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A review of remote sensing of invasive weeds and example of the early detection of spotted knapweed (Centaurea maculosa) and babysbreath (Gypsophila paniculata) with a hyperspectral sensor

Published online by Cambridge University Press:  20 January 2017

Lawrence W. Lass ,
Timothy S. Prather ,
Nancy F. Glenn ,
Keith T. Weber ,
Jacob T. Mundt  and
Jeffery Pettingill
Timothy S. Prather
Affiliation:
Department of Plant, Soil, and Entomological Sciences, University of Idaho, Moscow, ID 83844-2339
Nancy F. Glenn
Affiliation:
Department of Geosciences, Idaho State University-Boise Center, Boise, ID 83713
Keith T. Weber
Affiliation:
GIS Training and Research Center, Idaho State University, Pocatello, ID, 83209-8130
Jacob T. Mundt
Affiliation:
Department of Geosciences, Idaho State University-Boise Center, Boise, ID 83713
Jeffery Pettingill
Affiliation:
Bonneville County Weed Department, Idaho Falls, ID 83402
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Abstract

Remote sensing technology is a tool for detecting invasive species affecting forest, rangeland, and pasture environments. This article provides a review of the technology, and algorithms used to process remotely sensed data when detecting weeds and a working example of the detection of spotted knapweed and babysbreath with a hyperspectral sensor. Spotted knapweed and babysbreath frequently invade semiarid rangeland and irrigated pastures of the western United States. Ground surveys to identify the extent of invasive species infestations should be more efficient with the use of classified images from remotely sensed data because dispersal of an invasive plant may have occurred before the discovery or treatment of an infestation. Remote sensing data were classified to determine if infestations of spotted knapweed and babysbreath were detectable in Swan Valley near Idaho Falls, ID. Hyperspectral images at 2-m spatial resolution and 400- to 953-nm spectral resolution with 12-nm increments were used to identify locations of spotted knapweed and babysbreath. Images were classified using the spectral angle mapper (SAM) algorithm at 1, 2, 3, 4, 5, and 10° angles. Ground validation of the classified images established that 57% of known spotted knapweed infestations and 97% of known babysbreath infestations were identified through the use of hyperspectral imagery and the SAM algorithm.

Keywords

Babysbreath, Gypsophila paniculata L. GYPPAspotted knapweed, Centaurea maculosa Lam. CENMAHyperspectral sensorimaging spectrometerinvasive plant detection
Type
Symposium
Information
Weed Science , Volume 53 , Issue 2 , April 2005 , pp. 242 - 251
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Anderson, G. L., Everitt, J. H., Escobar, D. E., Spencer, N. R., and Andrascik, R. J. 1996. Mapping leafy spurge (Euphorbia esula) infestations using aerial photography and geographic information systems. Geocarto Int 11:81–89.CrossRefGoogle Scholar
Anderson, G. L., Everitt, J. H., Richardson, A. J., and Escobar, D. E. 1993. Using satellite data to map false broomweed (Ericameria austrotexana) infestations on south Texas rangelands. Weed Technol 7:865–871.Google Scholar
Anderson, G. L., Hanson, J. D., and Hart, G. F. 1992. Developing Relationships Between Green Biomass and Landsat Thematic Mapper Derived Vegetation Indices on Semi-Arid Rangelands. Washington, DC: ASPRS/ACSM/RT 92. Volume 4. Pp. 355–363.Google Scholar
Anderson, G. L. and Yang, C. 1996. Multispectral videography and geographic information systems for site-specific farm management. Pages 681–692 in Proceedings of the 3rd International Conference on Precision Agriculture. Madison, WI: ASA/CSSA/SSSA.Google Scholar
