Timely detection and remediation of volunteer cotton plants in
both cultivated and non-cultivated habitats is critical for completing boll
weevil eradication in Central and South Texas.  The study investigates the use
of aerial imagery and ground-based remotely sensed data for the discrimination
of different crop types and timely detection of cotton plants over large areas.
Airborne multispectral imagery and ground-based spectral reflectance data were
acquired at the same time over three large agricultural farms in Brazos County
in Texas during the 2010 growing season. The performances of imagery data and
handheld data for the discrimination were examined individually; then
multisensor data fusion technique was applied on both aerial and ground
datasets in order to improve the accuracy of the discrimination. The
classification accuracy with fused data was 100%; fused data did have better
performance on discrimination analysis than the data taken with a single
sensor alone.