Abstract
Accurately mapping the spatial distribution of weeds within a field is a first step towards effective Site-specific Weed Management. The main objective of this study was to investigate if the multivariate geostatistical method of cokriging (COK) can be used to improve the accuracy of Papaver rhoeas L. infestations maps in winter wheat fields using high-resolution UAV imagery as ancillary information. The primary variable was obtained by intensive grid weed density field samplings and the secondary variables were derived from the UAV imagery taken the same day as the weed field samplings (e.g. wavebands and derivative products, such as band ratios and vegetation indexes). Univariate Ordinary Kriging (OK) and multivariate cokriging (COK) interpolation methods were used and compared for Papaver density mapping. The performances of the different methods were assessed by cross-validation. The results indicated that COK outperformed OK in the spatial interpolation of Papaver density. COK reduced the prediction errors and enhanced the accuracy of Papaver estimates maps. The best performances were obtained when COK was performed with the UAV-secondary variables that yielded the highest correlation with Papaver density and produced the strongest spatial cross-semivariograms. On average, the COK with UAV-derived ancillary variables improved the accuracy of mapping Papaver density by 11 to 21% compared with OK. The results suggest the great potential of high-resolution UAV imagery as a source of ancillary information to improve the accuracy of spatial mapping of sparsely sampled target variables using COK.
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References
Adjorlolo, C., & Mutanga, O. (2013). Integrating remote sensing and geostatistics to estimate woody vegetation in an African savanna. Journal of Spatial Science, 58(2), 305–322.
Barrero, O., & Perdomo, S. A. (2018). RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields. Precision Agriculture. https://doi.org/10.1007/s11119-017-9558-x.
Barroso, J., Fernández-Quintanilla, C., Ruiz, D., Hernaiz, P., & Rew, R. J. (2004). Spatial stability of Avena sterilis ssp. ludoviciana populations under annual. Weed Research, 44(3), 178–186.
Barroso, J., Ruiz, D., Fernandez-Quintanilla, C., Leguizamon, E. S., Hernaiz, P., Ribeiro, A., et al. (2005). Comparison of sampling methodologies for site-specific management of Avena sterilis. Weed Research, 45(2), 165–174.
Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton, S. H., Jakeman, A. J., et al. (2013). Characterising performance of environmental models. Environmental Modelling & Software, 40(1), 1–20.
Blanco-Moreno, J. M., Chamorro, L., & Sans, F. X. (2006). Spatial and temporal patterns of Lolium rigidum–Avena sterilis mixed populations in a cereal field. Weed Research, 46(3), 207–218.
Camargo-Neto, J. (2004). A combined statistical—soft computing approach for classification and mapping weed species in minimum tillage systems. Lincoln, NE: University of Nebraska.
Cambardella, C. A., & Karlen, D. L. (1999). Spatial analysis of soil fertility parameters. Precision Agriculture, 1(1), 5–11.
Cantero-Martínez, C., Angás, P., & Lampurlanés, J. (2007). Long-term yield and water use efficiency under various tillage systems in Mediterranean rainfed conditions. Annals of Applied Biology, 150(3), 293–305.
Cardina, J., Jonson, G. A., & Sparrow, D. H. (1997). The nature and consequence of weed spatial distribution. Weed Science, 45(3), 364–373.
Castaldi, F., Pelosi, F., Pascucci, S., & Casa, R. (2017). Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precision Agriculture, 18(1), 76–94.
Chilès, J. P., & Delfiner, P. (2012). Geostatistics: modeling spatial uncertainty (2nd ed.). New York: Wiley.
Christensen, S., Søgaard, H. T., Kudsk, P., Nørremark, M., Lund, I., Nadimi, S., et al. (2009). Site specific weed control technologies. Weed Research, 49(3), 233–241.
Colbach, N., Forcella, F., & Johnson, G. A. (2000). Spatial and temporal stability of weed populations over five years. Weed Science, 48(3), 366–377.
