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Hakim Abdi. Photo.

Hakim Abdi

Researcher

Hakim Abdi. Photo.

Integrating Open Access Geospatial Data to Map the Habitat Suitability of the Declining Corn Bunting (Miliaria calandra)

Author

  • Hakim Abdi

Summary, in English

The efficacy of integrating open access geospatial data to produce habitat suitability maps for the corn bunting (Miliaria calandra) was investigated. Landsat Enhanced Thematic Mapper Plus (ETM+), Shuttle Radar Topography Mission (SRTM) and Corine (Coordination of Information on the Environment) land cover data for the year 2000 (CLC2000) were processed to extract explanatory variables and divided into three sets; Satellite (ETM+, SRTM), CLC2000 and Combined (CLC2000 + Satellite). Presence-absence data for M. calandra, collected during structured surveys for the Catalan Breeding Bird Atlas, were provided by the Catalan Ornithological Institute. The dataset was partitioned into an equal number of presence and absence points by dividing it into five groups, each composed of 88 randomly selected presence points to match the number of absences. A logistic regression model was then built for each group. Models were evaluated using area under the curve (AUC) of the receiver operating characteristic (ROC). Results of the five groups were averaged to produce mean Satellite, CLC2000 and Combined models. The mean AUC values were 0.69, 0.81 and 0.90 for the CLC2000, Satellite and the Combined model, respectively. The probability of M. calandra presence had the strongest positive correlation with land surface temperature, modified soil adjusted vegetation index, coefficient of variation for ETM+ band 5 and the fraction of non-irrigated arable land.

Publishing year

2013

Language

English

Pages

935-954

Publication/Series

ISPRS International Journal of Geo-Information

Volume

2

Issue

4

Document type

Journal article

Publisher

MDPI AG

Topic

  • Ecology
  • Remote Sensing
  • Environmental Sciences

Keywords

  • agricultural intensification
  • Landscape ecology
  • corn bunting
  • Remote sensing
  • Earth observation
  • species distribution modeling

Status

Published

ISBN/ISSN/Other

  • ISSN: 2220-9964