Arnold, G. W., Ozanne, P. G., Galbraith, K. A., and Dandridge, F. 1985. The capeweed content of pastures in south-west Western Australia. Aust. J. Exp. Agric 25:117–123.CrossRefGoogle Scholar
Bellmund, S. and Kitchens, W. 1997. Landscape Ecological Indices in Ecological and Precursor Success Criteria for South Florida Ecosystem Restoration. www.fiu.edu/∼glades/taskforce/precursor/chapter7.html.Google Scholar
Boardman, J. W., Kruse, F. A., and Green, R. O. 1995. Mapping target signature via partial unmixing of AVIRIS data. Pages 23–26 in Summaries of the Fifth JPL Airborne Geoscience Workshop JPL Publication 95-1. Pasadena, CA: NSAS Jet Propulsion Laboratory.Google Scholar
Bostater, C. R., Ghir, T., Bassetti, L., Hall, C., Reyeier, E., Lowers, R., Holloway-Adkins, K., and Virnstein, R. 2004. Hyperspectral remote sensing protocol development for submerged aquatic vegetation in shallow waters. Proc. SPIE Int. Soc. Opt. Eng 5233:199–215.Google Scholar
Campbell, J. B. 2002. Introduction to Remote Sensing. 3rd ed. New York: Gulford. 622 p.Google Scholar
Card, D. H. 1982. Using known map category marginal frequencies to improve estimates of thematic map accuracy. Photogram. Eng. Remote Sens 48:431–439.Google Scholar
Carson, H. W., Lass, L. W., and Callihan, R. H. 1995. Detection of yellow hawkweed with high resolution digital images. Weed Technol 9:477–483.CrossRefGoogle Scholar
Chang, C. I. 1999. Least squares error theory for linear mixing problems with mixed pixel classification for hyperspectral imagery. Recent Res. Dev. Opt. Eng 2:214–268.Google Scholar
Chang, C. I. and Ren, H. 2000. An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens 38:1044–1063.Google Scholar
Chang, C. I., Zhao, X., Althouse, M. L. G., and Pan, J. J. 1998. A posteriori least squares orthogonal subspace projection approach to mixed pixel classification in hyperspectral images. IEEE Trans. Geosci. Remote Sens 36:898–912.CrossRefGoogle Scholar
Chavez, P. S. 1996. Image-based atmospheric corrections—revisited and improved. Photogram. Eng. Remote Sens 62:1025–1036.Google Scholar
Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ 37:35–46.Google Scholar
Congalton, R. G., Oderwald, R., and Mead, R. A. 1983. Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogram. Eng. Remote Sens 49:1671–1678.Google Scholar
Darant, A. L. 1975. The biology of Canadian weeds. Gypsophila paniculata L. Can. J. Plant Sci 55:1049–1058.CrossRefGoogle Scholar
Darant, A. L. and Coupland, R. T. 1966. Life history of Gypsophila paniculata . Weeds 14:313–318.Google Scholar
Dewey, S. A. and Price, K. P. 1991. Satellite remote sensing to predict potential distribution of dyers woad (Isatis tinctora). Weed Technol 5:479–484.Google Scholar
DiPietro, D. Y. 2002. Mapping the Invasive Plant Arundo donax and Associated Riparian Vegetation Using Hyperspectral Remote Sensing. www.great.jussieu.fr/great/theses/master_Dipietro_2002.pdf.Google Scholar
Everitt, J. H., Alaniz, M. A., and Escobar, D. E. 1992. Using remote sensing to distinguish common goldenweed (Isocoma coronopifolia) and Drummon goldenweed (Isocoma drummondii). Weed Sci 40:621–628.Google Scholar
Everitt, J. H. and Deloach, C. J. 1990. Remote sensing of Chinese Tamarisk (Tamarix chinensis) and associated vegetation. Weed Sci 38:273–278.Google Scholar
Everitt, J. H., Escobar, D. E., Alaniz, M. A., and Davis, M. R. 1987. Using airborne middle-infrared (1.45–2.0 μm) video imagery for distinguishing plant species and soil conditions. Remote Sens. Environ 22:423–428.CrossRefGoogle Scholar
Everitt, J. H., Escobar, D. E., Alaniz, M. A., and Davis, M. R. 1991. Light reflectance characteristics and video remote-sensing of prickly pear. J. Range Manage 44:587–592.Google Scholar
