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ISPRS Int. J. Geo-Inf. 2013, 2(1), 220-236; doi:10.3390/ijgi2010220
Article

Mapping Urban Tree Species Using Very High Resolution Satellite Imagery: Comparing Pixel-Based and Object-Based Approaches

1,* , 1
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 and
1,2,*
Received: 1 January 2013 / Revised: 25 February 2013 / Accepted: 26 February 2013 / Published: 13 March 2013
(This article belongs to the Special Issue Geospatial Monitoring and Modelling of Environmental Change)
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Abstract

We assessed the potential of multi-spectral GeoEye imagery for biodiversity assessment in an urban context in Bangalore, India. Twenty one grids of 150 by 150 m were randomly located in the city center and all tree species within these grids mapped in the field. The six most common species, collectively representing 43% of the total trees sampled, were selected for mapping using pixel-based and object-based approaches. All pairs of species were separable based on spectral reflectance values in at least one band, with Peltophorum pterocarpum being most distinct from other species. Object-based approaches were consistently superior to pixel-based methods, which were particularly low in accuracy for tree species with small canopy sizes, such as Cocos nucifera and Roystonea regia. There was a strong and significant correlation between the number of trees determined on the ground and from object-based classification. Overall, object-based approaches appear capable of discriminating the six most common species in a challenging urban environment, with substantial heterogeneity of tree canopy sizes.
Keywords: urban ecology; tree diversity; ecological monitoring; hyperspatial imagery; India urban ecology; tree diversity; ecological monitoring; hyperspatial imagery; India
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Agarwal, S.; Vailshery, L.S.; Jaganmohan, M.; Nagendra, H. Mapping Urban Tree Species Using Very High Resolution Satellite Imagery: Comparing Pixel-Based and Object-Based Approaches. ISPRS Int. J. Geo-Inf. 2013, 2, 220-236.

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