitats. Our objective was to evaluate how different flight parameters affect the detectability of spider monkeys in videos recorded by a drone equipped with a thermal infrared camera and examine the level of agreement between coders. We used generalized linear mixed models to evaluate the impact of flight speed (2, 4, 6 m/s), flight height (40, 50 m above ground level), and camera angle (-45°, -90°) on spider monkey counts in a closed-canopy forest in the Yucatan Peninsula, Mexico. Our results indicate that none of the three flight parameters affected the number of detected spider monkeys. Agreement between coders was "substantial" (Fleiss' kappa coefficient = 0.61-0.80) in most cases for high thermal-contrast zones. Our study contributes to the development of standardized flight protocols, which are essential to obtain accurate data on the presence and abundance of wild populations. Based on our results, we recommend performing drone surveys for spider monkeys and other medium-sized arboreal mammals with a small commercial drone at a 4 m/s speed, 15 m above canopy height, and with a -90° camera angle. However, these recommendations may vary depending on the size and noise level produced by the drone model.
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Affiliation(s)
- Eduardo José Pinel-Ramos
- Instituto de Neuroetología, Universidad Veracruzana, Av. Dr. Luis Castelazo Ayala, Xalapa 91190, Veracruz, Mexico
- ConMonoMaya, A.C., Km 5.4 Carretera Chemax-Coba, Chemax 97770, Yucatán, Mexico
| | - Filippo Aureli
- Instituto de Neuroetología, Universidad Veracruzana, Av. Dr. Luis Castelazo Ayala, Xalapa 91190, Veracruz, Mexico
- ConMonoMaya, A.C., Km 5.4 Carretera Chemax-Coba, Chemax 97770, Yucatán, Mexico
- School of Biological and Environmental Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool L3 3AF, UK
| | - Serge Wich
- School of Biological and Environmental Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool L3 3AF, UK
| | - Steven Longmore
- Astrophysics Research Institute, Liverpool John Moores University, 146 Brownlow Hill, Liverpool L3 5RF, UK
| | - Denise Spaan
- Instituto de Neuroetología, Universidad Veracruzana, Av. Dr. Luis Castelazo Ayala, Xalapa 91190, Veracruz, Mexico
- ConMonoMaya, A.C., Km 5.4 Carretera Chemax-Coba, Chemax 97770, Yucatán, Mexico
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3
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Samiappan S, Krishnan BS, Dehart D, Jones LR, Elmore JA, Evans KO, Iglay RB. Aerial Wildlife Image Repository for animal monitoring with drones in the age of artificial intelligence. Database (Oxford) 2024; 2024:baae070. [PMID: 39043628 PMCID: PMC11265857 DOI: 10.1093/database/baae070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 05/31/2024] [Accepted: 07/08/2024] [Indexed: 07/25/2024]
Abstract
Drones (unoccupied aircraft systems) have become effective tools for wildlife monitoring and conservation. Automated animal detection and classification using artificial intelligence (AI) can substantially reduce logistical and financial costs and improve drone surveys. However, the lack of annotated animal imagery for training AI is a critical bottleneck in achieving accurate performance of AI algorithms compared to other fields. To bridge this gap for drone imagery and help advance and standardize automated animal classification, we have created the Aerial Wildlife Image Repository (AWIR), which is a dynamic, interactive database with annotated images captured from drone platforms using visible and thermal cameras. The AWIR provides the first open-access repository for users to upload, annotate, and curate images of animals acquired from drones. The AWIR also provides annotated imagery and benchmark datasets that users can download to train AI algorithms to automatically detect and classify animals, and compare algorithm performance. The AWIR contains 6587 animal objects in 1325 visible and thermal drone images of predominantly large birds and mammals of 13 species in open areas of North America. As contributors increase the taxonomic and geographic diversity of available images, the AWIR will open future avenues for AI research to improve animal surveys using drones for conservation applications. Database URL: https://projectportal.gri.msstate.edu/awir/.
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Affiliation(s)
- Sathishkumar Samiappan
- Geosystems Research Institute, Mississippi State University, 2 Research Blvd, Starkville, MS 39759, United States
| | - B. Santhana Krishnan
- Geosystems Research Institute, Mississippi State University, 2 Research Blvd, Starkville, MS 39759, United States
| | - Damion Dehart
- Geosystems Research Institute, Mississippi State University, 2 Research Blvd, Starkville, MS 39759, United States
- Computer Sciences and Computer Engineering, University of Southern Mississippi, 118 College Drive, Hattiesburg, MS 39406, United States
| | - Landon R Jones
- Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Stone Blvd, Mississippi State, MS 39762, United States
| | - Jared A Elmore
- Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Stone Blvd, Mississippi State, MS 39762, United States
| | - Kristine O Evans
- Geosystems Research Institute, Mississippi State University, 2 Research Blvd, Starkville, MS 39759, United States
- Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Stone Blvd, Mississippi State, MS 39762, United States
| | - Raymond B Iglay
- Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Stone Blvd, Mississippi State, MS 39762, United States
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Müller R. Bioinspiration from bats and new paradigms for autonomy in natural environments. BIOINSPIRATION & BIOMIMETICS 2024; 19:033001. [PMID: 38452384 DOI: 10.1088/1748-3190/ad311e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 03/07/2024] [Indexed: 03/09/2024]
Abstract
Achieving autonomous operation in complex natural environment remains an unsolved challenge. Conventional engineering approaches to this problem have focused on collecting large amounts of sensory data that are used to create detailed digital models of the environment. However, this only postpones solving the challenge of identifying the relevant sensory information and linking it to action control to the domain of the digital world model. Furthermore, it imposes high demands in terms of computing power and introduces large processing latencies that hamper autonomous real-time performance. Certain species of bats that are able to navigate and hunt their prey in dense vegetation could be a biological model system for an alternative approach to addressing the fundamental issues associated with autonomy in complex natural environments. Bats navigating in dense vegetation rely on clutter echoes, i.e. signals that consist of unresolved contributions from many scatters. Yet, the animals are able to extract the relevant information from these input signals with brains that are often less than 1 g in mass. Pilot results indicate that information relevant to location identification and passageway finding can be directly obtained from clutter echoes, opening up the possibility that the bats' skill can be replicated in man-made autonomous systems.
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Affiliation(s)
- Rolf Müller
- Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, United States of America
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Couturier T, Gaillard L, Vadier A, Dautrey E, Mathey J, Besnard A. Airborne imagery does not preclude detectability issues in estimating bird colony size. Sci Rep 2024; 14:3673. [PMID: 38351024 PMCID: PMC10864377 DOI: 10.1038/s41598-024-53961-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 02/07/2024] [Indexed: 02/16/2024] Open
Abstract
Aerial images obtained by drones are increasingly used for ecological research such as wildlife monitoring. Yet detectability issues resulting from animal activity or visibility are rarely considered, although these may lead to biased population size and trend estimates. In this study, we investigated detectability in a census of Malagasy pond heron Ardeola idae colonies on the island of Mayotte. We conducted repeated drone flights over breeding colonies in mangrove habitats during two breeding seasons. We then identified individuals and nests in the images and fitted closed capture-recapture models on nest-detection histories. We observed seasonal variation in the relative abundance of individuals, and intra-daily variation in the relative abundance of individuals-especially immature birds-affecting the availability of nests for detection. The detection probability of nests estimated by capture-recapture varied between 0.58 and 0.74 depending on flyover days and decreased 25% from early to late morning. A simulation showed that three flyovers are necessary to detect a 5-6% decline in colonies of 50 to 200 nests. These results indicate that the detectability of nests of forest-canopy breeding species from airborne imagery can vary over space and time; we recommend the use of capture-recapture methods to control for this bias.
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Affiliation(s)
- Thibaut Couturier
- CEFE, IRD, CNRS, University of Montpellier, EPHE-PSL University, Montpellier, France.
| | - Laurie Gaillard
- GEPOMAY, Groupe d'Études et de Protection des Oiseaux de Mayotte, 4 Impasse Tropina, Miréréni, Tsingoni, Mayotte, France
| | - Almodis Vadier
- GEPOMAY, Groupe d'Études et de Protection des Oiseaux de Mayotte, 4 Impasse Tropina, Miréréni, Tsingoni, Mayotte, France
| | - Emilien Dautrey
- GEPOMAY, Groupe d'Études et de Protection des Oiseaux de Mayotte, 4 Impasse Tropina, Miréréni, Tsingoni, Mayotte, France
| | - Jérôme Mathey
- DroneGo, Quartier Hadoume, Bp33 Poste de Combani, Tsingoni, Mayotte, France
| | - Aurélien Besnard
- CEFE, IRD, CNRS, University of Montpellier, EPHE-PSL University, Montpellier, France
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Rahman DA, Herliansyah R, Subhan B, Hutasoit D, Imron MA, Kurniawan DB, Sriyanto T, Wijayanto RD, Fikriansyah MH, Siregar AF, Santoso N. The first use of a photogrammetry drone to estimate population abundance and predict age structure of threatened Sumatran elephants. Sci Rep 2023; 13:21311. [PMID: 38042901 PMCID: PMC10693614 DOI: 10.1038/s41598-023-48635-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/28/2023] [Indexed: 12/04/2023] Open
Abstract
Wildlife monitoring in tropical rainforests poses additional challenges due to species often being elusive, cryptic, faintly colored, and preferring concealable, or difficult to access habitats. Unmanned aerial vehicles (UAVs) prove promising for wildlife surveys in different ecosystems in tropical forests and can be crucial in conserving inaccessible biodiverse areas and their associated species. Traditional surveys that involve infiltrating animal habitats could adversely affect the habits and behavior of elusive and cryptic species in response to human presence. Moreover, collecting data through traditional surveys to simultaneously estimate the abundance and demographic rates of communities of species is often prohibitively time-intensive and expensive. This study assesses the scope of drones to non-invasively access the Bukit Tigapuluh Landscape (BTL) in Riau-Jambi, Indonesia, and detect individual elephants of interest. A rotary-wing quadcopter with a vision-based sensor was tested to estimate the elephant population size and age structure. We developed hierarchical modeling and deep learning CNN to estimate elephant abundance and age structure. Drones successfully observed 96 distinct individuals at 8 locations out of 11 sampling areas. We obtained an estimate of the elephant population of 151 individuals (95% CI [124, 179]) within the study area and predicted more adult animals than subadults and juvenile individuals in the population. Our calculations may serve as a vital spark for innovation for future UAV survey designs in large areas with complex topographies while reducing operational effort.
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Affiliation(s)
- Dede Aulia Rahman
- Department of Forest Resources Conservation and Ecotourism, Faculty of Forestry and Environment, IPB University, Bogor, 16680, Indonesia.
