Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near‐surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on‐the‐ground phenological observations, leaf‐level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower‐based CO2flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter‐dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy‐level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature‐based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color‐based vegetation indices.
Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
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.
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- NSF-PAR ID:
- 10228631
- Date Published:
- Journal Name:
- Drones
- Volume:
- 4
- Issue:
- 3
- ISSN:
- 2504-446X
- Page Range / eLocation ID:
- 56
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Premise of the Study We investigated the spatial and temporal patterns of vegetation phenology with phenometrics derived from PhenoCam imagery. Specifically, we evaluated the Bioclimatic Law proposed by Hopkins, which relates phenological transitions to latitude, longitude, and elevation.
Methods “Green‐up” and “green‐down” dates—representing the start and end of the annual cycles of vegetation activity—were estimated from measures of canopy greenness calculated from digital repeat photography. We used data from 65 deciduous broadleaf (
DB ) forest sites, 18 evergreen needleleaf (EN ) forest sites, and 21 grassland (GR ) sites.Results DB green‐up dates were well correlated with mean annual temperature and varied along spatial gradients consistent with the Bioclimatic Law. Interannual variation inDB phenology was most strongly associated with temperature anomalies during a relatively narrow window of time.EN phenology was not well correlated with either climatic factors or spatial gradients, but similar toDB phenology, interannual variation was most closely associated with temperature anomalies. ForGR sites, mean annual precipitation explained most of the spatial variation in the duration of vegetation activity, whereas both temperature and precipitation anomalies explained interannual variation in phenology.Discussion PhenoCam data provide an objective and consistent means by which spatial and temporal patterns in vegetation phenology can be investigated.
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Abstract In dryland ecosystems, vegetation within different plant functional groups exhibits distinct seasonal phenologies that are affected by the prevailing hydroclimatic forcing. The seasonal variability of precipitation, atmospheric evaporative demand, and streamflow influences root-zone water availability to plants in water-limited environments. Increasing interannual variations in climate forcing of the local water balance and uncertainty regarding climate change projections have raised the potential for phenological shifts and changes to vegetation dynamics. This poses significant risks to plant functional types across large areas, especially in drylands and within riparian ecosystems. Due to the complex interactions between climate, water availability, and seasonal plant water use, the timing and amplitude of phenological responses to specific hydroclimate forcing cannot be determined a priori , thus limiting efforts to dynamically predict vegetation greenness under future climate change. Here, we analyze two decades (1994–2021) of remote sensing data (soil adjusted vegetation index (SAVI)) as well as contemporaneous hydroclimate data (precipitation, potential evapotranspiration, depth to groundwater, and air temperature), to identify and quantify the key hydroclimatic controls on the timing and amplitude of seasonal greenness. We focus on key phenological events across four different plant functional groups occupying distinct locations and rooting depths in dryland SE Arizona: semi-arid grasses and shrubs, xeric riparian terrace and hydric riparian floodplain trees. We find that key phenological events such as spring and summer greenness peaks in grass and shrubs are strongly driven by contributions from antecedent spring and monsoonal precipitation, respectively. Meanwhile seasonal canopy greenness in floodplain and terrace vegetation showed strong response to groundwater depth as well as antecedent available precipitation (aaP = P − PET) throughout reaches of perennial and intermediate streamflow permanence. The timings of spring green-up and autumn senescence were driven by seasonal changes in air temperature for all plant functional groups. Based on these findings, we develop and test a simple, empirical phenology model, that predicts the timing and amplitude of greenness based on hydroclimate forcing. We demonstrate the feasibility of the model by exploring simple, plausible climate change scenarios, which may inform our understanding of phenological shifts in dryland plant communities and may ultimately improve our predictive capability of investigating and predicting climate-phenology interactions in the future.more » « less
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Satellite and aerial imagery have been used extensively for mapping the abundance and distribution of giant kelp ( Macrocystis pyrifera ) in southern California. There is now great potential for using unoccupied aerial vehicles (UAVs) to map kelp canopy at very high resolutions. However, tides and currents have been shown to affect the amount of floating kelp canopy on the water surface, and the impacts of these processes on remotely sensed kelp estimates in this region have not been fully quantified. UAVs were used to map fine-scale changes in canopy area due to tidal height and current speed at kelp forests off the coast of Palos Verdes, CA and Santa Barbara, CA. An automated method for detecting kelp canopy was developed that was 67% accurate using red-green-blue (RGB) UAV imagery and 93% accurate using multispectral UAV imagery across a range of weather, ocean, and illumination conditions. Increases in tidal height of 1 m reduced the amount of floating kelp canopy by 15% in Santa Barbara and by over 30% in Palos Verdes. The effect of current speed on visible kelp canopy was inconclusive, but there was a trend towards lower canopy area with increased current speed. Therefore, while tidal height and current speed can introduce significant variability to estimates of kelp abundance, the magnitude of this variability is site specific. Still, UAVs are a valuable tool for mapping of kelp canopy and can provide greater spatial resolution and temporal coverage than is possible from many satellite sensors. This data can provide insight into the patterns and drivers of high frequency fluctuations in kelp abundance.more » « less
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Land surface phenology (LSP) enables global-scale tracking of ecosystem processes, but its utility is limited in drylands due to low vegetation cover and resulting low annual amplitudes of vegetation indices (VIs). Due to the importance of drylands for biodiversity, food security, and the carbon cycle, it is necessary to understand the limitations in measuring dryland dynamics. Here, using simulated data and multitemporal unmanned aerial vehicle (UAV) imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, VI uncertainty, and two different detection algorithms. Using simulated data, we found that plants with distinct VI signals, such as deciduous shrubs, can require up to 60% fractional cover to consistently detect LSP. Evergreen plants, with lower seasonal VI amplitude, require considerably higher cover and can have undetectable phenology even with 100% vegetation cover. Our evaluation of two algorithms showed that neither performed the best in all cases. Even with adequate cover, biases in phenological metrics can still exceed 20 days and can never be 100% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. We showed how high-resolution UAV imagery enables LSP studies in drylands and highlighted important scale effects driven by within-canopy VI variation. With high-resolution imagery, the open canopies of drylands are beneficial as they allow for straightforward identification of individual plants, enabling the tracking of phenology at the individual level. Drylands thus have the potential to become an exemplary environment for future LSP research.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/drones4030056
