Object-based mapping of native vegetation and para grass (Urochloa mutica) on a monsoonal wetland of Kakadu NP using a Landsat 5 TM Dry-season time series

Autor:	Penelope Wurm, Karen E. Joyce, James Boyden, Guy S. Boggs
Rok vydání:	2013
Předmět:	Atmospheric Science geography.geographical_feature_category Contextual image classification Perennial plant biology National park Geography Planning and Development Wetland Forestry biology.organism_classification General Energy Geography Dry season medicine Urochloa medicine.symptom Vegetation (pathology) Weed Remote sensing
Zdroj:	Journal of Spatial Science. 58:53-77
ISSN:	1836-5655 1449-8596
DOI:	10.1080/14498596.2012.759086
Popis:	This paper evaluates the use of multi-temporal Landsat 5 TM for object-based classification of native wetland vegetation and the perennial aquatic weed para grass within Kakadu National Park, Northern Territory, Australia. Using identical training data and segmentation, a nearest-neighbour classification produced from a four-image (dry season) time-series was compared with four ‘single-date’ classifications produced from the individual images of the same series. A 15-class vegetation map generated from the multi-date classification produced an overall accuracy of 82 percent (kappa = 0.80). This was an average increase in accuracy of 25 percent (kappa = 0.28) compared to single-date classifications. The multi-date image composite also improved segmentation quality and spectral separability of vegetation classes. Reliable maps of wetland vegetation, potentially useful for strategic conservation, can be produced by integrated, object-based, analysis of multi-temporal Landsat.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::59604e8bf738c8a728f39cffe9425f59 https://doi.org/10.1080/14498596.2012.759086 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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