Using phenological cameras to track the green up in a cerrado savanna and its on-the-ground validation

Plant phenology has gained new importance in the context of global change research, stimulating the development of novel technologies for phenological observations. Regular digital cameras have been effectively used as three-channel imaging sensors, providing measures of leaf color change or phenological shifts in plants. We monitored a species rich Brazilian cerrado savanna to assess the reliability of digital images to detect leaf-changing patterns. Analysis was conducted by extracting color information from selected parts of the image named regions of interest (ROIs). We aimed to answer the following questions: (i) Do digital cameras capture leaf changes in cerrado savanna vegetation? (ii) Can we detect differences in phenological changes among species crowns and the cerrado community? (iii) Is the greening pattern detected for each species by digital camera validated by our on-the-ground leafing phenology (direct observation of tree leaf changes)? We analyzed daily sequences of five images per hour, taken from 6:00 to 18:00 h, recorded during the cerrado main leaf flushing season. We defined 24 ROIs in the original digital image, including total or partial regions and crowns of six plant species. Our results indicated that: (i) for the studied period, single plant species ROIs were more sensitive to changes in relative green values than the community ROIs, (ii) three leaf strategies could be depicted from the species' ROI patterns of green color change, and (iii) the greening patterns and leaf functional groups were validated by our on-the-ground phenology. We concluded that digital cameras are reliable tools to monitor high diverse tropical seasonal vegetation and it is sensitive to inter-species differences of leafing patterns.     

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground.     

基于数字相机的冬小麦物候和碳交换监测

利用数字相机自动、连续监测植被冠层物候变化,逐渐引起人们的广泛关注。依托中国陆地生态系统通量观测研究网络(ChinaFLUX),探讨了数字相机在监测冬小麦生长状况及生态系统碳交换方面的作用,得到如下结果:(1)利用数字相机图像提取的比值绿度指数G/R能较好地反映冬小麦冠层物候变化,通过分析比值绿度指数G/R的时间序列,得到了较为准确的冬小麦关键生育日期(与人工观测数据比较,误差<3 d),表明数字相机可以作为物候监测的一种有效手段;(2)数字相机图像获取的比值绿度指数能较好地模拟冬小麦总生态系统碳交换量GEE,R2为0.66,叶片最大光合同化速率与比值绿度指数G/R变化趋势基本一致。表明利用数字相机技术在一定程度上能够表征作物生理生态过程。从而为我国开展不同陆地生态系统自动连续物候监测,深入研究不同生态系统物候和碳循环的关系提供支持。     

Field evidence for earlier leaf-out dates in alpine grassland on the eastern Tibetan Plateau from 1990 to 2006

Worldwide, many plant species are experiencing an earlier onset of spring phenophases due to climate warming. Rapid recent temperature increases on the Tibetan Plateau (TP) have triggered changes in the spring phenology of the local vegetation. However, remote sensing studies of the land surface phenology have reached conflicting interpretations about green-up patterns observed on the TP since the mid-1990s. We investigated this issue using field phenological observations from 1990 to 2006, for 11 dominant plants on the TP at the levels of species, families (Gramineae—grasses and Cyperaceae—sedges) and vegetation communities (alpine meadow and alpine steppe). We found a significant trend of earlier leaf-out dates for one species (Koeleria cristata). The leaf-out dates of both Gramineae and Cyperaceae had advanced (the latter significantly, starting an average of 9 days later per year than the former), but the correlation between them was significant. The leaf-out dates of both vegetation communities also advanced, but the pattern was only significant in the alpine meadow. This study provides the first field evidence of advancement in spring leaf phenology on the TP and suggests that the phenology of the alpine steppe can differ from that of the alpine meadow. These findings will be useful for understanding ecosystem responses to climate change and for grassland management on the TP.     

Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States

Phenological events, such as bud burst, are strongly linked to ecosystem processes in temperate deciduous forests. However, the exact nature and magnitude of how seasonal and interannual variation in air temperatures influence phenology is poorly understood, and model‐based phenology representations fail to capture local‐ to regional‐scale variability arising from differences in species composition. In this paper, we use a combination of surface meteorological data, species composition maps, remote sensing, and ground‐based observations to estimate models that better represent how community‐level species composition affects the phenological response of deciduous broadleaf forests to climate forcing at spatial scales that are typically used in ecosystem models. Using time series of canopy greenness from repeat digital photography, citizen science data from the USA National Phenology Network, and satellite remote sensing‐based observations of phenology, we estimated and tested models that predict the timing of spring leaf emergence across five different deciduous broadleaf forest types in the eastern United States. Specifically, we evaluated two different approaches: (i) using species‐specific models in combination with species composition information to ‘upscale’ model predictions and (ii) using repeat digital photography of forest canopies that observe and integrate the phenological behavior of multiple representative species at each camera site to calibrate a single model for all deciduous broadleaf forests. Our results demonstrate variability in cumulative forcing requirements and photoperiod cues across species and forest types, and show how community composition influences phenological dynamics over large areas. At the same time, the response of different species to spatial and interannual variation in weather is, under the current climate regime, sufficiently similar that the generic deciduous forest model based on repeat digital photography performed comparably to the upscaled species‐specific models. More generally, results from this analysis demonstrate how in situ observation networks and remote sensing data can be used to synergistically calibrate and assess regional parameterizations of phenology in models.     

Public Internet‐connected cameras used as a cross‐continental ground‐based plant phenology monitoring system

Plant phenology is highly sensitive to changes in environmental conditions and can vary widely across landscapes. Current observation methods are either manual for small‐scale, high precision measurements or by satellite remote sensing for large‐scale, low spatial resolution measurement. The development of inexpensive approaches is necessary to advance large scale, high precision phenology monitoring. The use of publicly available, Internet‐connected cameras, often associated with airports, national parks, and roadway conditions, for detecting and monitoring plant phenology at a continental scale can augment existing ground and satellite‐based methodologies. We collected twice‐daily images from over 1100 georeferenced public cameras across North America from February 2008 to 2009. Using a test subset of these cameras, we compared modeled spring ‘green‐up’ with that from co‐occurring remote sensing products. Although varying image exposure and color correction introduced noise to camera measurements, we were able to correlate spring green‐up across North America with visual validation from images and detect a latitudinal trend. Public cameras had an equivalent or higher ability to detect spring compared with satellite‐based data for corresponding locations, with fewer numbers of poor quality days, shorter continuous bad data days, and significantly lower errors of spring estimates. Manual image segmentation into deciduous, evergreen, and understory vegetation allowed detection of spring and fall onset for multiple vegetation types. Additional advantages of a public camera‐based monitoring system include frequent image capture (subdaily) and the potential to detect quantitative responses to environmental changes in organisms, species, and communities. Public cameras represent a relatively untapped and freely available resource for supporting large‐scale ecological and environmental monitoring.     

Using digital cameras for comparative phenological monitoring in an evergreen broad-leaved forest and a seasonal rain forest

Digital cameras have been used in phenological observations for their high accuracy and low labor cost. Most studies successfully use greenness indices derived from digital images for timing the events related to leaf development. However, when timing the leaf senescence events, wide discrepancies between actual and estimated dates are common. In this study, images of three species (two from an evergreen broad-leaved forest and one from a seasonal rain forest) were used to estimate three phenological events of leaf development and senescence. Other than the greenness index, a redness index was also employed. Different annual patterns in color indices developed among the species. The redness index was more accurate when estimating leaf senescence, while the greenness index was more accurate for estimating leaf development events in Acer heptalobum and Machilus bombycina. The absolute differences in estimations of phenological events ranged from − 3 to 1 day, which is more accurate than estimates based on the greenness index only (− 2 to 27 days). With the introduction of the redness index, this technique has been much improved and is possible to be applied to more species. Furthermore, variations of color indices during periods of phenological events were highly related to the climatic factors with a time lag of around 10 days. Because of the ease of use and efficiency (i.e., automatic daily data output), digital cameras are expected to be used in ecosystem process modeling, networks of phenology assessment and validation of the remote sensing results from satellites.     

