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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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References

Bourchier, R, Hansen, R, Lym, R, Norton, A, Olson, D, Bell Randall, C, Schwarzländer, M, Skinner, L (2006) Biology and Biological Control of Leafy Spurge. Morgantown, VA: U.S. Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team. 125 pGoogle Scholar
Carter, GA, Lucas, KL, Blossom, GA, Lassitter, CL, Holiday, DM, Mooneyhan, DS, Fastring, DR, Holcombe, TR, Griffith, JA (2009) Remote sensing and mapping of tamarisk along the Colorado River, USA: a comparative use of summer-acquired Hyperion, Thematic Mapper and QuickBird data. Remote Sens 1:318–329CrossRefGoogle Scholar
de Castro, AI, López-Granados, F, Jurado-Expósito, M (2013) Broad-scale cruciferous weed patch classification in winter wheat using QuickBird imagery for in-season site-specific control. Precis Agric 14:392–413CrossRefGoogle Scholar
Garcia-Ruiz, FJ, Wulfsohn, D, Rasmussen, J (2015) Sugar beet (Beta vulgaris L.) and thistle (Cirsium arvensis L.) discrimination based on field spectral data. Biosyst Eng 139:1–15CrossRefGoogle Scholar
Girma, K, Mosali, J, Raun, WR, Freeman, KW, Martin, KL, Solie, JB, Stone, ML (2005) Identification of optical spectral signatures for detecting cheat and ryegrass in winter wheat. Crop Sci 45:477–485CrossRefGoogle Scholar
Glenn, NF, Mundt, JT, Weber, KT, Prather, TS, Lass, LW, Pettingill, J (2005) Hyperspectral data processing for repeat detection of small infestations of leafy spurge. Remote Sens Environ 95:399–412CrossRefGoogle Scholar
Hung, C, Xu, Z, Sukkarieh, S (2014) Feature learning based approach for weed classification using high resolution aerial images from a digital camera mounted on a UAV. Remote Sens 6:12037–12054CrossRefGoogle Scholar
Hunt, ER, Daughtry, CST, Kim, MS, Parker Williams, AE (2007) Using canopy reflectance models and spectral angles to assess potential of remote sensing to detect invasive weeds. J Appl Remote Sens 1:013506CrossRefGoogle Scholar
Hunt, ER, Gillham, JH, Daughtry, CST (2010) Improving potential geographic distribution models for invasive plants by remote sensing. Rangel Ecol Manag 63:505–513CrossRefGoogle Scholar
Hunt, ER, Parker Williams, AE (2006) Detection of flowering leafy spurge with satellite multispectral imagery. Rangel Ecol Manag 59:494–499CrossRefGoogle Scholar
Kloppenburg, C (2014) Detecting Leafy Spurge in Native Grassland Using Hyperspectral Image Analysis. MS thesis. Lethbridge, AB, Canada: University of Lethbridge. 93 pGoogle Scholar
Knezevic, SZ, Smith, D, Kulm, R, Doty, D, Kinkaid, D, Goodrich, M, Stolcpart, R (2004) Purple loosestrife (Lythrum salicaria) control with herbicides: single-year application. Weed Technol 18:1255–1260CrossRefGoogle Scholar
Laba, M, Blair, B, Downs, R, Monger, B, Philpot, W, Smith, S, Sullivan, P, Baveye, PC (2010) Use of textural measurements to map invasive wetland plants in the Hudson River National Estuarine Research Reserve with IKONOS satellite imagery. Remote Sens Environ 114:876–886CrossRefGoogle Scholar
Laba, M, Tsai, F, Ogurcak, D, Smith, S, Richmond, ME (2005) Field determination of optimal dates for the discrimination of invasive wetland plant species using derivative spectral analysis. Photogramm Eng Remote Sensing 71:603–611CrossRefGoogle Scholar
Lym, RG (1998) The biology and integrated management of leafy spurge (Euphorbia esula) on North Dakota rangeland. Weed Technol 12:367–373CrossRefGoogle Scholar
Mafanya, M, Tsele, P, Botai, J, Manyama, P, Swart, B, Monate, T (2017) Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study. ISPRS J Photogramm Remote Sens 129:1–11CrossRefGoogle Scholar
