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Spectral and physiological uniqueness of perennial pepperweed (Lepidium latifolium)

Published online by Cambridge University Press:  20 January 2017

Margaret E. Andrew  and
Susan L. Ustin
Susan L. Ustin
Affiliation:
California Space Institute Center of Excellence, One Shields Avenue, The Barn, University of California, Davis, CA 95616
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Abstract

Perennial pepperweed is an aggressive, exotic weed invading wetland and riparian areas in California, including the San Francisco Bay/Sacramento–San Joaquin Delta Estuary. Effective management will require detailed and accurate maps of its distribution. Remote sensing technologies offer the capability to map weed species over broad areas and with rapid return intervals. As a first step in assessing the potential to map perennial pepperweed with hyperspectral remote sensing data, this study determined its spectral uniqueness relative to co-occurring species. Spectral measurements were conducted during summer drought conditions in the Sacramento–San Joaquin Delta region. Reflectance spectra of perennial pepperweed and seven co-occurring species were collected with a portable spectrometer. Nineteen physiological indexes were calculated from the reflectance data. Physiological indexes are sensitive to narrow spectral features and encapsulate reflectance information in ecologically relevant ways. Classification trees generated from these indexes were able to discriminate both flowering and fruiting perennial pepperweed from co-occurring species with high levels of cross-validated accuracy when using the original spectrometer data and also when this data set was resampled to simulate the spectral resolution of two widely used airborne hyperspectral imagers. Perennial pepperweed's characteristic white flowers are the major component of the spectral uniqueness of this species. Phenological state influenced reflectance spectra more strongly than variation in intraseasonal maturity. Field spectrometer spectra were qualitatively and quantitatively similar to perennial pepperweed spectra extracted from airborne image data. These results suggest that hyperspectral remote sensing will be a powerful tool for the mapping and monitoring of perennial pepperweed. Future work will extend these analyses to image data encompassing the San Francisco Bay/Sacramento–San Joaquin Delta region.

