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Accurate prediction of bird species richness patterns in an urban environment using Landsat-derived NDVI and spectral unmixing

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Abstract:

Urban landscapes are expanding rapidly and are reshaping the distribution of many animal and plant species. With these changes, the need to understand and to include urban biodiversity patterns in research and management programmes is becoming vital. Recent studies have shown that remote sensing tools can be useful in studies examining biodiversity patterns in natural landscapes. The present study aimed to explore whether remote sensing tools can be applied in biodiversity research in an urban landscape. More specifically, the study examined whether the Landsat-derived Normalized Difference Vegetation Index (NDVI) and linear spectral unmixing of urban land cover can predict bird richness in the city of Jerusalem. Bird richness was sampled in 40 1-ha sites over a range of urban environments in 329 surveys. NDVI and the per cent cover of built-up area were strongly and negatively correlated with each other, and were both very successful in explaining the number of bird species in the study sites. Mean NDVI in each site was positively correlated with the site bird species richness. A hump-shaped relationship between NDVI and species richness was observed (when calculated over increasing spatial scales), with a maximum value (Pearson's R = 0.87, p<0.001, n = 40) at a scale of 15 ha. We suggest that remote sensing approaches may provide planners and conservation biologists with an efficient and cost-effective method to study and estimate biodiversity across urban environments that range between densely built-up areas, residential neighbourhoods, urban parks and the peri-urban environment.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/01431160701772534

Affiliations: 1: The Biodiversity Research Group, Department of Evolution, Systematics and Ecology, The Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, 91904 Israel 2: Centre for Remote Sensing and Spatial Information Science, School of Geography, Planning and Architecture, The University of Queensland, Brisbane, Queensland, 4072 Australia

Publication date: January 1, 2008

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