- Primate Research Center, Institute of Research and Community Service, IPB University, Bogor, 16151, Indonesia.
| | - Riki Herliansyah
- School of Statistics, Kalimantan Institute of Technology, Balikpapan, 76127, Indonesia
- School of Mathematics and Maxwell Institute for Mathematical Sciences, University of Edinburgh, Edinburgh, EH9 3FD, UK
| | - Beginer Subhan
- Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University, Bogor, 16680, Indonesia
| | - Donal Hutasoit
- Jambi Natural Resources Conservation Agency, Jambi, 36361, Indonesia
| | | | | | - Teguh Sriyanto
- Jambi Natural Resources Conservation Agency, Jambi, 36361, Indonesia
| | - Raden Danang Wijayanto
- Tropical Biodiversity Conservation Program, Faculty of Forestry and Environment, IPB University, Bogor, 16680, Indonesia
- Yogyakarta Natural Resources Conservation Agency, D.I. Yogyakarta, 55514, Indonesia
| | | | - Ahmad Faisal Siregar
- Tropical Biodiversity Conservation Program, Faculty of Forestry and Environment, IPB University, Bogor, 16680, Indonesia
| | - Nyoto Santoso
- Department of Forest Resources Conservation and Ecotourism, Faculty of Forestry and Environment, IPB University, Bogor, 16680, Indonesia
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7
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vanVuuren M, vanVuuren R, Silverberg LM, Manning J, Pacifici K, Dorgeloh W, Campbell J. Ungulate responses and habituation to unmanned aerial vehicles in Africa's savanna. PLoS One 2023; 18:e0288975. [PMID: 37490471 PMCID: PMC10368239 DOI: 10.1371/journal.pone.0288975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 07/09/2023] [Indexed: 07/27/2023] Open
Abstract
This article tests the hypothesis that "the likelihood that the species will react and level at which they do to the unmanned aerial vehicle (UAV) is related to the altitude, number of passes, sound intensity, type of UAV, takeoff distance, and species." This paper examined the behavioral responses of a group of free ranging ungulate species (Oryx, Kudu, Springbok, Giraffe, Eland, Hartebeest, and Impala) found in an animal reserve in Namibia to the presence of different in-flight UAV models. The study included 397 passes (trials) over 99 flights at altitudes ranging from 15 to 55 meters in three categories of response level: No response, Alert, and Movement. The ungulates were unhabituated to the UAVs and the study was conducted in the presence of stress-inducing events that occur naturally in the environment. Certain species were found to be more reactive than others, in addition to several displaying different response levels in single or mixed herd environments. Zebras were found to be less responsive in mixed herd environments while Oryx were present, as compared to when the Oryx were not; suggesting that some species may respond based on other species perception of threat or their relative fitness levels. The UAVs also produced inconsistent response rates between movement and alert behavior. The reference vehicle, Phantom 3 was much more likely than the Mavic to induce an alert response, while both having similar probabilities of inducing a movement response. Furthermore, the Custom X8 showed significantly more alert and movement responses than the other UAVs. This shows there may be several aspects to the UAVs that affect the responses of the ungulates. For instance, the sound intensity may alert the species more often, but close proximity may induce a movement response. More generally, the data shows that when the UAV is flying above 50 meters and has a measured sound intensity below 50 dB, the likelihood of inducing a movement response on an ungulate species is below 6% regardless of the vehicle on the first pass over the animals. Additionally, with each subsequent pass the likelihood of response dropped by approximately 20 percent. The results suggest a stronger correlation between flight altitude and response across the different ungulates, and the evidence suggests rapid habituation to the UAVs.
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Affiliation(s)
| | | | - Larry M Silverberg
- Mechanical & Aerospace Engineering, North Carolina State University, Raleigh, North Carolina
| | - Joe Manning
- Mechanical & Aerospace Engineering, North Carolina State University, Raleigh, North Carolina
| | - Krishna Pacifici
- Center of Geospatial Analytics, Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina
| | - Werner Dorgeloh
- Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina
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Sikora A, Marchowski D. The use of drones to study the breeding productivity of Whooper Swan Cygnus cygnus. THE EUROPEAN ZOOLOGICAL JOURNAL 2023. [DOI: 10.1080/24750263.2023.2181414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023] Open
Affiliation(s)
- A. Sikora
- Ornithological Station, Museum and Institute of Zoology, Polish Academy of Sciences, Gdańsk, Poland
| | - D. Marchowski
- Ornithological Station, Museum and Institute of Zoology, Polish Academy of Sciences, Gdańsk, Poland
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9
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Krishnan BS, Jones LR, Elmore JA, Samiappan S, Evans KO, Pfeiffer MB, Blackwell BF, Iglay RB. Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys. Sci Rep 2023; 13:10385. [PMID: 37369669 PMCID: PMC10300091 DOI: 10.1038/s41598-023-37295-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 06/19/2023] [Indexed: 06/29/2023] Open
Abstract
Visible and thermal images acquired from drones (unoccupied aircraft systems) have substantially improved animal monitoring. Combining complementary information from both image types provides a powerful approach for automating detection and classification of multiple animal species to augment drone surveys. We compared eight image fusion methods using thermal and visible drone images combined with two supervised deep learning models, to evaluate the detection and classification of white-tailed deer (Odocoileus virginianus), domestic cow (Bos taurus), and domestic horse (Equus caballus). We classified visible and thermal images separately and compared them with the results of image fusion. Fused images provided minimal improvement for cows and horses compared to visible images alone, likely because the size, shape, and color of these species made them conspicuous against the background. For white-tailed deer, which were typically cryptic against their backgrounds and often in shadows in visible images, the added information from thermal images improved detection and classification in fusion methods from 15 to 85%. Our results suggest that image fusion is ideal for surveying animals inconspicuous from their backgrounds, and our approach uses few image pairs to train compared to typical machine-learning methods. We discuss computational and field considerations to improve drone surveys using our fusion approach.
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Affiliation(s)
- B Santhana Krishnan
- Geosystems Research Institute, Mississippi State University, Mississippi State, Mississippi State, MS, 39762, USA
| | - Landon R Jones
- Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Box 9690, Mississippi State, MS, 39762, USA
| | - Jared A Elmore
- Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Box 9690, Mississippi State, MS, 39762, USA
- Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC, 29634, USA
| | - Sathishkumar Samiappan
- Geosystems Research Institute, Mississippi State University, Mississippi State, Mississippi State, MS, 39762, USA
| | - Kristine O Evans
- Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Box 9690, Mississippi State, MS, 39762, USA
| | - Morgan B Pfeiffer
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Ohio Field Station, Sandusky, OH, 44870, USA
| | - Bradley F Blackwell
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Ohio Field Station, Sandusky, OH, 44870, USA
| | - Raymond B Iglay
- Department of Wildlife, Fisheries, and Aquaculture, Mississippi State University, Box 9690, Mississippi State, MS, 39762, USA.
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10
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Rathore A, Sharma A, Shah S, Sharma N, Torney C, Guttal V. Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings. PeerJ 2023; 11:e15573. [PMID: 37397020 PMCID: PMC10309051 DOI: 10.7717/peerj.15573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 05/25/2023] [Indexed: 07/04/2023] Open
Abstract
Aerial imagery and video recordings of animals are used for many areas of research such as animal behaviour, behavioural neuroscience and field biology. Many automated methods are being developed to extract data from such high-resolution videos. Most of the available tools are developed for videos taken under idealised laboratory conditions. Therefore, the task of animal detection and tracking for videos taken in natural settings remains challenging due to heterogeneous environments. Methods that are useful for field conditions are often difficult to implement and thus remain inaccessible to empirical researchers. To address this gap, we present an open-source package called Multi-Object Tracking in Heterogeneous environments (MOTHe), a Python-based application that uses a basic convolutional neural network for object detection. MOTHe offers a graphical interface to automate the various steps related to animal tracking such as training data generation, animal detection in complex backgrounds and visually tracking animals in the videos. Users can also generate training data and train a new model which can be used for object detection tasks for a completely new dataset. MOTHe doesn't require any sophisticated infrastructure and can be run on basic desktop computing units. We demonstrate MOTHe on six video clips in varying background conditions. These videos are from two species in their natural habitat-wasp colonies on their nests (up to 12 individuals per colony) and antelope herds in four different habitats (up to 156 individuals in a herd). Using MOTHe, we are able to detect and track individuals in all these videos. MOTHe is available as an open-source GitHub repository with a detailed user guide and demonstrations at: https://github.com/tee-lab/MOTHe-GUI.
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Affiliation(s)
- Akanksha Rathore
- Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India
| | - Ananth Sharma
- Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India
| | - Shaan Shah
- Department of Electrical Engineering, Indian Institute of Technology, Bombay, Mumbai, India
| | - Nitika Sharma
- Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States of America
| | - Colin Torney
- School of Mathematics and Statistics, University of Glasgow, Glasgow, United Kingdom
| | - Vishwesha Guttal
- Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India
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11
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Wu Z, Zhang C, Gu X, Duporge I, Hughey LF, Stabach JA, Skidmore AK, Hopcraft JGC, Lee SJ, Atkinson PM, McCauley DJ, Lamprey R, Ngene S, Wang T. Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape. Nat Commun 2023; 14:3072. [PMID: 37244940 DOI: 10.1038/s41467-023-38901-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 05/19/2023] [Indexed: 05/29/2023] Open
Abstract
New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.
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Affiliation(s)
- Zijing Wu
- Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands
| | - Ce Zhang
- Lancaster Environment Center, Lancaster University, Lancaster, UK
- UK Centre for Ecology & Hydrology, Lancaster, UK
| | - Xiaowei Gu
- School of Computing, University of Kent, Canterbury, UK
| | - Isla Duporge
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- U.S. Army Research Laboratory, Army Research Office, Durham, NC, USA
- The National Academies of Sciences, Washington, D.C., USA
| | - Lacey F Hughey
- Conservation Ecology Center, Smithsonian National Zoo and Conservation Biology Institute, Front Royal, VA, USA
| | - Jared A Stabach
- Conservation Ecology Center, Smithsonian National Zoo and Conservation Biology Institute, Front Royal, VA, USA
| | - Andrew K Skidmore
- Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands
- School of Natural Sciences, Macquarie University, Sydney, NSW, Australia
| | - J Grant C Hopcraft
- Institute of Biodiversity, Animal Health, and Comparative Medicine, University of Glasgow, Glasgow, UK
| | - Stephen J Lee
- U.S. Army Research Laboratory, Army Research Office, Durham, NC, USA
| | - Peter M Atkinson
- Lancaster Environment Center, Lancaster University, Lancaster, UK
- Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Douglas J McCauley
- Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA, USA
| | - Richard Lamprey
- Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands
| | - Shadrack Ngene
- Wildlife Research and Training Institute, Naivasha, Kenya
| | - Tiejun Wang
- Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands.
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12
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Trujillano F, Garay GJ, Alatrista-Salas H, Byrne I, Nunez-del-Prado M, Chan K, Manrique E, Johnson E, Apollinaire N, Kouame Kouakou P, Oumbouke WA, Tiono AB, Guelbeogo MW, Lines J, Carrasco-Escobar G, Fornace K. Mapping Malaria Vector Habitats in West Africa: Drone Imagery and Deep Learning Analysis for Targeted Vector Surveillance. REMOTE SENSING 2023; 15:2775. [PMID: 37324796 PMCID: PMC7614662 DOI: 10.3390/rs15112775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Côte d'Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs.