Keywords

Perennial pepperweed, Lepidium latifolium L. LEPLAAirborne Visible/Infrared Imaging Spectrometer (AVIRIS)classification and regression trees (CART)HyMaphyperspectralphysiological indexesremote sensingspectral indexes
Type
Research Article
Information
Weed Science , Volume 54 , Issue 6 , December 2006 , pp. 1051 - 1062
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Asner, G. P. and Vitousek, P. M. 2005. Remote analysis of biological invasion and biogeochemical change. Proc. Natl. Acad. Sci. U S A. 102:4383–4386.CrossRefGoogle ScholarPubMed
Barnes, J. D., Balaguer, L., Manrique, E., Elvira, S., and Davison, A. W. 1992. A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants. Environ. Exp. Bot. 32:85–100.CrossRefGoogle Scholar
Blank, R. R., Qualls, R. G., and Young, J. A. 2002. Lepidium latifolium: plant nutrient competition-soil interactions. Biol. Fertil. Soils. 35:458–464.Google Scholar
Blank, R. R. and Young, J. A. 2002. Influence of the exotic invasive crucifer, Lepidium latifolium, on soil properties and elemental cycling. Soil Sci. 167:821–829.CrossRefGoogle Scholar
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. 1998. Classification and Regression Trees. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar
Cochrane, M. A. 2000. Using vegetation reflectance variability for species level classification of hyperspectral data. Int. J. Remote Sens. 21:2075–2087.CrossRefGoogle Scholar
Cocks, T., Jennsen, R., Stewart, A., Wilson, I., and Shields, T. 1998. The HyMap airborne hyperspectral sensor: the system, calibration and performance. Pages 37–42 in Schaepman, M., Schlapfer, D., and Itten, F. I. eds. Proceedings of the 1st EARSEL Workshop on Imaging Spectroscopy, Zurich, Switzerland. Paris: European Association of Remote Sensing Laboratories.Google Scholar
De'ath, G. and Fabricius, K. E. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology. 81:3178–3192.Google Scholar
Drewitz, J. 2000. Conium maculatum L. Pages 120–123 in Bossard, C. C., Randall, J. M., and Hoshovsky, M. C. eds. Invasive Plants of California's Wildlands. Berkeley, CA: University of California.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. 73:87–102.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
Fuentes, D. A., Gamon, J. A., Qiu, H-L., Sims, D. A., and Roberts, D. A. 2001. Mapping Canadian boreal forest vegetation using pigment and water absorption features derived from the AVIRIS sensor. J. Geophys. Res. 106:33565–33577.Google Scholar
Gao, B-C. 1996. NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58:257–266.CrossRefGoogle 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. 39:1480–1490.Google Scholar
Gillham, J. H., Hild, A. L., Johnson, J. H., Hunt, E. R., and Whitson, T. D. 2004. Weed invasion susceptibility prediction (WISP) model for use with geographic information systems. Arid Land Res. Manag. 18:1–12.Google Scholar
Green, R. O., Eastwood, M. L., and Sarture, C. M. et al. 1998. Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sens. Environ. 65:227–248.Google Scholar
Herold, M., Roberts, D. A., Gardner, M. E., and Dennison, P. E. 2004. Spectrometry for urban area remote sensing—development and analysis of a spectral library from 350 to 2,400 nm. Remote Sens. Environ. 91:304–319.CrossRefGoogle Scholar
Howald, A. 2000. Lepidium latifolium L. Pages 222–227 in Bossard, C. C., Randall, J. M., and Hoshovsky, M. C. eds. Invasive Plants of California's Wildlands. Berkeley, CA: University of California.Google Scholar
Jacquemoud, S. and Baret, F. 1990. PROSPECT: A model of leaf optical properties spectra. Remote Sens. Environ. 34:75–91.Google Scholar
Laba, M., Tsai, F., Ogurcak, D., Smith, S., and Richmond, M. E. 2005. Field determination of optimal dates for the discrimination of invasive wetland plant species using derivative spectral analysis. Photogramm. Eng. Remote Sens. 71:603–611.Google Scholar
Lewis, M., Jooste, V., and de Gasparis, A. A. 2001. Discrimination of arid vegetation with airborne multispectral scanner hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 39:1471–1479.Google Scholar
Lichtenthaler, H. K., Lang, M., Sowinska, M., Heisel, F., and Miehe, J. A. 1996. Detection of vegetation stress via a new high resolution fluorescence imaging system. J. Plant Physiol. 148:599–612.Google Scholar
Lu, D., Batistella, M., and Moran, E. 2004. Multitemporal spectral mixture analysis for Amazonian land-cover change detection. Can. J. Remote Sens. 30:87–100.CrossRefGoogle Scholar
Mack, R. N., Simberloff, D., Lonsdale, W. M., Evans, H., Clout, M., and Bazzaz, F. A. 2000. Biotic invasions: causes, epidemiology, global consequences, and control. Ecol. Appl. 10:689–710.Google Scholar
