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Flowering leafy spurge (Euphorbia esula) detection using unmanned aerial vehicle imagery in biological control sites: Impacts of flight height, flight time and detection method

  • Xiaohui Yang (a1), Anne M. Smith (a1), Robert S. Bourchier (a1), Kim Hodge (a2) and Dustin Ostrander (a3)...


Leafy spurge, a noxious perennial weed, is a major threat to the prairie ecosystem in North America. Strategic planning to control leafy spurge requires monitoring its spatial distribution and spread. The ability to detect flowering leafy spurge at two biological control sites in southern Saskatchewan, Canada, was investigated using an unmanned aerial vehicle (UAV) system. Three flight missions were conducted on June 30, 2016, during the leafy spurge flowering period. Imagery was acquired at four flight heights and one or two acquisition times, depending on the site. The sites were reflown on June 28, 2017, to evaluate the change in flowering leafy spurge over time. Mixture tuned matched filtering (MTMF) and hue, intensity, and saturation (HIS) threshold analyses were used to determine flowering leafy spurge cover. Flight height of 30 m was optimal; the strongest relationships between UAV and ground estimates of leafy spurge cover (r2 = 0.76 to 0.90; normalized root mean square error [NRMSE] = 0.10 to 0.13) and stem density (r2 = 0.72 to 0.75) were observed. Detection was not significantly affected by the image analysis method (P > 0.05). Flowering leafy spurge cover estimates were similar using HIS (1.9% to 14.8%) and MTMF (2.1% to 10.3%) and agreed with the ground estimates (using HIS: r2 = 0.64 to 0.93, NRMSE = 0.08 to 0.25; using MTMF: r2 = 0.64 to 0.90, NRMSE = 0.10 to 0.27). The reduction in flowering leafy spurge cover between 2016 and 2017 detected using UAV images and HIS (8.1% at site 1 and 2.7% at site 2) was consistent with that based on ground digital photographs (10% at site 1 and 1.8% at site 2). UAV imagery is a useful tool for accurately detecting flowering leafy spurge and could be used for routine monitoring purposes in a biological control program.


Corresponding author

Author for correspondence: Anne M. Smith, Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, Alberta, CanadaT1J 4B1. Email:


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Associate Editor: Prashant Jha, Iowa State University



