Using the unique spectral signature of guano to identify unknown seabird colonies

Fretwell, Peter T.; Phillips, Richard A.; Brooke, M. de L.; Fleming, Andrew; MacArthur, Alasdair

doi:10.1016/j.rse.2014.10.011

Cited by 32 publications

(29 citation statements)

References 23 publications

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“…This reflects the potential elasticity available to the operator to refine rules. In most cases, our changes to the fuzzy membership functions associated with the class guano are designed to capture the spectral and textural characteristics that distinguish guano stains from the substrate dominating clay minerals like illite and kaolinite [19]. Based on the adapted versions of the master ruleset, study sites like BAIL, BIRD, and ZAVO exhibited superior classification results compared to that of the reference image.…”

Section: Discussionmentioning

confidence: 99%

An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images

Witharana

Lynch

2016

Remote Sensing

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Abstract:The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA) methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR) satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. We systematically gauged the segmentation quality, classification accuracy, and the reproducibility of fuzzy rules. A master ruleset was developed based on one study site and it was re-tasked "without adaptation" and "with adaptation" on candidate image scenes comprising guano stains. Our results suggest that object-based methods incorporating the spectral, textural, spatial, and contextual characteristics of guano are capable of successfully detecting guano stains. Reapplication of the master ruleset on candidate scenes without modifications produced inferior classification results, while adapted rules produced comparable or superior results compared to the reference image. This work provides a road map to an operational "image-to-assessment pipeline" that will enable Antarctic wildlife researchers to seamlessly integrate VHSR imagery into on-demand penguin population census.

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Section: Discussionmentioning

confidence: 99%

An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images

Witharana

Lynch

2016

Remote Sensing

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“…Computer‐automated detection of guano in satellite images has also been used to detect colonies of seabirds other than penguins (Fretwell et al. ). The overall coarser resolution of satellite images compared to images from aircraft lends itself better to traditional pixel‐based spectral analysis, which these studies have employed almost exclusively, in most cases using the supervised Maximum Likelihood Classification approach with ArcGIS's Spatial Analyst extension.…”

Section: Overview Of Image‐analysis Techniquesmentioning

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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“…Recent satellitesupported surveys of wildlife in the Antarctic have included penguins (Pygoscelis spp. and Emperor Penguin, Aptenodytes forsteri; Barber-Meyer et al, 2007;Fretwell and Trathan, 2009;Fretwell et al, 2012;Lynch et al, 2012;Schwaller et al, 2013;LaRue et al, 2014;Lynch and LaRue, 2014;Fretwell et al, 2015), seals (Weddell seal, Leptonychotes weddellii, LaRue et al, 2011;Ainley et al 2015;elephant seal, Mirounga leonina, McMahon et al, 2014), and whales (Abileah, 2002; southern right whale, Eubalaena australis; Fretwell et al, 2014).…”

Section: Remote Sensing For Antarctic Wildlifementioning

confidence: 99%

Benchmarking of data fusion algorithms in support of earth observation based Antarctic wildlife monitoring

Witharana

LaRue

Lynch

2016

ISPRS Journal of Photogrammetry and Remote Sensing

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a b s t r a c tRemote sensing is a rapidly developing tool for mapping the abundance and distribution of Antarctic wildlife. While both panchromatic and multispectral imagery have been used in this context, image fusion techniques have received little attention. We tasked seven widely-used fusion algorithms: Ehlers fusion, hyperspherical color space fusion, high-pass fusion, principal component analysis (PCA) fusion, University of New Brunswick fusion, and wavelet-PCA fusion to resolution enhance a series of single-date QuickBird-2 and Worldview-2 image scenes comprising penguin guano, seals, and vegetation. Fused images were assessed for spectral and spatial fidelity using a variety of quantitative quality indicators and visual inspection methods. Our visual evaluation elected the high-pass fusion algorithm and the University of New Brunswick fusion algorithm as best for manual wildlife detection while the quantitative assessment suggested the Gram-Schmidt fusion algorithm and the University of New Brunswick fusion algorithm as best for automated classification. The hyperspherical color space fusion algorithm exhibited mediocre results in terms of spectral and spatial fidelities. The PCA fusion algorithm showed spatial superiority at the expense of spectral inconsistencies. The Ehlers fusion algorithm and the wavelet-PCA algorithm showed the weakest performances. As remote sensing becomes a more routine method of surveying Antarctic wildlife, these benchmarks will provide guidance for image fusion and pave the way for more standardized products for specific types of wildlife surveys. Ó

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Using the unique spectral signature of guano to identify unknown seabird colonies

Cited by 32 publications

References 23 publications

An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images

An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images

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

Benchmarking of data fusion algorithms in support of earth observation based Antarctic wildlife monitoring

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