# Mapping Wild Leek through the Forest Canopy Using a UAV
## 1 Introduction
In contrast, some studies demonstrated that the late leaf retention of the invasive Amur honeysuckle allowed for the detection of the species using Landsat images obtained during a short window of time [23; 24; 25]. Similarly, other studies detected evergreen understory vegetation from Landsat images during the leaf-off period in deciduous forests [26; 27]. However, high resolution imagery is often necessary to map species that occur in small patches and provide more accurate distribution estimates and aerial photography has been used to identify species in wetlands, forested, and urban environments with high accuracies [28; 29; 30]. Multispectral and hyperspectral data has also proven to be effective in classifying invasive species using object-based classification methods [31; 32; 33]. An important disadvantage of using such data is the typically high cost associated with obtaining or producing the data. Figure 1: (a) Small and (b) bigger patch of wild leek in Gatineau Park, Quebec. Photos taken on 12 May 2016. Wild leek form dense patches that can cover large areas, unlike other spring ephemerals that tend to grow more sporadically. Recent developments in the use of Unmanned Aerial Vehicles (UAV) make these a low-cost option to acquire high resolution imagery for local-scale investigations of vegetation dynamics and ecological processes [34]. Recently, UAVs have gained interest as a data acquisition tool for targeted monitoring, largely for the management of invasive species where timely and repeated assessments are required [35; 36]. In comparison to more conventional methods for acquiring high resolution imagery, such as images obtained from aircraft or satellites, the operational requirements and cost of UAVs are reduced as they can be operated by personnel with basic training. Although the spectral information produced by the cameras on standard UAVs is often limited to three visible bands (RGB), the use of UAVs can provide important information at the individual and species level that would otherwise require detailed and time-consuming field sampling [37; 38; 39]. UAV imagery from the visible range has in fact been used to delineate the invasive _Phragmites_ in wetlands with an overall accuracy of 85% [40], although for other species, such as the invasive yellow flag iris, an automated classification yielded poor results compared to the manual interpretation of the imagery [41]. In addition to providing a high spatial resolution, being light-weight, and easy to use, UAVs can avoid cloud contamination that often obscures features of interest in satellite imagery. Frequent and user-controlled revisit times offered by UAV-based systems are therefore important advantages that allow the capture of short phenological events for which cloud contamination is problematic [31; 34]. When used to map the forest floor, there can be substantial contamination of the imagery from canopy features even before the leaves develop. Because spring ephemerals are forest floor species, the removal of elements of the canopy layer is necessary to obtain accurate information about ground features. The generation of point clouds based on imagery from UAV platforms has emerged as an attractive method for the retrieval of surface elevation information, allowing separation of the forest floor and canopy objects [42; 43; 44]. The Structure from Motion (SfM) framework performs image reconstruction by matching features in overlapping images to reconstruct both the camera pose and the imaged 3D environment. This technique allows the acquisition of automated and accurate 3D information on landscape features. It possesses significant advantages over traditional stereo photogrammetric methods because it eliminates the requirement for separate camera calibration, and estimates camera pose and scene geometry automatically through bundle adjustment [43; 45]. Simple and automated processes for detecting important plant species using high-resolution RGB color imagery are still lacking.