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Estimating Yellow Starthistle (Centaurea solstitialis) Leaf Area Index and Aboveground Biomass with the Use of Hyperspectral Data

Published online by Cambridge University Press:  20 January 2017

Shaokui Ge ,
Ming Xu ,
Gerald L. Anderson  and
Raymond I. Carruthers
Shaokui Ge*
Affiliation:
USDA—Agricultural Research Service, Exotic and Invasive Weeds Research Unit, 800 Buchanan St., Albany, CA 94710, and Earth and Planetary Sciences Department, University of California at Santa Cruz, 1156 High Street, Santa Cruz, CA 95064
Ming Xu
Affiliation:
Center for Remote Sensing and Spatial Analysis, Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ 08901
Gerald L. Anderson
Affiliation:
USDA—Agricultural Research Service, Agriculture Research Systems Unit, 1500 N Central Ave. Sidney, MT 59270
Raymond I. Carruthers
Affiliation:
USDA—Agricultural Research Service, Exotic and Invasive Weeds Research Unit, 800 Buchanan St. Albany, CA 94710
*
Corresponding author's E-mail: shaokui@pw.usda.gov or gesk@nature.berkeley.edu
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Abstract

Hyperspectral remote-sensed data were obtained via a Compact Airborne Spectrographic Imager-II (CASI-II) and used to estimate leaf-area index (LAI) and aboveground biomass of a highly invasive weed species, yellow starthistle (YST). In parallel, 34 ground-based field plots were used to measure aboveground biomass and LAI to develop and validate hyperspectral-based models for estimating these measures remotely. Derivatives of individual hyperspectral bands improved the correlations between imaged data and actual on-site measurements. Six derivative-based normalized difference vegetation indices (DNDVI) were developed; three of them were superior to the commonly used normalized difference vegetation index (NDVI) in estimating aboveground biomass of YST, but did not improve estimates of LAI. The locally integrated derivatives-based vegetation indices (LDVI) from adjacent bands within three different spectral regions (the blue, red, and green reflectance ranges) were used to enhance absorption characteristics. Three LDVIs outperformed NDVI in estimating LAI, but not biomass. Multiple regression models were developed to improve the estimation of LAI and aboveground biomass of YST, and explained 75% and 53% of the variance in biomass and LAI, respectively, based on validation assessments with actual ground measurements.

