AJPS  Vol.11 No.12 , December 2020
Assessing Hyperspectral Vegetation Indices Responses of Six Pigweed Species
Abstract: Pigweeds (Amaranthus species), negatively impact crop production systems throughout the world. They are distinguished from each other using manual methods that are tedious and time-consuming to complete. Hyperspectral light reflectance properties of plant leaves and canopies have shown promise for detecting and mapping weeds in crop production systems. Vegetation indices derived from hyperspectral reflectance data enhance differences between plants, leading to better detection of them from other targets. The objective was to evaluate the biomass and structural index, the biochemical index, the red edge index, the water and moisture index, the light-use efficiency index, and the lignin cellulose index for measuring differences among six pigweed species: Amaranthus albus (L), A. hybridus (L), A. palmeri (S. Wats.), A. retroflexus (L), A. spinosus (L), and A. tuberculatus [(Moq.) Sauer]. Two experiments were conducted under greenhouse conditions. Hyperspectral reflectance measurements were collected from the plant canopies on two dates for each experiment. Analysis of variance (ANOVA) and Tukey’s honest significant difference (HSD) test were used to determine if statistical differences (P ≤ 0.05) existed among the pigweed species canopies and to identify which species were statistically different for a vegetation index, respectively. The ANOVA analysis detected statistical differences among the canopy vegetation index values. Tukey’s HSD showed that the biochemical index and the red edge index detected differences between two to three pigweeds species on all dates of data collection. However, the differences were date-specific. Furthermore, statistical differences were not observed for all six species for any vegetation index. On the data collection dates, A. albus and A. tuberculatus index values were statistically different from other pigweed species for one or more of the vegetation indices. Future research should focus on using the vegetation indices in combination with each other to measure differences between the pigweed species and between them and other weeds and crops.
Cite this paper: Fletcher, R. (2020) Assessing Hyperspectral Vegetation Indices Responses of Six Pigweed Species. American Journal of Plant Sciences, 11, 1934-1948. doi: 10.4236/ajps.2020.1112138.

[1]   Légère, A. and Schreiber, M.M. (1989) Competition and Canopy Architecture as Affected by Soybean (Glycine max) Row Width and Density of Redroot Pigweed (Amaranthus retroflexus). Weed Science, 37, 84-92.

[2]   Klingaman, T.E. and Oliver, L.R. (1994) Palmer Amaranth (Amaranthus palmeri) Interference in Soybeans (Glycine max). Weed Science, 42, 523-527.

[3]   Knezevic, S.Z., Weise, S.F. and Swanton, C.J. (1994) Interference of Redroot Pigweed (Amaranthus retroflexus) in Corn (Zea mays). Weed Science, 42, 568-573.

[4]   Ma, X., Wu, H., Jiang, W., Ma, Y. and Ma, Y. (2015) Interference between Redroot Pigweed (Amaranthus retroflexus L.) and Cotton (Gossypium hirsutum L.): Growth Analysis. PLoS ONE, 10, e0130475.

[5]   Heap, I. (2020) The International Survey of Herbicide-Resistant Weed Database.

[6]   Wax, L.M. (1995) Pigweeds of the Midwest—Distribution, Importance and Management. Proceedings of the Integrated Crop Management Conference, Iowa State University, Ames, Iowa, 29-30 November 1995, 239-242.

[7]   Weaver, S.E. and McWilliams, E.L. (1980) The Biology of Canadian Weeds: 44. Amaranthus retroflexus L., A. powellii S. Wats. and A. hybridus L. Canadian Journal of Plant Science, 60, 1215-1234.

[8]   Costea, F.J. and Tardif, M. (2003) The Biology of Canadian Weeds. 126. Amaranthus albus L., A. blitoides S. Watson and A. blitum L. Canadian Journal of Plant Science, 83, 1039-1066.

[9]   Mitchell, J. and Rook, A. (1979) Botanical Dermatology: Plants and Plant Products Injurious to the Skin. Greengrass, Vancouver, Canada.

[10]   Würtzen, P.A., Nelson, H.S., Lowenstein, H. and Ipsen, H. (1995) Characterization of Chenopodiales (Amaranthus retroflexus, Chenopodium album, Kochia scoparia, Salsola pestifer) Pollen Allergens. Allergy, 50, 489-497.

[11]   Horak, M.J., Peterson, D.E., Chessman, D.J. and Wax, L.M. (2019) Pigweed Identification: A Pictorial Guide to the Common Pigweeds of the Great Plains. Kansas State University, Manhattan, KS, 13.

[12]   Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J. and Strachan, I.B. (2004) Hyperspectral Vegetation Indices and Novel Algorithms for Predicting Green LAI of Crop Canopies: Modeling and Validation in the Context of Precision Agriculture. Remote Sensing of Environment, 90, 337-352.

[13]   Bian, M., Skidmore, A.K., Schlerf, M., Fei, T., Liu, Y. and Wang, T. (2010) Reflectance Spectroscopy of Biochemical Components as Indicators of Tea (Camellia sinensis) Quality. Photogrammetric Engineering and Remote Sensing, 76, 1385-1392.

[14]   Pott, L.P., Amado, T.J., Schwalbert, R.A., Sebem, E., Jugulam, M. and Ciampitti, I.A. (2020) Pre-Planting Weed Detection Based on Ground Field Spectral Data. Pest Management Science, 76, 1173-1182.

[15]   Pu, R. and Gong, P. (2011) Hyperspectral Remote Sensing of Vegetation Bioparameters. In: Weng, Q.H., Ed., Advances in Environmental Remote Sensing: Sensors, Algorithm, and Applications, CRC Press, Boca Raton, 101-142.

