# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 1 Introduction  
Spring phenology in temperate forests is a biological indicator of the near-term impacts of climate change [1]--regulating photosynthesis [2]; thus, driving primary productivity and carbon cycling. Warming trends have resulted in both an advancement in the onset of spring vegetation activity phenophase, or leaf-out, and an extension of the growing season across the globe [3, 4]. It is necessary to quantify changes in the timing of phenophases that accompany anthropogenic climate change in order to constrain uncertainties in modeling the Earth system [5]. Near-surface optical remote sensing, specifically suborbital and ground-based methods, are well-suited for phenologicalobservation [6], with the most commonly used sensors being RGB cameras--cameras that capture reflectance in the red, green, and blue channels. For phenological observation, RGB cameras can be used either above-canopy (from the air, unoccupied aerial vehicles (UAV)), below-canopy (tripod-based or ground-based, facing upwards into the canopy), or at oblique-view angles (often tower-based, looking across the top-of-the-canopy). Comparisons of optical sensors have shown they are robust [7; 8; 9; 10; 11], but cross-platform validation of how view angle or observation perspective of the canopy influences canopy-level phenological assessment is necessary to inform scaling. UAV based sensors offer substantial potential to upscale plot observations to the stand or landscape level at finer resolutions than spaceborne platforms (e.g., MODIS, Landsat), but this potential must be informed by in situ measurements to be fully realized as remote sensing methods are best when informed by ground-validation. UAV-based sensors partially fill an extant gap in observational resolution and extent, while offering substantially higher temporal resolution than can realistically be garnered with spaceborne mapping. In phenological, as well as ecological research broadly, there are trade-offs in observational extent and resolution, in both space and time. This becomes an optimization problem where either resolution or extent must be prioritized. For example, spaceborne sensors that provide global coverage such as MODIS and AVIRIS that have relatively short return intervals provide relatively high temporal resolution on the order of 10 s of days, but do so by sacrificing spatial resolution. Conversely, small commercial satellites (e.g., CubeSats) can provide sub-meter spatial resolution with daily return intervals, but only over specific, highly limited spatial extents. UAV imagery can be collected over areas on the order of hectares in just a few hours; thus, providing an advantage in spatial extent over stationary RGB imagery [12; 13]. There are trade-offs however, as UAVs do require licensed operators and their use may be restricted by local ordinances or laws which almost universally require UAVs to remain in \"line-of-sight\". Further, UAVs may also be constrained by weather and battery life which can limit or prevent data collection. The nadir view-angle of UAV-based sensors (i.e., downward-facing) may introduce potential obfuscation and errors that are not associated with the oblique angle of tower-based sensors. These limitations have not hindered the adoption of UAV imagery for phenological study as the advantages outweigh the disadvantages [10; 14], however, forested ecosystems have thus far been comparatively underrepresented [9; 15; 16; 17; 18]. Research on tree-level phenology in temperate [16] and tropical systems [18] illustrates the potential of UAV imagery as a means to account for site heterogeneity and more precisely quantify the spatial distribution of phenological timing. Multi-temporal UAV-based studies in forest systems can provide detailed insight into the spatial drivers of phenological phenomenon and must be evaluated in a range of ecosystems.