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Mapping the richness and composition of British breeding birds from coarse spatial resolution satellite sensor imagery

Mapping the richness and composition of British breeding birds from coarse spatial resolution satellite sensor imagery
Mapping the richness and composition of British breeding birds from coarse spatial resolution satellite sensor imagery
Remote sensing has great potential as a source of information on biodiversity over large areas. Past studies have generally focused on species richness, used aspatial statistical techniques and highlighted scale-dependent results. Here, a fuller assessment of avian biodiversity, considering species richness and composition, was undertaken for breeding bird species in Great Britain from NDVI and temperature data derived from NOAA AVHRR imagery. Broad classes of bird species composition defined by an ordination analysis exhibited a high degree of separability, with classification accuracies (based on training data) of up to 77.3% observed. Although only 18.1% of the variance in species richness could be explained by a conventional aspatial regression analysis it was apparent from geographically weighted regression analyses that the relationship between species richness and the remotely sensed response was significantly non-stationary. Relative to the standard regression, geographically weighted analyses yielded models that provided stronger relationships and highlighted the spatial dependence of the relationship. Marked spatial variation in the regression model parameters and explanatory power were evident within and between scales. The results indicated the ability to characterize aspects of biodiversity from coarse spatial resolution remote sensing data and highlight the need to accommodate for the effects of spatial non-stationarity in the relationship.
aerial photography, geography, remote sensing, environmental sciences
0143-1161
3943 -3956
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37
Foody, G.M.
06e50027-603d-4a5b-88f5-af2bb6235a37

Foody, G.M. (2005) Mapping the richness and composition of British breeding birds from coarse spatial resolution satellite sensor imagery. International Journal of Remote Sensing, 26 (18), 3943 -3956. (doi:10.1080/01431160500165716).

Record type: Article

Abstract

Remote sensing has great potential as a source of information on biodiversity over large areas. Past studies have generally focused on species richness, used aspatial statistical techniques and highlighted scale-dependent results. Here, a fuller assessment of avian biodiversity, considering species richness and composition, was undertaken for breeding bird species in Great Britain from NDVI and temperature data derived from NOAA AVHRR imagery. Broad classes of bird species composition defined by an ordination analysis exhibited a high degree of separability, with classification accuracies (based on training data) of up to 77.3% observed. Although only 18.1% of the variance in species richness could be explained by a conventional aspatial regression analysis it was apparent from geographically weighted regression analyses that the relationship between species richness and the remotely sensed response was significantly non-stationary. Relative to the standard regression, geographically weighted analyses yielded models that provided stronger relationships and highlighted the spatial dependence of the relationship. Marked spatial variation in the regression model parameters and explanatory power were evident within and between scales. The results indicated the ability to characterize aspects of biodiversity from coarse spatial resolution remote sensing data and highlight the need to accommodate for the effects of spatial non-stationarity in the relationship.

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More information

Published date: September 2005
Keywords: aerial photography, geography, remote sensing, environmental sciences

Identifiers

Local EPrints ID: 58267
URI: http://eprints.soton.ac.uk/id/eprint/58267
ISSN: 0143-1161
PURE UUID: 77e64dac-2ce7-4f77-aab8-8a4fb004c797

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Date deposited: 12 Aug 2008
Last modified: 15 Mar 2024 11:10

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Author: G.M. Foody

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