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Volume 52, No. 1

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Finding colonies of Black-headed Gulls Chroicocephalus ridibundus using Google Earth.


Authors

ROBERTO G. VALLE1, ALEJANDRO CORREGIDOR-CASTRO2 & FRANCESCO SCARTON3

1Rialto 571, San Polo, I-30125, Venice, Italy (robertovalle@libero.it)
2Dipartimento di Biologia, Università di Padova, Via Ugo Bassi 58/B, I-35131, Padova, Italy; National Biodiversity Future Centre, Piazza Marina 61, 90133, Palermo, Italy
3Via Franchetti 192, I-31022, Preganziol, Italy

Citation

VALLE, R.G., CORREGIDOR-CASTRO, A. & SCARTON, F. 2024. Finding colonies of Black-headed Gulls Chroicocephalus ridibundus using Google Earth. . Marine Ornithology 52: 105 - 111
http://doi.org/10.5038/2074-1235.52.1.1567

Received 02 January 2023, accepted 07 August 2023

Date Published: 2024/04/15
Date Online: 2024/04/09

Key words: Black-headed Gull, breeding, Google Earth, Chroicocephalus ridibundus, satellite imagery, Lagoon of Venice

Abstract

We explore the possibility of identifying Black-headed Gull Chroicocephalus ridibundus colonies in the saltmarshes of the Lagoon of Venice, Italy, using Google Earth satellite images. One reproductive season was considered (June 2017), based on the images available on Google Earth. This species builds nests clustered around tidal pools and tidal creeks, providing a dark background to reveal the white gulls. Images of the southern part of the lagoon (excluding fish farms) were analyzed by dividing it into sectors (n = 403) using the Google Earth grid at an elevation of 100 m above ground level. The results of the satellite count were compared with field data collected in the same season. Image analysis revealed five colonies, with excellent sensitivity (100%) but only good specificity (88%), due to the presence of numerous clear areolae falling within the spectral range of nests; these consisted of plastic litter and dry, stranded vegetation. Overall, our results indicate that Black-headed Gull colonies can be found in marsh-island habitat using Google Earth. While this approach presents sub-optimal specificity due to both the abundance of whitish debris and low image resolution, future developments in software capabilities hold the potential to overcome these limitations and enhance the accuracy of the proposed approach.

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