► Webcam images were analysed by a Bayesian multiple change point analysis. ► The Bayesian method was compared with formerly applied methods. ► The Bayesian analysis appears to estimate reliably ...phenological transition dates. ► A delay of green-up of about 2.5 days per 100m altitude was estimated for Fagus sylvatica trees.
Phenological observations have a long tradition. By contrast, digital webcam-based phenological research has only developed in recent years, prompted by the development of cheaper user-friendly digital camera systems and by higher staff costs. Webcam photography provides spectral information in red, green and blue (RGB) wavelengths which mirror the seasonal colour changes in trees during bud burst, leaf unfolding, senescence and leaf fall.
Recent publications have mainly used two types of image data analysis to define onset dates of certain phenological stages and to compare species and growing seasons. These methods work well, but require high quality of the webcam images. However, changing light and weather conditions complicate data analysis particularly at increasing camera-to-subject distances. We investigated a series of images providing colour information on different tree species, e.g. Fagus sylvatica, Populus tremula, as well as of trees at different altitudes (700–1200m) in the Bavarian Forest National Park, Germany. Webcam images were analysed by the two previously published methods and compared with results derived from a newly developed Bayesian multiple change point analysis. In particular, transition dates of leaf development were identified in the green, as well as the red, colour channel.
The Bayesian analysis described phenological transition dates in spring and autumn and specified the uncertainties of the model fit. By contrast, previously employed methods have shortcomings associated with unrealistic asymptotic assumptions in logistic model fits or inability to cope with noise in data series.
The change point analysis at different elevations showed how the Bayesian approach coped with increasingly degraded image quality. A delay in green-up of about 2.5 days per 100m of altitude was estimated for Fagus sylvatica in the study area. Autumn phenology at different altitudes did not show clear patterns.
The Bayesian model approach allows not only the calculation of phenological change points during the year but also estimates the probability of those changes occurring on a particular day. This method appears to estimate reliably phenological events in the growing season, especially when handling low quality webcam data, either from poor weather conditions or when the subject is at a considerable distance from the camera.
Rain microstructure parameters assessed by disdrometers are commonly used to classify rain into convective and stratiform. However, different types of disdrometer result in different values for these ...parameters. This in turn potentially deteriorates the quality of rain type classifications. Thies disdrometer measurements at two sites in Bavaria in southern Germany were combined with cloud observations to construct a set of clear convective and stratiform intervals. This reference dataset was used to study the performance of classification methods from the literature based on the rain microstructure. We also explored the possibility of improving the performance of these methods by tuning the decision boundary. We further identified highly discriminant rain microstructure parameters and used these parameters in five machine-learning classification models. Our results confirm the potential of achieving high classification performance by applying the concepts of machine learning compared to already available methods. Machine-learning classification methods provide a concrete and flexible procedure that is applicable regardless of the geographical location or the device. The suggested procedure for classifying rain types is recommended prior to studying rain microstructure variability or any attempts at improving radar estimations of rain intensity.
Phenological variation among trees of different heights provides a small-scale ecological distinction within the forest, allowing the modulation of light interception and, consequently, net carbon ...gain. While spring phenological variations in temperate forests are well studied, for autumn phenology it is still unclear whether canopy vertical position (exposure), ontogeny or microclimatic factors are more decisive. We observed leaf colouring (LC) and leaf fall (LF) phenology of 166 Fagus sylvatica L. individuals (European beech; Fagaceae), twice/three times a week during autumn 2012 in a mixed forest in southern Germany. We aimed to determine: (1) the extent of variations in leaf senescence among F. sylvatica trees occurring in three different vertical canopy positions in the forest (overstorey, mesostorey and understorey); and (2) whether phenology varies between three different canopy height levels (upper, intermediate and lower). Possible microenvironmental drivers, such as air temperature, air humidity and light, were analysed in relation to autumn phenology. Air temperature and humidity data were obtained from HOBO loggers and light conditions from hemispheric fisheye photographs. Overstorey individuals were the first to start autumn phenology followed by mesostorey and understorey trees. For understorey individuals, the onset of LC and LF were observed 31 and 24 days later than for overstorey trees. The upper canopy parts of individual trees were characterised by the earliest appearance of autumn phases; mean difference in onset date of LC and LF between the upper and lower level was in each case -11 days. As peak dates did not differ, the duration of autumn phases were shorter at the lowest canopy height levels. We find a remarkable phenological avoidance of understorey trees and lower leaves compared to overstorey trees and upper canopy parts in F. sylvatica. We suggest that the observed differences were related to vertical variations in relative humidity and light availability, but also have an ontogenic cue. Since phenological variation in forest stands alters a range of environmental conditions, our study is useful from an ecological and microclimatic viewpoint. Moreover, since phenological development was shown to differ considerably, generalisations are limited when considering trees of different life stages within a forest. Further studies should focus on light conditions to investigate their influence on autumn phenology and importance for phenological avoidance.