近地遥感在森林冠层物候动态监测中的应用

近地遥感技术是原位观测森林冠层物候的重要手段,具有高时间分辨率的优点,而且空间尺度适中,是实现物候尺度推绎的有力工具.本研究首先评述了利用3种光学传感器(辐射表、光谱仪和数码相机)监测森林物候的近地遥感方法;结合帽儿山通量观测站的实测数据分析识别物候期的不确定性来源,发现最重要的误差来自物候提取方法;剖析近地遥感与其他物候观测方法的衔接以及该技术自身存在的问题.最后提出该领域的重点研究方向: 加强冠层光学(或冠层结构)物候与功能(生理、生态过程)物候的联系;整合各区域冠层物候观测网络,实现冠层尺度的全球物候联网观测与数据共享;充分发挥近地遥感的优势,整合多源多尺度物候数据;发展近地遥感物候模型,改进动态全球植被模型中物候模拟.     

Use of digital webcam images to track spring green-up in a deciduous broadleaf forest

Understanding relationships between canopy structure and the seasonal dynamics of photosynthetic uptake of CO₂ by forest canopies requires improved knowledge of canopy phenology at eddy covariance flux tower sites. We investigated whether digital webcam images could be used to monitor the trajectory of spring green-up in a deciduous northern hardwood forest. A standard, commercially available webcam was mounted at the top of the eddy covariance tower at the Bartlett AmeriFlux site. Images were collected each day around midday. Red, green, and blue color channel brightness data for a 640 × 100-pixel region-of-interest were extracted from each image. We evaluated the green-up signal extracted from webcam images against changes in the fraction of incident photosynthetically active radiation that is absorbed by the canopy (f _APAR), a broadband normalized difference vegetation index (NDVI), and the light-saturated rate of canopy photosynthesis (A _max), inferred from eddy flux measurements. The relative brightness of the green channel (green %) was relatively stable through the winter months. A steady rising trend in green % began around day 120 and continued through day 160, at which point a stable plateau was reached. The relative brightness of the blue channel (blue %) also responded to spring green-up, although there was more day-to-day variation in the signal because blue % was more sensitive to changes in the quality (spectral distribution) of incident radiation. Seasonal changes in blue % were most similar to those in f _APAR and broadband NDVI, whereas changes in green % proceeded more slowly, and were drawn out over a longer period of time. Changes in A _max lagged green-up by at least a week. We conclude that webcams offer an inexpensive means by which phenological changes in the canopy state can be quantified. A network of cameras could offer a novel opportunity to implement a regional or national phenology monitoring program.     

A systematic review of vegetation phenology in Africa

The study of vegetation phenology is important because it is a sensitive indicator of climate changes and it regulates carbon, energy and water fluxes between the land and atmosphere. Africa, which has 17% of the global forest cover, contributes significantly to the global carbon budget and has been identified as potentially highly vulnerable to climate change impacts. In spite of this, very little is known about vegetation phenology across Africa and the factors regulating vegetation growth and dynamics. Hence, this review aimed to provide a synthesis of studies of related Africa's vegetation phenology and classify them based on the methods and techniques used in order to identify major research gaps. Significant increases in the number of phenological studies in the last decade were observed, with over 70% of studies adopting a satellite-based remote sensing approach to monitor vegetation phenology. Whereas ground based studies that provide detailed characterisation of vegetation phenological development, occurred rarely in the continent. Similarly, less than 14% of satellite-based remote sensing studies evaluated vegetation phenology at the continental scale using coarse spatial resolution datasets. Even more evident was the lack of research focusing on the impacts of climate change on vegetation phenology. Consequently, given the importance and the uniqueness of both methods of phenological assessment, there is need for more ground-based studies to enable greater understanding of phenology at the species level. Likewise, finer spatial resolution satellite sensor data for regional phenological assessment is required, with a greater focus on the relationship between climate change and vegetation phenological changes. This would contribute greatly to debates over climate change impacts and, most importantly, climate change mitigation strategies.     