De Castro, A. I., Torres-Sánchez, J., Peña, J. M., Jiménez-Brenes, F. M., Csillik, O., & López-Granados, F. (2018). An automatic random forest-OBIA algorithm for early weed mapping between and within crop Rows using UAV imagery. Remote Sensing, 10(2), 285. https://doi.org/10.3390/rs10020285.
Dille, J. A., Milner, M., Groeteke, J. J., Mortensen, D. A., & Williams, M. M. (2003). How good is your weed map? A comparison of spatial interpolators. Weed Science, 51(1), 44–55.
Donald, W. W. (1994). Geostatistics for mapping weeds, with a Canada thistle (Cirsium arvense) patch as a case study. Weed Science, 42(4), 648–657.
Emery, X. (2012). Cokriging random fields with means related by known linear combinations. Computers & Geosciences, 38(1), 136–144.
Everitt, J. H., & Villarreal, R. (1987). Detecting huisache (Acacia farnesiana) and mexican palo-verde (Parkinsonia aculeata) by aerial photography. Weed Science, 35, 427–432.
Gerhards, R., & Christensen, S. (2006). Site-Specific Weed Management. In A. Srinivasan (Ed.), Handbook of Precision Agriculture principles and Applications (pp. 185–206). New York: Food Products Press, The Haworth Press.
Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1), 76–87.
Goovaerts, P. (1997). Geostatistics for natural resources evaluation. New York: Oxford University Press.
Goovaerts, P. (2000). Geostatistical approches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228(1–2), 113–129.
Heisel, T., Andreasen, C., & Ersbøll, A. K. (1996). Annual weed densities can be mapped with kriging. Weed Research, 36(4), 325–337.
Heisel, T., Ersboll, A., & Andreasen, C. (1999). Weed mapping with co-kriging using soil properties. Precision Agriculture, 1(1), 39–52.
Hernández-Stefanoni, J. L., Gallardo-Cruz, J. A., Meave, J. A., & Dupuy, J. M. (2011). Combining geostatistical models and remotely sensed data to improve tropical tree richness mapping. Ecological Indicators, 11(5), 1046–1056.
Hevesi, J. A., Istok, J. D., & Flint, A. L. (1992). Precipitation estimation in mountains terrain using multivariate geostatistics Part I: structural analysis. Journal of Applied Meteorology, 31(1), 661–676.
Holm, L., Doll, J., Holm, E., Pancho, J., & Herbereger, J. (1997). Papaver rhoeas L. In John Wiley and Sons (Ed.), World weeds natural histories and distribution (pp. 555–561). New York: Academic Press.
Huang, Y., Reddy, K. N., Fletcher, R. S., & Pennington, D. (2017). UAV low-altitude remote sensing for precision weed management. Weed Technology. https://doi.org/10.1017/wet.2017.89.
Isaaks, E. H., & Srivastava, R. M. (1989). An introduction to applied geostatistics. New York: Oxford University.
Izquierdo, J., Blanco-Moreno, J., Chamorro, L., Recasens, J., & Sans, F. (2009). Spatial distribution and temporal stability of prostrate knotweed (Polygonum aviculare) and corn poppy (Papaver rhoeas) Seed bank in a cereal field. Weed Science, 57(5), 505–511.
Jordan, C. F. (1969). Derivation of leaf area index from quality of light on the forest floor. Ecology, 50(4), 663–666.
Journel, A., & Huijbregts, C. (1978). Mining geostatistics. New York: Academic Press.
Jurado-Expósito, M., López-Granados, F., García-Torres, L., García-Ferrer, A., Sánchez de la Orden, M., & Atenciano, S. (2003). Multi-species weed spatial variability and site-specific management maps in cultivated sunflower. Weed Science, 51(3), 319–328.
Jurado-Expósito, M., López-Granados, F., Peña-Barragán, J. M., & García-Torres, L. (2009). A digital elevation model to aid geostatistical mapping of weeds in sunflower crops. Agronomy for Sustainable Development, 29(2), 391–400.
