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Using remote sensing for identification of late-season grass weed patches in wheat

Published online by Cambridge University Press:  20 January 2017

Francisca López-Granados ,
Montse Jurado-Expósito ,
Jose M. Peña-Barragán  and
Luis García-Torres
Francisca López-Granados
Affiliation:
Institute for Sustainable Agriculture, CSIC, Apartado 4084,14080 Cordoba, Spain
Montse Jurado-Expósito
Affiliation:
Institute for Sustainable Agriculture, CSIC, Apartado 4084,14080 Cordoba, Spain
Jose M. Peña-Barragán
Affiliation:
Institute for Sustainable Agriculture, CSIC, Apartado 4084,14080 Cordoba, Spain
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Abstract

Field research was conducted to determine the potential of hyperspectral and multispectral imagery for late-season discrimination and mapping of grass weed infestations in wheat. Differences in reflectance between weed-free wheat and wild oat, canarygrass, and ryegrass were statistically significant in most 25-nm-wide wavebands in the 400- and 900-nm spectrum, mainly due to their differential maturation. Visible (blue, B; green, G; red, R) and near infrared (NIR) wavebands and five vegetation indices: Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), R/B, NIR-R and (R − G)/(R + G), showed potential for discriminating grass weeds and wheat. The efficiency of these wavebands and indices were studied by using color and color-infrared aerial images taken over three naturally infested fields. In StaCruz, areas infested with wild oat and canarygrass patches were discriminated using the indices R, NIR, and NDVI with overall accuracies (OA) of 0.85 to 0.90. In Florida–West, areas infested with wild oat, canarygrass, and ryegrass were discriminated with OA from 0.85 to 0.89. In Florida–East, for the discrimination of the areas infested with wild oat patches, visible wavebands and several vegetation indices provided OA of 0.87 to 0.96. Estimated grass weed area ranged from 56 to 71%, 43 to 47%, and 69 to 80% of the field in the three locations, respectively, with per-class accuracies from 0.87 to 0.94. NDVI was the most efficient vegetation index, with a highly accurate performance in all locations. Our results suggest that mapping grass weed patches in wheat is feasible with high-resolution satellite imagery or aerial photography acquired 2 to 3 wk before crop senescence.