In mountainous regions, inversion situations with cold-air pools in the valleys occur frequently, especially in fall and winter. With the accumulation of inversion days, trees in lower elevations ...experience lower temperature sums than those in middle elevations. In a two-year observational study, deciduous trees, such as
Acer pseudoplatanus
and
Fagus sylvatica
, on altitudinal transects responded in their fall leaf senescence phenology. Phenological phases were advanced and senescence duration was shortened by the cold temperatures in the valley. This effect was more distinct for late phases than for early phases since they experienced more inversion days. The higher the inversion frequency, the stronger the signal was.
Acer pseudoplatanus
proved to be more sensitive to cold temperatures compared to
Fagus sylvatica
. We conclude that cold-air pools have a considerable impact on the vegetation period of deciduous trees. Considering this effect, trees in the mid hillside slopes gain advantages compared to lower elevations. Our findings will help to improve knowledge about ecological drivers and responses in mountainous forest ecosystems.
Rain properties vary spatially and temporally for several reasons. In particular, rain types (convective and stratiform) affect the rain drop size distribution (DSD). It has also been established ...that local weather conditions are influenced by large-scale circulations. However, the effect of these circulations on rain microstructures has not been sufficiently addressed. Based on DSD measurements from 16 disdrometers located in Lausanne, Switzerland, we present evidence that rain DSD differs among general weather patterns (GWLs). GWLs were successfully linked to significant variations in the rain microstructure characterized by the most important rain properties: rain intensity (R), mass weighted rain drop diameter (Dm), and rain drop concentration (N), as well as Z = ARb parameters. Our results highlight the potential to improve radar-based estimations of rain intensity, which is crucial for several hydrological and environmental applications.
The identification of changes in observational data relating to the climate change hypothesis remains a topic of paramount importance. In particular, scientifically sound and rigorous methods for ...detecting changes are urgently needed. In this paper, we develop a Bayesian approach to nonparametric function estimation. The method is applied to blossom time series of Prunus avium L., Galanthus nivalis L. and Tilia platyphyllos SCOP. The functional behavior of these series is represented by three different models: the constant model, the linear model and the one change point model. The one change point model turns out to be the preferred one in all three data sets with considerable discrimination of the other alternatives. In addition to the functional behavior, rates of change in terms of days per year were also calculated. We obtain also uncertainty margins for both function estimates and rates of change. Our results provide a quantitative representation of what was previously inferred from the same data by less involved methods.
Contemporary climate change leads to earlier spring phenological events in Europe. In forests, in which overstory strongly regulates the microclimate beneath, it is not clear if further change ...equally shifts the timing of leaf unfolding for the over- and understory of main deciduous forest species, such as Fagus sylvatica L. (European beech). Furthermore, it is not known yet how this vertical phenological (mis)match—the phenological difference between overstory and understory—affects the remotely sensed satellite signal. To investigate this, we disentangled the start of season (SOS) of overstory F.sylvatica foliage from understory F. sylvatica foliage in forests, within nine quadrants of 5.8 × 5.8 km, stratified over a temperature gradient of 2.5 °C in Bavaria, southeast Germany, in the spring seasons of 2019 and 2020 using time lapse cameras and visual ground observations. We explained SOS dates and vertical phenological (mis)match by canopy temperature and compared these to Sentinel-2 derived SOS in response to canopy temperature. We found that overstory SOS advanced with higher mean April canopy temperature (visual ground observations: −2.86 days per °C; cameras: −2.57 days per °C). However, understory SOS was not significantly affected by canopy temperature. This led to an increase of vertical phenological mismatch with increased canopy temperature (visual ground observations: +3.90 days per °C; cameras: +2.52 days per °C). These results matched Sentinel-2-derived SOS responses, as pixels of higher canopy height advanced more by increased canopy temperature than pixels of lower canopy height. The results may indicate that, with further climate change, spring phenology of F. sylvatica overstory will advance more than F. sylvatica understory, leading to increased vertical phenological mismatch in temperate deciduous forests. This may have major ecological effects, but also methodological consequences for the field of remote sensing, as what the signal senses highly depends on the pixel mean canopy height and the vertical (mis)match.
