Introduction and Testing of a Monitoring and Colony-Mapping Method for Waterbird Populations That Uses High-Speed and Ultra-Detailed Aerial Remote Sensing

Bakó, Gábor; Tolnai, Márton; Takács, Ádám

doi:10.3390/s140712828

Cited by 19 publications

(19 citation statements)

References 18 publications

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“…Similar studies of waterbirds that used manned aerial photography have also recommended this level of spatial resolution (Bakó, Tolnai, and Takács, 2014). It is important to note that UAS vibration and sensor issues like blurring and over-or underexposure will greatly impair the investigator's ability to identify birds regardless of the calculated GSD.…”

Section: Waterbird Species Identificationmentioning

confidence: 97%

Environmental Reviews and Case Studies: Applications of Unmanned Aircraft Systems (UAS) for Waterbird Surveys

Dulava

Bean

Richmond

2015

Environmental Practice

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Utilizing unmanned aircraft systems (UAS) can be an efficient and repeatable means of surveying wildlife, especially waterbirds. As with any technology in its infancy, case studies offer opportunities to explore drawbacks and limitations, both anticipated and unanticipated. We examined the relationship between flight altitude and camera focal length on bird identification. We then conducted a post-hoc analysis to examine the effect of flight altitude on bird flushing behavior. We flew UAS missions at three locations in California and Nevada to assess the use of UAS for censusing non-nesting waterbirds. A minimum pixel resolution of approximately 5 mm was needed be able to identify most waterbird species from imagery. Sensors needed to be carefully calibrated in order to obtain images of sufficient quality to identify waterbirds over open water. Our results suggest that gas-powered UAS may result in increased rates of flushing at low flight altitudes for some waterbirds. With careful design of surveys and processing workflow, UAS show promise for censusing and monitoring waterbirds. Environmental Practice 17: 201-210 (2015)

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Section: Waterbird Species Identificationmentioning

confidence: 97%

Environmental Reviews and Case Studies: Applications of Unmanned Aircraft Systems (UAS) for Waterbird Surveys

Dulava

Bean

Richmond

2015

Environmental Practice

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“…Remote sensing technologies, such as drones with high optical resolution (Bakó et al , ) multi‐spectral cameras (Peña‐Barragán et al , ), may change flower resource sampling in the near future. Several pollinator studies have already used remote sensing to estimate the amount of resource or habitat quality on the landscape scale (Osborne et al , ; Henry et al , ), or time‐lapse photography to investigate flowering dynamics (Crimmins & Crimmins, ).…”

Section: Recommendationsmentioning

confidence: 99%

Measuring floral resource availability for insect pollinators in temperate grasslands – a review

Szigeti

Kőrösi

Harnos

et al. 2016

Ecological Entomology

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Abstract. 1. The relationship between pollinators and flowering plants plays a crucial role in the function of terrestrial ecosystems. Although pollinators use floral nectar and pollen as food resources, no general methodology for floral resource availability estimates exists.2. A brief review is provided on floral resource sampling methods frequently used in pollination studies. The focus is on how representative vegetation samples are both spatially and temporally, and how these are constrained by sampling effort.3. Field studies investigating flowering plant abundance for insect pollinators in temperate grasslands were selected. We categorised the reviewed studies according to aims, sampling units, and count variables used and provide a descriptive summary on methodology. We also searched for trade-offs between different aspects of sampling investment.4. One hundred and fifty-eight pollination studies were reviewed. Large methodological differences were found, and vegetation sampling was presented in many studies insufficiently. Sampling covered a small proportion (median: 0.69%) of the study sites, with long intervals (median: 30 days), and most studies lasted only a few years. The most often used count variables were indirect proxies of floral resources. Negative relationships were found in some of the different aspects of sampling, e.g. the proportion of site covered with sampling decreased with increasing site area.5. By tailoring sampling methods to specific research questions, research effort should be optimally allocated to obtain proper spatio-temporal resolution and data coverage. Guidelines were suggested to design sampling, e.g. to increase coverage and frequency. Further field work on optimising sampling techniques is mandatory.

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“…With digital cameras now ubiquitous in aerial surveys (Buckland et al 2012) and continuously improving in performance and resolution (Bako et al 2014), image-analysis software becoming ever more sophisticated, and computer processing speeds ever increasing, circumstances seem favorable for a breakthrough in the use of computer-automated bird censuses using aerial images. Efforts to automate bird detection and counts in aerial images using computer software go back nearly three decades (Gilmer et al 1988, Bajzak andPiatt 1990) and continue to the present (Laliberte and Ripple 2003, Descamps et al 2011, Groom et al 2013.…”

Section: Introductionmentioning

confidence: 99%

Computer-automated bird detection and counts in high-resolution aerial images: a review

2016

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Bird surveys conducted using aerial images can be more accurate than those using airborne observers, but can also be more time‐consuming if images must be analyzed manually. Recent advances in digital cameras and image‐analysis software offer unprecedented potential for computer‐automated bird detection and counts in high‐resolution aerial images. We review the literature on this subject and provide an overview of the main image‐analysis techniques. Birds that contrast sharply with image backgrounds (e.g., bright birds on dark ground) are generally the most amenable to automated detection, in some cases requiring only basic image‐analysis software. However, the sophisticated analysis capabilities of modern object‐based image analysis software provide ways to detect birds in more challenging situations based on a variety of attributes including color, size, shape, texture, and spatial context. Some techniques developed to detect mammals may also be applicable to birds, although the prevalent use of aerial thermal‐infrared images for detecting large mammals is of limited applicability to birds because of the low pixel resolution of thermal cameras and the smaller size of birds. However, the increasingly high resolution of true‐color cameras and availability of small unmanned aircraft systems (drones) that can fly at very low altitude now make it feasible to detect even small shorebirds in aerial images. Continued advances in camera and drone technology, in combination with increasingly sophisticated image analysis software, now make it possible for investigators involved in monitoring bird populations to save time and resources by increasing their use of automated bird detection and counts in aerial images. We recommend close collaboration between wildlife‐monitoring practitioners and experts in the fields of remote sensing and computer science to help generate relevant, accessible, and readily applicable computer‐automated aerial photographic census techniques.

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Introduction and Testing of a Monitoring and Colony-Mapping Method for Waterbird Populations That Uses High-Speed and Ultra-Detailed Aerial Remote Sensing

Cited by 19 publications

References 18 publications

Environmental Reviews and Case Studies: Applications of Unmanned Aircraft Systems (UAS) for Waterbird Surveys

Environmental Reviews and Case Studies: Applications of Unmanned Aircraft Systems (UAS) for Waterbird Surveys

Measuring floral resource availability for insect pollinators in temperate grasslands – a review

Computer-automated bird detection and counts in high-resolution aerial images: a review

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