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Affiliation(s)
- Fedra Trujillano
- Health Innovation Laboratory, Institute of Tropical Medicine “Alexander von Humboldt”, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow G12 8QQ, UK
| | - Gabriel Jimenez Garay
- Health Innovation Laboratory, Institute of Tropical Medicine “Alexander von Humboldt”, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
- Department of Engineering and Computer Science, Faculty of Science and Engineering, Sorbonne University, 75005 Paris, France
| | - Hugo Alatrista-Salas
- Escuela de Posgrado Newman, Tacna 23001, Peru
- Science and Engineering School, Pontificia Universidad Católica del Perú (PUCP), Lima 15088, Peru
| | - Isabel Byrne
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Miguel Nunez-del-Prado
- Peru Research, Development and Innovation Center (Peru IDI), Lima 15076, Peru
- The World Bank, Washington, DC 20433, USA
| | - Kallista Chan
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Edgar Manrique
- Health Innovation Laboratory, Institute of Tropical Medicine “Alexander von Humboldt”, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
| | - Emilia Johnson
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow G12 8QQ, UK
| | - Nombre Apollinaire
- Centre National de Recherche et de Formation sur le Paludisme, Ouagadougou 01 BP 2208, Burkina Faso
| | | | - Welbeck A. Oumbouke
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Innovative Vector Control Consortium, Liverpool School of Tropical Medicine, London L3 5QA, UK
| | - Alfred B. Tiono
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Moussa W. Guelbeogo
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Jo Lines
- Department of Infection Biology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Gabriel Carrasco-Escobar
- Health Innovation Laboratory, Institute of Tropical Medicine “Alexander von Humboldt”, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093, USA
| | - Kimberly Fornace
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, Glasgow G12 8QQ, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 119077, Singapore
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13
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Walker SE, Sheaves M, Waltham NJ. Barriers to Using UAVs in Conservation and Environmental Management: A Systematic Review. ENVIRONMENTAL MANAGEMENT 2023; 71:1052-1064. [PMID: 36525068 DOI: 10.1007/s00267-022-01768-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 12/03/2022] [Indexed: 06/17/2023]
Abstract
The ability to adopt novel tools continues to become more important for governments and environmental managers tasked with balancing economic development, social needs and environmental protection. An example of an emerging technology that can enable flexible, cost-effective data collection for conservation and environmental management is Unmanned Aerial Vehicles (UAVs). It is clear that UAVs are beginning to be adopted for a diversity of purposes, identification of barriers to their use is the first step in increasing their uptake amongst the environmental management community. Identifying the barriers to UAV usage will enable research and management communities to confidently utilise these powerful pieces of technology. However, the implementation of this technology for environmental research has received little overall assessment attention. This systematic literature review has identified 9 barrier categories (namely Technological, Analytical and Processing, Regulatory, Cost, Safety, Social, Wildlife impact, work suitability and others) inhibiting the uptake of UAV technologies. Technological barriers were referenced in the literature most often, with the inability of UAVs to perform in poor weather (such as rain or windy conditions) commonly mentioned. Analytical and Processing and Regulatory barriers were also consistently reported. It is likely that some barriers identified will lessen with time (e.g. technological and analytical barriers) as this technology continues to evolve.
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Affiliation(s)
- S E Walker
- TropWATER, Centre for Tropical Water and Aquatic Ecosystem Research, James Cook University, Townsville, Australia.
- Marine Data Technology Hub, College of Science and Engineering, James Cook University, Townsville, Australia.
| | - M Sheaves
- Marine Data Technology Hub, College of Science and Engineering, James Cook University, Townsville, Australia
| | - N J Waltham
- TropWATER, Centre for Tropical Water and Aquatic Ecosystem Research, James Cook University, Townsville, Australia
- Marine Data Technology Hub, College of Science and Engineering, James Cook University, Townsville, Australia
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14
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Camacho AM, Perotto-Baldivieso HL, Tanner EP, Montemayor AL, Gless WA, Exum J, Yamashita TJ, Foley AM, DeYoung RW, Nelson SD. The broad scale impact of climate change on planning aerial wildlife surveys with drone-based thermal cameras. Sci Rep 2023; 13:4455. [PMID: 36932162 PMCID: PMC10023802 DOI: 10.1038/s41598-023-31150-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 03/07/2023] [Indexed: 03/19/2023] Open
Abstract
Helicopters used for aerial wildlife surveys are expensive, dangerous and time consuming. Drones and thermal infrared cameras can detect wildlife, though the ability to detect individuals is dependent on weather conditions. While we have a good understanding of local weather conditions, we do not have a broad-scale assessment of ambient temperature to plan drone wildlife surveys. Climate change will affect our ability to conduct thermal surveys in the future. Our objective was to determine optimal annual and daily time periods to conduct surveys. We present a case study in Texas, (United States of America [USA]) where we acquired and compared average monthly temperature data from 1990 to 2019, hourly temperature data from 2010 to 2019 and projected monthly temperature data from 2021 to 2040 to identify areas where surveys would detect a commonly studied ungulate (white-tailed deer [Odocoileus virginianus]) during sunny or cloudy conditions. Mean temperatures increased when comparing the 1990-2019 to 2010-2019 periods. Mean temperatures above the maximum ambient temperature in which white-tailed deer can be detected increased in 72, 10, 10, and 24 of the 254 Texas counties in June, July, August, and September, respectively. Future climate projections indicate that temperatures above the maximum ambient temperature in which white-tailed deer can be detected will increase in 32, 12, 15, and 47 counties in June, July, August, and September, respectively when comparing 2010-2019 with 2021-2040. This analysis can assist planning, and scheduling thermal drone wildlife surveys across the year and combined with daily data can be efficient to plan drone flights.
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Affiliation(s)
- Annalysa M Camacho
- Caesar Kleberg Wildlife Research Institute, Texas A&M University-Kingsville, Kingsville, TX, 78363, USA
| | | | - Evan P Tanner
- Caesar Kleberg Wildlife Research Institute, Texas A&M University-Kingsville, Kingsville, TX, 78363, USA
| | - Amanda L Montemayor
- Caesar Kleberg Wildlife Research Institute, Texas A&M University-Kingsville, Kingsville, TX, 78363, USA
| | - Walter A Gless
- Caesar Kleberg Wildlife Research Institute, Texas A&M University-Kingsville, Kingsville, TX, 78363, USA
| | - Jesse Exum
- Caesar Kleberg Wildlife Research Institute, Texas A&M University-Kingsville, Kingsville, TX, 78363, USA
| | - Thomas J Yamashita
- Caesar Kleberg Wildlife Research Institute, Texas A&M University-Kingsville, Kingsville, TX, 78363, USA
| | - Aaron M Foley
- Caesar Kleberg Wildlife Research Institute, Texas A&M University-Kingsville, Kingsville, TX, 78363, USA
| | - Randy W DeYoung
- Caesar Kleberg Wildlife Research Institute, Texas A&M University-Kingsville, Kingsville, TX, 78363, USA
| | - Shad D Nelson
- Dick and Mary Lewis Kleberg College of Agriculture and Natural Resources, Texas A&M University-Kingsville, Kingsville, TX, 78363, USA
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15
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Lalach LAR, Bradley DW, Bertram DF, Blight LK. Using drone imagery to obtain population data of colony-nesting seabirds to support Canada’s transition to the global Key Biodiversity Areas program. NATURE CONSERVATION 2023. [DOI: 10.3897/natureconservation.51.96366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Identifying of global or national biodiversity ‘hotspots’ has proven important for focusing and prioritizing conservation efforts worldwide. Canada has nearly 600 Important Bird and Biodiversity Areas (IBAs) identified by quantitative criteria to help guide avian conservation and management. Marine IBAs capture critical waterbird habitats such as nesting colonies, foraging sites, and staging areas. However, due to their remote locations, many lack recent population counts. Canada has begun transitioning IBAs into the global Key Biodiversity Areas (KBA) program; KBAs identify areas that are important for the persistence of biodiversity and encompass a wider scope of unique, rare, or vulnerable taxa. Assessing whether IBAs qualify as KBAs requires current data – as will future efforts to manage these biologically important sites. We conducted a pilot study in the Chain Islets and Great Chain Island IBA, in British Columbia, to assess the effectiveness of using drones to census surface-nesting seabirds in an IBA context. This IBA was originally designated for supporting a globally significant breeding colony of Glaucous-winged Gulls (Larus glaucescens). Total nest counts derived from orthomosaic imagery (1012 nesting pairs) show that this site now falls below the Global and National IBA designation criterion threshold, a finding consistent with regional declines in the species. Our trial successfully demonstrates a flexible and low cost approach to obtaining population data at an ecologically sensitive KBA site. We explore how drones will be a useful tool to assess and monitor species and habitats within remote, data-deficient IBAs, particularly during the transition to KBAs.
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16
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Chen A, Jacob M, Shoshani G, Charter M. Using computer vision, image analysis and UAVs for the automatic recognition and counting of common cranes (Grus grus). JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 328:116948. [PMID: 36516707 DOI: 10.1016/j.jenvman.2022.116948] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 10/22/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
Long-term monitoring of wildlife numbers traditionally uses observers, which are frequently inefficient and inaccurate due to their variable experience/training, are costly and difficult to sustain over time. Furthermore, there are other inhibiting factors for wildlife counting, such as: inhabiting inaccessible areas, fear of humans, and nocturnal behavior. There is a need to develop new technologies that will automatically identify and count wild animals in order to determine the appropriate management protocol. In this study, an advanced and accurate method for automatically calculating the number of cranes (Grus grus), using thermal cameras at night and visible light (RGB) cameras during the day onboard unmanned aerial vehicles (UAVs), based on image analysis and computer vision, was developed. The cranes congregate at night in a large communal roost, making it possible to count the birds while they are relatively static and all together. Each bird was counted individually by creating a standardized tool to determine population numbers for management, using image analysis and automatic processing. A dedicated algorithm was developed that aimed to identify the cranes based on their spectral characteristics (typical temperature, shape, size) and to effectively separate the cranes from the typical background. The automatic segmentation and counting of roosting common cranes using UAV nighttime thermal images had an Overall Accuracy (OA) of 91.47%, User's Accuracy (UA) of 99.68%, and Producer's Accuracy (PA) of 91.74%. The computer vision and machine learning algorithm based on the YOLO v3 platform of daytime RGB UAV images of common cranes at the feeding station yielded an overall loss accuracy level of 2.25%, with a mean square error of 1.87, OA of 94.51%, UA of 99.91%, PA of 94.59%. These results are highly encouraging, and although the algorithms were developed for the purpose of counting cranes, they could be adapted for other counting purposes for wildlife management.
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Affiliation(s)
- Assaf Chen
- MIGAL Galilee Research Institute, Kiryat Shmona, 11016, Israel.
| | - Moran Jacob
- MIGAL Galilee Research Institute, Kiryat Shmona, 11016, Israel
| | - Gil Shoshani
- MIGAL Galilee Research Institute, Kiryat Shmona, 11016, Israel
| | - Motti Charter
- Shamir Research Institute, University of Haifa, Katzrin 1290000, Israel; Department of Geography and Environmental Studies, University of Haifa, Mount Carmel, Haifa 3498838, Israel
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17
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Elmore JA, Schultz EA, Jones LR, Evans KO, Samiappan S, Pfeiffer MB, Blackwell BF, Iglay RB. Evidence on the efficacy of small unoccupied aircraft systems (UAS) as a survey tool for North American terrestrial, vertebrate animals: a systematic map. ENVIRONMENTAL EVIDENCE 2023; 12:3. [PMID: 39294790 PMCID: PMC11378819 DOI: 10.1186/s13750-022-00294-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 12/06/2022] [Indexed: 09/21/2024]
Abstract
BACKGROUND Small unoccupied aircraft systems (UAS) are replacing or supplementing occupied aircraft and ground-based surveys in animal monitoring due to improved sensors, efficiency, costs, and logistical benefits. Numerous UAS and sensors are available and have been used in various methods. However, justification for selection or methods used are not typically offered in published literature. Furthermore, existing reviews do not adequately cover past and current UAS applications for animal monitoring, nor their associated UAS/sensor characteristics and environmental considerations. We present a systematic map that collects and consolidates evidence pertaining to UAS monitoring of animals. METHODS We investigated the current state of knowledge on UAS applications in terrestrial animal monitoring by using an accurate, comprehensive, and repeatable systematic map approach. We searched relevant peer-reviewed and grey literature, as well as dissertations and theses, using online publication databases, Google Scholar, and by request through a professional network of collaborators and publicly available websites. We used a tiered approach to article exclusion with eligible studies being those that monitor (i.e., identify, count, estimate, etc.) terrestrial vertebrate animals. Extracted metadata concerning UAS, sensors, animals, methodology, and results were recorded in Microsoft Access. We queried and catalogued evidence in the final database to produce tables, figures, and geographic maps to accompany this full narrative review, answering our primary and secondary questions. REVIEW FINDINGS We found 5539 articles from our literature searches of which 216 were included with extracted metadata categories in our database and narrative review. Studies exhibited exponential growth over time but have levelled off between 2019 and 2021 and were primarily conducted in North America, Australia, and Antarctica. Each metadata category had major clusters and gaps, which are described in the narrative review. CONCLUSIONS Our systematic map provides a useful synthesis of current applications of UAS-animal related studies and identifies major knowledge clusters (well-represented subtopics that are amenable to full synthesis by a systematic review) and gaps (unreported or underrepresented topics that warrant additional primary research) that guide future research directions and UAS applications. The literature for the use of UAS to conduct animal surveys has expanded intensely since its inception in 2006 but is still in its infancy. Since 2015, technological improvements and subsequent cost reductions facilitated widespread research, often to validate UAS technology to survey single species with application of descriptive statistics over limited spatial and temporal scales. Studies since the 2015 expansion have still generally focused on large birds or mammals in open landscapes of 4 countries, but regulations, such as maximum altitude and line-of-sight limitations, remain barriers to improved animal surveys with UAS. Critical knowledge gaps include the lack of (1) best practices for using UAS to conduct standardized surveys in general, (2) best practices to survey whole wildlife communities in delineated areas, and (3) data on factors affecting bias in counting animals from UAS images. Promising advances include the use of thermal sensors in forested environments or nocturnal surveys and the development of automated or semi-automated machine-learning algorithms to accurately detect, identify, and count animals from UAS images.
