Detection of urban effect on vegetation in a less built-up Hungarian city by hyperspectral remote sensing

Jung, András; Kardeván, Péter; Tőkei, László

doi:10.1016/j.pce.2004.08.041

Cited by 28 publications

(22 citation statements)

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“…This can also be explained for the classification of open green which was mixed with urban areas and mostly tree canopy. As previously mentioned, Jung et al (2005) found that by discriminating different types of vegetation, the SWIR bands are most reliable; in this study, the SWIR bands detected vegetation on a micro-scale environment. In addition, urban areas were predominantly mixed with tree canopy, and with segmented Hyperion urban areas were mixed with open green areas.…”

Section: Overall Classificationsupporting

confidence: 60%

“…With the advanced technology of hyperspectral sensors, accuracy results when performed on dense urban imagery may be increased. Thus, these advantages have led to recent scholarly explorations of classification and mapping for land cover and vegetation with the application of hyperspectral imagery (HSI) (Heiden et al, 2012;Jung et al, 2005). A complete list of spaceborne hyperspectral satellites currently in orbit and set to launch is found in Buckingham and Staenz (2008).…”

Section: The Value Of Greenspacementioning

confidence: 99%

“…Hyperspectral sensors have the ability to derive urban land cover for complex analyses of large dense areas, thus enabling the update of surface material databases for use in urban development. Jung et al (2005) examined hyperspectral imagery and its detection of vegetation in an urban landscape and found that when discriminating different types of vegetation, the SWIR bands are most reliable. They attempted to detect the effect of vegetation on micro-scale environmental problems among built-up conditions.…”

Section: Applications Of Hyperspectral Remote Sensing In Land Resourcesmentioning

confidence: 99%

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Comparing Hyperspectral and Multispectral Imagery for Land Classification of the Lower Don River, Toronto

Ferrato¹,

Forsythe²

2013

JGG

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Urban greenspace is important for the health of cities. Up-to-date databases and information are vital to maintain and monitor growth in cities. During the last decade, advances in spaceborne hyperspectral sensors have resulted in some advantages being gained over multispectral sensors for land cover monitoring (due to increased spectral resolution). The objective of this research was to compare Earth Observing-1 (EO-1) Hyperion hyperspectral data to Landsat 5 Thematic Mapper (TM) and Satellite Probatoire d'Observation de la Terre (SPOT) 5 multispectral data for land cover classification in a dense urban landscape. For comparative analysis, orthorectified aerial imagery provided by the Toronto and Region Conservation Authority (TRCA) was used as ground truth data for accuracy assessment. This study utilized conventional and segmented principal components (CPCA and SPCA) for data compression on the Hyperion imagery, and used principal components analysis (PCA) as a visual enhancement technique for multispectral imagery. Image processing including the generation of the normalized difference vegetation index (NDVI), and mean texture was also performed for both Landsat and SPOT sensors. Unsupervised iterative self-organizing data analysis (ISODATA) classification procedures were performed on all images to produce land cover classification maps for a portion of the Lower Don River in Toronto, Ontario, Canada. Experiments conducted in this research demonstrated that hyperspectral imagery produced a higher overall accuracy (5-6% better) than multispectral data with the same resolution for defining vegetation cover. In addition, SPOT generated greater accuracy results than Landsat or Hyperion for vegetation classes. It was found that conventional Hyperion and segmented Hyperion methods outperformed the Landsat 5 TM sensor for vegetation differences (for tree canopy and open green spaces).

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Section: Overall Classificationsupporting

confidence: 60%

Section: The Value Of Greenspacementioning

confidence: 99%

Section: Applications Of Hyperspectral Remote Sensing In Land Resourcesmentioning

confidence: 99%

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Comparing Hyperspectral and Multispectral Imagery for Land Classification of the Lower Don River, Toronto

Ferrato¹,

Forsythe²

2013

JGG

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“…HSI sensor does not present only spectral data in detail consisting of hundreds of bands in one combination [9]. So, these features have resulted in new governmental and scholarly exploration of mapping and classification of vegetation and land cover with HSI application [10] [13].…”

Section: A Advantages Of Hyperspectral Over Multispectral Datamentioning

confidence: 99%

Performance of Spectral Angle Mapper and Parallelepiped Classifiers in Agriculture Hyperspectral Image

El_Rahman¹

2016

ijacsa

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Abstract-Hyperspectral Imaging (HSI) is used to provide a wealth of information which can be used to address a variety of problems in different applications. The main requirement in all applications is the classification of HSI data. In this paper, supervised HSI classification algorithms are used to extract agriculture areas that specialize in wheat growing and get a classified image. In particular, Parallelepiped and Spectral Angel Mapper (SAM) algorithms are used. They are implemented by a software tool used to analyse and process geospatial images that is an Environment of Visualizing Images (ENVI). They are applied on Al-Kharj, Saudi Arabia as the study area. The overall accuracy after applying the algorithms on the image of the study area for SAM classification was 66.67%, and 33.33% for Parallelepiped classification. Therefore, SAM algorithm has provided a better a study area image classification.

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“…We therefore needed to construct a vegetation index that can consider 10 or more bands. We further needed to consider thermal bands to study differences in surface temperature within the city [1], [9], [10]. Beyond these general aims our specific goals were to determine indices that could characterize built-up urban areas on the basis of a hyperspectral data cube and thermal sensor data as follows:…”

Section: Introductionmentioning

confidence: 99%

Application of Airborne Hype Rspectral and Thermal Images to Analyse Urban Microclimate

Jung¹

2007

Appl Ecol Env Res

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Detection of urban effect on vegetation in a less built-up Hungarian city by hyperspectral remote sensing

Cited by 28 publications

References 1 publication

Comparing Hyperspectral and Multispectral Imagery for Land Classification of the Lower Don River, Toronto

Comparing Hyperspectral and Multispectral Imagery for Land Classification of the Lower Don River, Toronto

Performance of Spectral Angle Mapper and Parallelepiped Classifiers in Agriculture Hyperspectral Image

Application of Airborne Hype Rspectral and Thermal Images to Analyse Urban Microclimate

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