Martens, H, Martens, M (2000) Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR). Food Qual Prefer 11:5–16CrossRefGoogle Scholar
Mirik, M, Ansley, RJ, Steddom, K, Jones, DC, Rush, CM, Michels, GJ, Elliott, NC (2013) Remote distinction of a noxious weed (musk thistle: Carduus nutans) using airborne hyperspectral imagery and the support vector machine classifier. Remote Sens 5:612–630CrossRefGoogle Scholar
Mitchell, JJ, Glenn, NF (2009) Leafy spurge (Euphorbia esula) classification performance using hyperspectral and multispectral sensors. Rangel Ecol Manag 62:16–27CrossRefGoogle Scholar
Mullin, BH (1998) The biology and management of purple loosestrife (Lythrum salicaria). Weed Technol 12:397–401CrossRefGoogle Scholar
Narumalani, S, Mishra, DR, Wilson, R, Reece, P, Kohler, A (2009) Detecting and mapping four invasive species along the floodplain of North Platte River, Nebraska. Weed Technol 23:99–107CrossRefGoogle Scholar
North Dakota Department of Agriculture (2017) Weed Survey Report. www.agdepartment.vision-technology.com/weedsurvey/report.asp. Accessed: December 9, 2018Google Scholar
Noujdina, NV, Ustin, SL (2008) Mapping downy brome (Bromus tectorum) using multidate AVIRIS data. Weed Sci 56:173–179CrossRefGoogle Scholar
O’Neill, M, Ustin, SL, Hager, S, Root, R (2000) Mapping the distribution of leafy spurge at Theodore Roosevelt National Park using AVIRIS. Pages 339–348 in Proceedings of the Ninth JPL Airborne Earth Science Workshop. Pasadena, CA: NASA Jet Propulsion LaboratoryGoogle Scholar
Parker Williams, A, Hunt, ER (2002) Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering. Remote Sens Environ 82:446–456CrossRefGoogle Scholar
Peña, JM, Torres-Sánchez, J, de Castro, AI, Kelly, M, López-Granados, F (2013) Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS ONE 8:e77151CrossRefGoogle ScholarPubMed
Pérez-Ortiz, M, Peña, JM, Gutiérrez, PA, Torres-Sánchez, J, Hervás-Martínez, C, López-Granados, F (2015) A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Appl Soft Comput 37:533–544CrossRefGoogle Scholar
Savitzky, A, Golay, MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627–1639CrossRefGoogle Scholar
Shapira, U, Herrmann, I, Karnieli, A, Bonfil, JD (2010) Weeds detection by ground-level hyperspectral data. Theory Pract 38:27–33Google Scholar
Shapira, U, Herrmann, I, Karnieli, A, Bonfil, DJ (2013) Field spectroscopy for weed detection in wheat and chickpea fields. Int J Remote Sens 34:6094–6108CrossRefGoogle Scholar
Shirzadifar, A, Bajwa, S, Mireei, SA, Howatt, K, Nowatzki, J (2018) Weed species discrimination based on SIMCA analysis of plant canopy spectral data. Biosyst Eng 171:143–154CrossRefGoogle Scholar
Swain, S, Narumalani, S, Mishra, DR (2011) Monitoring invasive species: detecting purple loosestrife and evaluating biocontrol along the Niobrara River, Nebraska. GIsci Remote Sens 48:225–244CrossRefGoogle Scholar
Torres-Sánchez, J, López-Granados, F, de Castro, AI, Peña-Barragán, JM (2013) Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS ONE 8:e58210CrossRefGoogle ScholarPubMed
Ustin, SL, Santos, MJ (2010) Spectral identification of native and non-native plant species. Pages 1–17 in Proceedings of ASD and IEEE GRS: Art, Science and Applications of Reflectance Spectroscopy Symposium. Volume 2. Boulder, CO: IEEEGoogle Scholar
Wilson, LM, Schwarzlaender, M, Blossey, B, Bell Randall, C (2004) Biology and Biological Control of Purple Loosestrife. Morgantown, VA: U.S. Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team. 78 pGoogle Scholar