Along with the global climate change, there is an increasing interest for its effect on phenological patterns such as start and end of the growing season. Scientific digital webcams are used for this ...purpose taking every day one or more images from the same natural motive showing for example trees or grassland sites. To derive phenological patterns from the webcam images, regions of interest are manually defined on these images by an expert and subsequently a time series of percentage greenness is derived and analyzed with respect to structural changes. While this standard approach leads to satisfying results and allows to determine dates of phenological change points, it is associated with a considerable amount of manual work and is therefore constrained to a limited number of webcams only. In particular, this forbids to apply the phenological analysis to a large network of publicly accessible webcams in order to capture spatial phenological variation. In order to be able to scale up the analysis to several hundreds or thousands of webcams, we propose and evaluate two automated alternatives for the definition of regions of interest, allowing for efficient analyses of webcam images. A semi-supervised approach selects pixels based on the correlation of the pixels' time series of percentage greenness with a few prototype pixels. An unsupervised approach clusters pixels based on scores of a singular value decomposition. We show for a scientific webcam that the resulting regions of interest are at least as informative as those chosen by an expert with the advantage that no manual action is required. Additionally, we show that the methods can even be applied to publicly available webcams accessed via the internet yielding interesting partitions of the analyzed images. Finally, we show that the methods are suitable for the intended big data applications by analyzing 13988 webcams from the AMOS database. All developed methods are implemented in the statistical software package R and publicly available in the R package phenofun. Executable example code is provided as supplementary material.
The precipitation diurnal cycle (PDC) varies with the season and location. Its link to large-scale weather circulations has been studied in different regions. However, comparable information is ...lacking for Central Europe. Two decades of hourly precipitation data were combined with records of objective weather patterns over Germany, focusing on the general atmospheric wind directions (WD). The PDC is characterized by the frequency and the average amount of hourly precipitation. The precipitation frequency generally has two peaks: one in the morning and the other in the afternoon. The morning peak of the precipitation amount is small compared to that of the afternoon peak. Remarkably, WD has a prominent influence on the PDC. Days with southwesterly WD have a high afternoon peak and a lower morning peak, while days with northwesterly WD have a high morning peak and a lower afternoon peak. Furthermore, the seasonal variations of PDC are dominated by the seasonal frequency of WD classes. This study presents a general overview of the PDC in Germany with regard to its variation with seasonality, geographical location, elevation, and WD.
Various indications for shifts in plant and animal phenology resulting from climate change have been observed in Europe. This analysis of phenological seasons in Germany of more than four decades ...(1951-96) has several major advantages: (i) a wide and dense geographical coverage of data from the phenological network of the German Weather Service, (ii) the 16 phenophases analysed cover the whole annual cycle and, moreover, give a direct estimate of the length of the growing season for four deciduous tree species. After intensive data quality checks, two different methods - linear trend analyses and comparison of averages of subintervals - were applied in order to determine shifts in phenological seasons in the last 46 years. Results from both methods were similar and reveal a strong seasonal variation. There are clear advances in the key indicators of earliest and early spring (-0. 18 to -0. 23 d y super(-1)) and notable advances in the succeeding spring phenophases such as leaf unfolding of deciduous trees (-0. 16 to -0. 08 d y super(-1)). However, phenological changes are less strong during autumn (delayed by + 0. 03 to + 0. 10 d y super(-1) on average). In general, the growing season has been lengthened by up to -0. 2 d y super(-1) (mean linear trends) and the mean 1974-96 growing season was up to 5 days longer than in the 1951-73 period. The spatial variability of trends was analysed by statistical means and shown in maps, but these did not reveal any substantial regional differences. Although there is a high spatial variability, trends of phenological phases at single locations are mirrored by subsequent phases, but they are not necessarily identical. Results for changes in the biosphere with such a high resolution with respect to time and space can rarely be obtained by other methods such as analyses of satellite data.
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