10 May 2012 Mapping urban vegetation cover using WorldView-2 imagery
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Abstract
There are clear indications that densification of built-up areas within cities and new developments in their outskirts, in conjunction with urban population activities, are at the origin of climate changes at the local level and have a direct impact on air and water quality. Densification of the vegetation cover is often mentioned as one of the most important means to mitigate the impacts of climate changes and to improve the quality of the urban environment. Decision making on vegetation cover densification presupposes that urban planners and managers know exactly the actual situation in terms of vegetation location, types and biomass. However, in many cities, inventories of vegetation cover are usually absent. This study examines the feasibility of an automatic system for vegetation cover inventory and mapping in urban areas based on WorldView-2 imagery. The city of Laval, Canada, was chosen as the experimental site. The principal conclusions are as follows: a) conversion of digital counts to ground reflectances is a crucial step in order to fully exploit the potential of WV-2 multispectral images for mapping vegetation cover and recognizing vegetation classes; b) the combined use of NDVIs computed using the three infrared available bands and the red band provides an accurate means of differentiating vegetation cover from other land covers; and c) it is possible to separate trees from other vegetation types and to identify tree species even in dense urban areas using spectral signature characteristics and segmentation algorithms.
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François Cavayas, Yuddy Ramos, André Boyer, "Mapping urban vegetation cover using WorldView-2 imagery", Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83900O (10 May 2012); doi: 10.1117/12.918655; https://doi.org/10.1117/12.918655
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KEYWORDS
Vegetation

Image segmentation

Reflectivity

Atmospheric modeling

Climate change

Climatology

Multispectral imaging

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