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Anderson, GL, Everitt, JH, Escobar, DE, Spencer, NR, Andrascik, RJ (1996) Mapping leafy spurge (Euphorbia escula) infestations using aerial photography and geographic information systems. Geocarto Int 11:8189
Baron, J, Hill, DJ, Elmiligi, H (2018) Combining image processing and machine learning to identify invasive plants in high-resolution images. Int J Remote Sens 39:50995118
Bourchier, RS, Hansen, R, Lym, R, Norton, A, Olsen, D, Randall, CB, Schwarzländer, M, Skinner, DL (2006) Biology and biological control of leafy spurge. Washington, DC: U.S. Department of Agriculture, Forest Health Technology Enterprise Team, Publication FHTET-2005-07
Bourchier, RS, Van Hezewijk, BH (2013) Euphorbia esula (L.) (leafy spurge) Euphorbiaceae. Pages 315320in Mason, PG, Gillespie, DR, eds. Biological Control Programmes in Canada 2001-2012. Wallingford, UK: CABI Publishing
Casady, GM, Hanley, RS, Seelan, SK (2005) Detection of leafy spurge (Euphorbia esula) using multidate high-resolution satellite Imagery. Weed Technol 19:462467
Dvořák, P, Müllerová, J, Bartaloš, T, Bruna, J (2015) Unmanned aerial vehicles for alien plant species detection and monitoring. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci XL-1/W4:8390
Everitt, JH, Anderson, GL, Escobar, DE, Davis, MR, Spencer, NR, Andrascik, RJ (1995) Use of remote sensing for detecting and mapping leafy spurge (Euphorbia esula). Weed Technol 9:599609
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 95:399412
Gunn, M (2016) Formula for determining resolution (cm/pix)? Accessed: June 10, 2019
Hill, DJ, Tarasoff, C, Whitworth, GE, Baron, J, Bradshaw, JL, Church, JS (2017) Utility of unmanned aerial vehicles for mapping invasive plant species: a case study on yellow flag iris (Iris pseudacorus L.). Int J Remote Sens 38:20832105
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 6:1203712054
Hunt, ER Jr, 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 1:119
Kirby, DR, Carlson, RB, Krabbenhoft, KD, Mundal, D, Kirby, MM (2000) Biological control of leafy spurge with introduced flea beetles (Aphthona spp.) J Range Manage 53:305308
Leistritz, FL, Bangsund, DA, Hodur, NM (2004) Assessing the economic impact of invasive weeds: the case of leafy spurge (Euphorbia esula). Weed Technol 18:13921395
Lin, LK (1992) Assay validation using the concordance correlation coefficient. Biometrics 48:599604
Lu, B, He, Y (2017) Species classification using unmanned aerial vehicle (UAV)–acquired high spatial resolution imagery in a heterogeneous grassland. ISPRS J Photogramm Remote Sens 128:7385
Lym, RG (1998) The biology and integrated management of leafy spurge (Euphorbia esula) on North Dakota rangeland. Weed Technol 12:367373
Lym, RG, Nelson, JA (2002) Integration of Aphthona spp. flea beetles and herbicides for leafy spurge (Euphorbia esula) control. Weed Sci 50:812819
Mangold, JM, Fuller, KB, Davis, SC, Rinella, MJ (2018) The economic cost of noxious weeds on Montana grazing lands. Invas Plant Sci Mana 11:96100
Mitchell, JJ, Glenn, NF (2009) Subpixel abundance estimates in mixture-tuned matched filtering classification of leafy spurge (Euphorbia esula L.). Int J Remote Sens 30:60996119
Mladinich, CS, Bustos, MR, Stitt, S, Root, R, Brown, K, Anderson, GL, Hager, S (2006) The use of Landsat 7 enhanced thematic mapper plus for mapping leafy spurge. Rangeland Ecol Manag 59:500506
Müllerová, J, Bartaloš, T, Brüna, J, Dvořák, P, Vítková, M (2017) Unmanned aircraft in nature conservation: an example from plant invasions. Int J Remote Sens 38:21772198
Mundt, JT, Streutker, DR, Glenn, NF (2007) Partial unmixing of hyperspectral imagery: theory and methods. Pages 4657in Proceedings of the American Society of Photogrammetry and Remote Sensing. Tampa, Florida: American Society of Photogrammetry and Remote Sensing
Parker-Williams, AE, Hunt, ER Jr (2002) Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering. Remote Sens Environ 82:446456
Parker-Williams, AE, Hunt, ER Jr (2004) Accuracy assessment for detection of leafy spurge with hyperspectral imagery. J Range Manage 57:106112
Peña, JM, Torres-Sánchez, J, Serrano-Pérez, A, de Castro, AI, López-Granados, F (2015) Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors 15: 56095626
Rempel, K, Eberts, D (2010) Economic impact assessment of leafy spurge in southern Manitoba final report. Brandon, MB, Canada: Rural Development Institute, Brandon University. 16 p
Smith, AM, Bourgeois, G, Teillet, PM, Freemantle, J, Nadeau, C (2008) A comparison of NDVI and MTVI2 for estimating LAI using CHRIS imagery: a case study in wheat. Can J Remote Sens 34:539548
[SSIS] Saskatchewan Soil Information System. Accessed: June 10, 2019
Stitt, S, Root, R, Brown, K, Hager, S, Mladinich, C, Anderson, GL, Dudek, K, Bustos, MR, Kokaly, R (2006) Classification of leafy spurge with Earth Observing-1 Advanced Land Imager. Rangeland Ecol Manag 59:507511
Stöcker, C, Nex, F, Koeva, M, Gerke, M (2017) Quality assessment of combined IMU/GNSS data for direct georeferenced in the context of UAV-based mapping. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci XLII-2/W6:355361
Tamouridou, AA, Alexandridis, TK, Pantazi, XE, Lagopodi, AL, Kashefi, J, Moshou, D (2017) Evaluation of UAV imagery for mapping Silybum marianum weed patches. Int J Remote Sens 38:22462259
Tang, L, Tian, L, Steward, BL (2000) Color image segmentation with genetic algorithm for in-field weed sensing. Trans ASAE 43:10191027
Tarbell, K, Reid, J (1991) A computer vision system for characterizing corn growth and development. Trans ASAE 34:22452255
Thilmony, BM, Lym, RG (2017) Leafy spurge (Euphorbia esula) control and soil seedbank composition fifteen years after release of Aphthona biological control agents. Invas Plant Sci Mana 10:180190
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 8:115


Flowering leafy spurge (Euphorbia esula) detection using unmanned aerial vehicle imagery in biological control sites: Impacts of flight height, flight time and detection method

  • Xiaohui Yang (a1), Anne M. Smith (a1), Robert S. Bourchier (a1), Kim Hodge (a2) and Dustin Ostrander (a3)...


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