Spectral and physiological uniqueness of perennial pepperweed (Lepidium latifolium)

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. Nomenclature: Perennial pepperweed, Lepidium latifolium L. LEPLA.

[1]  James A. Young,et al.  Lepidium latifolium: plant nutrient competition-soil interactions , 2002, Biology and Fertility of Soils.

[2]  P. Vitousek,et al.  Remote analysis of biological invasion and biogeochemical change. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[3]  J. H. Everitt,et al.  Remote Sensing of Chinese Tamarisk (Tamarix chinensis) and Associated Vegetation , 1990, Weed Science.

[4]  Susan L. Ustin,et al.  Detection of interannual vegetation responses to climatic variability using AVIRIS data in a coastal savanna in California , 2001, IEEE Trans. Geosci. Remote. Sens..

[5]  Jessica A. Faust,et al.  Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .

[6]  Thomas Schmid,et al.  Multisensor approach to determine changes of wetland characteristics in semiarid environments (central Spain) , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Megan M. Lewis,et al.  Discrimination of arid vegetation with airborne multispectral scanner hyperspectral imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[8]  C. Daughtry,et al.  Plant Litter and Soil Reflectance , 2000 .

[9]  Dengsheng Lu,et al.  Multitemporal spectral mixture analysis for Amazonian land-cover change detection , 2004 .

[10]  J. C. Price.  How unique are spectral signatures , 1994 .

[11]  I. Filella,et al.  Reflectance assessment of mite effects on apple trees , 1995 .

[12]  M. Cochrane.  Using vegetation reflectance variability for species level classification of hyperspectral data , 2000 .

[13]  Francine Heisel,et al.  Detection of vegetation stress via a new high resolution fluorescence imaging system , 1996 .

[14]  James A. Young,et al.  The Ecology and Control of Perennial Pepperweed (Lepidium latifolium L.) , 1998, Weed Technology.

[15]  Milo E. Richmond,et al.  Field Determination of Optimal Dates for the Discrimination of Invasive Wetland Plant Species Using Derivative Spectral Analysis , 2005 .

[16]  Dar A. Roberts,et al.  Modeling spatially distributed ecosystem flux of boreal forest using hyperspectral indices from AVIRIS imagery , 2001 .

[17]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[18]  Margaret E. Gardner,et al.  Spectrometry for urban area remote sensing—Development and analysis of a spectral library from 350 to 2400 nm , 2004 .

[19]  R. Jenssen,et al.  1 THE HYMAP TM AIRBORNE HYPERSPECTRAL SENSOR : THE SYSTEM , CALIBRATION AND PERFORMANCE , 1998 .

[20]  S. Ustin,et al.  Mapping nonnative plants using hyperspectral imagery , 2003 .

[21]  E. Hunt,et al.  Weed Invasion Susceptibility Prediction (WISP) Model for Use with Geographic Information Systems , 2004 .

[22]  Raymond F. Kokaly,et al.  Comparison of AVIRIS and EO-1 Hyperion for Classification and Mapping of Invasive Leafy Spurge in Theodore Roosevelt National , 2002 .

[23]  Robert O. Green,et al.  Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS , 1997 .

[24]  Francisca López-Granados,et al.  Spectral discrimination of Ridolfia segetum and sunflower as affected by phenological stage , 2006 .

[25]  J. Peñuelas,et al.  Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals , 2002 .

[26]  D. Roberts,et al.  A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery , 2002 .

[27]  James A. Young,et al.  INFLUENCE OF THE EXOTIC INVASIVE CRUCIFER, LEPIDIUM LATIFOLIUM, ON SOIL PROPERTIES AND ELEMENTAL CYCLING , 2002 .

[28]  D. Roberts,et al.  Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments , 2001 .

[29]  S. Ustin,et al.  A Comparison of Spatial and Spectral Image Resolution for Mapping Invasive Plants in Coastal California , 2007, Environmental management.

[30]  A. Skidmore,et al.  Spectral discrimination of vegetation types in a coastal wetland , 2003 .

[31]  S. Elvira,et al.  A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants , 1992 .

[32]  G. H. Rosenfield,et al.  A coefficient of agreement as a measure of thematic classification accuracy. , 1986 .

[33]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[34]  D. Simberloff,et al.  BIOTIC INVASIONS: CAUSES, EPIDEMIOLOGY, GLOBAL CONSEQUENCES, AND CONTROL , 2000 .

[35]  G. De’ath,et al.  CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS , 2000 .

[36]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[37]  L. Mehrhoff.  Lepidium latifolium L. , 2001 .

[38]  J. Peñuelas,et al.  Estimation of plant water concentration by the reflectance Water Index WI (R900/R970) , 1997 .

[39]  Dar A. Roberts,et al.  Mapping Canadian boreal forest vegetation using pigment and water absorption features derived from the AVIRIS sensor , 2001 .

[40]  D. Lobell,et al.  Quantifying vegetation change in semiarid environments: precision and accuracy of spectral mixture analysis and the normalized difference vegetation index. , 2000 .