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Affiliation(s)
- Jared A Elmore
- Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Thompson Hall, Box 9690, Mississippi State, MS, 39762, USA.
- Forestry and Environmental Conservation, Clemson University, Clemson, SC, 29634, USA.
| | - Emma A Schultz
- Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Thompson Hall, Box 9690, Mississippi State, MS, 39762, USA
| | - Landon R Jones
- Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Thompson Hall, Box 9690, Mississippi State, MS, 39762, USA
| | - Kristine O Evans
- Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Thompson Hall, Box 9690, Mississippi State, MS, 39762, USA
| | - Sathishkumar Samiappan
- Geosystems Research Institute, Mississippi State University, Mississippi State, MS, 39762, USA
| | - Morgan B Pfeiffer
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Ohio Field Station, Sandusky, OH, USA
| | - Bradley F Blackwell
- U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Ohio Field Station, Sandusky, OH, USA
| | - Raymond B Iglay
- Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Thompson Hall, Box 9690, Mississippi State, MS, 39762, USA
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18
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Krivek G, Mahecha EPN, Meier F, Kerth G, van Schaik J. Counting in the dark: estimating population size and trends of bat assemblages at hibernacula using infrared light barriers. Anim Conserv 2023. [DOI: 10.1111/acv.12856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Affiliation(s)
- G. Krivek
- Zoological Institute and Museum, Applied Zoology and Nature Conservation, University of Greifswald Greifswald Germany
| | - E. P. N. Mahecha
- Zoological Institute and Museum, Applied Zoology and Nature Conservation, University of Greifswald Greifswald Germany
| | - F. Meier
- Zoological Institute and Museum, Applied Zoology and Nature Conservation, University of Greifswald Greifswald Germany
| | - G. Kerth
- Zoological Institute and Museum, Applied Zoology and Nature Conservation, University of Greifswald Greifswald Germany
| | - J. van Schaik
- Zoological Institute and Museum, Applied Zoology and Nature Conservation, University of Greifswald Greifswald Germany
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19
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Varela-Jaramillo A, Rivas-Torres G, Guayasamin JM, Steinfartz S, MacLeod A. A pilot study to estimate the population size of endangered Galápagos marine iguanas using drones. Front Zool 2023; 20:4. [PMID: 36703215 PMCID: PMC9878759 DOI: 10.1186/s12983-022-00478-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 11/21/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Large-scale species monitoring remains a significant conservation challenge. Given the ongoing biodiversity crisis, the need for reliable and efficient methods has never been greater. Drone-based techniques have much to offer in this regard: they allow access to otherwise unreachable areas and enable the rapid collection of non-invasive field data. Herein, we describe the development of a drone-based method for the estimation of population size in Galápagos marine iguanas, Amblyrhynchus cristatus. As a large-bodied lizard that occurs in open coastal terrain, this endemic species is an ideal candidate for drone surveys. Almost all Amblyrhynchus subspecies are Endangered or Critically Endangered according to the IUCN yet since several colonies are inaccessible by foot, ground- based methods are unable to address the critical need for better census data. In order to establish a drone-based approach to estimate population size of marine iguanas, we surveyed in January 2021 four colonies on three focal islands (San Cristobal, Santa Fe and Espanola) using three techniques: simple counts (the standard method currently used by conservation managers), capture mark-resight (CMR), and drone-based counts. The surveys were performed within a 4-day window under similar ambient conditions. We then compared the approaches in terms of feasibility, outcome and effort. RESULTS The highest population-size estimates were obtained using CMR, and drone-based counts were on average 14% closer to CMR estimates-and 17-35% higher-than those obtained by simple counts. In terms of field-time, drone-surveys can be faster than simple counts, but image analyses were highly time consuming. CONCLUSION Though CMR likely produces superior estimates, it cannot be performed in most cases due to lack of access and knowledge regarding colonies. Drone-based surveys outperformed ground-based simple counts in terms of outcome and this approach is therefore suitable for use across the range of the species. Moreover, the aerial approach is currently the only credible solution for accessing and surveying marine iguanas at highly remote colonies. The application of citizen science and other aids such as machine learning will alleviate the issue regarding time needed to analyze the images.
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Affiliation(s)
- Andrea Varela-Jaramillo
- grid.9647.c0000 0004 7669 9786Institute of Biology, Molecular Evolution and Systematics of Animals, University of Leipzig, Leipzig, Saxony Germany ,3Diversity, Quito, Pichincha, Ecuador
| | - Gonzalo Rivas-Torres
- grid.412251.10000 0000 9008 4711Laboratorio de Biología Evolutiva, Colegio de Ciencias Biológicas y Ambientales COCIBA, Instituto Biósfera, Universidad San Francisco de Quito USFQ, Calle Diego de Robles s/n y Pampite, Cumbayá, Pichincha, Quito Ecuador ,Galápagos Science Center, GSC, San Cristóbal, Galápagos, Ecuador ,grid.15276.370000 0004 1936 8091Wildlife Ecology and Conservation, University of Florida, FL Gainesville, USA
| | - Juan M. Guayasamin
- grid.412251.10000 0000 9008 4711Laboratorio de Biología Evolutiva, Colegio de Ciencias Biológicas y Ambientales COCIBA, Instituto Biósfera, Universidad San Francisco de Quito USFQ, Calle Diego de Robles s/n y Pampite, Cumbayá, Pichincha, Quito Ecuador ,Galápagos Science Center, GSC, San Cristóbal, Galápagos, Ecuador
| | - Sebastian Steinfartz
- grid.9647.c0000 0004 7669 9786Institute of Biology, Molecular Evolution and Systematics of Animals, University of Leipzig, Leipzig, Saxony Germany
| | - Amy MacLeod
- grid.9647.c0000 0004 7669 9786Institute of Biology, Molecular Evolution and Systematics of Animals, University of Leipzig, Leipzig, Saxony Germany
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20
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Aucone E, Kirchgeorg S, Valentini A, Pellissier L, Deiner K, Mintchev S. Drone-assisted collection of environmental DNA from tree branches for biodiversity monitoring. Sci Robot 2023; 8:eadd5762. [PMID: 36652506 DOI: 10.1126/scirobotics.add5762] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The protection and restoration of the biosphere is crucial for human resilience and well-being, but the scarcity of data on the status and distribution of biodiversity puts these efforts at risk. DNA released into the environment by organisms, i.e., environmental DNA (eDNA), can be used to monitor biodiversity in a scalable manner if equipped with the appropriate tool. However, the collection of eDNA in terrestrial environments remains a challenge because of the many potential surfaces and sources that need to be surveyed and their limited accessibility. Here, we propose to survey biodiversity by sampling eDNA on the outer branches of tree canopies with an aerial robot. The drone combines a force-sensing cage with a haptic-based control strategy to establish and maintain contact with the upper surface of the branches. Surface eDNA is then collected using an adhesive surface integrated in the cage of the drone. We show that the drone can autonomously land on a variety of branches with stiffnesses between 1 and 103 newton/meter without prior knowledge of their structural stiffness and with robustness to linear and angular misalignments. Validation in the natural environment demonstrates that our method is successful in detecting animal species, including arthropods and vertebrates. Combining robotics with eDNA sampling from a variety of unreachable aboveground substrates can offer a solution for broad-scale monitoring of biodiversity.
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Affiliation(s)
- Emanuele Aucone
- Environmental Robotics Laboratory, Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland.,Swiss Federal Institute for Forest, Snow, and Landscape Research WSL, Birmensdorf, Switzerland
| | - Steffen Kirchgeorg
- Environmental Robotics Laboratory, Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland.,Swiss Federal Institute for Forest, Snow, and Landscape Research WSL, Birmensdorf, Switzerland
| | | | - Loïc Pellissier
- Swiss Federal Institute for Forest, Snow, and Landscape Research WSL, Birmensdorf, Switzerland.,Ecosystems and Landscape Evolution Group, Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland
| | - Kristy Deiner
- Environmental DNA Group, Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland
| | - Stefano Mintchev
- Environmental Robotics Laboratory, Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland.,Swiss Federal Institute for Forest, Snow, and Landscape Research WSL, Birmensdorf, Switzerland
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21
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Lenzi J, Barnas AF, ElSaid AA, Desell T, Rockwell RF, Ellis-Felege SN. Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys. Sci Rep 2023; 13:947. [PMID: 36653478 PMCID: PMC9849265 DOI: 10.1038/s41598-023-28240-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 01/16/2023] [Indexed: 01/19/2023] Open
Abstract
Imagery from drones is becoming common in wildlife research and management, but processing data efficiently remains a challenge. We developed a methodology for training a convolutional neural network model on large-scale mosaic imagery to detect and count caribou (Rangifer tarandus), compare model performance with an experienced observer and a group of naïve observers, and discuss the use of aerial imagery and automated methods for large mammal surveys. Combining images taken at 75 m and 120 m above ground level, a faster region-based convolutional neural network (Faster-RCNN) model was trained in using annotated imagery with the labels: "adult caribou", "calf caribou", and "ghost caribou" (animals moving between images, producing blurring individuals during the photogrammetry processing). Accuracy, precision, and recall of the model were 80%, 90%, and 88%, respectively. Detections between the model and experienced observer were highly correlated (Pearson: 0.96-0.99, P value < 0.05). The model was generally more effective in detecting adults, calves, and ghosts than naïve observers at both altitudes. We also discuss the need to improve consistency of observers' annotations if manual review will be used to train models accurately. Generalization of automated methods for large mammal detections will be necessary for large-scale studies with diverse platforms, airspace restrictions, and sensor capabilities.
