Spectral discrimination of vegetation types in a coastal wetland

Remote sensing is an important tool for mapping and monitoring vegetation. Advances in sensor technology continually improve the information content of imagery for airborne, as well as space-borne, systems. This paper investigates whether vegetation associations can be differentiated using hyperspec...

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Vydáno v:	Remote sensing of environment Ročník 85; číslo 1; s. 92 - 108
Hlavní autoři:	Schmidt, K.S., Skidmore, A.K.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY Elsevier Inc 25.04.2003 Elsevier Science
Témata:	Animal, plant and microbial ecology Bhattacharyya distance Biological and medical sciences Brackish Continuum removal Fundamental and applied biological sciences. Psychology General aspects. Techniques Hyperspectral methods Jeffries–Matusita distance Leerstoelgroep Resource Ecology Marine PE&RC Plantae Resource Ecology Saltmarsh Statistical analysis Teledetection and vegetation maps Netherlands Europe ANE, Wadden Sea Jeffries–Matusita distance Statistical analysis Continuum removal Bhattacharyya distance Hyperspectral methods Saltmarsh Vegetation map Salt marsh Vegetation type Remote sensing Reflectance
ISSN:	0034-4257, 1879-0704
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Remote sensing is an important tool for mapping and monitoring vegetation. Advances in sensor technology continually improve the information content of imagery for airborne, as well as space-borne, systems. This paper investigates whether vegetation associations can be differentiated using hyperspectral reflectance in the visible to shortwave infrared spectral range, and how well species can be separated based on their spectra. For this purpose, the field reflectance spectra of 27 saltmarsh vegetation types of the Dutch Waddenzee wetland were analysed in three steps. Prior to analysis, the spectra were smoothed with an innovative wavelet approach. In the first stage of the analysis, the reflectance spectra of the vegetation types were tested for differences between type classes. It was found that the reflectance spectra of saltmarsh vegetation types are statistically significantly different for various spectral regions. Secondly, it was tested whether this statistical difference could be enhanced by using continuum removal as a normalisation technique. For vegetation spectra, continuum removal improves the statistical difference between vegetation types in the visible spectrum, but weakens the statistical difference of the spectra in the near-infrared and shortwave infrared part of the spectrum. Thirdly, after statistical differences were found, it was determined how distant in spectral space the vegetation type classes were from each other, using the Bhattacharyya (BH) and the Jeffries–Matusita (JM) distance measures. We selected six wavelengths for this, based on the statistical analysis of the first step. The potential of correct classification of the saltmarsh vegetation types using hyperspectral remote sensing is predicted by these distance measures. It is concluded that the reflectance of vegetation types is statistically different. With high quality radiometric calibration of hyperspectral imagery, it is anticipated that vegetation species may be identified from imagery using spectral libraries that were measured in the field during the time of image acquisition.
