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Discrimination of leafy spurge (Euphorbia esula) and purple loosestrife (Lythrum salicaria) based on field spectral data

Published online by Cambridge University Press:  25 September 2019

Kathryn M. Hooge Hom Open the ORCID record for Kathryn M. Hooge Hom [Opens in a new window] ,
Sreekala G. Bajwa ,
Rodney G. Lym  and
John F. Nowatzki
Kathryn M. Hooge Hom*
Affiliation:
Graduate Student, Natural Resources Management Program, North Dakota State University, Fargo, ND, USA
Sreekala G. Bajwa
Affiliation:
Department Chair, Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND, USA; current: Montana Agricultural Experiment Station and College of Agriculture, Montana State University, Bozeman, MT, USA
Rodney G. Lym
Affiliation:
Professor, Plant Sciences, North Dakota State University, Fargo, ND, USA
John F. Nowatzki
Affiliation:
Agricultural Machines Specialist, Agricultural and Biosystems Engineering Department, North Dakota State University, Fargo, ND, USA
*
Author for correspondence: Kathryn M. Hooge Hom, Natural Resources Management Program, North Dakota State University, Fargo, ND 58102. (Email: k.hooge@ndsu.edu)
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Abstract

Leafy spurge (Euphorbia esula L.) and purple loosestrife (Lythrum salicaria L.) are invasive weeds that displace native vegetation. Herbicides are often applied to these weeds during flowering, so it would be ideal to identify them early in the season, possibly by the leaves. This paper evaluates the spectral separability of the inflorescences and leaves of these plants from surrounding vegetation. Leafy spurge, purple loosestrife, and surrounding vegetation were collected from sites in southeastern North Dakota and subjected to spectral analysis. Partial least-squares discriminant analysis (PLS-DA) was used to separate the spectral signatures of these weeds in the visible and near-infrared wavelengths. Using PLS-DA, the weeds were discriminated from their surroundings with R2 values of 0.86 to 0.92. Analysis of the data indicated that the bands contributing the most to each model were in the red and red-edge spectral regions. Identifying these weeds by the leaves allows them to be mapped earlier in the season, providing more time for herbicide application planning. The spectral signatures identified in this proof of concept study are the first step before using ultra–high resolution aerial imagery to classify and identify leafy spurge and purple loosestrife.

Keywords

Prashant Jha, Iowa State UniversityLeafy spurge, Euphorbia esula L.purple loosestrife, Lythrum salicaria L.Classificationpartial least-squares discriminant analysisspectral analysis
Type
Research Article
Information
Weed Technology , Volume 34 , Issue 2 , April 2020 , pp. 250 - 259
Copyright
© Weed Science Society of America, 2019

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