Assessing ground cover at patch and hillslopescale in semi-arid woody vegetation and pasture using fused Quickbird data

Carlos Muñoz-Robles, Paul Frazier, Matthew Tighe, Nick Reid, Sue Briggs, Brian Wilson

    Research output: Contribution to journalArticle

    8 Citations (Scopus)

    Abstract

    The amount and distribution of vegetation and groundcover are important factors that influence resource transfer (e.g. runoff, sediment) in patterned semi-arid landscapes. Identifying and describing these features in detail is an essential part of measuring and understanding ecohydrological processes at hillslopescales that can then be applied at broader scales. The aim of this study was to develop a comprehensive methodology to map groundcover using high resolution Quickbird imagery in woody and non-woody (pasture) vegetation. The specific goals were to: (1) investigate the use of several techniques of image fusion, namely principal components analysis (PCA), Brovey transform, modified intensityâ¿¿hueâ¿¿saturation (MIHS) and wavelet transform to increase the spatial detail of multispectral Quickbirddata; (2) evaluate the performance of the red and near-infra-red bands (NIR), the difference vegetation index (DVI), and the normalised difference vegetation index (NDVI) in estimating groundcover, and (3) map and assess spatial and temporal changes in groundcover at hillslopescale using the most appropriate method or combination of methods. Estimates of groundcover from the imagery were compared with a subset of observed groundcover estimates to determine map accuracy. The MIHS algorithm produced images that best preserved spectral and spatial integrity, while the red band fused with the panchromatic band produced the most accurate groundcover maps. The patch size of the groundcover beneath canopies was similar to canopy size, and percent groundcover (mainly litter) increased with canopy size. Groundcover was mapped with relative accuracies of 84% in the woodyvegetation and 86% in the pasture. From 2008 to 2009, groundcover increased from 55% to 65% in the woodyvegetation and from 40% to 45% in the pasture. These groundcover maps can be used to explore the spatial ecohydrological interactions between areas of different groundcover at hillslopescale with application to management at broader scales.
    Original languageEnglish
    Pages (from-to)94-102
    Number of pages9
    JournalInternational Journal of Applied Earth Observation and Geoinformation
    Volume14
    Issue number1
    DOIs
    Publication statusPublished - 2011

    Fingerprint

    QuickBird
    ground cover
    pasture
    vegetation
    canopy
    imagery
    transform
    Image fusion
    patch size
    vegetation index
    Runoff
    NDVI
    Principal component analysis
    Wavelet transforms
    wavelet
    principal component analysis
    near infrared
    Sediments
    litter
    runoff

    Cite this

    Muñoz-Robles, Carlos ; Frazier, Paul ; Tighe, Matthew ; Reid, Nick ; Briggs, Sue ; Wilson, Brian. / Assessing ground cover at patch and hillslopescale in semi-arid woody vegetation and pasture using fused Quickbird data. In: International Journal of Applied Earth Observation and Geoinformation. 2011 ; Vol. 14, No. 1. pp. 94-102.
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    abstract = "The amount and distribution of vegetation and groundcover are important factors that influence resource transfer (e.g. runoff, sediment) in patterned semi-arid landscapes. Identifying and describing these features in detail is an essential part of measuring and understanding ecohydrological processes at hillslopescales that can then be applied at broader scales. The aim of this study was to develop a comprehensive methodology to map groundcover using high resolution Quickbird imagery in woody and non-woody (pasture) vegetation. The specific goals were to: (1) investigate the use of several techniques of image fusion, namely principal components analysis (PCA), Brovey transform, modified intensity{\^a}¿¿hue{\^a}¿¿saturation (MIHS) and wavelet transform to increase the spatial detail of multispectral Quickbirddata; (2) evaluate the performance of the red and near-infra-red bands (NIR), the difference vegetation index (DVI), and the normalised difference vegetation index (NDVI) in estimating groundcover, and (3) map and assess spatial and temporal changes in groundcover at hillslopescale using the most appropriate method or combination of methods. Estimates of groundcover from the imagery were compared with a subset of observed groundcover estimates to determine map accuracy. The MIHS algorithm produced images that best preserved spectral and spatial integrity, while the red band fused with the panchromatic band produced the most accurate groundcover maps. The patch size of the groundcover beneath canopies was similar to canopy size, and percent groundcover (mainly litter) increased with canopy size. Groundcover was mapped with relative accuracies of 84{\%} in the woodyvegetation and 86{\%} in the pasture. From 2008 to 2009, groundcover increased from 55{\%} to 65{\%} in the woodyvegetation and from 40{\%} to 45{\%} in the pasture. These groundcover maps can be used to explore the spatial ecohydrological interactions between areas of different groundcover at hillslopescale with application to management at broader scales.",
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    Assessing ground cover at patch and hillslopescale in semi-arid woody vegetation and pasture using fused Quickbird data. / Muñoz-Robles, Carlos; Frazier, Paul; Tighe, Matthew; Reid, Nick; Briggs, Sue; Wilson, Brian.

    In: International Journal of Applied Earth Observation and Geoinformation, Vol. 14, No. 1, 2011, p. 94-102.

    Research output: Contribution to journalArticle

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    T1 - Assessing ground cover at patch and hillslopescale in semi-arid woody vegetation and pasture using fused Quickbird data

    AU - Muñoz-Robles, Carlos

    AU - Frazier, Paul

    AU - Tighe, Matthew

    AU - Reid, Nick

    AU - Briggs, Sue

    AU - Wilson, Brian

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    AB - The amount and distribution of vegetation and groundcover are important factors that influence resource transfer (e.g. runoff, sediment) in patterned semi-arid landscapes. Identifying and describing these features in detail is an essential part of measuring and understanding ecohydrological processes at hillslopescales that can then be applied at broader scales. The aim of this study was to develop a comprehensive methodology to map groundcover using high resolution Quickbird imagery in woody and non-woody (pasture) vegetation. The specific goals were to: (1) investigate the use of several techniques of image fusion, namely principal components analysis (PCA), Brovey transform, modified intensityâ¿¿hueâ¿¿saturation (MIHS) and wavelet transform to increase the spatial detail of multispectral Quickbirddata; (2) evaluate the performance of the red and near-infra-red bands (NIR), the difference vegetation index (DVI), and the normalised difference vegetation index (NDVI) in estimating groundcover, and (3) map and assess spatial and temporal changes in groundcover at hillslopescale using the most appropriate method or combination of methods. Estimates of groundcover from the imagery were compared with a subset of observed groundcover estimates to determine map accuracy. The MIHS algorithm produced images that best preserved spectral and spatial integrity, while the red band fused with the panchromatic band produced the most accurate groundcover maps. The patch size of the groundcover beneath canopies was similar to canopy size, and percent groundcover (mainly litter) increased with canopy size. Groundcover was mapped with relative accuracies of 84% in the woodyvegetation and 86% in the pasture. From 2008 to 2009, groundcover increased from 55% to 65% in the woodyvegetation and from 40% to 45% in the pasture. These groundcover maps can be used to explore the spatial ecohydrological interactions between areas of different groundcover at hillslopescale with application to management at broader scales.

    KW - Ecohydrology

    KW - Image fusion

    KW - Inter-patches

    KW - Modified intensityâ¿¿hueâ¿¿saturation transform

    KW - Patches

    KW - Australia

    U2 - 10.1016/j.jag.2011.08.010

    DO - 10.1016/j.jag.2011.08.010

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