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Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
| Content Provider | MDPI |
|---|---|
| Author | Atkins, Jeff Stovall, Atticus Yang, Xi |
| Copyright Year | 2020 |
| 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. |
| Starting Page | 56 |
| e-ISSN | 2504446X |
| DOI | 10.3390/drones4030056 |
| Journal | Drones |
| Issue Number | 3 |
| Volume Number | 4 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-09-10 |
| Access Restriction | Open |
| Subject Keyword | Drones Remote Sensing Phenology Scaling Rgb Imagery Ndvi |
| Content Type | Text |
| Resource Type | Article |
