Hostname: page-component-76fb5796d-2lccl Total loading time: 0 Render date: 2024-04-26T23:44:42.729Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  13 January 2020

Xiaohui Yang Open the ORCID record for Xiaohui Yang [Opens in a new window] ,
Anne M. Smith ,
Robert S. Bourchier ,
Kim Hodge  and
Dustin Ostrander
Xiaohui Yang
Affiliation:
Data Analyst and Research Scientists, Agriculture and Agri-Food Canada, Lethbridge, Alberta, Canada
Anne M. Smith*
Affiliation:
Data Analyst and Research Scientists, Agriculture and Agri-Food Canada, Lethbridge, Alberta, Canada
Robert S. Bourchier
Affiliation:
Data Analyst and Research Scientists, Agriculture and Agri-Food Canada, Lethbridge, Alberta, Canada
Kim Hodge
Affiliation:
Research Geographer, Agriculture and Agri-Food Canada, Regina, Saskatchewan, Canada
Dustin Ostrander
Affiliation:
Biologist, Agriculture and Agri-Food Canada, Swift Current, Saskatchewan, Canada
*
Author for correspondence: Anne M. Smith, Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, Lethbridge, Alberta, CanadaT1J 4B1. Email: anne.smith@canada.ca
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

Leafy spurgemixture tuned matched filterhue, intensity, and saturationspatial resolutionfractional coverstem densityLeafy spurge, Euphorbia esula L. EPHES
Type
Research Article
Information
Weed Technology , Volume 34 , Issue 4 , August 2020 , pp. 575 - 588
Copyright
© Her Majesty the Queen in Right of Canada as represented by the Minister of Agriculture and Agri-Food Canada 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Associate Editor: Prashant Jha, Iowa State University

References

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 11:81–89CrossRefGoogle Scholar
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 39:5099–5118CrossRefGoogle Scholar
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-2005-07Google Scholar
Bourchier, RS, Van Hezewijk, BH (2013) Euphorbia esula (L.) (leafy spurge) Euphorbiaceae. Pages 315–320in Mason, PG, Gillespie, DR, eds. Biological Control Programmes in Canada 2001-2012. Wallingford, UK: CABI Publishing 10.1079/9781780642574.0315CrossRefGoogle Scholar
Casady, GM, Hanley, RS, Seelan, SK (2005) Detection of leafy spurge (Euphorbia esula) using multidate high-resolution satellite Imagery. Weed Technol 19:462–46710.1614/WT-03-182R1CrossRefGoogle Scholar
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/W4:83–9010.5194/isprsarchives-XL-1-W4-83-2015CrossRefGoogle Scholar
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 9:599–60910.1017/S0890037X00023915CrossRefGoogle 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
Gunn, M (2016) Formula for determining resolution (cm/pix)? http://diydrones.com/forum/topics/formula-for-determining-resolution-cm-pix. Accessed: June 10, 2019Google Scholar
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 38:2083–210510.1080/01431161.2016.1264030CrossRefGoogle 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 6:12037–1205410.3390/rs61212037CrossRefGoogle Scholar
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 1:1–1910.1117/1.2536275CrossRefGoogle Scholar
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 53:305–30810.2307/4003437CrossRefGoogle Scholar
Leistritz, FL, Bangsund, DA, Hodur, NM (2004) Assessing the economic impact of invasive weeds: the case of leafy spurge (Euphorbia esula). Weed Technol 18:1392–139510.1614/0890-037X(2004)018[1392:ATEIOI]2.0.CO;2CrossRefGoogle Scholar
Lin, LK (1992) Assay validation using the concordance correlation coefficient. Biometrics 48:599–604CrossRefGoogle Scholar
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 128:73–8510.1016/j.isprsjprs.2017.03.011CrossRefGoogle Scholar
Lym, RG (1998) The biology and integrated management of leafy spurge (Euphorbia esula) on North Dakota rangeland. Weed Technol 12:367–37310.1017/S0890037X00043955CrossRefGoogle Scholar
Lym, RG, Nelson, JA (2002) Integration of Aphthona spp. flea beetles and herbicides for leafy spurge (Euphorbia esula) control. Weed Sci 50:812–81910.1614/0043-1745(2002)050[0812:IOASFB]2.0.CO;2CrossRefGoogle Scholar
Mangold, JM, Fuller, KB, Davis, SC, Rinella, MJ (2018) The economic cost of noxious weeds on Montana grazing lands. Invas Plant Sci Mana 11:96–10010.1017/inp.2018.10CrossRefGoogle Scholar
Mitchell, JJ, Glenn, NF (2009) Subpixel abundance estimates in mixture-tuned matched filtering classification of leafy spurge (Euphorbia esula L.). Int J Remote Sens 30:6099–6119CrossRefGoogle Scholar
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 59:500–50610.2111/06-027R1.1CrossRefGoogle Scholar
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 38:2177–219810.1080/01431161.2016.1275059CrossRefGoogle Scholar
Mundt, JT, Streutker, DR, Glenn, NF (2007) Partial unmixing of hyperspectral imagery: theory and methods. Pages 46–57in Proceedings of the American Society of Photogrammetry and Remote Sensing. Tampa, Florida: American Society of Photogrammetry and Remote SensingGoogle Scholar
Parker-Williams, AE, Hunt, ER Jr (2002) Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering. Remote Sens Environ 82:446–456CrossRefGoogle Scholar
Parker-Williams, AE, Hunt, ER Jr (2004) Accuracy assessment for detection of leafy spurge with hyperspectral imagery. J Range Manage 57:106–11210.2307/4003961CrossRefGoogle Scholar
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: 5609–562610.3390/s150305609CrossRefGoogle ScholarPubMed
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 pGoogle Scholar
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 34:539–54810.5589/m08-071CrossRefGoogle Scholar
[SSIS] Saskatchewan Soil Information System. http://sksis.usask.ca/#/map. Accessed: June 10, 2019Google Scholar
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 59:507–51110.2111/06-052R1.1CrossRefGoogle Scholar
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/W6:355–361CrossRefGoogle Scholar
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 38:2246–225910.1080/01431161.2016.1252475CrossRefGoogle Scholar
Tang, L, Tian, L, Steward, BL (2000) Color image segmentation with genetic algorithm for in-field weed sensing. Trans ASAE 43:1019–1027CrossRefGoogle Scholar
Tarbell, K, Reid, J (1991) A computer vision system for characterizing corn growth and development. Trans ASAE 34:2245–2255CrossRefGoogle Scholar
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 10:180–190CrossRefGoogle 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 8:1–1510.1371/journal.pone.0058210CrossRefGoogle ScholarPubMed