English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/173565
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

Drone monitoring of breeding waterbird populations: the case of the glossy ibis

AuthorsAfán, Isabel ; Máñez, Manuel ; Díaz-Delgado, Ricardo
KeywordsUAV
Aerial survey
Long-term monitoring
Plegadis falcinellus
Bird censuses
Supervised classification
Image processing
Issue Date1-Dec-2018
PublisherMultidisciplinary Digital Publishing Institute
CitationDrones 2(4): 42 (2018)
AbstractWaterbird communities are potential indicators of ecological changes in threatened wetland ecosystems and consequently, a potential object of ecological monitoring programs. Waterbirds often breed in largely inaccessible colonies in flooded habitats, so unmanned aerial vehicle (UAV) surveys provide a robust method for estimating their breeding population size. Counts of breeding pairs might be carried out by manual and automated detection routines. In this study we surveyed the main breeding colony of Glossy ibis (Plegadis falcinellus) at the Doñana National Park. We obtained a high resolution image, in which the number and location of nests were determined manually through visual interpretation by an expert. We also suggest a standardized methodology for nest counts that would be repeatable across time for long-term monitoring censuses, through a supervised classification based primarily on the spectral properties of the image and a subsequent automatic size and form based count. Although manual and automatic count were largely similar in the total number of nests, accuracy between both methodologies was only 46.37%, with higher variability in shallow areas free of emergent vegetation than in areas dominated by tall macrophytes. We discuss the potential challenges for automatic counts in highly complex images.
Publisher version (URL)https://dx.doi.org/10.3390/drones2040042
URIhttp://hdl.handle.net/10261/173565
DOI10.3390/drones2040042
E-ISSN2504-446X
Appears in Collections:(EBD) Artículos
Files in This Item:
File Description SizeFormat 
drone_ibis_2018.pdf6,84 MBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.