2024-03-28T18:57:41Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/1272282020-05-27T13:50:26Zcom_10261_74com_10261_6col_10261_327
Torres-Sánchez, Jorge
Peña Barragán, José Manuel
Castro, Ana Isabel de
López Granados, Francisca
2015-12-29T07:52:18Z
2015-12-29T07:52:18Z
2014-03-15
Computers and Electronics in Agriculture 103: 104- 113 (2014)
http://hdl.handle.net/10261/127228
10.1016/j.compag.2014.02.009
http://dx.doi.org/10.13039/501100003339
http://dx.doi.org/10.13039/501100000780
Mapping vegetation in crop fields is an important step in remote sensing applications for precision agriculture. Traditional aerial platforms such as planes and satellites are not suitable for these applications due to their low spatial and temporal resolutions. In this article, a UAV equipped with a commercial camera (visible spectrum) was used for ultra-high resolution image acquisition over a wheat field in the early-season period. From these images, six visible spectral indices (CIVE, ExG, ExGR, Woebbecke Index, NGRDI, VEG) and two combinations of these indices were calculated and evaluated for vegetation fraction mapping, to study the influence of flight altitude (30 and 60 m) and days after sowing (DAS) from 35 to 75 DAS on the classification accuracy. The ExG and VEG indices achieved the best accuracy in the vegetation fraction mapping, with values ranging from 87.73% to 91.99% at a 30 m flight altitude and from 83.74% to 87.82% at a 60 m flight altitude. These indices were also spatially and temporally consistent, allowing accurate vegetation mapping over the entire wheat field at any date. This provides evidence that visible spectral indices derived from images acquired using a low-cost camera onboard a UAV flying at low altitudes are a suitable tool to use to discriminate vegetation in wheat fields in the early season. This opens the doors for the utilisation of this technology in precision agriculture applications such as early site specific weed management in which accurate vegetation fraction mapping is essential for crop-weed classification
eng
closedAccess
Mosaicked image
Digital camera
Classification
Early site-specific weed mapping (ESSWM)
Visible vegetation indices
Narrow row crop
Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images UAV
artículo