Estimation of yellow starthistle abundance through CASI-2 hyperspectral imagery using linear spectral mixture models

The invasive weed yellow starthistle (Centaurea solstitialis) has infested between 4 and 6 million hectares in California. It often forms dense infestations and rapidly depletes soil moisture, preventing the establishment of other species. Precise assessment of its canopy cover, especially low-density abundance in the earlier growing season, is the key to effective management. Compact Airborne Spectrographic Imager 2 (CASI-2) hyperspectral imagery was acquired at the western edge of California's Central Valley grasslands on July 15, 2003. Four linear spectral mixture models (LSMM) were investigated from the original CASI-2 data. Band selections based upon residual analysis and feature extraction (PCA) were further explored to reduce the data dimension. All approaches, except four band-selection unconstrained LSMMs, provide consistent results. The uncertainty of the PCA-based LSMM was estimated through a Monte-Carlo simulation. The maximum standard deviation was approximately 11%. The results suggest that unmixing CASI-2 imagery could be used for estimating and mapping yellow starthistle for larger regional areas.

[1]  D. Lobell,et al.  A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation , 2000 .

[2]  A. Karnieli,et al.  A review of mixture modeling techniques for sub‐pixel land cover estimation , 1996 .

[3]  D. Pimentel,et al.  Environmental and Economic Costs of Nonindigenous Species in the United States , 2000 .

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

[5]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[6]  S. Tompkins,et al.  Optimization of endmembers for spectral mixture analysis , 1997 .

[7]  T. Prather,et al.  Detecting Spotted Knapweed (Centaurea maculosa) with Hyperspectral Remote Sensing Technology1 , 2002, Weed Technology.

[8]  Sarah M. Swope,et al.  Detection of yellow starthistle through band selection and feature extraction from hyperspectral imagery. , 2007 .

[9]  D. Roberts,et al.  Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data , 1993 .

[10]  Susan L. Ustin,et al.  Hyperspectral remote sensing for invasive species detection and mapping , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[11]  Alan R. Gillespie,et al.  Vegetation in deserts. I - A regional measure of abundance from multispectral images. II - Environmental influences on regional abundance , 1990 .

[12]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[13]  Chein-I Chang,et al.  Estimation of subpixel target size for remotely sensed imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Jeff J. Settle,et al.  On the relationship between spectral unmixing and subspace projection , 1996, IEEE Trans. Geosci. Remote. Sens..

[15]  J. Settle,et al.  Linear mixing and the estimation of ground cover proportions , 1993 .

[16]  C. Pieters,et al.  Remote geochemical analysis : elemental and mineralogical composition , 1993 .

[17]  Paul E. Johnson,et al.  A semiempirical method for analysis of the reflectance spectra of binary mineral mixtures , 1983 .

[18]  David B. Lobell,et al.  Subpixel canopy cover estimation of coniferous forests in Oregon using SWIR imaging spectrometry , 2001 .

[19]  D. Wilcove,et al.  QUANTIFYING THREATS TO IMPERILED SPECIES IN THE UNITED STATES , 1998 .

[20]  Gregory Asner,et al.  Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis , 2000, IEEE Trans. Geosci. Remote. Sens..

[21]  J. Ditomaso.  Invasive weeds in rangelands: Species, impacts, and management , 2000, Weed Science.

[22]  John R. Miller,et al.  Forest canopy closure from classification and spectral unmixing of scene components-multisensor evaluation of an open canopy , 1994, IEEE Trans. Geosci. Remote. Sens..

[23]  Martin Brown,et al.  Linear spectral mixture models and support vector machines for remote sensing , 2000, IEEE Trans. Geosci. Remote. Sens..

[24]  Darrel L. Williams.  A comparison of spectral reflectance properties at the needle, branch, and canopy level for selected Conifer species , 1991 .

[25]  Benoit Rivard,et al.  Derivative spectral unmixing of hyperspectral data applied to mixtures of lichen and rock , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Christoph C. Borel,et al.  The radiosity method in optical remote sensing of structured 3-D surfaces , 1991 .

[27]  Yosio Edemir Shimabukuro,et al.  The least-squares mixing models to generate fraction images derived from remote sensing multispectral data , 1991, IEEE Trans. Geosci. Remote. Sens..

[28]  B. Xu,et al.  Oakwood crown closure estimation by unmixing Landsat TM data , 2003 .

[29]  F. J. García-Haro,et al.  Linear spectral mixture modelling to estimate vegetation amount from optical spectral data , 1996 .

[30]  Dar A. Roberts,et al.  Predicted distribution of visible and near-infrared radiant flux above and below a transmittant leaf , 1990 .

[31]  D. Roberts,et al.  Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE , 2003 .

[32]  Carle M. Pieters,et al.  Deconvolution of mineral absorption bands: An improved approach , 1990 .

[33]  Paul E. Johnson,et al.  Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site , 1986 .

[34]  S. M. Jong,et al.  Improving the results of spectral unmixing of Landsat thematic mapper imagery by enhancing the orthogonality of end-members , 2000 .

[35]  Martin Brown,et al.  Support vector machines for optimal classification and spectral unmixing , 1999 .

[36]  John A. Richards,et al.  Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[37]  John F. Mustard,et al.  Spectral unmixing , 2002, IEEE Signal Process. Mag..

[38]  Eric D. Kolaczyk,et al.  Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing , 2003 .

[39]  Peter M. Vitousek,et al.  Biological invasions and ecosystem processes : towards an integration of population biology and ecosystem studies , 1990 .

[40]  Maria Petrou,et al.  Confidence in linear spectral unmixing of single pixels , 1999, IEEE Trans. Geosci. Remote. Sens..

[41]  C. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[42]  D. Dey,et al.  A First Course in Linear Model Theory , 2001 .

[43]  D. Horler,et al.  The red edge of plant leaf reflectance , 1983 .

[44]  S. Gerstl,et al.  Nonlinear spectral mixing models for vegetative and soil surfaces , 1994 .

[45]  Antonio J. Plaza,et al.  A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Robert A. Neville,et al.  Spectral unmixing of hyperspectral imagery for mineral exploration: comparison of results from SFSI and AVIRIS , 2003 .

[47]  N. A. Quarmby,et al.  Linear mixture modelling applied to AVHRR data for crop area estimation , 1992 .

[48]  Charles L. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[49]  Brian Curtiss,et al.  A method for manual endmember selection and spectral unmixing , 1996 .

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

[51]  John B. Adams,et al.  Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon , 1995 .

[52]  Amy E. Parker Williams,et al.  Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering , 2002 .

[53]  Jiang Li,et al.  Correction to "Wavelet-Based Feature Extraction for Improved Endmember Abundance Estimation in Linear Unmixing of Hyperspectral Signals" , 2004 .

[54]  D. A. Stow,et al.  Using multiple image endmember spectral mixture analysis to study chaparral regrowth in southern California , 2003 .

[55]  M. Cochrane.  Linear mixture model classification of burned forests in the Eastern Amazon , 1998 .