Reproductive phenology of coastal plain Atlantic forest vegetation: comparisons from seashore to foothills

The diversity of tropical forest plant phenology has called the attention of researchers for a long time. We continue investigating the factors that drive phenological diversity on a wide scale, but we are unaware of the variation of plant reproductive phenology at a fine spatial scale despite the high spatial variation in species composition and abundance in tropical rainforests. We addressed fine scale variability by investigating the reproductive phenology of three contiguous vegetations across the Atlantic rainforest coastal plain in Southeastern Brazil. We asked whether the vegetations differed in composition and abundance of species, the microenvironmental conditions and the reproductive phenology, and how their phenology is related to regional and local microenvironmental factors. The study was conducted from September 2007 to August 2009 at three contiguous sites: (1) seashore dominated by scrub vegetation, (2) intermediary covered by restinga forest and (3) foothills covered by restinga pre-montane transitional forest. We conducted the microenvironmental, plant and phenological survey within 30 transects of 25 m × 4 m (10 per site). We detected significant differences in floristic, microenvironment and reproductive phenology among the three vegetations. The microenvironment determines the spatial diversity observed in the structure and composition of the flora, which in turn determines the distinctive flowering and fruiting peaks of each vegetation (phenological diversity). There was an exchange of species providing flowers and fruits across the vegetation complex. We conclude that plant reproductive patterns as described in most phenological studies (without concern about the microenvironmental variation) may conceal the fine scale temporal phenological diversity of highly diverse tropical vegetation. This phenological diversity should be taken into account when generating sensor-derived phenologies and when trying to understand tropical vegetation responses to environmental changes.     

Variations in satellite-derived phenology in China's temperate vegetation

The relationship between vegetation phenology and climate is a crucial topic in global change research because it indicates dynamic responses of terrestrial ecosystems to climate changes. In this study, we investigate the possible impact of recent climate changes on growing season duration in the temperate vegetation of China, using the advanced very high resolution radiometer (AVHRR)/normalized difference vegetation index (NDVI) biweekly time-series data collected from January 1982 to December 1999 and concurrent mean temperature and precipitation data. The results show that over the study period, the growing season duration has lengthened by 1.16 days yr⁻¹ in temperate region of China. The green-up of vegetation has advanced in spring by 0.79 days yr⁻¹ and the dormancy delayed in autumn by 0.37 days yr⁻¹. The dates of onset for phenological events are most significantly related with the mean temperature during the preceding 2–3 months. A warming in the early spring (March to early May) by 1°C could cause an earlier onset of green-up of 7.5 days, whereas the same increase of mean temperature during autumn (mid-August through early October) could lead to a delay of 3.8 days in vegetation dormancy. Variations in precipitation also influenced the duration of growing season, but such influence differed among vegetation types and phenological phases.     

