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Comparing phenocam color indices with phenological observations of black spruce in the boreal forest

Li Xiaoxia, Khare Suyash, Khare Siddhartha, Jiang Nan, Liang Eryuan, Deslauriers Annie et Rossi Sergio. (2023). Comparing phenocam color indices with phenological observations of black spruce in the boreal forest. Ecological Informatics, 76, (e102149), p. 1-9.

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URL officielle: https://dx.doi.org/doi:10.1016/j.ecoinf.2023.10214...

Résumé

Bud phenology identifies the growing period of trees and determines the pattern of mass and energy exchanges between forest and atmosphere over time and space. Canopy color metrics derived from phenocams have been widely used to investigate tree phenology. However, it remains unclear which color-based index better tracks the seasonal variations of tree phenology in evergreen forest ecosystems. Herein, we compared four color metrics (red chromatic coordinate (RCC), green chromatic coordinate (GCC), vegetation contrast index (VCI) and excess green index (ExG)) derived from phenocam images with bud phenological phases recorded in black spruce [Picea mariana (Mill.) B.S·P] during 2017–2020 at a boreal forest site in Quebec, Canada. Canopy redness (RCC) and greenness (GCC, ExG, and VCI) showed a bimodal and bell-shaped seasonal pattern, respectively. The phases of bud burst and bud set lasted from end-May to end-June and from mid-July to end-September, respectively. The neural network model indicated that GCC had the best predictive ability in capturing the sequential phases of bud phenology. Bud phenological phases predicted by GCC showed the highest correlation with actual bud phenological phases among four indices, with R2 above 0.9 and RMSE lower than 0.5. Overall, color indices performed better when representing bud burst than bud set. Our findings improve the efficiency and confidence of the phenocam greenness index to characterize the growing season of evergreen forests.

Type de document:Article publié dans une revue avec comité d'évaluation
ISSN:15749541
Volume:76
Numéro:e102149
Pages:p. 1-9
Version évaluée par les pairs:Oui
Date:Septembre 2023
Nombre de pages:1
Identifiant unique:10.1016/j.ecoinf.2023.102149
Sujets:Sciences naturelles et génie > Sciences naturelles > Biologie et autres sciences connexes
Département, module, service et unité de recherche:Départements et modules > Département des sciences fondamentales
Unités de recherche > Centre de recherche sur la Boréalie (CREB)
Mots-clés:Remote sensing, tree phenology, digital repeat photography, evergreen conifers, PhenoCam
Déposé le:02 févr. 2024 00:11
Dernière modification:02 févr. 2024 00:11
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