Nagler, P. L., Daughtry, C. S. T., and Goward, S. N. 2000. Plant litter and soil reflectance. Remote Sens. Environ. 71:207–215.Google Scholar
Okin, G. S., Roberts, D. A., Murray, B., and Okin, W. J. 2001. Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sens. Environ. 77:212–225.Google Scholar
Peña-Barragan, J. M., Lopez-Granados, F., Jurado-Exposito, M., and Garcia-Torres, L. 2006. Spectral discrimination of Ridolfia segetum and sunflower as affected by phenological stage. Weed Res. 46:10–21.Google Scholar
Peñuelas, J., Baret, F., and Filella, I. 1995a. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica. 31:221–230.Google Scholar
Peñuelas, J., Filella, I., Lloret, P., Muñoz, F., and Vilajeliu, M. 1995b. Reflectance assessment of mite effects on apple trees. Int. J. Remote Sens. 16:2727–2733.Google Scholar
Peñuelas, J., Pinol, J., Ogaya, R., and Filella, I. 1997. Estimation of plant water concentration by the reflectance Water Index WI (R900/R970). Int. J. Remote Sens. 18:2869–2875.Google Scholar
Price, J. C. 1994. How unique are spectral signatures? Remote Sens. Environ. 49:181–186.Google Scholar
Rahman, A. F., Gamon, J. A., Fuentes, D. A., Roberts, D. A., and Prentiss, D. 2001. Modeling spatially distributed ecosystem flux of boreal forest using hyperspectral indices from AVIRIS imagery. J. Geophys. Res. 106:33579–33591.Google Scholar
Roberts, D. A., Batista, G. T., Pereira, J. L. G., Waller, E. K., and Nelson, B. W. 1998. Change identification using multitemporal spectral mixture analysis: applications in Eastern Amazonia. Pages 137–161 in Lunetta, R. S. and Elvidge, C. D. eds. Remote Sensing Change Detection: Environmental Monitoring Methods and Applications. Chelsea, MI: Ann Arbor.Google Scholar
Roberts, D. A., Green, R. O., and Adams, J. B. 1997. Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS. Remote Sens. Environ. 62:223–240.Google Scholar
Rogan, J., Franklin, J., and Roberts, D. A. 2002. A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery. Remote Sens. Environ. 80:143–156.Google Scholar
Root, R., Ustin, S., and Zarco-Tejada, P. et al. 2002. Comparison of AVIRIS and EO-1 Hyperion for classification and mapping of invasive leafy spurge in Theodore Roosevelt National Park. Proceedings of the 11th Earth Science Airborne Workshop. Pasadena, CA: Jet Propulsion Laboratory. (http:popo.jlp.nasa.gov/pub/docs/workshops/02_docs/toc.html).Google Scholar
Rosenfield, G. H. and Fitzpatrick-Lins, K. 1986. A coefficient of agreement as a measure of thematic classification accuracy. Photogramm. Eng. Remote Sens. 52:223–227.Google Scholar
Schmid, T., Koch, M., and Gumuzzio, J. 2005. Multisensor approach to determine changes of wetland characteristics in semiarid environments (Central Spain). IEEE Trans. Geosci. Remote Sens. 43:2516–2525.Google Scholar
Schmidt, K. S. and Skidmore, A. K. 2003. Spectral discrimination of vegetation types in a coastal wetland. Remote Sens. Environ. 85:92–108.Google Scholar
Serrano, L., Peñuelas, J., and Ustin, S. L. 2002. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sens. Environ. 81:355–364.Google Scholar
Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8:127–150.Google Scholar
Underwood, E., Ustin, S., and DiPietro, D. 2003. Mapping nonnative plants using hyperspectral imagery. Remote Sens. Environ. 86:150–161.Google Scholar
Underwood, E., Ustin, S. L., and Ramirez, C. M. 2006. A comparison of spatial and spectral image resolution for mapping invasive plants in coastal California. Environ. Manag. In press.Google Scholar
Ustin, S. L., Rajapakse, S. S., and Khanna, S. et al. 2005. Mapping Invasive Plant Species in the Sacramento–San Joaquin Delta Region Using Hyperspectral Imagery: Report Submitted to California Department of Boating and Waterways. Davis, CA: University of California.Google Scholar
Williamson, M. 1999. Invasions. Ecography. 22:5–12.Google Scholar
Young, J. A., Palmquist, D. E., and Blank, R. R. 1998. The ecology and control of perennial pepperweed (Lepidium latifolium L). Weed Technol. 12:402–405.CrossRefGoogle Scholar
Young, J. A., Palmquist, D. E., and Wotring, S. O. 1997. The invasive nature of Lepidium latifolium: a review. Pages 59–68 in Brock, J. H., Wade, M., Pysek, P., and Green, D. eds. Plant Invasions: Studies from North America and Europe. Leiden, The Netherlands: Backhuys.Google Scholar
Zarco-Tejada, P. J. 1998. Optical Indices as Bioindicators of Forest Sustainability: Research Evaluation Course. Toronto, Canada: York University.Google Scholar