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Affiliation(s)
- Javier Lenzi
- Department of Biology, University of North Dakota, Grand Forks, ND, 58202, USA.
| | - Andrew F Barnas
- Department of Biology, University of North Dakota, Grand Forks, ND, 58202, USA
- School of Environmental Studies, University of Victoria, Victoria, BC, V8W 2Y2, Canada
| | - Abdelrahman A ElSaid
- Department of Computer Science, University of North Carolina Wilmington, Wilmington, NC, USA
| | - Travis Desell
- Department of Software Engineering, Rochester Institute of Technology, Rochester, NY, USA
| | - Robert F Rockwell
- Vertebrate Zoology, American Museum of Natural History, New York, NY, 10024, USA
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22
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Espíndola WD, Cruz‐Mendoza A, Garrastazú A, Nieves MA, F. Rivera‐Milán F, Carlo TA. Estimating population size of red‐footed boobies using distance sampling and drone photography. WILDLIFE SOC B 2023. [DOI: 10.1002/wsb.1406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Walter D. Espíndola
- Department of Biology and Ecology Program The Pennsylvania State University 414A Mueller Laboratory University Park PA 16802 USA
| | - Alberto Cruz‐Mendoza
- Department of Biology and Ecology Program The Pennsylvania State University 414A Mueller Laboratory University Park PA 16802 USA
| | - Aralcy Garrastazú
- Departamento de Recursos Naturales y Ambientales de Puerto Rico Carretera 8838 km. 6.3, Sector El Cinco San Juan PR 00927 USA
| | - Miguel A. Nieves
- Departamento de Recursos Naturales y Ambientales de Puerto Rico Carretera 8838 km. 6.3, Sector El Cinco San Juan PR 00927 USA
| | - Frank F. Rivera‐Milán
- U.S. Fish and Wildlife Service Division of Migratory Bird Management Laurel MD 20708 USA
| | - Tomás A. Carlo
- Department of Biology and Ecology Program The Pennsylvania State University 414A Mueller Laboratory University Park PA 16802 USA
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23
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Desai B, Patel A, Patel V, Shah S, Raval MS, Ghosal R. Identification of free-ranging mugger crocodiles by applying deep learning methods on UAV imagery. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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24
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Racimo F, Valentini E, Rijo De León G, Santos TL, Norberg A, Atmore LM, Murray M, Hakala SM, Olsen FA, Gardner CJ, Halder JB. The biospheric emergency calls for scientists to change tactics. eLife 2022; 11:e83292. [PMID: 36342018 PMCID: PMC9640186 DOI: 10.7554/elife.83292] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 10/19/2022] [Indexed: 11/09/2022] Open
Abstract
Our current economic and political structures have an increasingly devastating impact on the Earth's climate and ecosystems: we are facing a biospheric emergency, with catastrophic consequences for both humans and the natural world on which we depend. Life scientists - including biologists, medical scientists, psychologists and public health experts - have had a crucial role in documenting the impacts of this emergency, but they have failed to drive governments to take action in order to prevent the situation from getting worse. Here we, as members of the movement Scientist Rebellion, call on life scientists to re-embrace advocacy and activism - which were once hallmarks of academia - in order to highlight the urgency and necessity of systemic change across our societies. We particularly emphasise the need for scientists to engage in nonviolent civil resistance, a form of public engagement which has proven to be highly effective in social struggles throughout history.
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Affiliation(s)
- Fernando Racimo
- University of CopenhagenCopenhagenDenmark
- Scientist Rebellion DenmarkCopenhagenDenmark
| | - Elia Valentini
- University of EssexColchesterUnited Kingdom
- Scientist Rebellion ItalyRomeItaly
- Scientist Rebellion UKColchesterUnited Kingdom
| | | | - Teresa L Santos
- Universidade de LisboaLisbonPortugal
- Scientist Rebellion PortugalLisboaPortugal
| | - Anna Norberg
- Norwegian University of Science and TechnologyTrondheimNorway
- Scientist Rebellion NorwayTrondheimNorway
| | - Lane M Atmore
- University of OsloOsloNorway
- Scientist Rebellion Turtle IslandTurtle IslandUnited States
| | - Myranda Murray
- Norwegian University of Science and TechnologyTrondheimNorway
- Scientist Rebellion NorwayTrondheimNorway
| | - Sanja M Hakala
- University of FribourgFribourgSwitzerland
- Scientist Rebellion SwitzerlandFribourgSwitzerland
| | | | - Charlie J Gardner
- University of KentCanterburyUnited Kingdom
- Scientist Rebellion UKCanterburyUnited Kingdom
| | - Julia B Halder
- Imperial CollegeLondonUnited Kingdom
- Scientist Rebellion UKLondonUnited Kingdom
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25
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A colonial-nesting seabird shows no heart-rate response to drone-based population surveys. Sci Rep 2022; 12:18804. [PMID: 36335150 PMCID: PMC9637139 DOI: 10.1038/s41598-022-22492-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 10/14/2022] [Indexed: 11/08/2022] Open
Abstract
Aerial drones are increasingly being used as tools for ecological research and wildlife monitoring in hard-to-access study systems, such as in studies of colonial-nesting birds. Despite their many advantages over traditional survey methods, there remains concerns about possible disturbance effects that standard drone survey protocols may have on bird colonies. There is a particular gap in the study of their influence on physiological measures of stress. We measured heart rates of incubating female common eider ducks (Somateria mollissima) to determine whether our drone-based population survey affected them. To do so, we used heart-rate recorders placed in nests to quantify their heart rate in response to a quadcopter drone flying transects 30 m above the nesting colony. Eider heart rate did not change from baseline (measured in the absence of drone survey flights) by a drone flying at a fixed altitude and varying horizontal distances from the bird. Our findings suggest that carefully planned drone-based surveys of focal species have the potential to be carried out without causing physiological impacts among colonial-nesting eiders.
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26
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Kyathanahally SP, Hardeman T, Reyes M, Merz E, Bulas T, Brun P, Pomati F, Baity-Jesi M. Ensembles of data-efficient vision transformers as a new paradigm for automated classification in ecology. Sci Rep 2022; 12:18590. [PMID: 36329061 PMCID: PMC9633651 DOI: 10.1038/s41598-022-21910-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 10/05/2022] [Indexed: 11/05/2022] Open
Abstract
Monitoring biodiversity is paramount to manage and protect natural resources. Collecting images of organisms over large temporal or spatial scales is a promising practice to monitor the biodiversity of natural ecosystems, providing large amounts of data with minimal interference with the environment. Deep learning models are currently used to automate classification of organisms into taxonomic units. However, imprecision in these classifiers introduces a measurement noise that is difficult to control and can significantly hinder the analysis and interpretation of data. We overcome this limitation through ensembles of Data-efficient image Transformers (DeiTs), which not only are easy to train and implement, but also significantly outperform the previous state of the art (SOTA). We validate our results on ten ecological imaging datasets of diverse origin, ranging from plankton to birds. On all the datasets, we achieve a new SOTA, with a reduction of the error with respect to the previous SOTA ranging from 29.35% to 100.00%, and often achieving performances very close to perfect classification. Ensembles of DeiTs perform better not because of superior single-model performances but rather due to smaller overlaps in the predictions by independent models and lower top-1 probabilities. This increases the benefit of ensembling, especially when using geometric averages to combine individual learners. While we only test our approach on biodiversity image datasets, our approach is generic and can be applied to any kind of images.
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Affiliation(s)
- S. P. Kyathanahally
- grid.418656.80000 0001 1551 0562Eawag, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - T. Hardeman
- grid.418656.80000 0001 1551 0562Eawag, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - M. Reyes
- grid.418656.80000 0001 1551 0562Eawag, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - E. Merz
- grid.418656.80000 0001 1551 0562Eawag, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - T. Bulas
- grid.418656.80000 0001 1551 0562Eawag, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - P. Brun
- grid.419754.a0000 0001 2259 5533WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
| | - F. Pomati
- grid.418656.80000 0001 1551 0562Eawag, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - M. Baity-Jesi
- grid.418656.80000 0001 1551 0562Eawag, Überlandstrasse 133, 8600 Dübendorf, Switzerland
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27
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Kaneko S, Gamper H. Large-scale simulation of bird localization systems in forests with distributed microphone arrays. JASA EXPRESS LETTERS 2022; 2:101201. [PMID: 36319217 DOI: 10.1121/10.0014809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Acoustic wildlife monitoring systems are important tools for capturing information about animal habitation in ecosystems. Previous work has demonstrated the effectiveness of audio-based bird localization techniques. However, few studies have investigated the performance and robustness of distributed systems in large forests. Here, the performance of distributed microphone arrays for localizing birds is examined by simulating forest scenes with added reverberation, ambient noise, and measurement errors. The simulation revealed the importance of the signal-to-noise ratio and the spectral weighting in the localization algorithm. These results may guide the design of large-scale wildlife monitoring systems and suggest promising directions for further improvements.
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Affiliation(s)
- Shoken Kaneko
- Department of Computer Science, University of Maryland, College Park, Maryland 20742, USA
| | - Hannes Gamper
- Audio and Acoustics Research Group, Microsoft Research, Redmond, Washington 98052, USA ,
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28
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Infantes E, Carroll D, Silva WTAF, Härkönen T, Edwards SV, Harding KC. An automated work-flow for pinniped surveys: A new tool for monitoring population dynamics. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.905309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Detecting changes in population trends depends on the accuracy of estimated mean population growth rates and thus the quality of input data. However, monitoring wildlife populations poses economic and logistic challenges especially in complex and remote habitats. Declines in wildlife populations can remain undetected for years unless effective monitoring techniques are developed, guiding appropriate management actions. We developed an automated survey workflow using unmanned aerial vehicles (drones) to quantify the number and size of individual animals, using the well-studied Scandinavian harbour seal (Phoca vitulina) as a model species. We compared ground-based counts using telescopes with manual flights, using a zoom photo/video, and pre-programmed flights producing orthomosaic photo maps. We used machine learning to identify and count both pups and older seals and we present a new method for measuring body size automatically. We evaluate the population’s reproductive success using drone data, historical counts and predictions from a Leslie matrix population model. The most accurate and time-efficient results were achieved by performing pre-programmed flights where individual seals are identified by machine learning and their body sizes are measured automatically. The accuracy of the machine learning detector was 95–97% and the classification error was 4.6 ± 2.9 for pups and 3.1 ± 2.1 for older seals during good light conditions. There was a clear distinction between the body sizes of pups and older seals during breeding time. We estimated 320 pups in the breeding season 2021 with the drone, which is well beyond the expected number, based on historical data on pup production. The new high quality data from the drone survey confirms earlier indications of a deteriorating reproductive rate in this important harbour seal colony. We show that aerial drones and machine learning are powerful tools for monitoring wildlife in inaccessible areas which can be used to assess annual recruitment and seasonal variations in body condition.
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29
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Cruz M, González‐Villa J, Lefebvre J, Gilliland SG, St‐Pierre F, English M, Lepage C. Multi‐image flock size estimation with
CountEm
: A case study with half a million Common Eiders and Greater Snow Geese. Ecosphere 2022. [DOI: 10.1002/ecs2.4174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Marcos Cruz
- Department of Mathematics Statistics and Computer Science, Universidad de Cantabria Santander Spain
| | - Javier González‐Villa
- Department of Mathematics Statistics and Computer Science, Universidad de Cantabria Santander Spain
| | - Josée Lefebvre
- Canadian Wildlife Service—Québec Region Environment and Climate Change Canada Québec City Québec Canada
| | - Scott G. Gilliland
- Canadian Wildlife Service—Atlantic Region Environment and Climate Change Canada Sackville New Brunswick Canada
| | - Francis St‐Pierre
- Canadian Wildlife Service—Québec Region Environment and Climate Change Canada Québec City Québec Canada
| | - Matthew English
- Canadian Wildlife Service—Atlantic Region Environment and Climate Change Canada Sackville New Brunswick Canada
| | - Christine Lepage
- Canadian Wildlife Service—Québec Region Environment and Climate Change Canada Québec City Québec Canada
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30
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Bertram MG, Martin JM, McCallum ES, Alton LA, Brand JA, Brooks BW, Cerveny D, Fick J, Ford AT, Hellström G, Michelangeli M, Nakagawa S, Polverino G, Saaristo M, Sih A, Tan H, Tyler CR, Wong BB, Brodin T. Frontiers in quantifying wildlife behavioural responses to chemical pollution. Biol Rev Camb Philos Soc 2022; 97:1346-1364. [PMID: 35233915 PMCID: PMC9543409 DOI: 10.1111/brv.12844] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 02/13/2022] [Accepted: 02/16/2022] [Indexed: 12/26/2022]
Abstract
Animal behaviour is remarkably sensitive to disruption by chemical pollution, with widespread implications for ecological and evolutionary processes in contaminated wildlife populations. However, conventional approaches applied to study the impacts of chemical pollutants on wildlife behaviour seldom address the complexity of natural environments in which contamination occurs. The aim of this review is to guide the rapidly developing field of behavioural ecotoxicology towards increased environmental realism, ecological complexity, and mechanistic understanding. We identify research areas in ecology that to date have been largely overlooked within behavioural ecotoxicology but which promise to yield valuable insights, including within- and among-individual variation, social networks and collective behaviour, and multi-stressor interactions. Further, we feature methodological and technological innovations that enable the collection of data on pollutant-induced behavioural changes at an unprecedented resolution and scale in the laboratory and the field. In an era of rapid environmental change, there is an urgent need to advance our understanding of the real-world impacts of chemical pollution on wildlife behaviour. This review therefore provides a roadmap of the major outstanding questions in behavioural ecotoxicology and highlights the need for increased cross-talk with other disciplines in order to find the answers.
