Investigating Habitat Association of Breeding Birds Using Public Domain Satellite Imagery and Land Cover Data

LDEO Publication: 
Publication Type  Thesis
Year of Publication  2010
Authors  Abdi, A.
Academic Department  Institute for Geoinformatics
Number of Pages  95
University  University of Muenster
City  Muenster, Germany
Degree  Master of Science
Key Words  Agricultural intensification; Corn Bunting; Landsat; Logistic regression; Species distribution modeling

Twenty-five years after the implementation of the Birds Directive in 1979, Europe‟s farmland bird species and long-distance migrants continue to decrease at an alarming rate. Farmland supports more bird species of conservation concern than any other habitat in Europe. Therefore, it is imperative to understand farmland species‟ relationship with their habitats.
Bird conservation requires spatial information; this understanding not only serves as a check on the individual species‟ populations, but also as a measure of the overall health of the ecosystem as birds are good indicators of the state of the environment. The target species in this study is the corn bunting Miliaria calandra, a bird whose numbers in northern and central Europe have declined sharply since the mid-1970s.
This study utilizes public domain data, namely Landsat imagery and CORINE land cover, along with the corn bunting‟s presence-absence data, to create a predictive distribution map of the species based on habitat preference. Each public domain dataset was preprocessed to extract predictor variables. Predictive models were built in R using logistic regression.
Three models resulted from the regression analysis; one containing the satellite-only variables, one containing the land cover variables and a combined model containing both satellite and land cover variables. The final model was the combined model because it exhibited the highest predictive accuracy (AUC=0.846) and the least unexplained variation (RD=276.11). The results have shown that the corn bunting is strongly influenced by land surface temperature and the modified soil adjusted vegetation index. Results have also shown that the species strongly prefers non-irrigated arable land and areas containing vegetation that has high moisture content while avoiding areas with steep slopes and areas near human activity.
This study has shown that the combination of public data from different sources is a viable method in producing models that reflect species‟ habitat preference and that the development of maps comprised of information from both satellites and land cover datasets are of importance for species whose habitat requirements are poorly known.