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Quantifying Leaf Phenology of Individual Trees and Species in a Tropical Forest Using Unmanned Aerial Vehicle (UAV) Images

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dc.contributor.author Park, John Y. en
dc.contributor.author Muller-Landau, Helene en
dc.contributor.author Lichstein, Jeremy W. en
dc.contributor.author Rifai, Sami W. en
dc.contributor.author Dandois, Jonathan P. en
dc.contributor.author Bohlman, Stephanie A. en
dc.date.accessioned 2019-08-23T02:01:00Z
dc.date.available 2019-08-23T02:01:00Z
dc.date.issued 2019
dc.identifier.citation Park, John Y., Muller-Landau, Helene, Lichstein, Jeremy W., Rifai, Sami W., Dandois, Jonathan P., and Bohlman, Stephanie A. 2019. "<a href="https://www.mdpi.com/2072-4292/11/13/1534; https://www.mdpi.com/2072-4292/11/13/1534">Quantifying Leaf Phenology of Individual Trees and Species in a Tropical Forest Using Unmanned Aerial Vehicle (UAV) Images</a>." <em>Remote Sensing</em>. 11 (13):1534. <a href="https://doi.org/10.3390/rs11131534">https://doi.org/10.3390/rs11131534</a> en
dc.identifier.issn 2072-4292
dc.identifier.uri https://hdl.handle.net/10088/98607
dc.description.abstract Tropical forests exhibit complex but poorly understood patterns of leaf phenology. Understanding species- and individual-level phenological patterns in tropical forests requires datasets covering large numbers of trees, which can be provided by Unmanned Aerial Vehicles (UAVs). In this paper, we test a workflow combining high-resolution RGB images (7 cm/pixel) acquired from UAVs with a machine learning algorithm to monitor tree and species leaf phenology in a tropical forest in Panama. We acquired images for 34 flight dates over a 12-month period. Crown boundaries were digitized in images and linked with forest inventory data to identify species. We evaluated predictions of leaf cover from different models that included up to 14 image features extracted for each crown on each date. The models were trained and tested with visual estimates of leaf cover from 2422 images from 85 crowns belonging to eight species spanning a range of phenological patterns. The best-performing model included both standard color metrics, as well as texture metrics that quantify within-crown variation, with r2 of 0.84 and mean absolute error (MAE) of 7.8% in 10-fold cross-validation. In contrast, the model based only on the widely-used Green Chromatic Coordinate (GCC) index performed relatively poorly (r2 = 0.52, MAE = 13.6%). These results highlight the utility of texture features for image analysis of tropical forest canopies, where illumination changes may diminish the utility of color indices, such as GCC. The algorithm successfully predicted both individual-tree and species patterns, with mean r2 of 0.82 and 0.89 and mean MAE of 8.1% and 6.0% for individual- and species-level analyses, respectively. Our study is the first to develop and test methods for landscape-scale UAV monitoring of individual trees and species in diverse tropical forests. Our analyses revealed undescribed patterns of high intraspecific variation and complex leaf cover changes for some species. en
dc.relation.ispartof Remote Sensing en
dc.title Quantifying Leaf Phenology of Individual Trees and Species in a Tropical Forest Using Unmanned Aerial Vehicle (UAV) Images en
dc.type Journal Article en
dc.identifier.srbnumber 152067
dc.identifier.doi 10.3390/rs11131534
rft.jtitle Remote Sensing
rft.volume 11
rft.issue 13
rft.spage 1534
dc.description.SIUnit STRI en
dc.description.SIUnit Peer-reviewed en
dc.citation.spage 1534
dc.relation.url https://www.mdpi.com/2072-4292/11/13/1534; https://www.mdpi.com/2072-4292/11/13/1534


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