Everitt, J. H., Escobar, D. E., Alaniz, M. A., Davis, M. R., and Richerson, J. V. 1996. Using spatial information technologies to map Chinese tamarisk (Tamarix chinensis) infestations. Weed Sci 44:194–201.Google Scholar
Everitt, J. H., Escobar, D. E., Blazquez, C. H., Hussey, M. A., and Nixon, P. R. 1986. Evaluation of the mid-infrared (1.45–2.0 μm) with a black-and-white infrared video camera. Photogram. Eng. Remote Sens 52:1655–1660.Google Scholar
Everitt, J. H. and Nixon, P. R. 1985. Video imagery: a new remote sensing tool for range management. J. Range Manage 38:421–424.Google Scholar
Forster, B. C. 1984. Derivation of atmospheric correction procedures for LANDSAT MSS with particular reference to urban data. Int. J. Remote Sens 5:799–817.CrossRefGoogle Scholar
Goodchild, M. F. and Gopal, S. eds. 1989. Accuracy of Spatial Databases. London: Taylor and Francis. 309 p.Google Scholar
Haefner, S. 2004. Kite Aerial Photography. www.thehaefners.com/kap/?page=kites.Google Scholar
King, D. J. 1995. Airborne multi-spectral digital camera and video sensors: a critical review of system designs and applications. Can. J. Remote Sens 21:245–273.Google Scholar
Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Hiebedrecht, K. B., Shapiro, A. T., Barloom, P. J., and Goetz, A. F. H. 1993. The spatial image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ 44:145–163.Google Scholar
Lass, L. W. and Callihan, R. H. 1997. The effect of phenological stage on detectability of yellow hawkweed (Hieracium partense) and oxeye daisy (Chrysanthemum leucanthemum) with remote multispectral digital imagery. Weed Technol 11:248–256.Google Scholar
Lass, L. W., Carson, H. W., and Callihan, R. H. 1996. Detection of yellow starthistle with high resolution multispectral digital images. Weed Technol 10:466–474.Google Scholar
Lass, L. W. and Prather, T. S. 2004. Detecting the locations of Brazilian pepper trees in the Everglades with a hyperspectral sensor. Weed Technol 18:437–442.Google Scholar
Lass, L. W., Shafii, B., Price, W. J., and Thill, D. C. 2000. Assessing agreement in multispectral images of yellow starthistle (Centaurea solstitialis) with ground truth data using a Bayesian methodology. Weed Technol 14:539–544.Google Scholar
Lass, L. W. and Thill, D. C. 2000. Detecting yellow starthistle (Centaurea solstitialis) with hyperspectral remote sensing technology. Proc. West. Soc. Weed Sci 53:11.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 16:426–432.Google Scholar
Lindholm, S. 2004. Aerial Digital Photography from a Balloon for Fifty Dollars. www.stanford.edu/∼lindholm/chpro_bal.html.Google Scholar
Louis, J., Lamb, D. W., McKenzie, G., Chapman, G., Edirisinghe, A., McCloud, I., and Pratley, J. 1995. Operational use and calibration of airborne video for agricultural and environmental land management applications. Pages 326–333 in Proceedings of the 15th Biennial Workshop on Colour Photography and Air Videography; Terrahoute, IN. The Regional Institute.Google Scholar
Manzer, F. E. and Cooper, G. R. 1982. Use of portable video taping for aerial infrared imaging of potato disease. Plant Dis 66:665–667.Google Scholar
O'Neill, A. L. 1996. Satellite-derived vegetation indices applied to semi-arid shrublands in Australia. Aust. Geogr 27:185–199.CrossRefGoogle Scholar
Parker-Williams, A. and Hunt, E. R. 2002. Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering. Remote Sens. Environ 82:446–456.Google Scholar
Parker-Williams, A. E. and Hunt, E. R. 2004. Accuracy assessment for detection of leafy spurge with hyperspectral imagery. J. Range Manage 57:106–112.Google Scholar
Pearlstine, L., Smith, S., Walsh, E., and Stenberg, J. 1998. Aerial Sampling for Brazilian Pepper on Canaveral National Seashore. Gainesville, FL: USGS Biological Resources Division, Florida Cooperative Fish and Wildlife Research Unit.Google Scholar
