# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 1 Introduction
RGB cameras are powerful phenological tools because indices of plant greenness, such as the Green Chromatic Coordinate (GCC), a ratio of the green channel to all RGB channels, can be derived from standard RGB imagery [19]. GCC is well correlated with canopy development, leaf-out, and photosynthesis [20; 21]. GCC is one index used widely by the PhenoCam network, a network of over 400 cameras across North America (as of 2020), each uploading images to a server every 30 min [22]. Data from these cameras are made publicly available free of charge, as are open-source software solutions to interpret and analyze PhenoCam data. Repeat RGB imagery, such as the PhenoCam network [22], has the advantage of consistency and high-temporality. The images collected can readily be compared to each other and end-users know the data are always the same areas of the canopy, from image to image. However, PhenoCam data provide snap shots of only one area of the canopy, which limits the assessment of site heterogeneity. Below-canopy observation is often conducted with cameras outfitted with hemispherical lenses, mounted on tripods, facing upwards into the canopy [23]. This method has the advantage of isolating the canopy, which removes any interference from the ground or understory vegetation. Ground-based methods of capturing phenology are limited by user mobility, image quality, sun angle, and sky conditions. On clear sky days, early morning or late evening hours are preferable. Isolating the canopy allows for the estimation of additional forest structural metrics, such as leaf area index (LAI)and canopy gap fraction, which are not necessarily directly inferable from all other sensors surveyed here [24]. In addition to, or in replace of, standard RGB channels, cameras can include near-infrared (NIR) channels, which allow the calculation of the normalized difference vegetation index (NDVI), a common index in spaceborne remote sensing that relates the red and NIR channels in imagery to estimate plant greenness and vitality [25; 26]. For near-surface remote sensing, the blue channel can be used in place of the red channel in calculating NDVI. There are large differences in the spatial extents that can be efficiently measured using the sensors we surveyed. Below-canopy cameras capture only point-measurements of limited areas of the canopy--capturing on the order of tens to thousands of square meters of canopy area as compared to thousands or more in the case of tower-based PhenoCams, or rather continuous UAV imagery, which can cover on the order of hectares [9]. The additional canopy structural information provided by below-canopy imagery could scale from the plot- to stand-scale with coincident UAV imagery. Given the difference in spatial extent, resolution, and perspective, however, it is necessary to quantify the resolution at which UAV imagery is most comparable to hemispherical imagery. The purpose of this study is to assess three methods of recording vegetation phenology: (1) a ground-based, below-canopy, upward-facing digital single-lens reflex (DSLR) camera commercially designed for vegetation studies with B, G, and NIR channels; (2) a UAV-based, downward-facing or nadir-view RGB camera; and (3) a tower-based, oblique-perspective, RGB camera specifically designed for phenological study as part of the PhenoCam network. Comparisons among these sensors are necessary in order to understand what elements of phenology are being represented from any specific perspective, as well as how combining these measurements may provide more representative measurements of vegetation phenology. To this end, we tested each of these approaches in a mixed, temperate forests across the spring phenophase transition to full canopy closure in 2018 to answer the following questions:  
1. At what spatial extent are above-canopy (UAV-based imagery) remote sensing metrics most representative of below-canopy (ground-based, hemispherical photography) vegetation metrics? 2.