Mapping seabird nesting habitats in Franz Josef Land, Russian High Arctic, using digital Landsat Thematic Mapper imagery

Abstract

Supervised classification of digital Landsat satellite images was used to locate seabird nesting habitats in the Russian High Arctic archipelago of Franz Josef Land, a region where the avifauna is poorly known and ecologically vulnerable. Major seabird nesting colonies are readily identifiable in Landsat Thematic Mapper (TM) imagery of the region due primarily to the distinctive spectral signature of vegetation on ornithogenically altered soils below bird cliffs. Supervised image classification was used to pinpoint areas displaying spectral characteristics typical of documented seabird nesting habitats. A total of 101 seabird nesting colony locations identified in Russian and Western literature from 1898 to 1996 was used as training sites to develop spectral signatures from a summer TM image mosaic for use in a supervised maximum likelihood classification. The classified image was thresholded and compared to a map of documented nesting locations. Of the 101 field-documented nesting sites, 96 were clearly identified in the classified image. An inventory was produced of all undocumented seabird habitats suggested by the classification, totalling over 300 sites. The methodology used may be applicable to other arctic regions and is intended as a first step when planning ecological protection zones in remote and inaccessible arctic regions.

First global census of the Adélie Penguin

“…Although the idea that penguin colonies can be identified in satellite imagery is several decades old (Schwaller et al 1984, 1986, 1989, Olson et al 1987, Williams and Dowdeswell 1988, increased pressure to monitor Southern Ocean ''sentinel'' species, combined with increased access to polar geospatial imagery, is driving a renaissance in the tracking of penguin populations using satellite imagery (Fretwell et al 2012, Lynch et al 2012b, Mustafa et al 2012, Schwaller et al 2013. In contrast to the recent Landsat survey reported by Schwaller et al (2013), our survey relied on manual identification and interpretation of Adélie Penguin colonies, an effort that was time consuming and required the extensive experience of two interpreters (H.J.L.…”

Surveying Wild Animals from Satellites, Manned Aircraft and Unmanned Aerial Systems (UASs): A Review

“…The early spaceborne surveys of wild animals focused on using medium/low (1-60 m)-spatial resolution spaceborne data to find wild animals by identifying some form of sign indicating that the animals have been in the area, such as fecal counts [24][25][26], food removal, and burrow counts [27,28], rather than performing direct observations of the animals themselves. Examples of the indirect surveying of species include monitoring the population increase of the king penguin (Aptenodytes patagonicus) using 10 m-resolution SPOT images in the southern Indian Ocean [87], developing a supervised classification algorithm for locating seabird nesting habitats from Landsat TM images in the Russian High Arctic archipelago of Franz Josef Land [88], surveying the distribution of the Adélie penguin (Pygoscelis adeliae) [25,26] and emperor penguin (Aptenodytes forsteri) [24] in Antarctica by analyzing guano and other debris in the Landsat TM imagery, and detecting hairy-nosed wombats (Lasiorhinus latifrons) by analyzing the degraded vegetation and bare ground caused by the animal's burrowing and mound building behaviors in the Nullarbor Plain of southern Australia based on 60 m-resolution Landsat imagery. VHR imagery, such as 1.8 m-resolution Worldview-2 imagery, has also been applied to detect gerbil burrows using a similar NDVI-based technique [28].…”

How do you find the green sheep? A critical review of the use of remotely sensed imagery to detect and count animals

“…LaRue et al., ; Lynch et al., ; Robinson et al., ). For example Löffler and Margules () were able to approximate wombat distributions ( Lasiorhinus latifrons ) by detecting burrows, Velasco () detected active marmot ( Marmota siberica ) mounds, Williams and Dowdeswell () used the unique spectral signatures of vegetation attributed to altered soils below seabird nesting colonies to indicate the presence of seabirds, and studies have estimated penguin populations from guano‐stained areas (e.g. Fretwell et al., ; LaRue et al., ; Lynch et al., ).…”