Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Abstract

Tree-grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity-GPP) at four tree-grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural

Improving the Performance of 3-D Radiative Transfer Model FLIGHT to Simulate Optical Properties of a Tree-Grass Ecosystem

“…The grass layer covers almost the totality of the soil, even under the tree canopy, and combines different functional types: grasses, forbs, and legumes [40]. This layer is highly dynamic [41] and biodiverse in space and time, depending on the availability of resources [42][43][44]. Its strong temporal dynamics are characterized by two seasonal biomass peaks with a maximum in spring and a second maximum after the summer decay, following the soil re-wetting phase.…”

Fate of N additions in a multiple resource‐limited Mediterranean oak savanna

“…At the same time, there was a slight decrease in soil N and a very noticeable decrease in extractable soil N pools which indicate that plant N uptake was great enough to reduce other surface N pools. The timing and magnitude of the dehesa springtime productivity peak are extremely sensitive to rainfall and temperature conditions of that year (Luo et al 2018). S3).…”

“…grow in an Abruptic Luvisol and sustained by~650 mm average annual precipitation, which falls mostly between winter and early spring. Since 2003, the site has been the location of an eddy covariance tower belonging to the FLUXNET network with the site identifier ES-LMa (Pacheco-Labrador et al 2017, El-Madany et al 2018, Luo et al 2018. It is worth mentioning that while this study took place at the same site as several others, the sampling plots were well outside of the tower footprints and the experiment itself was separate from nutrient manipulation experiments reported elsewhere.…”

“…At the same time, there was a slight decrease in soil N and a very noticeable decrease in extractable soil N pools which indicate that plant N uptake was great enough to reduce other surface N pools. Likely, the increase in plant N is a result of interannual variability rather than a delayed fertilization effect as the growing period of 2017 was characterized by low productivity due to low precipitation during the spring and summer periods (Luo et al 2018) with rainfall only 60% of the annual average (data not shown, but see Appendix S1: Fig. S3).…”

“…S3). The timing and magnitude of the dehesa springtime productivity peak are extremely sensitive to rainfall and temperature conditions of that year (Luo et al 2018). Because soil was already relatively dry during fertilization in 2017, it is likely that plants were not able to fully utilize added nutrients until the following growing season.…”