Hu Haibo

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Hu
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Haibo
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  • Article
    Relationship between leaf physiologic traits and canopy color indices during the leaf expansion period in an oak forest
    (John Wiley & Sons, 2015-12-11) Liu, Zhunqiao ; Hu, Haibo ; Yu, Hua ; Yang, Xi ; Yang, Hualei ; Ruan, Cunxin ; Wang, Yan ; Tang, Jianwu
    Plant phenology has a significant impact on the forest ecosystem carbon balance. Detecting plant phenology by capturing the time-series canopy images through digital camera has become popular in recent years. However, the relationship between color indices derived from camera images and plant physiological characters are elusive during the growing season in temperate ecosystems. We collected continuous images of forest canopy, leaf size, leaf area index (LAI) and leaf chlorophyll measured by a soil plant analysis development (SPAD) analyzer in a northern subtropical oak forest in China. Our results show that (1) the spring peak of color indices, Gcc (Green Chromatic Coordinates) and ExG (Excess Green), was 18 days earlier than the 90% maximum SPAD value; (2) the 90% maximum SPAD value coincided with the change point of Gcc and ExG immediately after their spring peak; and (3) the spring curves of Gcc and ExG before their peaks were highly synchronous with the expansion of leaf size and the development of LAI value. We suggest it needs to be adjusted if camera-derived Gcc or ExG is used as a proxy of chlorophyll or gross primary productivity, and images observation should be complemented with field phenological and physiological information to interpret the physiological meaning of leaf seasonality.
  • Article
    Using canopy greenness index to identify leaf ecophysiological traits during the foliar senescence in an oak forest
    (Ecological Society of America, 2018-07-31) Liu, Zhunqiao ; An, Shuqing ; Lu, Xiaoliang ; Hu, Haibo ; Tang, Jianwu
    Camera‐based observation of forest canopies allows for low‐cost, continuous, high temporal‐spatial resolutions of plant phenology and seasonality of functional traits. In this study, we extracted canopy color index (green chromatic coordinate, Gcc) from the time‐series canopy images provided by a digital camera in a deciduous forest in Massachusetts, USA. We also measured leaf‐level photosynthetic activities and leaf area index (LAI) development in the field during the growing season, and corresponding leaf chlorophyll concentrations in the laboratory. We used the Bayesian change point (BCP) approach to analyze Gcc. Our results showed that (1) the date of starting decline of LAI (DOY 263), defined as the start of senescence, could be mathematically identified from the autumn Gcc pattern by analyzing change points of the Gcc curve, and Gcc is highly correlated with LAI after the first change point when LAI was decreasing (R2 = 0.88, LAI < 2.5 m2/m2); (2) the second change point of Gcc (DOY 289) started a more rapid decline of Gcc when chlorophyll concentration and photosynthesis rates were relatively low (13.4 ± 10.0% and 23.7 ± 13.4% of their maximum values, respectively) and continuously reducing; and (3) the third change point of Gcc (DOY 295) marked the end of growing season, defined by the termination of photosynthetic activities, two weeks earlier than the end of Gcc curve decline. Our results suggested that with the change point analysis, camera‐based phenology observation can effectively quantify the dynamic pattern of the start of senescence (with declining LAI) and the end of senescence (when photosynthetic activities terminated) in the deciduous forest.