(a) The optimal segmentation layer for the 11 cm dataset (false color composite) with overlaid training data points from the four vegetation classes. (b) The optimal segmentation layer for the 3 cm imagery of the same area and training data points.

(a) The optimal segmentation layer for the 11 cm dataset (false color composite) with overlaid training data points from the four vegetation classes. (b) The optimal segmentation layer for the 3 cm imagery of the same area and training data points.

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Detecting newly established invasive plants is key to prevent further spread. Traditional field surveys are challenging and often insufficient to identify the presence and extent of invasions. This is particularly true for wetland ecosystems because of difficult access, and because floating and submergent plants may go undetected in the understory...

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... implemented the ArcGIS Mean Shift Segmentation algorithm through the SegOptim package in R, a newer approach that closely integrates optimization, segmentation and classification algorithms [43]. To optimize the three segmentation parameters in this function ('Spectral Detail', 'Spatial Detail', and 'Minimum Segment Size'), we ran a genetic algorithm model to obtain the best "fitness" values, or those parameters that yielded highest classification accuracies from subsequent RF model runs of 200-500 image segmentations of the datasets (depending on spatial resolution and time constraints; see Figure 3 for optimal outputs for each spatial resolution). To obtain these values, we set value ranges for each segmentation parameter that we thought to be reasonable in representing the realistic conditions of spectral and spatial ranges, along with minimum segment size, in the imagery. ...
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... emphasize that this was only the case for the Emergent and Floating classes, whereas we achieved higher producer accuracy for Submergent vegetation at the very-high resolution. One explanation for this trend is that the smaller 3 cm pixels, along with distinct spectral and structural values for this class ( Figure S3), better captured the smaller, interspersed areas of Submergent vegetation in the masked image footprint. Previous research in UAS forestry classification also found higher classification accuracies in imagery of coarser spatial resolution depending on the density of tree stands [46]. ...