Determination of vegetation cover index under different soil management systems of cover plants by using an unmanned aerial vehicle with an onboard digital photographic camera

Adnane Beniaich, Marx Leandro Naves Silva, Fabio Arnaldo Pomar Avalos, Michele Duarte de Menezes, Bernardo Moreira Cândido

Abstract


The permanent monitoring of vegetation cover is important to guarantee a sustainable management of agricultural activities, with a relevant role in the reduction of water erosion. This monitoring can be carried out through different indicators such as vegetation cover indices. In this study, the vegetation cover index was obtained using uncalibrated RGB images generated from a digital photographic camera on an unmanned aerial vehicle (UAV). In addition, a comparative study with 11 vegetation indices was carried out. The vegetation indices CIVE and EXG presented a better performance and the index WI presented the worst performance in the vegetation classification during the cycles of jack bean and millet, according to the overall accuracy and Kappa coefficient. Vegetation indices were effective tools in obtaining soil cover index when compared to the standard Stocking method, except for the index WI. Architecture and cycle of millet and jack bean influenced the behavior of the studied vegetation indices. Vegetation indices generated from RGB images obtained by UAV were more practical and efficient, allowing a more frequent monitoring and in a wider area during the crop cycle.

Keywords


Vegetation cover index; RGB image; Vegetation index; Unmanned aerial vehicle.

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References


AGISOFT. Agisoft PhotoScan User Manual. Petersburg: Agisoft LLC, 2017. Available at: . Accessed at: 6 aug. 2017.

ARROYO, J.; GUIJARRO, M.; PAJARES, G. An instance-based learning approach for thresholding in crop images under different outdoor conditions. Computers and Electronics in Agriculture, Athens, v. 127, p. 669-679, 2016.

BENDIG, J.; YU, K.; AASEN, H.; BOLTEN, A.; BENNERTZ, S.; BROSCHEIT, J.; GNYP, M. L.; BARETH, G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, Amsterdam, v. 39, p. 79-87, 2015.

CAMPBELL, J. B.; WYNNE, R. H. Digital imagery. In: CAMPBELL, J. B.; WYNNE, R. H. (Ed.). Introduction to remote sensing. 5th ed. New York: The Guilford Press, 2011, p. 101-130.

CARDOSO, D. P.; SILVA, M. L. N.; CARVALHO, G. J. de; FREITAS, D. A. F. de; AVANZI, J. C. Plantas de cobertura no controle das perdas de solo, água e nutrientes por erosão hídrica. Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 16, n. 6, p. 632-638, 2012.

CARUSO, G.; TOZZINI, L.; RALLO, G.; PRIMICERIO, J.; MORIONDO, M.; PALAI, G.; GUCCI, R. Estimating biophysical and geometrical parameters of grapevine canopies (’Sangiovese’) by an unmanned aerial vehicle (UAV) and VIS-NIR cameras. Vitis - Journal of Grapevine Research, Siebeldingen, v. 56, n. 2, p. 63-70, 2017.

DANDOIS, J. P.; OLANO, M.; ELLIS, E. C. Optimal altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure. Remote Sensing, Basel, v. 7, n. 10, p. 13895-13920, 2015.

FAUSTOLO, D.; BISPO, A.; LEANDRO, M.; SILVA, N.; PONTES, L. M.; GUIMARÃES, D. V. Soil, water , nutrients and soil organic matter losses by water erosion as a function of soil management in the Posses sub-watershed , Extrema , Minas Gerais , Brazil. Semina: Ciências Agrarias, Londrina, v. 38, n. 4, p. 1813-1824, 2017.

FERREIRA, D. F. Estatística básica. Lavras: Editora UFLA, 2005. 664 p.

FOODY, G. Assessing the accuracy of remotely sensed data: principles and practices. The Photogrammetric Record, Nottingham, v. 25, n. 130, p. 204-205, 2010.

GILABERT, M.; GONZÁLEZ-PIQUERAS, J.; GARCI?A-HARO, F.; MELIÁ, J. A generalized soil-adjusted vegetation index. Remote Sensing of Environment, Amsterdam, v. 82, n. 2-3, p. 303-310, 2002.

GUIJARRO, M.; PAJARES, G.; RIOMOROS, I.; HERRERA, P. J.; BURGOS-ARTIZZU, X. P.; RIBEIRO, A. Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture, Athens, v. 75, n. 1, p. 75-83, 2011.

GUIMARAES, D. V.; SILVA, M. L. N.; BISPO, D. F. A.; MARTINS, S. G.; MELO NETO, J. D. O.; MARTINS, R. P.; CURI, N. Water erosion associated with rainfall patterns in the extreme South of Bahia in eucalyptus post-planting. Semina: Ciências Agrárias, Londrina, v. 38, n. 4, p. 2463, 2017. Supplement 1.

HAGUE, T.; TILLETT, N. D.; WHEELER, H. Automated crop and weed monitoring in widely spaced cereals. Precision Agriculture, Berlin, v. 7, n. 1, p. 21-32, 2006.

HILBE, J. M. Logistic regression models. New York: Taylor & Francis.2009. 53 p.

HUNT, E. R. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture, Berlin, v. 6, n. 4, p. 359-378, 2005.

JAFARI GOLDARAG, Y.; MOHAMMADZADEH, A.; ARDAKANI, A. S. Fire risk assessment using neural network and logistic regression. Journal of the Indian Society of Remote Sensing, Berlin, v. 44, n. 6, p. 885-894, 2016.

KATAOKA, T.; KANEKO, T.; OKAMOTO, H.; HATA, S. Crop growth estimation system using machine vision. Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Kobe, v. 2, n. 1, p. 1079-1083, 2003.

