Comparing phenocam color indices with phenological observations of black spruce in the boreal forest |
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| Institution: | 1. State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China;2. Laboratoire sur les écosystèmes terrestres boréaux, Département des Sciences Fondamentales, Université du Québec à Chicoutimi, 555, Boulevard de l''Université, Chicoutimi, QC G7H2B1, Canada;3. Geomatics Engineering Division, Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India;4. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China;1. Ecology and Future Research Institute, Busan 46228, Republic of Korea;3. Bureau of Conservation and Assessment Research, National Institute of Ecology, Seocheon 33657, Republic of Korea;4. Environment Team, Samsung Electronics, Hwaseong 18448, Republic of Korea;5. Duru Institute of Environmental Ecology, Daegu 41069, Republic of Korea;6. Department of Biological Sciences, Andong National University, Andong 36729, Republic of Korea;7. Environmental Research Center, Andong National University, Andong 36729, Republic of Korea;8. Watershed and Total Load Management Research Division, National Institute of Environmental Research, Incheon 22689, Republic of Korea;9. Department of Biology, Kyung Hee University, Seoul 02447, Republic of Korea;1. Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, 723 Xingke Road, Tianhe District, Guangzhou 510650, China;2. University of Chinese Academy of Sciences, 19(A) Yuquan Road, Shijingshan District, Beijing 100049, China;3. MOE Key Laboratory of Biosystems Homeostasis and Protection, College of Life Sciences, Zhejiang University, Hangzhou 310000, China;4. Key Laboratory of Tree-Ring Physical and Chemical Research, CMA/Xinjiang Key Laboratory of Tree-Ring Ecology, Institute of Desert Meteorology, China Meteorological Administration, Urumqi 830002, China;5. CSIC, Global Ecology Unit CREAF-CSIC-UAB Bellaterra, Barcelona 08193, Catalonia, Spain;6. CREAF, Bellaterra, Barcelona 08193, Catalonia, Spain;7. Guangzhou Institute of Forestry and Landscape Architecture, Guangzhou 510405, China;1. Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze-Huaihe River Basin, School of Geography and Tourism, Anhui Normal University, Wuhu 241000, China;2. DendroLab, Department of Natural Resources and Environmental Science, University of Nevada, Reno, NV 89557, USA;3. Department of Geography, Johannes Gutenberg University, Mainz, Germany |
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| Abstract: | 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. |
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