Kalivas, D. P., Christos, E. V., Garifalia, E., & Paraskevi, D. (2012). Regional mapping of perennial weeds in cotton with the use of geostatistics. Weed Science, 60(2), 233–243.
Kerry, R., & Oliver, M. (2003). Variograms of ancillary data of aid sampling for soil surveys. Precision Agriculture, 4(3), 261–278.
Kumar, L., Schmidt, K. S., Dury, S., & Skidmore, A. K. (2001). Imaging spectrometry and vegetation science. In F. van der Meer & S. M. de Jong (Eds.), Imaging spectrometry (pp. 111–155). Dordrecht: Kluwer Academic.
Lambert, J. P. T., Hicks, H. L., Childs, D. Z., & Freckleton, R. P. (2018). Evaluating the potential of Unmanned Aerial Systems for mapping weeds at field scales: a case study with Alopecurus myosuroides. Weed Research, 58(1), 35–45.
Li, J., & Heap, A. D. (2011). A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics, 6, 228–241.
López-Granados, F., Jurado-Expósito, M., Peña-Barragán, J. M., & García-Torres, L. (2005). Using geoestatistical and remote sensing approaches for mapping soil properties. European Journal of Agronomy, 23(3), 279–289.
López-Granados, F., Torres-Sánchez, J., De Castro, A. I., Serrano-Pérez, A., Mesas-Carrascosa, F. J., & Peña, J. M. (2016). Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery. Agronomy for Sustainable Development, 36, 67. https://doi.org/10.1007/s13593-016-0405-7.
Lottes, P., Khanna, R., Pfeifer, J., Siegwart, R., & Stachniss, C. (2017). UAV-based crop and weed classification for smart farming. In IEEE International Conference on Robotics and Automation (ICRA), 3024–3031, https://doi.org/10.1109/icra.2017.7989347.
Matheron, G. (1970). The theory of regionalized variables and its applications. Ecole Nationale Supérieure des Mine, 5, 212.
Meng, Q. M., Cieszewski, C., & Madden, M. (2009). Large area forest inventory using Landsat ETM plus: A geostatistical approach. ISPRS Journal of Photogrammetry and Remote Sensing, 64(1), 27–36.
Mesas-Carrascosa, F. J., Clavero-Rumbao, I., Torres-Sánchez, J., García-Ferrer, A., Peña, J. M., & López-Granados, F. (2017). Accurate ortho-mosaicked six-band multispectral UAV images as affected by mission planning for precision agriculture proposes. International Journal of Remote Sensing, 38(8–10), 2161–2176.
Mesas-Carrascosa, F. J., Torres-Sánchez, J., Clavero-Rumbao, I., García-Ferrer, A., Peña, J. M., Borra-Serrano, I., et al. (2015). Assessing optimal flight parameters for generating accurate multispectral orthomosaicks by UAV to support site-specific crop management. Remote Sensing, 7(10), 12793–12814.
Meyer, G. E., Camargo-Neto, J., Jones, D. D., & Hindman, T. W. (2004). Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from colour images. Computers and Electronics in Agriculture, 42, 161–180.
Mutanga, O., & Rugege, D. (2006). Integrating remote sensing and spatial statistics to model biomass distribution in a tropical savanna. International Journal of Remote Sensing, 27(19), 3499–3514.
National Research Council. (1997). Precision agriculture in the 21st century: Geospatial and information technologies in crop management. Washington, DC: National Academy Press.
Oliver, M. A. (2010). Geostatistical applications for precision agriculture. New York, NY: Springer.
Papritz, A., & Stein, A. (1999). Spatial prediction by linear kriging. In A. Stein (Ed.), Spatial statistics for remote sensing. Dordrecht: Kluwer.
Peña, J. M., Torres-Sánchez, J., de Castro, A. I., 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(10), e77151. https://doi.org/10.1371/journal.pone.0077151.
Rasmussen, J., Nielsen, J., Garcia-Ruiz, F., Christensen, S., & Streibig, J. C. (2013). Potential uses of small unmanned aircraft systems (UAS) in weed research. Weed Research, 53(4), 242–248.