Keywords

Wild oat (Avena fatua L. AVEFA and A. sterilis L. AVEST)canarygrass (Phalaris paradoxa L. PHAPA and P. minor Retz PHAMI)ryegrass, Lolium rigidum Gaudin LOLRIwheat, Triticum durum L. ‘Mexicali.’Hyperspectralmultispectralsite-specific weed managementvegetation indices
Type
Weed Management
Information
Weed Science , Volume 54 , Issue 2 , April 2006 , pp. 346 - 353
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Anonymous. 2004. Spanish Ministry of Agriculture, Fisheries and Food. www.mapya.es/es/estadistica.Google Scholar
Bajwa, S. G. and Tian, L. F. 2001. Aerial CIR remote sensing for weed density mapping in soybean field. Trans ASAE 44:1965–1974.Google Scholar
Barroso, J. 2004. Tratamientos localizados contra poblaciones de Avena sterilis L. presentes en cultivos de cereal en la región centro de España. Tesis Doctoral, Universidad Politécnica de Madrid. 117 p.Google Scholar
Barroso, J., Fernández-Quintanilla, C., Ruiz, P., Hernáiz, P., and Rew, L. J. 2004. Spatial stability of Avena sterilis ssp. Ludoviciana populations under annual applications of low rates of imazamethabenz. Weed Res 44:178–186.CrossRefGoogle Scholar
Blanco-Moreno, J. M., Chamorro, L., Masalles, R. M., Recasens, J., and Sans, F. X. 2004. Spatial distribution of Lolium rigidum seedling following seed dispersal by combine harvesters. Weed Res 44:375–387.CrossRefGoogle Scholar
Chang, J., Clay, S. A., Clay, D. E., and Kevin, D. 2004. Detecting weed-free and weed-infested areas of a soybean field using near-infrared spectral data. Weed Sci 52:642–646.Google Scholar
Elmore, A. J., Mustard, J. F., Manning, S. J., and Lobell, D. B. 2000. Quantifying vegetation change in semiarid environments: precision and accuracy of spectral mixture analysis and the normalized difference vegetation index. Remote Sensing Environ 73:87–102.Google Scholar
Everitt, J. H. and Deloach, C. J. 1990. Remote sensing of Chinese tamarisk (Tamarix chinensis) and associated vegetation. Weed Sci 38:273–278.Google Scholar
Everitt, J. H., Richerson, J. V., Alaniz, M. A., Escobar, D. E., and Davis, M. R. 1994. Light reflectance characteristics and remote sensing of big bend loco (Astragalus mollissimus var. earlei) and wooton loco (Astragalus wootonii). Weed Sci 42:115–122.Google Scholar
Everitt, J. H. and Villarreal, R. 1987. Detecting huisache (Acacia farnesiana) and Mexican palo-verde (Parkinsonia aculeata) by aerial photography. Weed Sci 35:427–432.Google Scholar
Felton, W. L., Doss, A. F., Nash, P. G., and Mccloy, F. 1991. To selectively spot spray weed. Am. Soc. Agric. Eng. Symp 11:427–432.Google Scholar
Felton, W. L., Alston, C. L., Haigh, C. B. M., Nash, P. G., Wicks, G. A., and Hanson, G. E. 2002. Using reflectance sensors in agronomy and weed science. Weed Technol 16:520–527.Google Scholar
Gibson, K. D., Dirks, R., Medlin, C. R., and Johnston, L. 2004. Detection of weed species in soybean using multispectral digital images. Weed Technol 18:742–749.Google Scholar
Girma, K., Mosali, J., Raun, W. R., Freeman, K. W., Martin, K. L., Solie, J. B., and Stone, M. L. 2005. Identification of optical spectral signature for detecting cheat and ryegrass in winter wheat. Crop Sci 45:477–485.Google Scholar
Hansen, P. M. and Schjoerring, J. K. 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing Environ 86:542–553.Google Scholar
Heisel, T., Andreasen, C., and Ersboll, A. K. 1996. Annual weed distributions can be mapped with kriging. Weed Res 36:325–337.CrossRefGoogle Scholar
Jackson, R. D. and Huete, A. R. 1991. Interpreting vegetation indices. Prev. Vet. Med 11:185–200.Google Scholar
Jordan, C. F. 1969. Derivation of leaf area index from quality of light on the forest floor. Ecology 50:663–666.Google Scholar
Jurado-Expósito, M., López-Granados, F., García-Torres, L., García-Ferrer, A., de la Orden, M. Sánchez, and Atenciano, S. 2003. Multi-species weed spatial variability and site-specific management maps in cultivated sunflower. Weed Sci 51:319–328.Google Scholar
Jurado-Expósito, M., López-Granados, F., González-Andujar, J. L., and García-Torres, L. 2004. Spatial and temporal analysis of Convolvulus arvensis L. populations over four growing seasons. Eur. J. Agron 21:287–296.Google Scholar
Koger, H. K., Shaw, D. R., Watson, C. E., and Reddy, K. N. 2003. Detecting late-season weed infestations in soybean (Glycine max). Weed Technol 17:696–704.CrossRefGoogle Scholar
Koger, H. K., Shaw, D. R., Reddy, K. N., and Bruce, L. M. 2004. Detection of pitted morningglory (Ipomoea lacunosa) with hyperspectral remote sensing. II. Effects of vegetation ground cover and reflectance properties. Weed Sci 52:230–235.CrossRefGoogle Scholar
