Using canopy greenness index to identify leaf ecophysiological traits during the foliar senescence in an oak forest

Abstract

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.

Detecting the onset of autumn leaf senescence in deciduous forest trees of the temperate zone

“…derivatives to determine peaks and trend changes, spline interpolation or Bayesian and Pruned Exact Linear Time change point techniques) are also possible depending on the time resolution and trend (i.e. gradual or abrupt) of the available data (Zhang et al, 2003;Klosterman et al, 2014;Liu et al, 2018;Richardson et al, 2018). (3) What are the best applications for the different methods?…”

“…In the present study we did not test the performance of the proxy based on the coloration change measured with repeated colour images of the forest canopy throughout the season (Cai et al, 2016;Klosterman & Richardson, 2017). This method can offer high temporal resolution and reduces the subjective character of the classical visual coloration observations, but it requires the installation of a permanent camera on a tall mast or frequent drone measurements, both options considered unpractical at our sites (Chipkin et al, 1975;Brown et al, 2016;Liu et al, 2018).…”

“…The main issue associated with these methods is the determination of the exact starting point of the seasonal decline. Statistical methods typically used to solve similar problems require high-frequency time series not easily available from the leaf N and Chl content measurement methods (Liu et al, 2018). Therefore, a common solution is to take a threshold in the loss of leaf N or Chl (e.g.…”

Seasonal variation in the canopy color of temperate evergreen conifer forests

“…In addition to showing that vegetation indices calculated from hyperspectral PhotoSpec data correlated well with pigment pools and ratios, we found that color indices derived from broadband phenocam imagery performed almost equally well ( Figure 5). Phenocam indices have been shown to have a nonlinear relationship with pigment concentrations in senescing deciduous foliage (Junker & Ensminger, 2016;Liu et al, 2018). Thus,…”

PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits

“…Anderson et al (2016) and Andresen et al (2018) investigated the growth of vegetation in high arctic regions and Snyder et al (2016) the PhenoPhases in a cold desert. Several studies have investigated grassland (Migliavacca et al, 2011;Julitta et al, 2014;Hufkens et al, 2016;Browning et al, 2017;Liu et al, 2017;Fan et al, 2018;Filippa et al, 2018) and forest phenology (Richardson et al, 2007(Richardson et al, , 2013(Richardson et al, , 2018bHufkens et al, 2012;Sonnentag et al, 2012;Keenan et al, 2014;Klosterman et al, 2014;Reid et al, 2016;Donnelly et al, 2017;Liu et al, 2017Liu et al, , 2018Filippa et al, 2018;Toda and Richardson, 2018). Throughout the last years, large datasets of PhenoCam imagery that cover several different vegetation types have been established (Brown et al, 2016;Hufkens et al, 2018b;Nagai et al, 2018;Richardson et al, 2018a).…”