Estimating yellow starthistle (Centaurea solstitialis) leaf area index and aboveground biomass with the use of hyperspectral data

Shaokui Ge, Ming Xu, Gerald L. Anderson, Raymond I. Carruthers

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)671-678
Number of pages8
JournalWeed Science
Issue number6
StatePublished - Nov 2007

All Science Journal Classification (ASJC) codes

  • Agronomy and Crop Science
  • Plant Science


  • Airborne hyperspectral data
  • Biophysical estimation
  • Hyperspectral remote sensing
  • Invasive species
  • Vegetation index


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