Keywords

Yellow starthistle, Centaurea solstitialis LInvasive specieshyperspectral remote sensingairborne hyperspectral datavegetation indexbiophysical estimation
Type
Special Topics
Information
Weed Science , Volume 55 , Issue 6 , December 2007 , pp. 671 - 678
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Anderson, G. L., Carruthers, R. I., Ge, S., and Gong, P. 2005. Cover: monitoring of invasive Tamarix distribution and effects of biological control with airborne hyperspectral remote sensing. Int. J. Remote Sens. 26:2487–2489.Google Scholar
Ben-Dor, E., Kruse, F. A., and Banin, A. 1994. Comparison of three calibration techniques for utilization of GER 63-channel aircraft scanner data of Makhtesh Ramon, Negev, Israel. Photogram. Eng. Remote Sens. 60:1339–1354.Google Scholar
Ben-Dor, E. and Levin, E. 2000. Determination of surface from raw hyperspectral data without simultaneous ground data measurements, a case study of the GER 63-channel sensor data acquired over Naan, Israel. Int. J. Remote Sens. 10:2053–2074.Google Scholar
Benefield, C. B., DiTomaso, J. M., Kyser, G. B., and Tschohl, A. 2001. Reproductive biology of yellow starthistle (Centaurea solstitialis): maximizing late season control. Weed Sci. 49:38–90.Google Scholar
Blackburn, G. A. 1998. Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens. Environ. 66:273–285.CrossRefGoogle Scholar
Blackburn, G. A. 1999. Relationships between spectral reflectance and pigment concentrations in stacks of deciduous broadleaves. Remote Sens. Environ. 70:224–237.CrossRefGoogle Scholar
Blackburn, G. A. 2002. Remote sensing of forest pigments using airborne imaging spectrometer and LIDAR imagery. Remote Sens. Environ. 82:311–321.CrossRefGoogle Scholar
Blackburn, G. A. and Steele, C. M. 1999. Towards the remote sensing of matorral vegetation physiology—adaptation and acclimation mechanisms. Remote Sens. Environ. 70:278–292.Google Scholar
Bongers, F. 2001. Methods to assess tropical rain forest canopy structure: an overview. Plant Ecol. 153:263–277.Google Scholar
Borge, N. H. and Leblanc, E. 2000. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens. Environ. 76:156–172.Google Scholar
Carruthers, R. I. 2003. Invasive species research in the USDA Agriculture Research Service. Pest Manag. Sci. 59:827–834.Google Scholar
Chappelle, E. W., Kim, M. S., and McMurtrey, J. E. I. 1992. Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sens. Environ. 39:239–247.Google Scholar
Chen, Z., Elvidge, C. D., and Groeneveld, D. P. 1998. Monitoring seasonal dynamics of arid land vegetation using AVIRIS data. Remote Sens. Environ. 65:255–266.Google Scholar
Cohen, W. B., Maiersperger, T. K., Gower, S. T., and Turner, D. P. 2002. An improved strategy for regression of biophysical variables and Landsat ETM+ data. Remote Sens. Environ. 58:1–11.Google Scholar
Collingham, Y. C., Hill, M. O., and Hulme, P. E. 1997. The use of a spatially explicit model to simulate the spread of riparian weeds. Pages 45–52. in Cooper, A., Power, J. eds. Proceedings of the International Association for Landscape Ecology: Species Dispersal and Land Use Processes. Belfast IALE.Google Scholar
Collingham, Y. C., Wadsworth, R. A., Huntley, B., and Hulme, P. E. 2000. Predicting the spatial distribution of alien riparian species; issues of spatial scale. J. Appl. Ecol. 37 (Suppl. 1):13–27.Google Scholar
DiTomaso, J. M. 2000. Invasive weeds in rangelands: species, impacts and management. Weed Sci. 48:255–265.Google Scholar
DiTomaso, J. M., Kyser, G. B., Orloff, S. B., and Enloe, S. F. 2000. Integrated approaches and control option considerations when developing a management strategy for yellow starthistle. Calif. Agric. 54:30–36.CrossRefGoogle Scholar
Elvidge, C. D. and Chen, Z. 1995. Comparison of broad-band and narrow-band red and near-infrared vegetation indexes. Remote Sens. Environ. 54:38–48.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., and Davis, M. R. 1995. Using remote sensing for detecting and mapping noxious plants. Weed Abstr. 44:639–649.Google Scholar
Ge, S., Everitt, J. H., Carruthers, R. I., Gong, P., and Anderson, G. 2006. Hyperspectral characteristics of canopy components and structure for phenological assessment of an invasive weed. Environ. Monit. Assess. 120:109–126.CrossRefGoogle ScholarPubMed
Harrod, R. J. and Taylor, R. J. 1995. Reproduction and pollination biology of Centaurea and Acroptilon species, with emphasis on C. diffusa . Northwest Sci. 69:97–105.Google Scholar
Kalkhan, M. A. and Stohlgren, T. J. 2000. Using multi-scale sampling and spatial cross-correlation to investigate patterns of plant species richness. Environ. Monit. Assess. 64:591–605.Google Scholar
Kerr, J. T. and Ostrovsky, M. 2003. From space to species: ecological applications for remote sensing. Trends Ecol. Evol. 18:299–305.Google Scholar
Lass, L. W., Carson, H. W., and Calihan, R. H. 1996. Detection of yellow starthistle (Centaurea solstitialis) and common St. Johnswort (Hypericum perforatum) with multispectral digital imagery. Weed Technol. 10:466–474.Google Scholar
Lass, L. W., Prather, T. S., Glenn, N. F., Weber, K. T., Mundt, J. T., and Pettingill, J. 2005. 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. Weed Sci. 53:242–251.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
Maddox, D. M. 1981. Introduction, Phenology, and Density of Yellow Starthistle in Coastal, Intercoastal, and Central Valley Situations in California. Washington, DC U.S. Department of Agriculture (USDA) ARS Rep. ARR-W-20. 1–33.Google Scholar
Maddox, D. M., Joley, D. B., Supkoff, D. M., and Mayfield, A. 1996. Pollination biology of yellow starthistle (Centaurea solstitialis) in California. Can. J. Bot. 74:262–267.CrossRefGoogle Scholar
Peñuelas, J., Gammon, J. A., Fredeen, A. L., Merino, J., and Field, C. B. 1994. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens. Environ. 48:135–146.Google Scholar
Schmidtlein, S. 2005. Imaging spectroscopy as a tool for mapping Ellenberg indicator values. J. Appl. Ecol. 42:966–974.Google Scholar
Selvin, S. 1998. Modern applied biostatistical methods: using S-plus. Oxford, UK Oxford University Press. 461.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.CrossRefGoogle Scholar
Sims, D. A. and Gamon, J. A. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and development stages. Remote Sens. Environ. 81:337–354.Google Scholar
Stohlgren, T. J., Falkner, M. B., and Schell, L. D. 1995. A modified-Whittaker nested vegetation sampling method. Vegetatio. 117:113–121.Google Scholar
Strachan, I. B., Pattey, E., and Boisvert, J. B. 2002. Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sens. Environ. 80:213–224.Google Scholar
Thenkabail, P. S., Smith, R. B., and De-Pauw, E. 2000. Hyperspectral vegetation indices for determining agricultural crop characteristics. Remote Sens. Environ. 71:158–182.Google Scholar
Tsai, F. and Philpot, W. 1998. Derivative analysis of hyperspectral data. Remote Sens. Environ. 66:41–51.Google Scholar
Turner, W., Spector, S., Gardiner, N., Fladeland, M., Sterling, E., and Steininger, M. 2003. Remote sensing for biodiversity science and conservation. Trends Ecol. Evol. 18:306–314.Google Scholar
Vane, G. and Goetz, A. F. H. 1993. Terrestrial images spectrometry: current status, future trends. Remote Sens. Environ. 44:117–126.CrossRefGoogle Scholar
Venter, J. H. and Snyman, J. L. J. 1995. A note on the generalized cross-validation criterion in linear model selection. Biometrika. 82:215–219.Google Scholar
Wadsworth, R. A., Collingham, Y. C., Willis, S. G., Huntley, B., and Hulme, P. E. 2000. Simulating the spread and management of alien riparian weeds: are they out of control? J. Appl. Ecol. 37 (Suppl. 1):28–38.Google Scholar
Yoder, B. J. and Pettigrew-Crosby, R. E. 1995. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales. Remote Sens. Environ. 53:199–211.Google Scholar