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Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based SensorsPhenology 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.
Document ID
20220000104
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
Jeff W. Atkins ORCID
(Virginia Commonwealth University Richmond, Virginia, United States)
Atticus E. L. Stovall
(University of Maryland University College Adelphi, Maryland, United States)
Xi Yang ORCID
(University of Virginia Charlottesville, Virginia, United States)
Date Acquired
January 18, 2022
Publication Date
September 10, 2020
Publication Information
Publication: Drones
Publisher: MDPI
Volume: 4
Issue: 3
Issue Publication Date: September 1, 2020
e-ISSN: 2504-446X
Subject Category
Earth Resources And Remote Sensing
Funding Number(s)
CONTRACT_GRANT: 80NSSC21K0342
CONTRACT_GRANT: 80NSSC17K0110
CONTRACT_GRANT: NSF 1837891
CONTRACT_GRANT: NSF 2005574
CONTRACT_GRANT: NSF 1655095
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Technical Review
External Peer Committee
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