QuickBird spots flamingos off Nalabana Island, Chilika Lake, India

Sasamal, S. K.; Chaudhury, S.; Samal, R. N.; Pattanaik, Ajit Kumar

doi:10.1080/01431160701814336

Cited by 16 publications

(11 citation statements)

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“…The entity that we want to detect are often much smaller than the area (spatial resolution of EO sensor) sampled by the sensor. However, in spite of the problems mentioned above, successful examples of identifying animals, as well as evidence of animal activity such as tunnelling or guano, from higher resolution imagery such as RapidEye have been developed for birds, penguins, flamingos; whales (Leyequien et al, 2007;Sasamal et al, 2008;Groom et al, 2011;Fretwell et al, 2014) and wildebeest in African savannahs (Zhang et al, 2014).…”

Section: Detecting Animal Species Using Eomentioning

confidence: 97%

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Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives

Lausch

Bannehr

Beckmann

et al. 2016

Ecological Indicators

147

145

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Section: Detecting Animal Species Using Eomentioning

confidence: 97%

“…Leyequien et al (2007);Sasamal et al (2008);Curtis et al (2009);Groom et al (2011);Fretwell et al (2014);Zhang et al (2014) Movement of animals − GPS tracking (e.g. storks, cranes, gulls, geese) TDWikelski et al (2007);Witt et al (2010);Kranstauber et al (2011);Safi et al (2013) Modelling of animal behaviour (e.g.…”

mentioning

confidence: 99%

Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives

Lausch

Bannehr

Beckmann

et al. 2016

Ecological Indicators

147

145

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“…Other species that have been manually detected in satellite images include flamingos (Sasamal et al. ) and nests of Masked Boobies ( Sula dactylatra ) (Hughes et al. ).…”

Section: Overview Of Image‐analysis Techniquesmentioning

confidence: 99%

“…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. Other species that have been manually detected in satellite images include flamingos (Sasamal et al 2008) and nests of Masked Boobies (Sula dactylatra) (Hughes et al 2011).…”

Section: Automated Bird Counts In Aerial Imagesmentioning

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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“…However, most authors utilize airborne data (Groom et al 2013), and only relatively little work is being undertaken based on data acquired from space. Sasamal et al (2008) observed the wintering habitat of flamingos in Chilika Lake, India, using high-resolution QuickBird and IKONOS data. Flamingo congregations, mapped as bright spots, contrasted strongly with the dark surrounding water, which enabled a precise count of the flamingos.…”

mentioning

confidence: 99%

Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks

Kuenzer

Ottinger²,

Wegmann³

et al. 2014

International Journal of Remote Sensing

154

112

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Many biologists, ecologists, and conservationists are interested in the possibilities that remote sensing offers for their daily work and study site analyses as well as for the assessment of biodiversity. However, due to differing technical backgrounds and languages, cross-sectorial communication between this group and remote-sensing scientists is often hampered. Hardly any really comprehensive studies exist that are directed towards the conservation community and provide a solid overview of available Earth observation sensors and their different characteristics. This article presents, categorizes, and discusses what spaceborne remote sensing has contributed to the study of animal and vegetation biodiversity, which different types of variables of value for the biodiversity community can be derived from remote-sensing data, and which types of spaceborne sensor data are available for which time spans, and at which spatial and temporal resolution. We categorize all current and important past sensors with respect to application fields relevant for biologists, ecologists, and conservationists. Furthermore, sensor gaps and current challenges for Earth observation with respect to data access and provision are presented.

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QuickBird spots flamingos off Nalabana Island, Chilika Lake, India

Cited by 16 publications

References 0 publications

Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives

Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives

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

Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks

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