AbstractList	Efforts aimed at mapping and monitoring vegetation have benefitted from the development of remote sensing technologies. A study was implemented to determine whether vegetation associations can be accurately differentiated based on analyses of hyperspectral reflectance in the visible to shortwave infrared spectral range. The study also considered how well different species can be separated based on their individual spectra. Tests were implemented using field reflectance spectra for 27 saltmarsh vegetation types of the Dutch Waddenzee wetland. The spectra utilized in the project were smoothed prior to analysis using a novel wavelet methodology. Evidence from the study indicated that reflectance data for vegetation types exhibited statistically significant differences. Remote sensing is an important tool for mapping and monitoring vegetation. Advances in sensor technology continually improve the information content of imagery for airborne, as well as space-borne, systems. This paper investigates whether vegetation associations can be differentiated using hyperspectral reflectance in the visible to shortwave infrared spectral range, and how well species can be separated based on their spectra. For this purpose, the field reflectance spectra of 27 saltmarsh vegetation types of the Dutch Waddenzee wetland were analysed in three steps. Prior to analysis, the spectra were smoothed with an innovative wavelet approach. In the first stage of the analysis, the reflectance spectra of the vegetation types were tested for differences between type classes. It was found that the reflectance spectra of saltmarsh vegetation types are statistically significantly different for various spectral regions. Secondly, it was tested whether this statistical difference could be enhanced by using continuum removal as a normalisation technique. For vegetation spectra, continuum removal improves the statistical difference between vegetation types in the visible spectrum, but weakens the statistical difference of the spectra in the near-infrared and shortwave infrared part of the spectrum. Thirdly, after statistical differences were found, it was determined how distant in spectral space the vegetation type classes were from each other, using the Bhattacharyya (BH) and the Jeffries–Matusita (JM) distance measures. We selected six wavelengths for this, based on the statistical analysis of the first step. The potential of correct classification of the saltmarsh vegetation types using hyperspectral remote sensing is predicted by these distance measures. It is concluded that the reflectance of vegetation types is statistically different. With high quality radiometric calibration of hyperspectral imagery, it is anticipated that vegetation species may be identified from imagery using spectral libraries that were measured in the field during the time of image acquisition. Remote sensing is an important tool for mapping and monitoring vegetation. Advances in sensor technology continually improve the information content of imagery for airborne, as well as space-borne, systems. This paper investigates whether vegetation associations can be differentiated using hyperspectral reflectance in the visible to shortwave infrared spectral range, and how well species can be separated based on their spectra. For this purpose, the field reflectance spectra of 27 saltmarsh vegetation types of the Dutch Waddenzee wetland were analysed in three steps. Prior to analysis, the spectra were smoothed with an innovative wavelet approach. In the first stage of the analysis, the reflectance spectra of the vegetation types were tested for differences between type classes. It was found that the reflectance spectra of saltmarsh vegetation types are statistically significantly different for various spectral regions. Secondly, it was tested whether this statistical difference could be enhanced by using continuum removal as a normalisation technique. For vegetation spectra, continuum removal improves the statistical difference between vegetation types in the visible spectrum, but weakens the statistical difference of the spectra in the near-infrared and shortwave infrared part of the spectrum. Thirdly, after statistical differences were found, it was determined how distant in spectral space the vegetation type classes were from each other, using the Bhattacharyya (BH) and the Jeffries-Matusita (JM) distance measures. We selected six wavelengths for this, based on the statistical analysis of the first step. The potential of correct classification of the saltmarsh vegetation types using hyperspectral remote sensing is predicted by these distance measures. It is concluded that the reflectance of vegetation types is statistically different. With high quality radiometric calibration of hyperspectral imagery, it is anticipated that vegetation species may be identified from imagery using spectral libraries that were measured in the field during the time of image acquisition.
Author	Skidmore, A.K. Schmidt, K.S.
Author_xml	– sequence: 1 givenname: K.S. surname: Schmidt fullname: Schmidt, K.S. email: schmidt@itc.nl – sequence: 2 givenname: A.K. surname: Skidmore fullname: Skidmore, A.K. email: Skidmore@itc.nl
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=14716078$$DView record in Pascal Francis
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Keywords	Jeffries–Matusita distance Statistical analysis Continuum removal Bhattacharyya distance Hyperspectral methods Saltmarsh Vegetation map Salt marsh Vegetation type Remote sensing Reflectance
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Snippet	Remote sensing is an important tool for mapping and monitoring vegetation. Advances in sensor technology continually improve the information content of imagery... Efforts aimed at mapping and monitoring vegetation have benefitted from the development of remote sensing technologies. A study was implemented to determine...
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SubjectTerms	Animal, plant and microbial ecology Bhattacharyya distance Biological and medical sciences Brackish Continuum removal Fundamental and applied biological sciences. Psychology General aspects. Techniques Hyperspectral methods Jeffries–Matusita distance Leerstoelgroep Resource Ecology Marine PE&RC Plantae Resource Ecology Saltmarsh Statistical analysis Teledetection and vegetation maps
Title	Spectral discrimination of vegetation types in a coastal wetland
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