[16]   Roberts, D., Roth, K. and Perroy, R. (2011) Hyperspectral Vegetation Indices. In: Thenkabail, P.S. and Lyon, J.G., Eds., Hyperspectral Remote Sensing of Vegetation, CRC Press, Boca Raton, 309-327.

[17]   Pu, R. (2017) Hyperspectral Remote Sensing: Fundamentals and Practices. CRC Press, Boca Raton.

[18]   Green, K., Congalton, R.G. and Tukman, M. (2017) Imagery and GIS: Best Practices for Extracting Information from Imagery. Esri Press, Redlands, CA.

[19]   Thenkabail, P., Gumma, M., Teluguntla, P. and Ahmed, M.I. (2014) Hyperspectral Remote Sensing of Vegetation and Agricultural Crops. Photogrammetric Engineering and Remote Sensing, 80, 697-709.

[20]   Thenkabail, P., Mariotto, I., Gumma, M., Middleton, E., Landis, D. and Huemmrich, K. (2013) Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion/EO-1 Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 427-439.

[21]   Galvao, L., Epiphanio, J., Breunig, F. and Formaggio, A. (2011) Crop Type Discrimination Using Hyperspectral Data. In: Thenkabail, P.S. and Lyon, J.G., Eds., Hyperspectral Remote Sensing of Vegetation, CRC Press, Boca Raton, 397-421.

[22]   Gitelson, A. (2011) Non-Destructive Estimation of Foliar Pigment (Chlorophylls, Carotenoids and Anthocyanins) Contents: Espousing a Semi-Analytical Three-Band Model. In: Thenkabail, P.S. and Lyon, J.G., Eds., Hyperspectral Remote Sensing of Vegetation, CRC Press, Boca Raton, 141-166.

[23]   Gamon, J.A., Serrano, L. and Surfus, J.S. (1997) The Photochemical Reflectance Index: An Optical Indicator of Photosynthetic Radiation Use Efficiency across Species, Functional Types, and Nutrient Levels. Oecologia, 112, 492-501.

[24]   Trotter, G.M., Whitehead, D. and Pinkney, E.J. (2002) The Photochemical Reflectance Index as a Measure of Photosynthetic Light Use Efficiency for Plants with Varying Foliar Nitrogen Contents. International Journal of Remote Sensing, 23, 1207-1212.

[25]   Middleton, E.M., Huemmrich, K.F., Cheng, Y.B. and Margolis, H. (2011) Spectral Bioindicators of Photosynthetic Efficiency and Vegetation Stress. In: Thenkabail, P.S. and Lyon, J.G., Eds., Hyperspectral Remote Sensing of Vegetation, CRC Press, Boca Raton, 265-288.

[26]   Danner, M., Locherer, M., Hank, T. and Richter, K. (2015) En Map Field Guides Technical Report Spectral Sampling with the ASD FIELDSPEC 4. GFZ Data Services.

[27]   Fletcher, R.S. and Turley, R.B. (2018) Comparing Canopy Hyperspectral Reflectance Properties of Palmer Amaranth to Okra and Super-Okra Leaf Cotton. American Journal of Plant Sciences, 9, 2708-2718.

[28]   Savitzky, A. and Golay, M.J.E. (1964) Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry, 36, 1627-1639.

[29]   R Core Team (2020) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.

[30]   Lehnert, L., Meyer, H., Obermeier, W., Silva, B., Regeling, B., Thies, B. and Bendix, J. (2019) Hyperspectral Data Analysis in R: The Hsdar Package. Journal of Statistical Software, 89, 1-23.

[31]   Thenkabail, P., Ed. (2018) Hyperspectral Remote Sensing for Terrestrial Applications. In: Thenkabail, P., Ed., Remote Sensing Handbook—Three Volume Set, CRC Press, Boca Raton, 935-968.

[32]   Jensen, J.R. (2016) Introductory Digital Image Processing: A Remote Sensing Perspective. Prentice Hall Press, Upper Saddle River, NJ.

[33]   Stanberry, L. (2013) Analysis of Variance. In: Dubitzky, W., Wolkenhauer, O., Cho, K.H. and Yokota, H., Eds., Encyclopedia of Systems Biology, Springer, New York, NY, 24-25.

[34]   Haynes, W. (2013) Tukey’s Test. In: Dubitzky, W., Wolkenhauer, O., Cho, K.H. and Yokota, H., Eds., Encyclopedia of Systems Biology, Springer, New York, NY, 2303-2304.

[35]   De Mendiburu, F. and Simon, R. (2015) Agricolae—Ten Years of an Open Source Statistical Tool for Experiments in Breeding, Agriculture and Biology. PeerJ, 3, e1404v1.

[36]   De Mendiburu, F. (2020) Agricolae: Statistical Procedures for Agricultural Research.

[37]   Huete, A.R. (2004) 11—Remote Sensing for Environmental Monitoring. In: Artiola, J.F., Pepper, I.L. and Brusseau, M.L., Eds., Environmental Monitoring and Characterization, Academic Press, Burlington, 183-206.

[38]   Lemaire, G., Francois, C., Soudani, K., Berveiller, D., Pontailler, J., Breda, N., Genet, H., Davi, H. and Dufrene, E. (2008) Calibration and Validation of Hyperspectral Indices for the Estimation of Broadleaved Forest Leaf Chlorophyll Content, Leaf Mass per Area, Leaf Area Index and Leaf Canopy Biomass. Remote Sensing of Environment, 112, 3846-3864.

[39]   Pena-Barragan, J.M., Lopez-Granados, F., Jurado-Exposito, M. and Garcia-Torres, L. (2006) Spectral Discrimination of Ridolfia segetum and Sunflower as Affected by Phenological Stage. Weed Research, 46, 10-21.