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Cultivation and grazing since the mid-nineteenth century in Texas has caused dramatic changes in grassland vegetation. Among these changes is the encroachment of native and introduced brush species. The distribution and quantity of brush can affect livestock production and water holding capacity of soil. Still, at the same time, brush can improve carbon sequestration and enhance agritourism and real estate value. The accurate identification of brush species and their distribution over large land tracts are important in developing brush management plans which may include herbicide application decisions. Near-real-time imaging and analyses of brush using an Unoccupied Aerial System (UAS) is a powerful tool to achieve such tasks. The use of multispectral imagery collected by a UAS to estimate the efficacy of herbicide treatment on noxious brush has not been evaluated previously. There has been no previous comparison of band combinations and pixel- and object-based methods to determine the best methodology for discrimination and classification of noxious brush species with Random Forest (RF) classification. In this study, two rangelands in southern Texas with encroachment of huisache (Vachellia farnesianna [L.] Wight & Arn.) and honey mesquite (Prosopis glandulosa Torr. var. glandulosa) were studied. Two study sites were flown with an eBee X fixed-wing to collect UAS images with four bands (Green, Red, Red-Edge, and Near-infrared) and ground truth data points pre- and post-herbicide application to study the herbicide effect on brush. Post-herbicide data were collected one year after herbicide application. Pixel-based and object-based RF classifications were used to identify brush in orthomosaic images generated from UAS images. The classification had an overall accuracy in the range 83–96%, and object-based classification had better results than pixel-based classification since object-based classification had the highest overall accuracy in both sites at 96%. The UAS image was useful for assessing herbicide efficacy by calculating canopy change after herbicide treatment. Different effects of herbicides and application rates on brush defoliation were measured by comparing canopy change in herbicide treatment zones. UAS-derived multispectral imagery can be used to identify brush species in rangelands and aid in objectively assessing the herbicide effect on brush encroachment. [ABSTRACT FROM AUTHOR]

The use of unmanned aircraft systems (UAS) delivering imaging technologies in agricultural settings has become more prevalent over the past five years and is growing in pest management programs. Here, spectral data from a three-band consumer-grade camera with a filter to obtain Near Infrared (NIR) data, mounted on a fixed-winged UAS, was used to assess the ability to detect cotton fleahopper, Pseudatomoscelis seriatus (Reuter) (Hemiptera: Miridae), injury to immature fruiting bodies on cotton. In a small plot experiment conducted two years and two planting periods each year, cotton fleahopper densities were manipulated with insecticide. Variable populations of cotton fleahopper across the plots were achieved in 2015, ranging between 0 and 3.5 cotton fleahopper-days over a five-week period when squares were forming. Derived from spectral data of multiple UAS flights, unexpected but inconsistent trends (by regression analysis) of increasing Normalized Difference Vegetation Index (NDVI), values with increasing cotton fleahopper days were detected in both plantings and years (five of 12 regressions were significant). Our preliminary data suggest that differences in cotton fleahopper activity on cotton may be reflected in NDVI values using a modified consumer-grade camera in-season. But the interpretation of NDVI may be complicated by the feeding site of cotton fleahopper, leading to unexpected and inconsistent regressions. Exploration of image resolution and bandwidth to define optical sensor needs appears important for cotton fleahopper, given its feeding habitat and injury to cotton. The application of UAS-derived remotely sensed data to detect insect-induced plant stress continues to have merit, but a merging of best suited UAS technology to the needs of detecting insect-induced cotton stress will be a researchintensive endeavor. [ABSTRACT FROM AUTHOR]