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Affiliation(s)
- Michael G. Bertram
- Department of Wildlife, Fish, and Environmental StudiesSwedish University of Agricultural SciencesSkogsmarksgränd 17UmeåVästerbottenSE‐907 36Sweden
| | - Jake M. Martin
- School of Biological SciencesMonash University25 Rainforest WalkMelbourneVictoria3800Australia
| | - Erin S. McCallum
- Department of Wildlife, Fish, and Environmental StudiesSwedish University of Agricultural SciencesSkogsmarksgränd 17UmeåVästerbottenSE‐907 36Sweden
| | - Lesley A. Alton
- School of Biological SciencesMonash University25 Rainforest WalkMelbourneVictoria3800Australia
| | - Jack A. Brand
- School of Biological SciencesMonash University25 Rainforest WalkMelbourneVictoria3800Australia
| | - Bryan W. Brooks
- Department of Environmental ScienceBaylor UniversityOne Bear PlaceWacoTexas76798‐7266U.S.A.
| | - Daniel Cerveny
- Department of Wildlife, Fish, and Environmental StudiesSwedish University of Agricultural SciencesSkogsmarksgränd 17UmeåVästerbottenSE‐907 36Sweden
- Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of HydrocenosesUniversity of South Bohemia in Ceske BudejoviceZátiší 728/IIVodnany389 25Czech Republic
| | - Jerker Fick
- Department of ChemistryUmeå UniversityLinnaeus väg 10UmeåVästerbottenSE‐907 36Sweden
| | - Alex T. Ford
- Institute of Marine SciencesUniversity of PortsmouthWinston Churchill Avenue, PortsmouthHampshirePO1 2UPU.K.
| | - Gustav Hellström
- Department of Wildlife, Fish, and Environmental StudiesSwedish University of Agricultural SciencesSkogsmarksgränd 17UmeåVästerbottenSE‐907 36Sweden
| | - Marcus Michelangeli
- Department of Wildlife, Fish, and Environmental StudiesSwedish University of Agricultural SciencesSkogsmarksgränd 17UmeåVästerbottenSE‐907 36Sweden
- Department of Environmental Science and PolicyUniversity of California350 E Quad, DavisCaliforniaCA95616U.S.A.
| | - Shinichi Nakagawa
- Evolution & Ecology Research Centre, School of Biological, Earth and Environmental SciencesUniversity of New South Wales, Biological Sciences West (D26)SydneyNSW2052Australia
| | - Giovanni Polverino
- School of Biological SciencesMonash University25 Rainforest WalkMelbourneVictoria3800Australia
- Centre for Evolutionary Biology, School of Biological SciencesUniversity of Western Australia35 Stirling HighwayPerthWA6009Australia
- Department of Ecological and Biological SciencesTuscia UniversityVia S.M. in Gradi n.4ViterboLazio01100Italy
| | - Minna Saaristo
- Environment Protection Authority VictoriaEPA Science2 Terrace WayMacleodVictoria3085Australia
| | - Andrew Sih
- Department of Environmental Science and PolicyUniversity of California350 E Quad, DavisCaliforniaCA95616U.S.A.
| | - Hung Tan
- School of Biological SciencesMonash University25 Rainforest WalkMelbourneVictoria3800Australia
| | - Charles R. Tyler
- Biosciences, College of Life and Environmental SciencesUniversity of ExeterStocker RoadExeterDevonEX4 4QDU.K.
| | - Bob B.M. Wong
- School of Biological SciencesMonash University25 Rainforest WalkMelbourneVictoria3800Australia
| | - Tomas Brodin
- Department of Wildlife, Fish, and Environmental StudiesSwedish University of Agricultural SciencesSkogsmarksgränd 17UmeåVästerbottenSE‐907 36Sweden
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Sellés-Ríos B, Flatt E, Ortiz-García J, García-Colomé J, Latour O, Whitworth A. Warm beach, warmer turtles: Using drone-mounted thermal infrared sensors to monitor sea turtle nesting activity. FRONTIERS IN CONSERVATION SCIENCE 2022. [DOI: 10.3389/fcosc.2022.954791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
For decades sea turtle projects around the world have monitored nesting females using labor-intensive human patrolling techniques. Here we describe the first empirical testing of a drone-mounted thermal infrared sensor for nocturnal sea turtle monitoring; on the Osa peninsula in Costa Rica. Preliminary flights verified that the drone could detect similar sea turtle activities as identified by on-the-ground human patrollers – such as turtles, nests and tracks. Drone observers could even differentiate tracks of different sea turtle species, detect sea turtle hatchlings, other wildlife, and potential poachers. We carried out pilot flights to determine optimal parameters for detection by testing different thermal visualization modes, drone heights, and gimbal angles. Then, over seven nights, we set up a trial to compare the thermal drone and operators’ detections with those observed by traditional patrollers. Our trials showed that thermal drones can record more information than traditional sea turtle monitoring methods. The drone and observer detected 20% more sea turtles or tracks than traditional ground-based patrolling (flights and patrols carried out across the same nights at the same time and beach). In addition, the drone operator detected 39 other animals/predators and three potential poachers that patrollers failed to detect. Although the technology holds great promise in being able to enhance detection rates of nesting turtles and other beach activity, and in helping to keep observers safer, we detail challenges and limiting factors; in drone imagery, current cost barriers, and technological advances that need to be assessed and developed before standardized methodologies can be adopted. We suggest potential ways to overcome these challenges and recommend how further studies can help to optimize thermal drones to enhance sea turtle monitoring efforts worldwide.
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32
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Robinson JM, Harrison PA, Mavoa S, Breed MF. Existing and emerging uses of drones in restoration ecology. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13912] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Jake M. Robinson
- Department of Landscape Architecture The University of Sheffield Sheffield UK
- College of Science and Engineering Flinders University Bedford Park SA Australia
| | - Peter A. Harrison
- ARC Training Centre for Forest Value and School of Natural Sciences University of Tasmania Hobart Australia
| | - Suzanne Mavoa
- Melbourne School of Population and Global Health University of Melbourne Melbourne Vic. Australia
| | - Martin F. Breed
- College of Science and Engineering Flinders University Bedford Park SA Australia
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33
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Meeting sustainable development goals via robotics and autonomous systems. Nat Commun 2022; 13:3559. [PMID: 35729171 PMCID: PMC9211790 DOI: 10.1038/s41467-022-31150-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 06/06/2022] [Indexed: 11/24/2022] Open
Abstract
Robotics and autonomous systems are reshaping the world, changing healthcare, food production and biodiversity management. While they will play a fundamental role in delivering the UN Sustainable Development Goals, associated opportunities and threats are yet to be considered systematically. We report on a horizon scan evaluating robotics and autonomous systems impact on all Sustainable Development Goals, involving 102 experts from around the world. Robotics and autonomous systems are likely to transform how the Sustainable Development Goals are achieved, through replacing and supporting human activities, fostering innovation, enhancing remote access and improving monitoring. Emerging threats relate to reinforcing inequalities, exacerbating environmental change, diverting resources from tried-and-tested solutions and reducing freedom and privacy through inadequate governance. Although predicting future impacts of robotics and autonomous systems on the Sustainable Development Goals is difficult, thoroughly examining technological developments early is essential to prevent unintended detrimental consequences. Additionally, robotics and autonomous systems should be considered explicitly when developing future iterations of the Sustainable Development Goals to avoid reversing progress or exacerbating inequalities. A horizon scan was used to explore possible impacts of robotics and automated systems on achieving the UN Sustainable Development Goals. Positive effects are likely. Iterative regulatory processes and continued dialogue could help avoid environmental damages and increases in inequality.
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34
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Unmanned aerial vehicle surveys reveal unexpectedly high density of a threatened deer in a plantation forestry landscape. ORYX 2022. [DOI: 10.1017/s0030605321001058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Abstract
The Vulnerable marsh deer Blastocerus dichotomus, the largest native cervid in South America, is declining throughout its range as a result of the conversion of wetlands and overhunting. Estimated densities in open wetlands of several types are 0.1–6.8 individuals per km2. We undertook the first unmanned aerial vehicle (UAV) survey of the marsh deer to estimate the density of this species in a 113.6 km2 area under forestry management in the lower delta of the Paraná River, Argentina. During 6–8 August 2019, at a time of year when canopy cover is minimal, we surveyed marsh deer using Phantom 4 Pro UAVs along 94 transects totalling 127.8 km and 8.6 km2 (8.1% of the study area). The 5,506 photographs obtained were manually checked by us and by a group of 39 trained volunteers, following a standardized protocol. We detected a total of 58 marsh deer, giving an estimated density of 6.90 individuals per km2 (95% CI 5.26–8.54), which extrapolates to 559–908 individuals in our 113.6 km2 study area. As it has generally been assumed that marsh deer prefer open habitats, this relatively high estimate of density within a forestry plantation matrix is unexpected. We discuss the advantages of using UAVs to survey marsh deer and other related ungulates.
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35
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Cline EE, Gehring TM, Etter DR. Evaluating unoccupied aerial vehicles for estimating relative abundance of muskrats. WILDLIFE SOC B 2022. [DOI: 10.1002/wsb.1306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Ellisif E. Cline
- Department of Biology, Institute for Great Lakes Research Central Michigan University Mount Pleasant MI 48859 USA
| | - Thomas M. Gehring
- Department of Biology, Institute for Great Lakes Research Central Michigan University Mount Pleasant MI 48859 USA
| | - Dwayne R. Etter
- Michigan Department of Natural Resources 4166 Legacy Parkway Lansing MI 48911 USA
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36
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Annan E, Guo J, Angulo-Molina A, Yaacob WFW, Aghamohammadi N, C Guetterman T, Yavaşoglu Sİ, Bardosh K, Dom NC, Zhao B, Lopez-Lemus UA, Khan L, Nguyen USDT, Haque U. Community acceptability of dengue fever surveillance using unmanned aerial vehicles: A cross-sectional study in Malaysia, Mexico, and Turkey. Travel Med Infect Dis 2022; 49:102360. [PMID: 35644475 DOI: 10.1016/j.tmaid.2022.102360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/01/2022] [Accepted: 05/19/2022] [Indexed: 11/29/2022]
Abstract
Surveillance is a critical component of any dengue prevention and control program. There is an increasing effort to use drones in mosquito control surveillance. Due to the novelty of drones, data are scarce on the impact and acceptance of their use in the communities to collect health-related data. The use of drones raises concerns about the protection of human privacy. Here, we show how willingness to be trained and acceptance of drone use in tech-savvy communities can help further discussions in mosquito surveillance. A cross-sectional study was conducted in Malaysia, Mexico, and Turkey to assess knowledge of diseases caused by Aedes mosquitoes, perceptions about drone use for data collection, and acceptance of drones for Aedes mosquito surveillance around homes. Compared with people living in Turkey, Mexicans had 14.3 (p < 0.0001) times higher odds and Malaysians had 4.0 (p = 0.7030) times the odds of being willing to download a mosquito surveillance app. Compared to urban dwellers, rural dwellers had 1.56 times the odds of being willing to be trained. There is widespread community support for drone use in mosquito surveillance and this community buy-in suggests a potential for success in mosquito surveillance using drones. A successful surveillance and community engagement system may be used to monitor a variety of mosquito spp. Future research should include qualitative interview data to add context to these findings.
