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Lookup NU author(s): Dr Elias Berra,
Dr Rachel GaultonORCiD,
Professor Stuart Barr
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. This article assesses the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data (5 cm spatial resolution, ~7 day temporal resolution) were acquired in tandem with an intensive ground campaign during the spring season of 2015 across a 15 ha mixed woodland. Phenophase transition dates were estimated at an individual tree-level using UAV time series of Normalized Difference Vegetation Index (NDVI) and Green Chromatic Coordinate (GCC) and validated against visual observations of tree phenology. UAV-derived start of season dates could be predicted with an accuracy of <1 week. The analysis was scaled to a plot level, where ground (visual assessment and understorey development), UAV and Landsat metrics were compared, indicating UAV data is effective for tracking canopy phenology, as opposed to ecosystem dynamics detected by satellites. The UAV data were used to automatically map phenological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This, and a large temporal gap in the Landsat series, accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R2 < 0.50) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, providing information which could improve characterization of vegetation phenology at multiple scales.
Author(s): Berra EF, Gaulton R, Barr SL
Publication type: Article
Publication status: Published
Journal: Remote Sensing of Environment
Print publication date: 15/03/2019
Online publication date: 28/01/2019
Acceptance date: 09/01/2019
Date deposited: 29/01/2019
ISSN (print): 0034-4257
ISSN (electronic): 1879-0704
Publisher: Elsevier Inc.
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