Identification of African antelope species: Using thermographic videos to test the efficacy of real‐time thermography

Goodenough, Anne E.; Carpenter, William S.; MacTavish, Lynne; Theron, Charles; Delbridge, Matthew; Hart, Adam G.

doi:10.1111/aje.12513

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…For example, a project on tools for quantifying biodiversity and assessing management outcomes in a South-African savannah ecosystem yielded three publications that included undergraduate students as co-authors but did not explicitly refer to this anywhere in the manuscript (Goodenough et al, 2017(Goodenough et al, , 2020Hart et al, 2020). It also yielded two publications that clearly described the inclusion of 50 undergraduate students in the methods section but did not include any of them as co-authors and did not list their names in the acknowledgements (Goodenough et al, 2018a(Goodenough et al, , 2018b. The project is an exemplar of RFEL yet is inconsistent in attributing credit to participants.…”

Section: Barriers To Real-world Impact and Visibilitymentioning

confidence: 99%

Improving Real-World Impact From Research-Focussed Experiential Learning In Higher Education; A Call-To-Arms Centring On Field Ecological Monitoring

Tolhurst

White

Friscia

et al. 2023

JPAAP

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Research-focussed experiential learning (RFEL), where students develop academic research skills by participating in research activities, is a valuable and widespread component of higher education. It is also a potentially important source of research currency for academic staff in teaching-focussed universities. Numerous initiatives facilitate this practice in an extra-curricular capacity, whilst its integration into curricula is less widespread, but is gaining momentum. Nonetheless, real-world impact deriving from RFEL is not as visible as might be expected, and we argue that the RFEL paradigm has yet to achieve its full potential. We discuss how to overcome barriers to visibility and effective implementation of RFEL in terms of real-world outcomes, for our own discipline – ecology. A capacity gap exists, particularly at the local level, in effective ecological site monitoring for biodiversity conservation and other forms of environmental management. We believe there is currently underused potential in integrating RFEL via student field experience (field trips, field work, field classes, or field programmes) to fill this gap. We show how the parallel process of citizen science, where participating university students and others outside academia collect data as volunteers, lends insights and best practice recommendations to the field experience RFEL model. RFEL is critically important for upskilling students for competitive career markets. We outline five ways in which it can be galvanised to additionally and effectively yield real-world research and management outcomes, adding value within the sector.

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Section: Barriers To Real-world Impact and Visibilitymentioning

confidence: 99%

Improving Real-World Impact From Research-Focussed Experiential Learning In Higher Education; A Call-To-Arms Centring On Field Ecological Monitoring

Tolhurst

White

Friscia

et al. 2023

JPAAP

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“…Surveying the large areas required for robust abundance estimates in a cost-effective way is also problematic [3,10]. In response to this, drones (also known as remotely piloted aircraft systems (RPAS) and unmanned aerial vehicles (UAV)) are rapidly being recognised as efficient and highly effective tools for wildlife monitoring [8,10,11,[13][14][15][16]. Drones can cover large areas systematically, using pre-programmed flight paths [16] and carry sensors that capture data at a resolution high enough for accurate wildlife detection [10,13,14,17], even for relatively small mammals such as koalas [18].…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Koalas with Deep Learning Ensembles

et al. 2022

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Effective management of threatened and invasive species requires regular and reliable population estimates. Drones are increasingly utilised by ecologists for this purpose as they are relatively inexpensive. They enable larger areas to be surveyed than traditional methods for many species, particularly cryptic species such as koalas, with less disturbance. The development of robust and accurate methods for species detection is required to effectively use the large volumes of data generated by this survey method. The enhanced predictive and computational power of deep learning ensembles represents a considerable opportunity to the ecological community. In this study, we investigate the potential of deep learning ensembles built from multiple convolutional neural networks (CNNs) to detect koalas from low-altitude, drone-derived thermal data. The approach uses ensembles of detectors built from combinations of YOLOv5 and models from Detectron2. The ensembles achieved a strong balance between probability of detection and precision when tested on ground-truth data from radio-collared koalas. Our results also showed that greater diversity in ensemble composition can enhance overall performance. We found the main impediment to higher precision was false positives but expect these will continue to reduce as tools for geolocating detections are improved. The ability to construct ensembles of different sizes will allow for improved alignment between the algorithms used and the characteristics of different ecological problems. Ensembles are efficient and accurate and can be scaled to suit different settings, platforms and hardware availability, making them capable of adaption for novel applications.

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Hot monkey, cold reality: surveying rainforest canopy mammals using drone-mounted thermal infrared sensors

Kays

Sheppard

McLean

et al. 2018

International Journal of Remote Sensing

105

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Animals of the rainforest canopies are often endangered by deforestation or hunting but are difficult to survey and study because of the inaccessibility of the treetops, combined with the visual camouflage of many species. Drone-based thermal sensors have the potential to overcome these hurdles by rapidly scanning large forested areas from above, detecting and mapping wildlife based on the contrast between their warm body temperatures and the cool tree canopies. We tested this concept by flying a drone-mounted thermal infrared radiometric sensor over the wildlife-rich rainforests of Barro Colorado Island, Panama. Arboreal mammals had body temperatures around 27°C and were conspicuous in the thermal infrared imagery at night and early morning when the forest canopy was cool (23-25°C), but were difficult to detect by mid-morning, by which time the direct sunshine had heated up canopy vegetation to over 30°C. Species were difficult to identify from thermal infrared imagery alone, but could be recognized from synchronized visual images taken during the daytime. Simultaneous drone and ground-based surveys of the same area proved that the aerial thermal camera did detect highcanopy species missed by the terrestrial observer, but that substantially more animals were detected by the human than by the drone. Because animal detection was so much better at night, when species ID was difficult, we suggest that future work could combine automated detection of animals from thermal infrared imagery with flash photography or IR illumination to enable species ID during nocturnal surveys. We conclude by discussing various logistical challenges that limit the utility of drone-based thermal infrared today, but that could be overcome by continued improvement of technology and collaboration with permitting agencies.

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Identification of African antelope species: Using thermographic videos to test the efficacy of real‐time thermography

Abstract: The University of Gloucestershire accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement.

Cited by 3 publications

References 32 publications

Improving Real-World Impact From Research-Focussed Experiential Learning In Higher Education; A Call-To-Arms Centring On Field Ecological Monitoring

Improving Real-World Impact From Research-Focussed Experiential Learning In Higher Education; A Call-To-Arms Centring On Field Ecological Monitoring

Automated Detection of Koalas with Deep Learning Ensembles

Hot monkey, cold reality: surveying rainforest canopy mammals using drone-mounted thermal infrared sensors

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