高寒矮生嵩草草甸主要植物物候特征对养分和水分添加的响应

植物物候特征对环境条件的季节和年际变化具有较强的指示作用, 因此研究植物物候特征对环境条件变化的响应, 对理解植物和环境之间的相互作用关系、植物的适应机制和生存策略, 以及应对全球变化都具有重要的意义。该研究基于2009-2011年高寒矮生嵩草(Kobresia humilis)草甸养分水分控制实验的植物物候观测数据资料, 采用巢式方差分析、物候指数和聚类分析方法, 开展了高寒矮生嵩草草甸主要植物物候特征对养分和水分添加的响应研究。结果表明: (1)养分添加处理之间植物返青期和枯黄期均无显著差异, 但养分添加中氮磷处理对主要物种作用较明显, 使莎草科、禾本科、杂类草主要代表植物的返青期和枯黄期推迟。(2)增雪处理效应明显, 主要优势物种无论是何种养分添加, 在增雪处理后均表现出花期物候提前的趋势(p < 0.01), 同时增雪处理使杂类草植物返青期显著提前(p < 0.05)。增水处理对植物的作用效果并不一致, 其中垂穗披碱草(Elymus nutans)和双柱头藨草(Scirpus distigmaticus)的枯黄期显著推迟(p < 0.05), 而杂类草枯黄期提前。(3)养分添加后, 不同物种的物候特征表现出显著差异(p < 0.01), 例如雪白委陵菜(Potentilla nivea)枯黄期显著推迟(p < 0.05), 而双柱头藨草的枯黄期显著提前(p < 0.05), 但物种对养分添加响应的差异以植物类群为单位, 禾本科植物表现为返青期推迟, 而莎草科植物表现为返青期提前。(4)矮生嵩草草甸主要植物营养生长期与果后营养期持续天数之间呈负相关关系, 主要植物物候特征经聚类分析可以分为3个类群, 3个类群经氮磷钾、钾和氮钾三个养分添加处理后植物物候特征变化较大。研究表明, 高寒矮生嵩草草甸植物物候特征在物种水平响应和水分添加后的响应表现出较大差异, 而对养分添加的响应不显著。     

基于多种遥感植被指数、叶绿素荧光与CO2通量数据的温带针阔混交林物候特征对比分析

植被物候学作为研究植被与环境条件相互作用的科学,在全球气候变化的大背景下已成为国际热点研究领域,其中森林植被在调节全球碳平衡、维护全球气候稳定的过程中有着至关重要的作用。随着遥感技术的发展,多种遥感指数被应用到森林植被物候研究中,其中以MODIS NDVI和EVI应用最为广泛,而叶绿素荧光(SIF)作为植被光合作用的"探针"也被广泛应用于森林植被物候研究中。为了探究3种指数在森林植被物候研究中的差异与特性,本文以长白山温带红松阔叶林通量观测站为研究区域,采用模型拟合结合动态阈值法提取2007—2013森林物候特征参数,并使用通量数据(总初级生产力GPP)进行验证。结果表明:NDVI与EVI、SIF相比,表现为生长季开始时间与结束时间的明显提前和滞后,与GPP数据偏差较大,且夏季生长季峰期曲线形态过宽且平坦,无法较好反映生长季变化特征;EVI相较于NDVI有所改善,整体变化趋势与SIF、GPP基本吻合,但依然存在秋季衰减时间稍迟于SIF与GPP的问题;SIF虽然存在夏季骤降现象,但依然与GPP数据一致性最好,可以较好反映出森林植被季节变化特征。SIF数据与植被光合作用的紧密关联使其在植被物候研究中具有优于植被指数的准确性,并随着遥感平台的增加和反演方法的改善,将会在多尺度、多类型的植被物候监测中发挥更加重要的作用。     

Utility of information in photographs taken upwards from the floor of closed-canopy deciduous broadleaved and closed-canopy evergreen coniferous forests for continuous observation of canopy phenology

Hemispherical photographs taken on forest floors are used to monitor seasonal changes in canopy openness or leaf area index in ecological studies. Those analyses usually use black and white images converted from the original colour images. Photographs taken by downwards-facing cameras installed on towers are used to provide detailed information on leaf expansion, maturation and senescence of various tree species through the analysis of red, green and blue ‘digital numbers’ (DN_RGB) extracted from those images. To examine the usefulness of colour information encoded in upwards hemispherical photographs in monitoring canopy phenological characteristics, we examined the consistency of DN_RGB values between downwards and upwards images in deciduous broadleaved and evergreen coniferous forests in Japan. In the deciduous broadleaved forest, the DN_RGB values in the upwards images were able to detect canopy phenology almost as well as those in the downwards images. However, we found the effects on DN_RGB of (1) the spatial heterogeneity among observed points, (2) low-vegetation (before the beginning of leaf-expansion and after the end of leaf-fall period) and (3) white balance settings. In the evergreen coniferous forest, in contrast, the DN_RGB values in the upwards images did not capture canopy phenology. These different results may be attributable to the light attenuation characteristics in the canopies due to the geometries of leaves and branches. Thus, the DN_RGB values obtained from upwards images are almost as good as those of downwards images for monitoring detailed canopy phenology in deciduous broadleaved forests with a closed canopy.     