Lamb, D. W. and Weedon, M. 1998. Evaluating accuracy of mapping weeds in fallow fields using airborne imaging: Panicum effusum in oil-seed rape stubble. Weed Res 38:443–451.Google Scholar
Lamb, D. W., Weedon, M., and Rew, R. J. 1999. Evaluating the accuracy of mapping weeds in seedling crops using airborne digital imagery: Avena spp in seedling triticale. Weed Res 39:481–492.Google Scholar
Landis, J. R. and Kock, G. G. 1977. The measurement of observer agreement for categorical data. Biometrics 33:159–174.Google Scholar
Lass, L. W. and Callihan, R. H. 1997. The effect of phenological stage on detectability of yellow hawkweed (Hieracium pratense) and oxeye daisy (Chysanthemum leucanthemum) with remote multispectral digital imagery. Weed Technol 11:248–256.CrossRefGoogle Scholar
Lass, L. W., Saffi, B., Price, W. J., and Thill, D. C. 2000. Assessing agreement in multispectral images of yellow starthistle (Centaurea solstitialis) with ground truth data using a Bayesian methodology. Weed Technol 14:539–544.Google Scholar
Medlin, C. R., Shaw, D. R., Gerard, P. D., and Lamastus, F. E. 2000. Using remote sensing to detect weed infestations in Glycine max . Weed Sci 48:393–398.Google Scholar
Radhakrishnan, J., Liang, S., Teasdale, J. R., and Shuey, C. J. 2002. Remote sensing of weed canopies. Pages 175–202 in Muttiah, Ranjan S. ed. From Laboratory Spectroscopy to Remotely Sensed Spectra of Terrestrial Ecosystem. Dordrecht: Kluwer.Google Scholar
Rew, L. J. and Cousens, R. D. 2000. Spatial distribution of weeds in arable crops: are current samplings and analytical methods appropriate? Weed Res 41:1–18.Google Scholar
Rouse, J. W., Hass, R. H., Schell, J. A., and Deering, D. W. 1973. Monitoring vegetation systems in the Great Plains with ERTS. Pages 309–317 in Proceedings of the Earth Research Technology Satellite Program, NASA SP-351. Volume 1. Washington, DC: NASA.Google Scholar
Saavedra, M., Cuevas, J., Mesa-García, J., and García-Torres, L. 1989. Grass weeds in winter cereals in southern Spain. Crop Protect 8:181–187.Google Scholar
Salisbury, J. W. 1999. Spectral measurements field guide. Report No. ADA362372. Washington, DC: Defense Technology Information Center.Google Scholar
Schmidt, K. S. and Skidmore, A. K. 2003. Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing Environ 85:92–108.Google Scholar
Smits, P. C., Dellepiane, S. G., and Schowengerdt, R. A. 1999. Quality assessment of image of image classification algorithm for land cover mapping: a review and proposal for a cost-based approach. Int. J. Remote Sensing 20:1461–1486.Google Scholar
Smith, A. M. and Blackshaw, R. E. 2003. Weed-crop discrimination using remote sensing: a detached leaf experiment. Weed Technol 17:811–820.Google Scholar
Stehman, S. V. and Czaplewski, R. L. 1998. Design and analysis for thematic map accuracy assessment: fundamental principles. Remote Sensing Environ 64:331–334.Google Scholar
Steckler, J. P. and Brown, R. B. 1993. Prescription map for herbicide sprayer control. St. Joseph, MI: American Society of Agricultural Engineers Paper No. 93-1070. 18 p.Google Scholar
Thomlison, J. R., Bolstad, P. V., and Cohen, W. B. 1999. Coordinating methodologies for scaling landcover classifications from site-specific to global: steps toward validating global map product. Remote Sensing Environ 70:16–28.Google Scholar
Thompson, J. F., Stafford, J. V., and Miller, P. C. H. 1991. Potential for automatic weed detection selective herbicide application. Crop Protect 10:254–259.Google Scholar
Timmermann, C., Gerhards, R., and Kuhbauch, W. 2003. The economic impact of site-specific weed control. Precis. Agric 4:249–260.Google Scholar
Thorp, K. R. and Tian, L. F. 2004. A review of remote sensing of weeds in agriculture. Prec. Agric 5:477–508.CrossRefGoogle Scholar
Tottman, D. R., Makepeace, R. J., and Broad, H. 1979. An explanation of the decimal code for the growth stages of cereals, with illustrations. Ann. Appl. Biol 93:221–234.Google Scholar
Vrindts, E., Baerdemaeker, J. D., and Ramon, H. 2002. Weed detection using canopy reflection. Prec. Agric 3:63–80.Google Scholar
Wallinga, J. R., Groeneveld, M. W., and Lotz, L. A. P. 1998. Measures to describe weed spatial patterns at different levels of resolution and their application for patch spraying weeds. Weed Res 38:351–359.Google Scholar
Wilson, B. J. and Brain, P. 1991. Long-term stability of distribution of Alopecurus myosuroides Huds. within cereal fields. Weed Res 31:367–373.Google Scholar
Zanin, G., Berti, A., and Riello, L. 1998. Incorporation of weed spatial variability into the weed control decision-making process. Weed Res 38:107–118.CrossRefGoogle Scholar