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ARS Home » Pacific West Area » Albany, California » Western Regional Research Center » Invasive Species and Pollinator Health » Research » Publications at this Location » Publication #204146
Title: Estimating leaf area index and aboveground biomass of an invasive weed (yellow starthistle, Centaurea solstitalis L.) using airborne hyperspectral data

Author
item GE, SHAOKUI - UC SANTA CRUZ
item XU, MING - RUTGERS UNIVERSITY
item Anderson, Gerald
item Carruthers, Raymond

Submitted to: Weed Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/1/2006
Publication Date: 4/16/2007
Citation: Ge, S., Xu, M., Anderson, G.L., Carruthers, R.I. 2007. Estimating leaf area index and aboveground biomass of an invasive weed (yellow starthistle, Centaurea solstitalis L.) using airborne hyperspectral data. Weed Science. 55:671-678.

Interpretive Summary: Yellow starthistle, Centaurea solstitialis, is one of the worst invasive weeds in the state of California, infesting over 22 million acres. It also is a major weed pest in the adjacent states of Oregon, Washington, Idaho and Nevada. It causes losses in forage quality and quantity, reduces livestock production and negatively affects wildlife viability. In severe infestations, it can cause brain lesions in horses that may lead to death. USDA-ARS in cooperation with the California Department of Food and Agriculture and the University of California are working together to develop and implement a variety of integrated management strategies to hep control this pest species. One of the primary needs is to make areawide assessment of yellow starthistle infestation levels which is both time consuming and expensive when conducted using ground crews of monitoring personnel. This study reports on the use of hyperspectral remote sensing that was successfully used to estimate yellow starthistle above ground biomass and leaf area index. New quantitative models link specific reflectance patterns to actual ground based measurements, the results showing reasonable detection capabilities for this technology. With additional research on detection within mixed YST and grass canopies, we believe that this technology will be effective in providing land managers with a new tool to assess YST infestations across entire watershed, thus helping in directing larger-scale strategic approaches to its control.

Technical Abstract: Hyperspectral remote sensed data was obtained via a Compact Airborne Spectrographic Imager (CASI) and used to estimate leaf area index (LAI) and aboveground biomass of a highly invasive weed species, yellow starthistle (Centaurea solstitialis L.). In parallel, 34 ground-based field plots were used to measure above-ground biomass and leaf area index (LAI) to calibrate and validate hyperspectral-based models for estimating these measures of invasive plant cover. It was found that the first-order derivatives calculated from individual hyperspectral bands significantly enhanced the correlations between imaged data and actual on-site measurements. Six derivative-based normalized difference vegetation indices (DNDVI) were proposed, and it was found that 3 of them were superior to the commonly-used normalized difference vegetation index (NDVI) in estimating aboveground biomass of YST. However, for LAI, no improvement in predictability was achieved by using a derivative-based index compared to the commonly used NDVI. The integrative derivatives from adjacent consecutive bands within 3 different spectral regions (i.e. the blue absorption, red absorption, and green reflectance range, respectively) were used to emphasize absorption and reflectance typical to characteristic bands of the CASI images. We found that three of local derivative indices (LDVI) outperformed NDVI in estimating LAI, but were slightly poorer in estimating biomass. Based on the above derivatives and indices, we developed multiple regression models to improve the estimation of LAI and aboveground biomass of YST. The optimal multiple regression models explained 75% and 53% of the variances of biomass and LAI, respectively, based on cross-validation assessments with actual ground measurements.