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the greant plains with ERTS. Proceedings of the Earth Resources Technology Satellite Symposium NASA SP-351, vol 1 (pp. 309–317). Washington, DC.
Schirrmann, M., Hamdorf, A., Giebel, A., Gleiniger, F., Pflanz, M., & Dammer, K. (2017). Regression kriging for improving crop height models fusing ultra-sonic Sensing with UAV imagery. Remote Sensing, 9, 665. https://doi.org/10.3390/rs9070665.
Simbahan, G. C., Dobermann, A., Goovearts, P., Ping, J., & Haddix, M. (2006). Fine resolution mapping of soil organic carbon based on multivariate secondary data. Geoderma, 132(3–4), 471–489.
Taberner, A., Anguera, R., Cirujeda, A., & Tarago, R. (2001). Situación actual de las resistencias de Lolium rigidum y Papaver rhoeas en cereales de invierno. Phytoma, 132, 33–35.
Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71, 158–182.
Torra, J., Cirujeda, A., Taberner, A., & Recasens, J. (2010). Evaluation of herbicides to manage herbicide-resistant corn poppy (Papaver rhoeas) in winter cereals. Crop Protection, 29(7), 731–736.
Torra, J., Gonzalez-Andujar, J. L., & Recasens, J. (2008). Modelling the population dynamics of Papaver rhoeas under various weed management systems in a Mediterranean climate. Weed Research, 48(2), 136–146.
Torra, J., & Recasens, J. (2008). Demography of corn poppy (Papaver rhoeas) in relation to emergence time and crop competition. Weed Science, 56(6), 826–833.
Torres-Sánchez, J., López-Granados, F., De Castro, A. I., & Peña-Barragán, J. M. (2013). Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. Plos ONE, 8(3), e58210. https://doi.org/10.1371/journal.pone.0058210.
Tucker, C. J. (1977). Asymptotic nature of grass canopy spectral reflectance. Applied Optics, 16(5), 1151–1156.
Van der Meer, F. (2012). Remote-sensing image analysis and geostatistics. International Journal of Remote Sensing, 33(18), 5644–5676.
Wackernagel, H. (2003). Multivariate Geostatistics: An introduction with applications (3rd ed.). Berlin: Springer-Verlag.
Walter, A. M., Christensen, S., & Simmelsgaard, S. E. (2002). Spatial correlation between weed species densities and soil properties. Weed Research, 42(1), 26–38.
Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists (2nd ed.). Chichester: John Wiley and Sons, Ltd.
Wilson, B. J., Wright, K. J., Brain, P., Clements, M., & Stephens, E. (1995). Predicting the competitive effects of weed and crop density on weed biomass, weed production and crop yield in wheat. Weed Research, 35(4), 265–278.
Woebbecke, D. M., Meyer, G. E., Von Bargen, K., & Mortensen, D. A. (1995). Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE, 38(1), 259–269.
Yao, H., & Huang, Y. (2013). Remote sensing applications to precision farming. In G. Wang & Q. Weng (Eds.), Remote sensing of natural resources (pp. 333–352). Boca Raton, FL: CRC.
Zhang, C., & Kovacs, J. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693–712.
Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture: A worldwide overview. Computers and Electronics in Agriculture, 36(2–3), 113–132.
Acknowledgements
This research was financed by the AGL2014-52465-C4-4-R and AGL2017-83325-C4-4-R MINECO (Spanish Ministry of Economy and Competition, FEDER Funds). Research of AI. de Castro was financed by Juan de la Cierva (MINECO) program. The authors thank Dr. Recasens and his group for their valuable help in field surveys.
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Jurado-Expósito, M., de Castro, A.I., Torres-Sánchez, J. et al. Papaver rhoeas L. mapping with cokriging using UAV imagery. Precision Agric 20, 1045–1067 (2019). https://doi.org/10.1007/s11119-019-09635-z
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DOI: https://doi.org/10.1007/s11119-019-09635-z