A cost-effective monitoring method using digital time-lapse cameras for detecting temporal and spatial variations of snowmelt and vegetation phenology in alpine ecosystems

Alpine ecosystems are particularly vulnerable to the effects of climate change. Although long-term and detailed monitoring is required to conserve alpine ecosystems, field surveillance and satellite remote sensing have difficulties in providing wide coverage or frequent observation in mountain areas. In this study, a new method for monitoring alpine ecosystems by digital cameras was developed in order to detect both snow-cover areas and vegetation phenology at the plant community or species level. We used images from cameras that have been installed at mountain lodges in the northern Japanese Alps (at elevations around 2350–3100 m). Red, green, and blue (RGB) digital numbers were derived from each pixel within the images. The snow-cover and snow-free pixels were statistically classified by analysis of variance of gray-level histograms. A flexible threshold was determined for each image to maximize the between-class variance. The temporal variations of the snowmelt rate showed site-specific characteristics and yearly variations. The snowmelt times reflected the local microtopography and differed among the habitats of various functional types of vegetation (i.e., evergreen dwarf pine, deciduous shrubs, evergreen Sasa, tall forbs, and snowbed plants). In addition, the vegetation phenology was quantified by using a vegetation index (green ratio) calculated from RGB digital numbers. An increase in the green ratio indicated the start of the growing period following snowmelt and a decrease indicated leaf senescence. By using pixel-based analysis of the temporal variations of the green ratio, local distributions of the start and end dates and length of the growing period were illustrated at the plant species level for the first time. The distribution of the start of the growing period strongly corresponded to the snowmelt gradient, whereas the end of the growing period was related to the vegetation type. Our results suggest that the length of the growing period mainly corresponded to the snowmelt gradient in relation to the local microtopography. Thus, commercially available digital time-lapse cameras enabled us to clarify the snow–vegetation relationships and the growing period at high temporal and spatial resolutions. This monitoring method should greatly improve our understanding of alpine ecosystems and help to assess the influence of future climate change.     

Detecting mammals in heterogeneous landscapes: implications for biodiversity monitoring and management

With terrestrial mammals facing worldwide declines there is an increasing need to effectively monitor populations so that appropriate conservation actions can be taken. There are many techniques available to survey terrestrial mammals and in recent years there have been a number of studies comparing the effectiveness of different methods. Most of these studies have not considered complementarity (the degree to which techniques detect unique species) and effectiveness across ecological gradients. In this study we examined three widely used techniques, camera trapping, live trapping and hair detection, for their complementarity across a vegetation and disturbance gradient. Overall, camera trapping detected more species than any other single technique, but live trapping complemented the cameras by consistently detecting unique species. Additionally, technique effectiveness differed between vegetation types; cameras alone were most effective in dry forest systems while cameras combined with live traps were most effective in wetter forest systems. These results suggest that care needs to be taken when sampling across heterogeneous landscapes because relying on one technique alone could result in certain taxa being systematically overlooked, leading to potentially erroneous conclusions.     

