Description: Herbaceous perennial with thin, branched rhizomes. Stems few or solitary, usually 25–50 cm tall, grayish with long, entangled, white hairs, often with short leafy branches in mid and upper leaf axils. Leaves bipinnatisect, usually oblong, green or grayish-green, more or less densely hairy; leaves of sterile shoots up to 25 cm long, long-petiolate; lower stem leaves 7–20 cm long, petiolate to subsessile; upper leaves sessile, usually 1–6 cm long. Inflorescences capitula arranged in loose, convex corymbs of unequal heights. Involucre cup-shaped; involucral bracts oviform, pale yellowish-green. Ray flower ligules pink, very rarely white. Fruits oblong, wedge-shaped achenes, truncated at the apex.

The term phenology was first introduced by Charles MorrenMorren Charles in 1849 in a public lecture on the 16th of December entitled “Le globe, le temps et la vie” (Morren 1849, 1851). Phenologyphenology definition which he took from the Greek ϕαινομαι, (Morren 1849), was defined as “apparaître, se manifester: phénologie, la science des phénomènes qui apparaissent successivement sur le globe.” This translates as: to show, to appear: the science of phenomena that appear successively on the globe.

A number of approaches using a variety of satellite remote sensing products have been used to derive metrics related to the timing of biological events (or land surface phenology, LSP). The advantages of utilizing remote sensing for phenology applications are the ability to capture the continuous expression of phenology patterns across the landscape and the ability to retrospectively observe phenology from archived satellite data sets (e.g. Landsat and Advanced Very High Resolution Radiometer). However, LSP databases have not yet been satisfactorily validated due to the difficulty in obtaining sufficiently extensive ground observations throughout the growing season. A multi-level validation approach that uses ground observations, dedicated web cameras, and high, medium, and coarse spatial resolution satellite data is needed to give scientists an improved level of confidence in utilizing the data. Many of these shortcomings are being addressed by phenology networks across the globe such as the U.S. National Phenology Network. Even without extensive validation, a number of applications areas have employed LSP data successfully, including studies on ecosystems analysis, disasters, land use, and climate change. Land surface phenology promises to continue contributing to these types of applications, and will also likely serve as an important early indicator of environmental effects of climate change

Landsat imagery is routinely used to characterize stand-level forest communities, but low temporal resolution makes pixel-wise characterization of phenology difficult. This limitation can be overcome by using multi-year imagery, but organizing Landsat scenes by calendar date ignores phenological gradients across the landscape as well as inter-annual differences in both scene- and pixel-wise phenology. We demonstrate how a spatially generalizable, phenologically-informed approach for re-ordering Landsat pixels can be used to characterize spatial variations in autumn senescence in several forest tree species. Using end-of-season estimates derived from MODIS phenology data, we determined the “days left in season” (DLiS) across Landsat images to produce a synthesized phenological trajectory of the normalized difference infrared index (NDII). We used ground-based species composition data in conjunction with the NDII trajectories to model autumn senescence by species. Absolute phenology differed by one and a half to 3 weeks between northern and southern Wisconsin, USA, but we show that the relative timing of phenology for individual species differs across regions by only 1–3 days when considering senescence with respect to the local end of the season. The progression of species senescence was consistent in lowland stands, starting with green and black ash, followed by silver maple, yellow birch, red maple, and tamarack. The image analyses suggest that senescence progressed more rapidly in southern than northern Wisconsin, starting earlier but taking about ten more days in the north. Our results support the use of MODIS phenological data with multi-year Landsat imagery to detect species with unique phenologies and identify how these vary across the landscape.

Phenology controls the seasonal activities of vegetation on land surfaces and thus plays a fundamental role in regulating photosynthesis and other ecosystem processes. Therefore, accurately simulating phenology and soil processes is critical to ecosystem and climate modeling. In this study, we present an integrated ecosystem model of plant productivity, plant phenology, and the soil freeze–thaw process to (1) improve the quality of simulations of soil thermal regimes and (2) estimate the seasonal variability of plant phenology and its effects on plant productivity in high-altitude seasonal frozen regions. We tested different model configurations and parameterizations, including a refined soil stratification scheme that included unfrozen water in frozen soil, a remotely sensed diagnostic phenology scheme, and a modified prognostic phenology scheme, to describe the seasonal variation in vegetation. After refined soil layering resolution and the inclusion of unfrozen water in frozen soil, the results show that the model adequately reproduced the soil thermal regimes and their interactions observed at the site. The inclusion of unfrozen water in frozen soil was found to have a significant effect on soil moisture simulation during the spring but only a small effect on soil temperature simulation at this site. Moreover, the performance of improved phenology schemes was good. The phenology model accurately predicted the start and end of phenology, and its precise prediction of phenology variation allows an improved simulation of vegetation production.

