Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-27T23:11:42.490Z Has data issue: false hasContentIssue false

Mapping Downy Brome (Bromus tectorum) Using Multidate AVIRIS Data

Published online by Cambridge University Press:  20 January 2017

Nina V. Noujdina*
Affiliation:
Center for Spatial Technologies and Remote Sensing (CSTARS), Department of Land, Air, and Water Resources, University of California, Davis, CA 95616
Susan L. Ustin
Affiliation:
Center for Spatial Technologies and Remote Sensing (CSTARS), Department of Land, Air, and Water Resources, University of California, Davis, CA 95616
*
Corresponding author's E-mail: [email protected]

Abstract

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.

Type
Special Topics
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Adams, J. B., Smith, M. O., and Johnson, P. E. 1986. Spectral mixture modeling: a new analysis of rock and soil types at the Viking Lander 1 site. J. Geophys. Res. 91:80988112.CrossRefGoogle Scholar
Boardman, J. W. 1998. Leveraging the high dimensionality of AVIRIS data for improved sub-pixel target unmixing and rejection of false positives: mixture tuned matched filtering. in. Summaries of the Seventh JPL Airborne Geoscience Workshop, JPL Publication 97-1. Pasadena, CA NASA Jet Propulsion Laboratory. 5556.Google Scholar
Boardman, J. W. and Kruse, F. A. 1994. Automated spectral analysis: a geological example using AVIRIS data, north Grapevine Mountains, Nevada. in. Proceedings of the 10th Thematic Conference on Geologic Remote Sensing. Ann Arbor, MI Environmental Research Institute of Michigan. I-407I-418.Google Scholar
Boardman, J. W., Kruse, F. A., and Green, R. O. 1995. Mapping target signatures via partial unmixing of AVIRIS data. in. Summaries of the fifth JPL Airborne Geoscience Workshop, JPL Publication 95-1. Pasadena, CA NASA Jet Propulsion Laboratory. 2326.Google Scholar
Bradley, B. A. and Mustard, J. F. 2005. Identifying land cover variability distinct from land cover change: cheatgrass in the Great Basine. Remote Sens. Environ. 94:204213.CrossRefGoogle Scholar
D'Antonio, C. M. and Vitousek, P. M. 1992. Biological invasions by exotic grasses/fire cycle, and global change. Ann. Rev. of Ecol. Syst. 23:6387.Google Scholar
DiPietro, D. Y. 2002. Mapping the Invasive Plant Arundo donax and Associated Riparian Vegetation Using Hyperspectral Remote Sensing. . Davis, CA: University of California, Davis, 49 p.Google Scholar
DiTomaso, J. M. 2000. Invasive weeds in rangelands: species, impact, and management. Weed Sci. 48:255265.Google Scholar
Elmore, A. J., Mustard, J. F., Manning, S. J., and Lobell, D. B. 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.Google Scholar
Garcia, M. and Ustin, S. L. 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.Google Scholar
Glenn, N. F., Mundt, J. T., Weber, K. T., Prather, T. S., Lass, L. W., and Pettingill, T. S. 2005. Hyperspectral data processing for repeat detection of small infestations of leafy spruge. Remote Sens. Environ. 95:399412.CrossRefGoogle Scholar
Harsanyi, J. C. and Chang, C. 1994. Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach. IEEE Trans. Geosci. Remote Sens. 32:779785.Google Scholar
Hobbs, R. J. and Humphries, S. E. 1995. An integrated approach to the ecology and management of plant invasions. Conserv. Biol. 9:761770.Google Scholar
Lass, L. W., Thill, D. C., Shafii, B., and Prather, T. S. 2002. Detecting spotted knapweed (Centaurea maculosa) with hyperspectral remote sensing technology. Weed Technol. 16:426432.CrossRefGoogle Scholar
Lass, L. W., Prather, T. S., Glenn, N. F., Mundt, J. T., and Pettingill, J. 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.Google Scholar
Lopez-Granados, F., Jurado-Exposito, M., Pena-Barragan, J. M., and Garcia-Torres, L. 2006. Using remote sensing for identification of late-season grass weed patches in wheat. Weed Sci. 54:346353.Google Scholar
Novak, S. J. 2004. Genetic analysis of downy brome (Bromus tectorum) and medusahead (Taeniatherum caput-medusae): management implications. Weed Technol. 18:14171421.CrossRefGoogle Scholar
O'Neill, M., Ustin, S. L., Hager, S., and Root, R. 2000. Mapping the distribution of leafy spurge at Theodore Roosevelt National Park using AVIRIS. in. Proceedings of the 9th AVIRIS Earth Science Workshop, JPL Publication 00-18. Pasadena, CA NASA Jet Propulsion Laboratory. 339348.Google Scholar
Parker Williams, A. E. P. and Hunt, E. R. 2002. Estimation of leafy spruge cover from hyperspectral imagery using mixture tuned matched filtering. Remote Sens. Environ. 82:446456.CrossRefGoogle Scholar
Parker Williams, A. E. P. and Hunt, E. R. 2004. Accuracy assessment for detection of leafy spruge with hyperspectral imagery. J. Range Manage. 57:106112.Google Scholar
Peterson, E. B. 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.Google Scholar
Pontius, J., Hallett, R., and Martin, M. 2005. Using AVIRIS to assess hemlock abundance and early decline in Catskills, New York. Remote Sens. Environ. 97:163173.Google Scholar
Root, R., Ustin, S., Zarco-Tejada, P., Pinilla, C., Kokaly, R., Anderson, G., Brown, K., Dudek, K., Hager, S., and Holroyd, E. 2002. Comparison of AVIRIS and EO-1 Hyperion for classification and mapping of invasive leafy spurge in Theodore Roosevelt National Park. Presented at the 11th Earth Science Airborne Workshop, March 5–8. Pasadena, CA NASA Jet Propulsion Laboratory.Google Scholar
Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., and Ohno, H. 2005. A crop phenology detection method using time-series MODIS data. Remote Sens. Environ. 96:366374.CrossRefGoogle Scholar
Schwartz, M. D. 1999. Advancing to full bloom: planning phenological research for the 21st century. Int. J. Biometeorol. 42:113118.Google Scholar
Smith, A. M. and Blackshaw, R. E. 2003. Weed-crop discrimination using remote sensing: a detached leaf experiment. Weed Technol. 17:811820.Google Scholar
Smith, M. O., Ustin, S. L., Adams, J. B., and Gillespie, A. R. 1990. Vegetation in deserts: I. a regional measure of abundance from multispectral images. Remote Sens. Environ. 31:126.Google Scholar
Thompson, R. and Clark, R. M. 2006. Spatio-temporal modelling and assessment of within-species phenological variability using thermal time methods. Int. J. Biometeorol. 50:312322.Google Scholar
Underwood, E., Ustin, S. L., and DiPietro, D. 2003. Mapping nonnative plants using hyperspectral imagery. Remote Sens. Environ. 86:150161.Google Scholar
van Wagtendonk, J. W. and Root, R. R. 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.Google Scholar
Vitousek, P. M., D'Antonio, C. M., Loope, L. L., and Westbrooks, R. 1996. Biological invasions and environmental change. Am. Sci. 84:468479.Google Scholar
Zhao, T. T. and Schwartz, M. D. 2003. Examining the onset of spring in Wisconsin. Climate Res. 24:5970.Google Scholar