VEGETATIVE VIGOR OF MAIZE CROP OBTAINED THROUGH VEGETATION INDEXES IN ORBITAL AND AERIAL SENSORS IMAGES

Authors

  • L.S. Santana
  • G.A e S. Ferraz
  • L.M. Santos
  • D.A. Maciel
  • R.A.P. Barata
  • É. F. Reynaldo
  • G. Rossi

DOI:

https://doi.org/10.18011/bioeng2019v13n3p195-206

Keywords:

remote sensing, vegetative vigor, precision agriculture, Unmanned Aircraft System (UAS)

Abstract

Currently, images from unmanned aerial vehicles (UAVs) are being used due to their high spatial and temporal resolution. Studies comparing different mobile data acquisition platforms, such as satellites, are important due to the limited spatial and temporal resolution of some satellites as well of the presence of clouds in such images. The objective of this study was to compare the vegetation indices (VIs) generated from images obtained by orbital (satellite) and sub-orbital (unmanned aerial vehicles - UAV) platforms. The experiment was conducted in a maize-growing area in Paraná, Brazil. Landsat 8 and UAV images of the study area were collected. Four VIs were applied: NDVI, VIgreen, ExG and VEG. The NDVI was selected as the control and compared with the other VIs. There was a good correlation (0.79) between the NDVI and the VEG for the UAV images. For the Landsat images, the highest correlation found was between the NDVI and the VIgreen derived from UAV images, which was 0.89. It is concluded that the images obtained by UAVs generated better indices, mainly in the dry season.

Downloads

Download data is not yet available.

References

ADÃO, T.; HRUÅ KA, J.; PÁDUA, L.; BESSA, J.; PERES, E.; MORAIS, R.; SOUSA, J. Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing, v. 9(11): 11-10, 2007.

AGISOFT. Software Agisoft PhotoScan. available in: http://www.agisoft.ru/products/photoscan/professional/buy/educational/. Access in: April, 2019.

DI LENA, P.; MARGARA, L. Optimal global alignment of signals by maximization of Pearson correlation. Information Processing Letters, v. 110, n. 16: 679-686, 2010.

DING, Y.; ZHAO, K.; ZHENG, X.; JIANG, T. Temporal dynamics of spatial heterogeneity over cropland quantified by time-series NDVI, near infrared and red reflectance of Landsat 8 OLI imagery. International Journal of Applied Earth Observation and Geoinformation, v. 30, n. 1: 139–145, 2014.

DUAN, T.; CHAPMAN, S. C.; GUO, Y.; ZHENG, B. Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle. Field Crops Research, v. 210: 71–80, 2017.

EMBRAPA – Empresa Brasileira de Pesquisas Agropecuária, Sistema Brasileiro de Classificação dos Solos, 590p. 5. ed. 2018.

GITELSON, A. A.; KAUFMAN, Y. J.; STARK, R.; RUNDQUIST, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, v. 80, n. 1: 76–87, 2002.

GRAESSER, J.; RAMANKUTTY, N. Detection of cropland field parcels from Landsat imagery. Remote Sensing of Environment, v. 201: 165–180, 2017.

HE, Z.; DU, J.; ZHAO, W.; YANG, J.; CHEN, L.; ZHU, X.; CHANG, X.; LIU, H. Assessing temperature sensitivity of subalpine shrub phenology in semi-arid mountain regions of China. Agricultural and Forest Meteorology, v. 213: 42–52, 2015.

JACKSON, T. J.; CHEN, D.; COSH, M.; LI, F.; ANDERSON, M.; WALTHALL, C.; DORIASWAMY, P.; HUNT, E. R. Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment, v. 92, n. 4: 475–482, 2004.

JARLAN, L.; MANGIAROTTI, S.; MOUGIN, E.; MAZZEGA, P.; HIERNAUX, P.; LE DANTEC, V. Assimilation of SPOT/VEGETATION NDVI data into a sahelian vegetation dynamics model. Remote Sensing of Environment, v. 112, n. 4: 1381–1394, 2008.

JIN, X.; KUMAR, L.; LI, Z.; FENG, H.; XU, X.; YANG, G.; WANG, J. A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, v. 92: 141–152, 2018.

MADUGUNDU, R.; AL-GAADI, K. A.; TOLA, E.; KAYAD, A. G.; JHA, C. S. Estimation of gross primary production of irrigated maize using Landsat-8 imagery and Eddy Covariance data. Saudi Journal of Biological Sciences, v. 24, n. 2: 410–420, 2017.

MATHER P, M. Computer processing of remotely-sensed images: an introduction. New York: John Wiley & Sons; 1999. p. 292.

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

MU, Y.; LIU, X.; WANG, L. A Pearson's correlation coefficient-based decision tree and its parallel implementation. Information Sciences, v. 435: 40–58, 2018.

QGIS DEVELOPMENT TEAM. QGIS Geographic Information System. Open Source Geospatial Foundation Project. URL:http://qgis.osgeo.org. Access in: April, 2019.

SILVA, R. DE S. V. Uso de imagens multiespectrais de baixo custo para classificar níveis de N aplicados ao solo em agricultura de precisão. Dissertação. 56p. 2016.

ROUSE, J. W.; HAAS, R. H.; SCHEEL, J. A.; DEERING, D. W. Monitoring Vegetation Systems in the Great Plains with ERTS. 3rd Earth Resource Technology Satellite (ERTS) Symposium. In Proc. Conf: 309-317, 1973.

SAYAGO, S.; OVANDO, G.; BOCCO, M. Landsat images and crop model for evaluating water stress of rainfed soybean. Remote Sensing of Environment, v. 198: 30–39, 2017.

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

WOEBBECKE, D. M.; MEYER, G. E.; VON BARGEN, K.; MORTENSEN, D. A. Shape features for identifying young weeds using image analysis. Transactions of the ASAE (American Society of Agricultural Engineers), v. 38, n. 1, p. 271–281, 1995.

ZHANG, C.; KOVACS, J. M. The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, v. 13, n. 6: 693–712, 2012.

ZHANG, J.; YANG C.; SONG, H.; HOFFMANN, W.; ZHANG, D.; ZHANG, G. Evaluation of an airborne remote sensing platform consisting of two consumer-grade cameras for crop identification. Remote Sensing, v. 8, n. 3, p. 257, 2016.

Downloads

Published

2019-09-30

How to Cite

Santana, L., Ferraz, G. e S., Santos, L., Maciel, D., Barata, R., Reynaldo, É. F., & Rossi, G. (2019). VEGETATIVE VIGOR OF MAIZE CROP OBTAINED THROUGH VEGETATION INDEXES IN ORBITAL AND AERIAL SENSORS IMAGES. Revista Brasileira De Engenharia De Biossistemas, 13(3), 195–206. https://doi.org/10.18011/bioeng2019v13n3p195-206

Issue

Section

Regular Section