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Affiliation(s)
- Esther Annan
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA.
| | - Jinghui Guo
- Department of Computer Science, The University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Aracely Angulo-Molina
- Department of Chemical and Biological Sciences, University of Sonora, Hermosillo, 83000, Sonora, Mexico
| | - Wan Fairos Wan Yaacob
- Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Kelantan, Kampus Kota Bharu, Lembah Sireh, 15050, Kota Bharu, Kelantan, Malaysia; Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Kompleks Al-Khawarizmi, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia
| | - Nasrin Aghamohammadi
- Centre for Epidemiology and Evidence-Based Practice, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, 50603, Malaysia
| | | | - Sare İlknur Yavaşoglu
- Department of Biology, Faculty of Science and Arts, Aydın Adnan Menderes University, Aydın, 09010, Turkey
| | - Kevin Bardosh
- Center for One Health Research, School of Public Health, University of Washington, USA
| | - Nazri Che Dom
- Faculty of Health Sciences, Universiti Teknologi MARA Cawangan Selangor, Selangor, Malaysia
| | - Bingxin Zhao
- Department of Statistics, Purdue University, 250 N. University St, West Lafayette, IN, 47907, USA
| | - Uriel A Lopez-Lemus
- Department of Health Sciences, Center for Biodefense and Global Infectious Diseases, Colima, 28078, Mexico
| | - Latifur Khan
- Department of Computer Science, The University of Texas at Dallas, Richardson, TX, 75080, USA
| | - Uyen-Sa D T Nguyen
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA
| | - Ubydul Haque
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA
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Das N, Padhy N, Dey N, Mukherjee A, Maiti A. Building of an edge enabled drone network ecosystem for bird species identification. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2021.101540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Castenschiold JHF, Bregnballe T, Bruhn D, Pertoldi C. Unmanned Aircraft Systems as a Powerful Tool to Detect Fine-Scale Spatial Positioning and Interactions between Waterbirds at High-Tide Roosts. Animals (Basel) 2022; 12:ani12080947. [PMID: 35454194 PMCID: PMC9030221 DOI: 10.3390/ani12080947] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/04/2022] [Accepted: 04/05/2022] [Indexed: 11/16/2022] Open
Abstract
The surveillance of behavioral interactions between individuals in bird populations is important to understand social dynamics and explain distribution patterns caused by competition for food and space. For waterbirds, little is known about interactions between individuals at high-tide roosts. In the present study, we used surveying with unmanned aircraft systems (UASs) to provide enhanced information on previously hidden aspects of the highly dynamic communities of roosting waterbirds in the non-breeding season. Fine-scale density estimations, derived from aerial photos obtained with UASs, were used as a measure to explain intra- and inter-species interactions for 10 selected waterbird species on a major roost site in the Danish Wadden Sea. Uniquely defined density distributions were detected, which, to some degree, were dependent on species and species size, with smaller waders exhibiting densely packed flocks (e.g., dunlin Calidris alpina and golden plover Pluvialis apricaria), whereas larger species, such as ducks and geese (Anatidae) exhibited lower densities. Multi-species flocks were observed to occur frequently (31.9%) and generally resulted in lower densities than single-species flocks for each of the species involved. Furthermore, it has been demonstrated that UAS aerial photos can be used both to assess positions for roosting waterbirds and to classify habitats (i.e., mudflats, vegetated areas, waterline, and flooded areas) during high-tide. This facilitated the collection of precise data for temporal habitat choices for individual species when using the studied roost site. Our study highlights UAS surveys as an effective tool to gather hitherto unobtainable data for individual occurrences of roosting waterbirds on a spatiotemporal scale.
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Affiliation(s)
- Johan H. Funder Castenschiold
- Department of Chemistry and Bioscience, Aalborg University, Fredrik Bajers Vej 7H, 9220 Aalborg, Denmark; (D.B.); (C.P.)
- Correspondence:
| | - Thomas Bregnballe
- Department of Ecoscience, Aarhus University, C.F. Møllers Allé 8, 8000 Aarhus, Denmark;
| | - Dan Bruhn
- Department of Chemistry and Bioscience, Aalborg University, Fredrik Bajers Vej 7H, 9220 Aalborg, Denmark; (D.B.); (C.P.)
| | - Cino Pertoldi
- Department of Chemistry and Bioscience, Aalborg University, Fredrik Bajers Vej 7H, 9220 Aalborg, Denmark; (D.B.); (C.P.)
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Ito TY, Miyazaki A, Koyama LA, Kamada K, Nagamatsu D. Antler detection from the sky: deer sex ratio monitoring using drone‐mounted thermal infrared sensors. WILDLIFE BIOLOGY 2022. [DOI: 10.1002/wlb3.01034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Takehiko Y. Ito
- Arid Land Research Center, Tottori Univ. Tottori Japan
- International Platform for Dryland Research and Education, Tottori Univ. Tottori Japan
| | - Atsushi Miyazaki
- Dept of Social Informatics, Graduate School of Informatics, Kyoto Univ. Kyoto Japan
| | - Lina A. Koyama
- Dept of Social Informatics, Graduate School of Informatics, Kyoto Univ. Kyoto Japan
| | - Kisa Kamada
- Faculty of Regional Sciences, Tottori Univ. Tottori Japan
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Tuia D, Kellenberger B, Beery S, Costelloe BR, Zuffi S, Risse B, Mathis A, Mathis MW, van Langevelde F, Burghardt T, Kays R, Klinck H, Wikelski M, Couzin ID, van Horn G, Crofoot MC, Stewart CV, Berger-Wolf T. Perspectives in machine learning for wildlife conservation. Nat Commun 2022; 13:792. [PMID: 35140206 PMCID: PMC8828720 DOI: 10.1038/s41467-022-27980-y] [Citation(s) in RCA: 79] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 12/08/2021] [Indexed: 11/08/2022] Open
Abstract
Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation.
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Affiliation(s)
- Devis Tuia
- School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Benjamin Kellenberger
- School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sara Beery
- Department of Computing and Mathematical Sciences, California Institute of Technology (Caltech), Pasadena, CA, USA
| | - Blair R Costelloe
- Max Planck Institute of Animal Behavior, Radolfzell, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Silvia Zuffi
- Institute for Applied Mathematics and Information Technologies, IMATI-CNR, Pavia, Italy
| | - Benjamin Risse
- Computer Science Department, University of Münster, Münster, Germany
| | - Alexander Mathis
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mackenzie W Mathis
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Tilo Burghardt
- Computer Science Department, University of Bristol, Bristol, UK
| | - Roland Kays
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA
- North Carolina Museum of Natural Sciences, Raleigh, NC, USA
| | - Holger Klinck
- Cornell Lab of Ornithology, Cornell University, Ithaca, NY, USA
| | - Martin Wikelski
- Max Planck Institute of Animal Behavior, Radolfzell, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
| | - Iain D Couzin
- Max Planck Institute of Animal Behavior, Radolfzell, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Grant van Horn
- Cornell Lab of Ornithology, Cornell University, Ithaca, NY, USA
| | - Margaret C Crofoot
- Max Planck Institute of Animal Behavior, Radolfzell, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Charles V Stewart
- Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Tanya Berger-Wolf
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH, USA
- Departments of Computer Science and Engineering; Electrical and Computer Engineering; Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH, USA
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41
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Mannocci L, Villon S, Chaumont M, Guellati N, Mouquet N, Iovan C, Vigliola L, Mouillot D. Leveraging social media and deep learning to detect rare megafauna in video surveys. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2022; 36:e13798. [PMID: 34153121 PMCID: PMC9291111 DOI: 10.1111/cobi.13798] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 05/19/2021] [Accepted: 06/02/2021] [Indexed: 05/04/2023]
Abstract
Deep learning has become a key tool for the automated monitoring of animal populations with video surveys. However, obtaining large numbers of images to train such models is a major challenge for rare and elusive species because field video surveys provide few sightings. We designed a method that takes advantage of videos accumulated on social media for training deep-learning models to detect rare megafauna species in the field. We trained convolutional neural networks (CNNs) with social media images and tested them on images collected from field surveys. We applied our method to aerial video surveys of dugongs (Dugong dugon) in New Caledonia (southwestern Pacific). CNNs trained with 1303 social media images yielded 25% false positives and 38% false negatives when tested on independent field video surveys. Incorporating a small number of images from New Caledonia (equivalent to 12% of social media images) in the training data set resulted in a nearly 50% decrease in false negatives. Our results highlight how and the extent to which images collected on social media can offer a solid basis for training deep-learning models for rare megafauna detection and that the incorporation of a few images from the study site further boosts detection accuracy. Our method provides a new generation of deep-learning models that can be used to rapidly and accurately process field video surveys for the monitoring of rare megafauna.
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Affiliation(s)
- Laura Mannocci
- MARBEC, Univ Montpellier, CNRS, Ifremer, IRDMontpellierFrance
- ENTROPIE (IRD, Université de la Réunion, Université de la Nouvelle Calédonie, CNRS, Ifremer), Laboratoire Excellence LABEX CorailCentre IRD NouméaNouméaNew Caledonia
- LIRMM, Univ MontpellierCNRSMontpellierFrance
| | - Sébastien Villon
- ENTROPIE (IRD, Université de la Réunion, Université de la Nouvelle Calédonie, CNRS, Ifremer), Laboratoire Excellence LABEX CorailCentre IRD NouméaNouméaNew Caledonia
| | - Marc Chaumont
- LIRMM, Univ MontpellierCNRSMontpellierFrance
- University of NîmesNîmesFrance
| | - Nacim Guellati
- MARBEC, Univ Montpellier, CNRS, Ifremer, IRDMontpellierFrance
| | - Nicolas Mouquet
- MARBEC, Univ Montpellier, CNRS, Ifremer, IRDMontpellierFrance
- FRB – CESABMontpellierFrance
| | - Corina Iovan
- ENTROPIE (IRD, Université de la Réunion, Université de la Nouvelle Calédonie, CNRS, Ifremer), Laboratoire Excellence LABEX CorailCentre IRD NouméaNouméaNew Caledonia
| | - Laurent Vigliola
- ENTROPIE (IRD, Université de la Réunion, Université de la Nouvelle Calédonie, CNRS, Ifremer), Laboratoire Excellence LABEX CorailCentre IRD NouméaNouméaNew Caledonia
| | - David Mouillot
- MARBEC, Univ Montpellier, CNRS, Ifremer, IRDMontpellierFrance
- Institut Universitaire de FranceParisFrance
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42
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Unlocking the Potential of Deep Learning for Migratory Waterbirds Monitoring Using Surveillance Video. REMOTE SENSING 2022. [DOI: 10.3390/rs14030514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Estimates of migratory waterbirds population provide the essential scientific basis to guide the conservation of coastal wetlands, which are heavily modified and threatened by economic development. New equipment and technology have been increasingly introduced in protected areas to expand the monitoring efforts, among which video surveillance and other unmanned devices are widely used in coastal wetlands. However, the massive amount of video records brings the dual challenge of storage and analysis. Manual analysis methods are time-consuming and error-prone, representing a significant bottleneck to rapid data processing and dissemination and application of results. Recently, video processing with deep learning has emerged as a solution, but its ability to accurately identify and count waterbirds across habitat types (e.g., mudflat, saltmarsh, and open water) is untested in coastal environments. In this study, we developed a two-step automatic waterbird monitoring framework. The first step involves automatic video segmentation, selection, processing, and mosaicking video footages into panorama images covering the entire monitoring area, which are subjected to the second step of counting and density estimation using a depth density estimation network (DDE). We tested the effectiveness and performance of the framework in Tiaozini, Jiangsu Province, China, which is a restored wetland, providing key high-tide roosting ground for migratory waterbirds in the East Asian–Australasian flyway. The results showed that our approach achieved an accuracy of 85.59%, outperforming many other popular deep learning algorithms. Furthermore, the standard error of our model was very small (se = 0.0004), suggesting the high stability of the method. The framework is computing effective—it takes about one minute to process a theme covering the entire site using a high-performance desktop computer. These results demonstrate that our framework can extract ecologically meaningful data and information from video surveillance footages accurately to assist biodiversity monitoring, fulfilling the gap in the efficient use of existing monitoring equipment deployed in protected areas.