Macrophenology: insights into the broad-scale patterns,drivers, and consequences of phenology

Plant phenology research has surged in recent decades, in part due to interest in phenological sensitivity to climate change and the vital role phenology plays in ecology. Many local-scale studies have generated important findings regarding the physiology, responses, and risks associated with shifts in plant phenology. By comparison, our understanding of regional- and global-scale phenology has been largely limited to remote sensing of green-up without the ability to differentiate among plant species. However, a new generation of analytical tools and data sources—including enhanced remote sensing products, digitized herbarium specimen data, and public participation in science—now permits investigating patterns and drivers of phenology across extensive taxonomic, temporal, and spatial scales, in an emerging field that we call macrophenology. Recent studies have highlighted how phenology affects dynamics at broad scales, including species interactions and ranges, carbon fluxes, and climate. At the cusp of this developing field of study, we review the theoretical and practical advances in four primary areas of plant macrophenology: (1) global patterns and shifts in plant phenology, (2) within-species changes in phenology as they mediate species' range limits and invasions at the regional scale, (3) broad-scale variation in phenology among species leading to ecological mismatches, and (4) interactions between phenology and global ecosystem processes. To stimulate future research, we describe opportunities for macrophenology to address grand challenges in each of these research areas, as well as recently available data sources that enhance and enable macrophenology research.     

A comparative study of satellite and ground-based phenology

Long time series of ground-based plant phenology, as well as more than two decades of satellite-derived phenological metrics, are currently available to assess the impacts of climate variability and trends on terrestrial vegetation. Traditional plant phenology provides very accurate information on individual plant species, but with limited spatial coverage. Satellite phenology allows monitoring of terrestrial vegetation on a global scale and provides an integrative view at the landscape level. Linking the strengths of both methodologies has high potential value for climate impact studies. We compared a multispecies index from ground-observed spring phases with two types (maximum slope and threshold approach) of satellite-derived start-of-season (SOS) metrics. We focus on Switzerland from 1982 to 2001 and show that temporal and spatial variability of the multispecies index correspond well with the satellite-derived metrics. All phenological metrics correlate with temperature anomalies as expected. The slope approach proved to deviate strongly from the temporal development of the ground observations as well as from the threshold-defined SOS satellite measure. The slope spring indicator is considered to indicate a different stage in vegetation development and is therefore less suited as a SOS parameter for comparative studies in relation to ground-observed phenology. Satellite-derived metrics are, however, very susceptible to snow cover, and it is suggested that this snow cover should be better accounted for by the use of newer satellite sensors.     

Temperature and snowfall trigger alpine vegetation green‐up on the world's roof

Rapid temperature increase and its impacts on alpine ecosystems in the Qinghai–Tibetan Plateau, the world's highest and largest plateau, are a matter of global concern. Satellite observations have revealed distinctly different trend changes and contradicting temperature responses of vegetation green‐up dates, leading to broad debate about the Plateau's spring phenology and its climatic attribution. Large uncertainties in remote‐sensing estimates of phenology significantly limit efforts to predict the impacts of climate change on vegetation growth and carbon balance in the Qinghai–Tibetan Plateau, which are further exacerbated by a lack of detailed ground observation calibration. Here, we revealed the spatiotemporal variations and climate drivers of ground‐based herbaceous plant green‐up dates using 72 green‐up datasets for 22 herbaceous plant species at 23 phenological stations, and corresponding daily mean air temperature and daily precipitation data from 19 climate stations across eastern and southern parts of the Qinghai–Tibetan Plateau from 1981 to 2011. Results show that neither the continuously advancing trend from 1982 to 2011, nor a turning point in the mid to late 1990s as reported by remote‐sensing studies can be verified by most of the green‐up time series, and no robust evidence for a warmer winter‐induced later green‐up dates can be detected. Thus, chilling requirements may not be an important driver influencing green‐up responses to spring warming. Moreover, temperature‐only control of green‐up dates appears mainly at stations with relatively scarce preseason snowfall and lower elevation, while coupled temperature and precipitation controls of green‐up dates occur mostly at stations with relatively abundant preseason snowfall and higher elevation. The diversified interactions between snowfall and temperature during late winter to early spring likely determine the spatiotemporal variations of green‐up dates. Therefore, prediction of vegetation growth and carbon balance responses to global climate change on the world's roof should integrate both temperature and snowfall variations.