# Mapping Wild Leek through the Forest Canopy Using a UAV
## Materials and Methods
### Remote Sensing Data, Acquisition and Processing  
On 10 and 11 May 2017, a nadir-pointing video was captured for Area 1 and Area 2 using a DJI Phantom 3 Standard UAV. Before the flights, brightly colored markers were placed at the four corners of each area and georeferenced with a Trimble R1 DGPS unit (typical accuracy on forest floor: 70-100 cm). At each area, the UAV was launched from the nearest trail, and then flown in a pre-programmed snaking pattern covering the entire site at 60 m above ground level while capturing video in a 2.7 K mode. Individual image frames were extracted to a jpeg format (2704 \(\times\) 1520 pixels) from these videos (1326 frames from Area 1; 1765 from Area 2), and the camera position for each frame was estimated from the UAVs GPS log. Point clouds for each area were generated by processing the frames using the SfM framework in Pix4D, using settings for the highest quality, slowest processing (full image scale, high point density). Pix4D uses each frame's georeference information as an initial guess, and then optimizes both the position and orientation of the camera for each image, as well as the camera's internal geometry and the positions of features identified in the image frames, using a bundle adjustment process [43]. The identified features and their location in each image are then used to detect additional features in the image set and determine their locations, resulting in a dense point cloud. Processing of the UAV imagery with Pix4D generated very dense point clouds (\(\sim\)50 million points) for each area. We then used the GPS coordinates of the markers, which were clearly visible in the dense point clouds, to improve the absolute georeference of each point cloud. All points in the point clouds were classified as ground or non-ground using a Multiscale Curvature Classification algorithm developed for LIDAR data (MCC-LIDAR) [49] in order to remove points located above ground level (i.e., in the canopy). MCC-LIDAR uses an iterative multiscale curvature classification algorithm to separate a point cloud into ground and non-ground data points. It employs two tunable parameters; the dynamic scale (s) and the curvature tolerance (t). The scale parameter (s) governs the sampling interval (post-spacing), and should be set with a consideration of the scale of the objects on the ground. A greater s parameter assumes a relatively smoother surface, but may erase the natural forms of the terrain [50]. The curvature tolerance parameter (t) indicates the threshold of the local deviations from surrounding points and should be tuned according to local topography and vegetation structure [49]. Using these two parameters, the algorithm iteratively compares the elevation of each point in the point cloud to a smoothed raster DEM based on points within the scale domain (determined by s), and eliminates those with an elevation too far (as determined by t) above the DEM. To process the point clouds, multiple combinations of s and t were tested. Based on a visual inspection of results produced with a range of variables, s = 0.1 and t = 1 gave the best results. Using these settings, points exceeding the curvature threshold were classified as non-ground and removed from the point clouds before further processing. For both areas, 5-cm resolution orthomosaics were created based on the remaining ground points, using CloudCompare [51]. Data gaps, largely due to the removal of tree trunks, were filled by interpolating RGB values from neighboring points during the rasterization process. From these orthomosaics, the Green Chromatic Coordinate (GCC) was calculated. The GCC correlates with measurements of GPP, NDVI, and EVI [52; 53; 54] and is a measure of greenness widely used to monitor canopy development and the changing levels of green pigmentation when only information from the visible spectrum is available. The GCC has been used mostly to follow phenological events of various plants and trees [55; 56; 37; 52; 54].