Assessing the use of camera-based indices for characterizing canopy phenology in relation to gross primary production in a deciduous broad-leaved and an evergreen coniferous forest in Japan
Highlights
► We assessed the use of camera-based indices for characterizing canopy phenology. ► We conducted camera and tower-flux observation at two different forests. ► We introduced green excess, green chromatic coordinate and HUE indices. ► We found the clear relationship between sunny GPP and camera-based indices. ► The different relationship appeared at different phenological stages.
Introduction
To investigate terrestrial ecosystem structure and functions under climate change, it is crucial to understand the relationships between canopy phenological features such as leaf expansion or fall and photosynthetic productivity (Ito, 2010, Muraoka et al., 2010, Polgar and Primack, 2011, Richardson et al., 2010). In situ and satellite remote sensing are promising tools for observations of canopy phenology. An in situ camera system for obtaining canopy surface phenological images can be operated at low cost and consumes little electrical power, making deployment of such a system in different ecosystems at tower flux sites around the world convenient and practical, thus facilitating comparative analyses (Bater et al., 2011, Wingate et al., 2008). Recent studies have shown that the seasonal variations in the digital numbers of the red, green, and blue bands (RGB_DN) in phenological images obtained by digital cameras are correlated with those in tower-flux-based gross primary production (GPP) (Ahrends et al., 2009, Ide et al., 2011, Richardson et al., 2009, Sonnentag et al., 2011) and with those in net ecosystem exchange (Kurc and Benton, 2010); these relationships imply that it may be possible to estimate the temporal and spatial distributions of photosynthetic productivity from RGB_DN data on the basis of the relationship between RGB_DN and GPP (Baldocchi et al., 2005).
However, the relationship between seasonal variations in photosynthetic capacity, which reflect leaf area and pigments, and those in tower-flux-based GPP, which reflect actual photosynthetic activity, is not always linear. For instance, Nagai et al. (2010) reported that the Enhanced Vegetation Index observed in situ increased earlier than the tower-flux-based GPP during the leaf-expansion period in a deciduous broad-leaved forest. Other studies have reported that photosynthetic productivity shows large temporal variations, depending on weather conditions such as light, temperature, and vapor pressure, in various evergreen and deciduous forests (Lindroth et al., 1998, Malhi et al., 1998, Saigusa et al., 2002, Saitoh et al., 2010, Takanashi et al., 2005). As a result, the seasonal variation in RGB_DN is not always correlated linearly with the variation in actual photosynthetic activity (i.e., tower-flux-based GPP).
In this study, we examined the relationship between RGB_DN and GPP obtained by tower CO2 flux observations at a daily time step over multiple years in a deciduous broad-leaved and an evergreen coniferous forest (the dominant forest types in East Asia) (Ito, 2008). However, to correctly detect leaf expansion and fall, we used three camera-based indices (i.e., the green excess index [GE], the green chromatic coordinate [rG], and HUE) instead of the individual RGB_DN values (Ide and Oguma, 2010, Richardson et al., 2007, Richardson et al., 2009, Sonnentag et al., 2012, Woebbecke et al., 1995). Our aim was to examine (1) the characteristics of seasonal variation in these camera-based indices and (2) the robustness of the relationship between tower flux-based GPP and the camera-based indices by assessing the relationship in two different forest ecosystems.
Section snippets
Study sites and period
Our observation sites were located in a deciduous broad-leaved forest (TKY, 36°08′N, 137°25′E, 1420 m a.s.l.) and an evergreen coniferous forest (TKC, 36°08′N, 137°22′E, 800 m a.s.l.) in Takayama, Japan, which has a cool temperate climate. The Takayama forests are part of the AsiaFlux (http://asiaflux.net) and the Japan Long-Term Ecological Research (JaLTER, http://www.jalter.org) networks. CO2 flux and concentration have been monitored continuously at the TKY site since 1993 (Saigusa et al., 2002)
GE, rG, and HUE
At the TKY site, GE and rG rapidly increased each year during DOY (Day of year) 120 to 158, gradually decreased until DOY 195, and then rapidly decreased to zero and 0.32, respectively on DOY 300 (Fig. 2). In contrast, at the TKC site, GE and rG rapidly increased during DOY 59 to 155, reaching a peak on DOY 155 and another on DOY 279 after a small decline. From DOY 279, GE and rG decreased to a minimum value of 10 and 0.35, respectively (Fig. 3). No short-term variation due to weather
Differences in canopy characteristics between the two forest ecosystems
The seasonal variations in GE, rG, and HUE may be due to two types of phenological information being recorded by these indices, namely, leaf area and the physiological pigments responsible for leaf color on the canopy surface. Although the leaf area index is almost constant throughout the year in the evergreen coniferous forest at the TKC site (Saitoh et al., 2010), the leaf color of the canopy surface changes from dark green to reddish green in winter (December to March) and from yellowish
Conclusion
A camera system for obtaining canopy surface images is inexpensive and consumes little electrical power, even though the spectrum information about the vegetation surface is less accurate than that obtained with a spectroradiometer. These features make it possible to deploy such a system at many different tower flux sites. Investigations of the seasonal variation in camera-based indices and the relationship between camera-based indices and tower-flux-based GPP under sunny conditions and in
Acknowledgments
We thank K. Kurumado, Y. Miyamoto, and Y. Yashiro (River Basin Research Centre, Gifu University), H. Mikami (University of Tsukuba), T. Motohka (University of Tsukuba and the Japan Aerospace Exploration Agency; JAXA), S. Murayama (National Institute of Advanced Industrial Science and Technology), K. Kuwata (University of Tokyo), M. Ishihara (National Institute for Environmental Studies), and T. Inoue (Waseda University) for their assistance in the field. We also thank all PEN (Phenological Eyes
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2019, Journal of Environmental ManagementCitation Excerpt :With the application of remote sensing technology to monitor vegetation phenology, vegetation indices that are based on satellite images have expanded vegetation phenology from the species level to regional and global scales. However, it is difficult to obtain high-quality and continuous image data because acquiring high-precision images is expensive, and image acquisitions are subject to cloud cover and atmospheric interference (Saitoh et al., 2012). The mid- and low-resolution images are limited by the temporal and spatial resolutions of the remote sensing data, which is not conducive to understanding the driving mechanisms of vegetation phenology at the community scale (Nijland et al., 2014).