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Mots RAMEAU représentant le fonds du centre de documentation
géomatique, géodésie, réseaux (géodésie), systèmes de localisation par satellites,
imagerie satellitaire, traitement d'images, photogrammétrie, lasergrammétrie,
systèmes d'information géographique, données géospatiales, cartographie,
géographie - base de données

Nouvelle recherche

Article: Article/Communication

Super-resolution image analysis as a means of monitoring bracken (Pteridium aquilinum) distributions / Jennie Holland in ISPRS Journal of photogrammetry and remote sensing, vol 75 (January 2013)

URL article

Public

[article]
inISPRS Journal of photogrammetry and remote sensing > vol 75 (January 2013) . - pp 48 - 63

Titre :	Super-resolution image analysis as a means of monitoring bracken (Pteridium aquilinum) distributions
Type de document :	Article/Communication
Auteurs :	Jennie Holland, Auteur ; Paul Alpin, Auteur
Année de publication :	2013
Article en page(s) :	pp 48 - 63
Note générale :	Bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classification dirigée [Termes IGN] Filicophyta [Termes IGN] image Ikonos [Termes IGN] image Landsat [Termes IGN] image multitemporelle [Termes IGN] plante non ligneuse [Termes IGN] surveillance de la végétation
Résumé :	(Auteur) The bracken (Pteridium aquilinum) fern is environmentally significant due to its great abundance and swift colonisation, and its perception as a problem plant in degrading agricultural or ecologically sensitive land. Various attempts have been made to map bracken using remote sensing, but these have proved relatively unsuccessful, often apparently constrained by the lack of spatial detail associated with medium spatial resolution satellite sensors such as the Landsat series. In this study, bracken was characterised using a combination of 30 m Landsat sensor imagery and 4 m IKONOS imagery. Different classification techniques were compared, including hard maximum likelihood classification and a super-resolution approach comprising soft classification and sub-pixel contouring. These techniques were applied to a range of image dates, including summer, winter and multitemporal images. Image analysis was supported by extensive field data collection, comprising both a land cover survey and stakeholder interviews. For the hard classified Landsat sensor imagery, the summer image proved least able to characterise bracken, due largely to the spectral similarity between (green) growing bracken and grasses and other vegetation. The winter images were more successful for identifying bracken due to the strong contrast between dead (brown/red) bracken and other vegetation. However, the multitemporal Landsat image was considerably more accurate than any of the single date images. The hard classified IKONOS image was more accurate overall than the Landsat sensor images for classifying land cover. Surprisingly, though, it was not comprehensively more accurate for mapping the bracken class. Notably, the producers accuracy of bracken was lower for the IKONOS image than the Landsat sensor images. This suggests image spatial resolution, although influential on the success of bracken characterisation, is not necessarily the sole or main determinant of classification accuracy. Also important are the temporal nature of image acquisition (here the multitemporal Landsat sensor image proved of considerable benefit) and the spectral characteristics of the imagery (here IKONOS’s four visible and near infrared spectral wavebands proved limited compared to the Landsat sensors’ six visible, near and shortwave infrared bands). Following soft classification of the multitemporal Landsat image, super-resolution sub-pixel contouring was applied to identify the boundary of bracken patches. Predicted bracken boundaries were assessed against actual boundaries identified using field observation and IKONOS image interpretation. For comparison, the bracken boundaries identified through hard classification (i.e. using pixel edges) were also assessed against the actual boundaries. Overall, the spatial accuracy of the super-resolution approach proved considerably higher than that of hard classification.
Numéro de notice :	A2013-032
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2012.10.002
En ligne :	https://doi.org/10.1016/j.isprsjprs.2012.10.002
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=32170

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
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