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43
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Kuhlmann K, Fontaine A, Brisson‐Curadeau É, Bird DM, Elliott KH. Miniaturization eliminates detectable impacts of drones on bat activity. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13807] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Kayla Kuhlmann
- Department of Natural Resource Sciences McGill University Montréal Canada
| | - Amélie Fontaine
- Department of Natural Resource Sciences McGill University Montréal Canada
| | | | - David M. Bird
- Department of Natural Resource Sciences McGill University Montréal Canada
| | - Kyle H. Elliott
- Department of Natural Resource Sciences McGill University Montréal Canada
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44
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Effectiveness of using drones and convolutional neural networks to monitor aquatic megafauna. Afr J Ecol 2022. [DOI: 10.1111/aje.12950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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45
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Saunders D, Nguyen H, Cowen S, Magrath M, Marsh K, Bell S, Bobruk J. Radio-tracking wildlife with drones: a viewshed analysis quantifying survey coverage across diverse landscapes. WILDLIFE RESEARCH 2022. [DOI: 10.1071/wr21033] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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46
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Davidson SC, Ruhs EC. Understanding the dynamics of Arctic animal migrations in a changing world. ANIMAL MIGRATION 2021. [DOI: 10.1515/ami-2020-0114] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
This is submitted as an introduction to the special collection on, “Arctic Migrations in a Changing World”.
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Affiliation(s)
- Sarah C. Davidson
- Department of Animal Migration , Max Plank Institute of Animal Behavior , Radolfzell , Germany ; Department of Biology , University of Konstanz , Konstanz , Germany Department of Civil, Environmental and Geodetic Engineering , The Ohio State University , Columbus , OH, USA
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47
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Obermoller TR, Norton AS, Michel ES, Haroldson BS. Use of Drones With Thermal Infrared to Locate White‐tailed Deer Neonates for Capture. WILDLIFE SOC B 2021. [DOI: 10.1002/wsb.1242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Tyler R. Obermoller
- Farmland Wildlife Populations and Research Group, Minnesota Department of Natural Resources 35365 800th Avenue Madelia MN 56062 USA
| | - Andrew S. Norton
- South Dakota Game, Fish and Parks 4130 Adventure Trail Rapid City SD 57702 USA
| | - Eric S. Michel
- Farmland Wildlife Populations and Research Group, Minnesota Department of Natural Resources 35365 800th Avenue Madelia MN 56062 USA
| | - Brian S. Haroldson
- Farmland Wildlife Populations and Research Group, Minnesota Department of Natural Resources 35365 800th Avenue Madelia MN 56062 USA
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48
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Howell LG, Clulow J, Jordan NR, Beranek CT, Ryan SA, Roff A, Witt RR. Drone thermal imaging technology provides a cost-effective tool for landscape-scale monitoring of a cryptic forest-dwelling species across all population densities. WILDLIFE RESEARCH 2021. [DOI: 10.1071/wr21034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abstract Context Drones, or remotely piloted aircraft systems, equipped with thermal imaging technology (RPAS thermal imaging) have recently emerged as a powerful monitoring tool for koala populations. Before wide uptake of novel technologies by government, conservation practitioners and researchers, evidence of greater efficiency and cost-effectiveness than with other available methods is required. Aims We aimed to provide the first comprehensive analysis of the cost-effectiveness of RPAS thermal imaging for koala detection against two field-based methods, systematic spotlighting (Spotlight) and the refined diurnal radial search component of the spot-assessment technique (SAT). Methods We conducted various economic comparisons, particularly comparative cost-effectiveness of RPAS thermal imaging, Spotlight and SAT for repeat surveys of a low-density koala population. We compared methods on cost-effectiveness as well as long-term costs by using accumulating cost models. We also compared detection costs across population density using a predictive cost model. Key results Despite substantial hardware, training and licensing costs at the outset (>A$49 900), RPAS thermal imaging surveys were cost-effective, detecting the highest number of koalas per dollar spent. Modelling also suggested that RPAS thermal imaging requires the lowest survey effort to detect koalas within the range of publicly available koala population densities (~0.006–18 koalas ha−1) and would provide long-term cost reductions across longitudinal monitoring programs. RPAS thermal imaging would also require the lowest average survey effort costs at a landscape scale (A$3.84 ha−1), providing a cost-effective tool across large spatial areas. Conclusions Our analyses demonstrated drone thermal imaging technology as a cost-effective tool for conservation practitioners monitoring koala populations. Our analyses may also form the basis of decision-making tools to estimate survey effort or total program costs across any koala population density. Implications Our novel approach offers a means to perform various economic comparisons of available survey techniques and guide investment decisions towards developing standardised koala monitoring approaches. Our results may assist stakeholders and policymakers to confidently invest in RPAS thermal imaging technology and achieve optimal conservation outcomes for koala populations, with standardised data collection delivered through evidence-based and cost-effective monitoring programs.
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49
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Myburgh A, Botha H, Downs CT, Woodborne SM. The Application and Limitations of a Low-Cost UAV Platform and Open-Source Software Combination for Ecological Mapping and Monitoring. AFRICAN JOURNAL OF WILDLIFE RESEARCH 2021. [DOI: 10.3957/056.051.0166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Albert Myburgh
- Centre for Functional Biodiversity, School of Life Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3209 South Africa
| | - Hannes Botha
- Scientific Services, Mpumalanga Tourism and Parks Agency, Nelspruit, South Africa
| | - Colleen T. Downs
- Centre for Functional Biodiversity, School of Life Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3209 South Africa
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50
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Braga‐Pereira F, Morcatty TQ, El Bizri HR, Tavares AS, Mere‐Roncal C, González‐Crespo C, Bertsch C, Rodriguez CR, Bardales‐Alvites C, von Mühlen EM, Bernárdez‐Rodríguez GF, Paim FP, Tamayo JS, Valsecchi J, Gonçalves J, Torres‐Oyarce L, Lemos LP, Vieira MAR, Bowler M, Gilmore MP, Perez NCA, Alves RR, Peres CA, Pérez‐Peña P, Mayor P. Congruence of local ecological knowledge (LEK)‐based methods and line‐transect surveys in estimating wildlife abundance in tropical forests. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13773] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Franciany Braga‐Pereira
- Departamento de Ecologia e Sistemática Universidade Federal da Paraíba João Pessoa Brazil
- Rede de Pesquisa para Estudos sobre Diversidade Conservação e Uso da Fauna na Amazônia (REDEFAUNA) Manaus Brazil
- Institut de Ciència i Tecnologia Ambientals Universitat Autònoma de Barcelona Barcelona Spain
- Department de Sanitat i Anatomia Animals Universitat Autònoma de Barcelona Barcelona Spain
| | - Thais Q. Morcatty
- Rede de Pesquisa para Estudos sobre Diversidade Conservação e Uso da Fauna na Amazônia (REDEFAUNA) Manaus Brazil
- Faculty of Humanities and Social Sciences Oxford Brookes University Oxford UK
- Instituto de Desenvolvimento Sustentável Mamirauá Estrada do Bexiga Tefé Brazil
| | - Hani R. El Bizri
- Rede de Pesquisa para Estudos sobre Diversidade Conservação e Uso da Fauna na Amazônia (REDEFAUNA) Manaus Brazil
- Instituto de Desenvolvimento Sustentável Mamirauá Estrada do Bexiga Tefé Brazil
- Comunidad de Manejo de Fauna Silvestre en la Amazonía y en Latinoamérica (COMFAUNA) Iquitos Peru
| | - Aline S. Tavares
- Núcleo de Estudos e Pesquisas das Cidades da Amazônia Brasileira Universidade Federal do Amazonas Manaus Brazil
| | - Carla Mere‐Roncal
- School of Environmental Science and Policy George Mason University Fairfax VA USA
| | - Carlos González‐Crespo
- Department de Sanitat i Anatomia Animals Universitat Autònoma de Barcelona Barcelona Spain
| | - Carolina Bertsch
- Laboratório de Manejo de Fauna Instituto Nacional de Pesquisas da Amazônia (INPA) Manaus Brazil
| | | | | | - Eduardo M. von Mühlen
- Departamento de Ecologia Universidade Federal do Rio Grande do Norte Natal Brazil
- Instituto Juruá Manaus Brazil
| | | | | | - Jhancy Segura Tamayo
- Servicio Nacional de Áreas Naturales Protegidas por el Estado (SERNANP) Urbanización Palomar Calle Lima Peru
| | - João Valsecchi
- Rede de Pesquisa para Estudos sobre Diversidade Conservação e Uso da Fauna na Amazônia (REDEFAUNA) Manaus Brazil
- Instituto de Desenvolvimento Sustentável Mamirauá Estrada do Bexiga Tefé Brazil
- Comunidad de Manejo de Fauna Silvestre en la Amazonía y en Latinoamérica (COMFAUNA) Iquitos Peru
| | - Jonas Gonçalves
- Laboratório de Manejo de Fauna Instituto Nacional de Pesquisas da Amazônia (INPA) Manaus Brazil
- Secretaria Executiva de Ciência Tecnologia e Inovação (SECTI/SEDECTI) Governo do Amazonas Manaus Brazil
| | | | - Lísley Pereira Lemos
- Rede de Pesquisa para Estudos sobre Diversidade Conservação e Uso da Fauna na Amazônia (REDEFAUNA) Manaus Brazil
- Instituto de Desenvolvimento Sustentável Mamirauá Estrada do Bexiga Tefé Brazil
| | - Marina A. R. Vieira
- Rede de Pesquisa para Estudos sobre Diversidade Conservação e Uso da Fauna na Amazônia (REDEFAUNA) Manaus Brazil
- RIVERS ERC Project Departamento de Ciencias Sociales Universidad Carlos III de Madrid Getafe (Madrid) Spain
| | - Mark Bowler
- School of Engineering, Arts, Science and Technology University of Suffolk Ipswich UK
| | - Michael P. Gilmore
- School of Environmental Science and Policy George Mason University Fairfax VA USA
| | | | - Rômulo Romeu Alves
- Departamento de Ecologia e Sistemática Universidade Federal da Paraíba João Pessoa Brazil
- Laboratory of Ethnobiology and Ethnoecology Universidade Estadual da Paraíba Campina Grande Brazil
| | - Carlos A. Peres
- Instituto Juruá Manaus Brazil
- School of Environmental Sciences University of East Anglia Norwich UK
| | - Pedro Pérez‐Peña
- Instituto de Investigaciones de la Amazonía Peruana (IIAP) Iquitos Peru
| | - Pedro Mayor
- Department de Sanitat i Anatomia Animals Universitat Autònoma de Barcelona Barcelona Spain
- Comunidad de Manejo de Fauna Silvestre en la Amazonía y en Latinoamérica (COMFAUNA) Iquitos Peru
- Museo de Culturas Indígenas Amazónicas Iquitos Peru
- Postgraduate Program in Animal Health and Production in Amazonia (PPGSPAA) Federal Rural University of the Amazon (UFRA) Belém Brazil
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