Abstract
Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.
Highlights
Spring phenology in temperate forests is a biological indicator of the near-term impacts of climate change [1]—regulating photosynthesis [2]; driving primary productivity and carbon cycling
Our analyses show that all three sensor platforms considered here effectively approximate phenological indicators, with above- and below-canopy methods estimating canopy closure earlier than the oblique-angle tower-based PhenoCams, but later than MODIS derived normalized difference vegetation index (NDVI)
We found there are optimal, but differing plot radii over which GCCUAV is averaged that best approximates below-canopy measured NDVI and structural attributes, leaf area index (LAI), and gap fraction
Summary
Spring phenology in temperate forests is a biological indicator of the near-term impacts of climate change [1]—regulating photosynthesis [2]; driving primary productivity and carbon cycling. Near-surface optical remote sensing, suborbital and ground-based methods, are well-suited for phenological. 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
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