KAZMI, W.; GARCIA-RUIZ, F. J.; NIELSEN, J.; RASMUSSEN, J.; JØRGEN ANDERSEN, H. Detecting creeping thistle in sugar beet fields using vegetation indices. Computers and Electronics in Agriculture, Athens, v. 112, p. 10-19, 2015.

LANDIS, J. R.; KOCH, G. G. The Measurement of observer agreement for categorical data. Biometrics, Washington, v. 33, n. 1, p. 159, 1977.

LI, Y.; CHEN, D.; WALKER, C. N.; ANGUS, J. F. Estimating the nitrogen status of crops using a digital camera. Field Crops Research, Amsterdam, v. 118, n. 3, p. 221-227, 2010.

LIMA, P. L. T.; SILVA, M. L. N.; CURI, N.; QUINTON, J. Soil loss by water erosion in areas under maize and jack beans intercropped and monocultures. Ciência e Agrotecnologia, Lavras, v. 38, n. 2, p. 129-139, 2014.

MARCHANT, J. A.; ONYANGO, C. M. Shadow-invariant classification for scenes illuminated by daylight. Journal of the Optical Society of America, Washington, v. 17, n. 11, p. 1952-1961, 2000.

MARRERO, D. F.; DELGADO, L. E. P.; IAÑEZ, N. C.; CALERO, C. M.; RODRÍGUEZ, M. L.; PÉREZ, L. R.; RODRÍGUEZ, L. C. Cubierta vegetal con Teramnus labialis en plantaciones citrícolas: efectos sobre algunas propiedades físicas del suelo. Semina: Ciências Agrárias, Londrina, v. 30, n. 4, p. 1073, 2009. Supplement 1.

MEYER, G. E.; NETO, J. C. Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture, Athens, v. 63, n. 2, p. 282-293, 2008.

MOTOHKA, T.; NASAHARA, K. N.; OGUMA, H.; TSUCHIDA, S. Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sensing, Amsterdam, v. 2, n. 10, p. 2369-2387, 2010.

PASSOS, R. R.; COSTA, L. M.; BURAK, D. L.; SANTOS, D. A. Quality indices in degraded pasture in hilly relief. Semina: Ciências Agrárias, Londrina, v. 36, n. 4, p. 2465-2482, 2015.

PURCELL, L. C.; MOZAFFARI, M.; KARCHER, D. E.; ANDY KING, C.; MARSH, M. C.; LONGER, D. E. Association of “Greenness” in corn with yield and leaf Nitrogen concentration. Agronomy Journal, Madison, v. 103, n. 2, p. 529-535, 2011.

SABERIOON, M. M.; AMIN, M. S. M.; ANUAR, A. R.; GHOLIZADEH, A.; WAYAYOK, A.; KHAIRUNNIZA-BEJO, S. Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. International Journal of Applied Earth Observation and Geoinformation, Amsterdam, v. 32, p. 35-45, 2014.

SANTOS, C. A. G.; SUZUKI, K.; WATANABE, M.; SRINIVASAN, V. S. Influência do tipo da cobertura vegetal sobre a erosão no semi-árido Paraibano. Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 4, n. 1, p. 92-96, 2000.

SPERANDIO, H. V.; CECÍLIO, R. A.; CAMPANHARO, W. A.; CARO, C. F. DEL; HOLLANDA, M. P. DE. Avaliação da erosão hidrica pela alteração na superfície do solo em diferentes coberturas vegetais de uma sub-bacia hidrográfica no Município de Alegre, ES. Semina:Ciâncias Agrarias, Londrina, v. 33, n. 4, p. 14110-1418, 2012.

STOCKING, M. A. Assessing vegetative cover and management effects. In: LAL, R. (Ed.). Soil erosion research methods. 2. ed. Ankeny: Soil and Water Conservation Society, 1994. p. 163-185.

TORRES-SÁNCHEZ, J.; PEÑA, J. M.; CASTRO, A. I. DE; LÓPEZ-GRANADOS, F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture, Athens, v. 103, p. 104-113, 2014.

TUCKER, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, Amsterdam, v. 8, n. 2, p. 127-150, 1979.

WOEBBECKE, D. M.; MEYER, G. E.; BARGEN, K. V. O. N.; MORTENSEN, D. A. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE, Michigan, v. 38, n. 1, p. 259-269, 1995.

YU, Z.; CAO, Z.; WU, X.; BAI, X.; QIN, Y.; ZHUO, W.; XIAO, Y.; ZHANG, X.; XUE, H. Automatic image-based detection technology for two critical growth stages of maize: Emergence and three-leaf stage. Agricultural and Forest Meteorology, Amsterdam, v. 174-175, p. 65-84, 2013.

ZHENG, Y.; ZHU, Q.; HUANG, M.; GUO, Y.; QIN, J. Maize and weed classification using color indices with support vector data description in outdoor fields. Computers and Electronics in Agriculture, Athens, v. 141, p. 215-222, 2017.

ZHONGMING, W.; LEES, B. G.; FENG, J.; WANNING, L.; HAIJING, S. Stratified vegetation cover index: A new way to assess vegetation impact on soil erosion. Catena, Amsterdam, v. 83, n. 1, p. 87-93, 2010.




DOI: http://dx.doi.org/10.5433/1679-0359.2019v40n1p49

Semina: Ciênc. Agrár.
Londrina - PR
E-ISSN 1679-0359
DOI: 10.5433/1679-0359
E-mail: semina.agrarias@uel.br
Este obra está licenciado com uma Licença Creative Commons Atribuição-NãoComercial 4.0 Internacional