Skip to main content
    • Aa
    • Aa

Mapping Downy Brome (Bromus tectorum) Using Multidate AVIRIS Data

  • Nina V. Noujdina (a1) and Susan L. Ustin (a1)

Invasive plants impose threats to both natural and managed ecosystems. Downy brome is among the most aggressive invasive weeds that has infested the shrub-steppe ecoregion of eastern Washington. Hyperspectral remote sensing has potential for early detection and for monitoring the spread of downy brome—information that is essential for developing effective management strategies. Two airborne hyperspectral Advanced Visible Infrared Imaging Spectrometer (AVIRIS) images (electromagnetic spectrum ranging from 400 to 2,500 nm) were acquired at a nominal 4-m ground resolution over a study area in south-central Washington on July 27, 2000 and May 5, 2003. We used a mixture-tuned matched filtering (MTMF) algorithm to classify downy brome and predict its percent cover in each dataset plus a merged multiseasonal dataset using the transformed bands from a minimum noise fraction (MNF) output. The correlation coefficient was 0.79, calculated for the multidate MTMF predicted downy brome abundance, compared to 0.41 and 0.51 derived from the July 2000 and May 2003 data, respectively. Although this study used high spatial resolution (∼3 to 4 m) hyperspectral imagery, this result shows that data acquired in different seasons is more effective for detection of downy brome invasion, compared to single-date datasets. These results support expanded use of multitemporal data for weed mapping to capitalize on spectral differences between seasons for weeds, in this case downy brome, and the surrounding environment.

Corresponding author
Corresponding author's E-mail:
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

J. B. Adams , M. O. Smith , and P. E. Johnson 1986. Spectral mixture modeling: a new analysis of rock and soil types at the Viking Lander 1 site. J. Geophys. Res. 91:80988112.

B. A. Bradley and J. F. Mustard 2005. Identifying land cover variability distinct from land cover change: cheatgrass in the Great Basine. Remote Sens. Environ. 94:204213.

C. M. D'Antonio and P. M. Vitousek 1992. Biological invasions by exotic grasses/fire cycle, and global change. Ann. Rev. of Ecol. Syst. 23:6387.

J. M. DiTomaso 2000. Invasive weeds in rangelands: species, impact, and management. Weed Sci. 48:255265.

A. J. Elmore , J. F. Mustard , S. J. Manning , and D. B. Lobell 2000. Quantifying vegetation change in semiarid environments: precision and accuracy of spectral mixture analysis and the Normalized Difference Vegetation Index. Remote Sens. Environ. 73:87102.

M. Garcia and S. L. Ustin 2001. Detection of interannual vegetation responses to climatic variability using AVIRIS data in a coastal savanna in California. IEEE Trans. Geosci. Remote Sens. 39:14801490.

N. F. Glenn , J. T. Mundt , K. T. Weber , T. S. Prather , L. W. Lass , and T. S. Pettingill 2005. Hyperspectral data processing for repeat detection of small infestations of leafy spruge. Remote Sens. Environ. 95:399412.

J. C. Harsanyi and C. Chang 1994. Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach. IEEE Trans. Geosci. Remote Sens. 32:779785.

R. J. Hobbs and S. E. Humphries 1995. An integrated approach to the ecology and management of plant invasions. Conserv. Biol. 9:761770.

L. W. Lass , D. C. Thill , B. Shafii , and T. S. Prather 2002. Detecting spotted knapweed (Centaurea maculosa) with hyperspectral remote sensing technology. Weed Technol. 16:426432.

L. W. Lass , T. S. Prather , N. F. Glenn , J. T. Mundt , and J. Pettingill 2005. Symposium. A review of remote sensing of invasive weeds and example of early detection of spotter knapweed (Centaura maculosa) and babysbreath (Gypsophila paniculata) with a hyperspectral sensor. Weed Sci. 53:242251.

F. Lopez-Granados , M. Jurado-Exposito , J. M. Pena-Barragan , and L. Garcia-Torres 2006. Using remote sensing for identification of late-season grass weed patches in wheat. Weed Sci. 54:346353.

S. J. Novak 2004. Genetic analysis of downy brome (Bromus tectorum) and medusahead (Taeniatherum caput-medusae): management implications. Weed Technol. 18:14171421.

A. E. P. Parker Williams and E. R. Hunt 2002. Estimation of leafy spruge cover from hyperspectral imagery using mixture tuned matched filtering. Remote Sens. Environ. 82:446456.

A. E. P. Parker Williams and E. R. Hunt 2004. Accuracy assessment for detection of leafy spruge with hyperspectral imagery. J. Range Manage. 57:106112.

E. B. Peterson 2005. Estimating cover of an invasive grass (Bromus tectorum) using tobit regression and phelology derived from two dates of Landsat ETM+ data. Int. J. Remote Sens. 26:24912507.

J. Pontius , R. Hallett , and M. Martin 2005. Using AVIRIS to assess hemlock abundance and early decline in Catskills, New York. Remote Sens. Environ. 97:163173.

T. Sakamoto , M. Yokozawa , H. Toritani , M. Shibayama , N. Ishitsuka , and H. Ohno 2005. A crop phenology detection method using time-series MODIS data. Remote Sens. Environ. 96:366374.

M. D. Schwartz 1999. Advancing to full bloom: planning phenological research for the 21st century. Int. J. Biometeorol. 42:113118.

A. M. Smith and R. E. Blackshaw 2003. Weed-crop discrimination using remote sensing: a detached leaf experiment. Weed Technol. 17:811820.

M. O. Smith , S. L. Ustin , J. B. Adams , and A. R. Gillespie 1990. Vegetation in deserts: I. a regional measure of abundance from multispectral images. Remote Sens. Environ. 31:126.

R. Thompson and R. M. Clark 2006. Spatio-temporal modelling and assessment of within-species phenological variability using thermal time methods. Int. J. Biometeorol. 50:312322.

E. Underwood , S. L. Ustin , and D. DiPietro 2003. Mapping nonnative plants using hyperspectral imagery. Remote Sens. Environ. 86:150161.

J. W. van Wagtendonk and R. R. Root 2003. The use of multi-temporal Landsat Normalized Difference Vegetation Index (NDVI) data for mapping fuels in Yosemite National Park, USA. Int. J. Remote Sens. 24:16391651.

T. T. Zhao and M. D. Schwartz 2003. Examining the onset of spring in Wisconsin. Climate Res. 24:5970.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Weed Science
  • ISSN: 0043-1745
  • EISSN: 1550-2759
  • URL: /core/journals/weed-science
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Abstract views

Total abstract views: 7 *
Loading metrics...

* Views captured on Cambridge Core between 20th January 2017 - 26th April 2017. This data will be updated every 24 hours.