Vegetation phenology describing the seasonal cycle of plants is currently one of the main concerns in the study of climate change and carbon balance estimation in ecosystems. Satellite-derived information has been demonstrated to be an important source for detecting vegetation phenology. A variety of methods have been developed to generate phenological metrics from satellite measurements varying from empirically, simple threshold of vegetation index to automated, elaborate logistic model. Each method provides certain advantages and paves the way for the success of satellite-derived vegetation phenology. The vegetation phenology derived from satellite measurements has been utilized for tracking vegetation dynamics, invasive species, and land use changes as well as assessing crop conditions, drought severity, and wildfire risk. Satellite sensors have their specific characteristics of temporal and spatial resolution, spatial coverage, and data quality and archive history. Each satellite takes advantages of its respective strengths to provide certain phenological applications. Despite the insights gained form satellite observations of vegetation phenology, the scale problem brings a big challenge for comparing satellite-derived vegetation phenology and ground records. In the future, more detailed information of ground records together with phenophases of individual species could be integrated to reflect the canopy phenology and compared with the satellite-derived phenology. The well-validated vegetation phenology from satellite measurements will contribute to the improvement in ecosystem process models.

Land surface phenology (LSP) is a key indicator of ecosystem dynamics under a changing environment. Over the last few decades, numerous studies have used the time series data of vegetation indices derived from land surface reflectance acquired by satellite-based optical sensors to delineate land surface phenology. Recent progress and data accumulation from CO₂ eddy flux towers offers a new perspective for delineating land surface phenology through either net ecosystem exchange of CO₂ (NEE) or gross primary production (GPP). In this chapter, we discussed the potential convergence of satellite observation approach and CO₂ eddy flux observation approach. We evaluated three vegetation indices (Normalized Difference Vegetation Index, Enhanced Vegetation Index, and Land Surface Water Index) in relation to NEE and GPP data from five CO₂ eddy flux tower sites, representing five vegetation types (deciduous broadleaf forests, evergreen needleleaf forest, temperate grassland, cropland, and tropical moist evergreen broadleaf forest). This chapter highlights the need for the community to combine satellite observation approach and CO₂ eddy flux observation approach, in order to develop better understanding of land surface phenology.

Pilosocereus leucocephalus produces flowers in discrete pulses, suggesting this cactus might exhibit pulsed flowering—a rare flowering pattern among angiosperms. In this study, we (1) describe the phenology of P. leucocephalus, (2) explore the influence of temperature, rainfall, and plant size on the flowering pattern, and (3) assess the effect of flowering phenology on the reproductive success of this cactus. Flowering phenology was characterized using the coefficient of variation in addition to traditional descriptors of flowering phenology: flowering onset, flowering duration, number of pulses and flowers, as well as flowering synchrony. The association between temperature, rainfall, plant size, and reproductive success (fruit set) with phenological descriptors was assessed using mixed-effects models. The flowering phenology of P. leucocephalus was confirmed as pulsed but was unexpectedly asynchronous. This cactus flowers during the warmest part of the year. We found a significant relationship between temperature and flower production. Plant size has a strong effect on all the flowering phenology descriptors we studied, except flowering synchrony. Of the phenological descriptors evaluated, only flowering onset has a significant and positive relationship with fruit set. These results strongly suggest that flowering phenology in P. leucocephalus (1) is mainly controlled by temperature and plant size and (2) influences its reproductive success.

Background and aims

Phenological variations in tropical forests are usually explained by climate. Nevertheless, considering that soil water availability and nutrient content also influence plant water status and metabolism, soil conditions may also be important in the regulation of plant reproductive and vegetative activities over time. We investigated whether phenological patterns and stem growth differ in trees growing in two types of soil that display contrasting water and nutrient availability, namely, Gleysol (moist and nutrient-poor) and Cambisol (drier and nutrient-rich).

Methods

Phenological observations (flushing, leaf fall, flowering and fruiting) and stem diameter growth were recorded for 120 trees fitted with fixed dendrometer bands, at 15 days intervals, for 1 year. Two species of contrasting deciduousness were investigated: Senna multijuga (semi-deciduous) and Citharexylum myrianthum (deciduous).

Results

Both species were seasonal in all phenophases, regardless of soil type. However, frequency, mean date and intensity of phenophases varied according to soil type. Girth increment of C. myrianthum was four times greater in Cambisol than in Gleysol, whereas the type of soil had no significant effect on that of S. multijuga.

Conclusions

These results show that soil characteristics also play an important role in determining phenological patterns and growth and must be considered when analysing phenological patterns in tropical forests.