Abstract
Plant phenology is a sensitive indicator of the effects of global change on terrestrial ecosystems and controls the timing of key ecosystem functions including photosynthesis and transpiration. Aerial drone imagery and photogrammetric techniques promise to advance the study of phenology by enabling the creation of distortion-free orthomosaics of plant canopies at the landscape scale, but with branch-level image resolution. The main goal of this study is to determine the leaf life cycle events corresponding to phenological metrics derived from automated analyses based on color indices calculated from drone imagery. For an oak-dominated, temperate deciduous forest in the northeastern USA, we find that plant area index (PAI) correlates with a canopy greenness index during spring green-up, and a canopy redness index during autumn senescence. Additionally, greenness and redness metrics are significantly correlated with the timing of budburst and leaf expansion on individual trees in spring. However, we note that the specific color index for individual trees must be carefully chosen if new foliage in spring appears red, rather than green—which we observed for some oak trees. In autumn, both decreasing greenness and increasing redness correlate with leaf senescence. Maximum redness indicates the beginning of leaf fall, and the progression of leaf fall correlates with decreasing redness. We also find that cooler air temperature microclimates near a forest edge bordering a wetland advance the onset of senescence. These results demonstrate the use of drones for characterizing the organismic-level variability of phenology in a forested landscape and advance our understanding of which phenophase transitions correspond to color-based metrics derived from digital image analysis.
Highlights
Phenology, the study of recurrent biological events, has been a focus of plant science for centuries [1]
While changes in canopy greenness have commonly been used to track spring budburst and leaf expansion in deciduous forests, we found that in some instances, these processes appeared to be more associated with changes in redness (Figures S2 and S3)
Using digital image processing techniques, we found that greenness metrics integrating several trees were correlated with canopy structural development (PAI) in springtime, while redness metrics were correlated with plant area index (PAI) decrease in autumn
Summary
The study of recurrent biological events, has been a focus of plant science for centuries [1]. Phenology responds to interannual and spatial variability in environmental conditions, temperature [2,3], and mediates key ecosystem functions, including carbon assimilation and evapotranspiration [4,5,6,7,8]. Tower- or building-mounted phenocams, typically located at positions just above the canopy [9,10,11], preserve a valuable visual record of vegetation phenology in forests and other ecosystems. “Near-surface” methods such as phenocams complement the phenology records of satellite remote sensing, which extensively observe entire landscapes, but at a spatial resolution that typically makes it impossible to discern individual plants [12].
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