Remote Sensing Image Data and Automated Analysis to Describe Marine Bird Distributions and Abundances

Journal Article

Title: Remote Sensing Image Data and Automated Analysis to Describe Marine Bird Distributions and Abundances
Publication Date:
March 01, 2013
Journal: Ecological Informatics
Volume: 14
Pages: 2-8
Publisher: Elsevier
Affiliation:
Receptor:

Document Access

Website: External Link

Citation

Groom, G.; Stjernholm, M.; Nielsen, R.; Fleetwood, A.; Petersen, I. (2013). Remote Sensing Image Data and Automated Analysis to Describe Marine Bird Distributions and Abundances. Ecological Informatics, 14, 2-8.
Abstract: 

Advances in image data capture with airborne digital cameras and in object-based image analysis (OBIA) have provided a basis for new arenas of applied remote sensing, one of which is the direct counting and mapping of animal individuals. The derived data represents significant inputs to population size estimation and study of animal–habitat interactions. One growing application is bird distributions and abundances in relation to EIAs for marine installations such as offshore wind farms and sea crossing transportation infrastructure. The ornithological surveys in relation to the EIA process concerning the Burbo Bank Extension and Walney Extension offshore wind farms that are being undertaken jointly by Aarhus University and BLOM-UK are an example of the former, with monthly surveys of two offshore areas (total area ca. 670 km2) in the Irish Sea. Image coverage comprises acquisition of 4 cm and 3 cm image data. Object based image analysis methods are used to localise marine birds. This paper describes the data processing and analysis procedures used. It presents examples of representations in these image data of five bird species. Examples of non-bird ‘background’ image patterns that can impact the bird localisation possibilities are presented along with developed countering-strategies. Results of initial assessments of levels of bird under-mapping by the OBIA method are given; the reported overall success level from 18 image frames with respect to under-mapping is over 92%.

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