Peters, A. J., Reed, B. C., and Eve, M. D. 1992. Remote sensing of broom snakeweed (Gutierrezia sarothrae) with NOAA-10 spectral image processing. Weed Technol 6:1015–1020.Google Scholar
Prather, T. S., Shafii, B., and Callihan, R. H. 1994. Predicting common crupina habitat with geographic and remote sensing data. Pages 122–135 in Kansas State University Conference on Applied Statistics in Agriculture. Manhattan, KS: Kansas State University.Google Scholar
Rejmánek, M. and Pitcairn, M. J. 2002. When is eradication of exotic pest plants a realistic goal?. Pages 249–253 in Veitch, C. R. and Clout, M. N. eds. Proceedings of the International Conference on Eradication of Island Invasives. Turning the Tide: The Eradication of Invasive Species. Gland, Switzerland: IUCN SSC Invasive Species Specialist Group.Google Scholar
Ren, H. and Chang, C-I. 1998. A computer-aided detection and classification method of concealed targets in hyperspectral imagery. Pages 1016–1018 in International Symposium Geosciences and Remote Sensing; Seattle, WA. Piscataway, NJ: International Geoscience and Remote Sensing Society.Google Scholar
Richards, J. A. 1986. Remote Sensing Digital Image Analysis: An Introduction. Berlin: Springer. 363 p.Google Scholar
Ringrose, S. and Matheson, W. 1987. Spectral assessment of indicators of range degradation in the Botswana Hardveld Environment. Remote Sens. Environ 23:379–396.Google Scholar
Roche, B. Jr. and Talbott Roche, C. 1994. Diffuse Knapweed Control in Feral Babysbreath. Washington State Univ. Ext. Bull. 1793. cru.cahe.wsu.edu/CEPublications/eb1793/eb1793.html.Google Scholar
Root, R. 2002. Identification, Canopy, Characterization and Mapping of Invasive Leafy Spurge with the EO-1 Hyperion Orbital Imaging Spectrometer, Final NASA EO-1 Science Validation Team Meeting.Google Scholar
RotorKraft. 2004. RotorKraft: Premium Radio Control Helicopters and Accessories. www.rotorkraft.com/index.htm.Google Scholar
Schlosser, W. E., Blatner, K. A., and Chapman, R. C. 1991. Economic and marketing implications of special forest products harvest in the Costal Pacific Northwest. West. J. Appl. For 6:67–72.Google Scholar
Settle, J. J. and Drake, N. A. 1993. Linear mixing and the estimation of ground proportions. Int. J. Remote Sens 14:1159–1177.Google Scholar
Shafii, B., Price, W. J., Prather, T. S., Lass, L. W., and Thill, D. C. 2004. Using landscape characteristics as prior information for bayesian classification of yellow starthistle. Weed Sci 52:948–953.Google Scholar
Shimabukuro, Y. E. and Smith, J. A. 1991. The least-squares mixing models to generate fraction images derived from remote sensing multispectral data. IEEE Trans. Geosci. Remote Sens 29:16–20.Google Scholar
Sohn, Y. and McCoy, R. M. 1997. Mapping desert shrub rangeland using spectra unmixing and modeling spectral mixtures with TM data. Photogram. Eng. Remote Sens 63:707–716.Google Scholar
Sun, X., Baker, J., and Hordon, R. 1997. Computerized airborne multicamera imaging system (CAMIS) and its 4-camera applications. Pages 799–806 in Proceedings of the Third International Airborne Remote-Sensing Conference and Exhibition; Copenhagen, Denmark. Ann Arbor, MI: Altarum Airborne Conferences.Google Scholar
Underwood, E., Ustin, S. L., and DiPietro, D. 2003. Mapping nonnative plants using hyperspectral imagery. Remote Sens. Environ 86:150–161.Google Scholar
[USGS] U.S. Geological Survey. 2003. Monitoring Changes in Vegetation and Land Surfaces by Remote Sensing—Detecting Infestations of Cheatgrass on the Colorado Plateau. climchange.cr.usgs.gov/info/sw/monitor/remote1.html.Google Scholar
Ustin, S. L., DiPietro, D., Olmstead, K., Underwood, E., and Scheer, G. J. 2002. Hyperspectral remote sensing for invasive species detection and mapping. Int. Geosci. Remote Sens. Symp 3:1658–1660.Google Scholar