# Mapping Wild Leek through the Forest Canopy Using a UAV
## 4 Discussion
Fully developed white trillium is the species that would most likely be confused with wild leek because of the color of its leaves and its tendency to grow in clusters. However, it does not form patches as dense as wild leek, especially at the beginning of wild leek emergence. This simplifies the classification, given the possibility that large and dense patches of non-target species could yield higher GCC values later in the season. Although in situ hyperspectral measurements of wild leek and white trillium have revealed higher NDVI values in white trillium leaves, the study demonstrated that patches of white trillium appeared as a fainter green from aerial imagery due to the lower plant density [18]. As a result, we suggest obtaining imagery at the early stage of wild leek appearance, when fewer spring species have emerged and white trillium leaves are not fully developed, to reduce confusion between wild leek and other green understory species. The simple and effective workflow outlined in this paper can be used as a framework for mapping and monitoring patches of wild leek in forested areas (Figure 7). In this workflow, the amount of human involvement is limited to the choice of the dynamic scale (s) and curvature tolerance (t) values for ground filtering, and it thus has potential for upscaling and/or repeat monitoring of an area, on the assumption that the GCC threshold value can be held constant across space and time, a question we have not investigated in this study. A study of multiple vegetation indices for tracking phenological events [59] examined the influences of camera type, weather, and time of day on the observed GCC values in forested environments and although they suggested using the GCC over alternative vegetation indices to follow canopy greenness due to its capacity to suppress most variation, these influences were not completely removed [58]. Unwanted variability due to scene illumination in imagery obtained during overcast vs. sunny days, or in sunlit vs. shaded areas, could potentially make comparisons more difficult. Therefore, it is important to interpret the outputs with care, as a change in GCC values caused by variations in illumination between years could lead to a biased classification and incorrect appearance of short-term changes in wild leek distribution. Minimizing sources of unwanted variability, e.g., in illumination, camera type, and altitude of the flight, will also improve the comparability of imagery between years and help draw more accurate conclusions concerning annual changes in wild leek distribution. To better exploit high resolution imagery, incorporating texture, size, pattern, and shape in an object-based classification could improve the classification results [29; 66]. When using high resolution imagery to detect bush honeyusckle, higher accuracies were obtained (90-95% overall accuracy) with an object-based image processing technique compared to pixel-based methods [29]. Even in more complex environments, an object-based method performed relatively well when incorporating textural attributes and vegetation indices for multiple species [32]. Using a set threshold has the benefit of being easily implemented, but might lack the flexibility and precision to separate other species present on the forest floor. Therefore, this method is not easily applicable to different species, compared to other methods based on phenological differences [21; 22]. Furthermore, improved accuracies might be achieved by using hyperspectral remote sensing data if available, to better discriminate wild leak from other forest floor species, which have been shown to display differences in spectral characteristics [18]. There is a trade-off between the extent of coverage produced by a given remote sensing technology, the accuracy with which it can be used to map wild leek, and the cost of using it.