Title: Potential for remote sensing to detect and predict herbicide injury on waterhyacinth (Eichhornia crassipes)
Abstract: Many large-scale management programs directed
toward the control of waterhyacinth rely on maintenance management with
herbicides. Improving the implementation of these programs could be achieved
through accurately detecting herbicide injury in order to evaluate efficacy.
Mesocosm studies were conducted in the fall and summer of 2006 and 2007 at the
R. R. Foil Plant Science Research Center, Mississippi State University, to
detect and predict herbicide injury on waterhyacinth treated with four different
rates of imazapyr and glyphosate. Herbicide rates corresponded to maximum
recommended rates of 0.6 and 3.4 kg ae ha−1 (0.5 and
3 lb ac−1) for imazapyr and glyphosate, respectively, and
three rates lower than recommended maximum. Injury was visually estimated using
a phytotoxicity rating scale, and reflectance measurements were collected using
a handheld hyperspectral sensor. Reflectance measurements were then transformed
into a Landsat 5 Thematic Mapper (TM) simulated data set to obtain pixel values
for each spectral band. Statistical analyses were performed to determine if a
correlation existed between bands 1, 2, 3, 4, 5, and 7 and phytotoxicity
ratings. Simulated data from Landsat 5 TM indicated that band 4 was the most
useful band to detect and predict herbicide injury of waterhyacinth by
glyphosate and imazapyr. The relationship was negative because pixel values of
band 4 decreased when herbicide injury increased. At 2 wk after treatment,
the relationship between band 4 and phytotoxicity was best (r2
of 0.75 and 0.90 for glyphosate and imazapyr, respectively), which served to
predict herbicide injury in the following weeks. [Wilfredo Robles, John D.
Madsen & Ryan M. Wersal (2010). Potential for remote sensing to detect
and predict herbicide injury on waterhyacinth (Eichhornia crassipes).
Invasive Plant Science and Management, 3(4):440-450. doi:
10.1614/IPSM-D-09-00040.1]