•We evaluated 6 phenology products against 57,000 leaf-out and flowering observations.•Recent releases of MODIS products show strong improvements in precision and bias.•However, all products had ...reduced variability compared to ground observations.•We conclude that remotely sensed phenology time series benefit from bias correction.
Vegetation phenology indices derived from multispectral remote sensing data are used to estimate primary productivity, track impacts of climate change and predict fire seasons. Such indices may, however, lack accuracy due to effects of snow and water, different vegetation types, and parameter choices for determining green-up and green-down. Here, we compare remotely sensed green-up dates with an extensive database of 57,000 leaf out and flowering observations from the Alberta PlantWatch citizen science network. We evaluate older global 5 km resolution VIP-NDVI and VIP-EVI2 v4 and v5 products, a regional 250 m resolution MOD09Q1-NDVI v6 product specifically designed for Alberta, and a recent 500 m resolution MCD12Q2-EVI2 v6 product. Overall, we find that MCD12Q2-EVI2 had the highest precision and least bias relative to ground observations, representing a significant advance over earlier phenology products. Different vegetation types showed a staged remotely sensed phenology in Alberta, with deciduous forest green-up first, followed by grasslands about 5 days later, and conifer forests green-up with a 10-day delay, allowing for corrections for different vegetation types. All products showed reduced interannual variability compared to ground observations, which may also lead to underestimating impacts of directional climate change. However, also in this respect MCD12Q2-EVI2 was superior, maintaining approximately 60% of the interannual variability. Nevertheless, the analysis shows that remotely sensed time series estimations of advances in leaf out may benefit from bias correction.
Satellite remote sensing has the potential to contribute to plant phenology monitoring at spatial and temporal scales relevant for regional and global scale studies. Historically, temporal composites ...of satellite data, ranging from 8
days to 16
days, have been used as a starting point for satellite-derived phenology data sets. In this study we assess how the temporal resolution of such composites affects the estimation of the start of season (SOS) by: 1) calibrating a relationship between satellite derived SOS with
in situ leaf unfolding (LU) of trembling aspen (
Populus tremuloides) across Canada and 2) quantifying the sensitivity of calibrated satellite SOS estimates and trends, over Canadian broadleaf forests, to the temporal resolution of NDVI data. SOS estimates and trends derived from daily NDVI data were compared to SOS estimates and trends derived from multiday NDVI composites that retain the exact date of the maximum NDVI value or that assume the midpoint of the multiday interval as the observation date.
In situ observations of LU dates were acquired from the PlantWatch Canada network. A new Canadian database of cloud and snow screened daily 1-km resolution National Oceanic and Atmospheric Administration advanced very high resolution radiometer surface reflectance images was used as input satellite data. The mean absolute errors of SOS dates with respect to
in situ LU dates ranged between 13 and 40
days. SOS estimates from NDVI composites that retain the exact date of the maximum NDVI value had smaller errors (~
13 to 20
days). The sensitivity analysis reinforced these findings: SOS estimates from NDVI composites that use the exact date had smaller absolute deviations from the LU date (0 to −
5
days) than the SOS estimates from NDVI composites that use the midpoint (−
2 to −
27
days). The SOS trends between 1985 and 2007 were not sensitive to the temporal resolution or compositing methods. However, SOS trends at individual ecozones showed significant differences with the SOS trends from daily NDVI data (Taiga plains and the Pacific maritime zones). Overall, our results suggest that satellite based estimates of vegetation green-up dates should preferably use sub-sampled NDVI composites that include the exact observation date of the maximum NDVI to minimize errors in both, SOS estimates and SOS trend analyses. For trend analyses alone, any of the compositing methods could be used, preferably with composite intervals of less than 28
days. This is an important finding, as it suggests that existing long-term 10-day or 15-day NDVI composites could be used for SOS trend analyses over broadleaf forests in Canada or similar areas. Future studies will take advantage of the growing
in situ phenology networks to improve the validation of satellite derived green-up dates.
► We examined how the temporal resolution of NDVI affects the start of season (SOS). ► We composed multi-day NDVI composites over 1000 sites between 1985 and 2007. ► SOS was not sensitive when using composites with the exact date of the maximum NDVI. ► SOS trends were not sensitive to the temporal resolution or composing method. ► Existing multi-day NDVI could be used for studying SOS trends of broad leaf forests.
Global warming is expected to have a major impact on plant distributions, an issue of key importance in biological conservation. However, very few models are able to predict species distribution ...accurately, although we know species respond individually to climate change. Here we show, using a process‐based model (PHENOFIT), that tree species distributions can be predicted precisely if the biological processes of survival and reproductive success only are incorporated as a function of phenology. These predictions showed great predictive power when tested against present distributions of two North American species – quaking aspen and sugar maple – indicating that on a broad scale, the fundamental niche of trees coincides with their realized niche. Phenology is shown here to be a major determinant of plant species range and should therefore be used to assess the consequences of global warming on plant distributions, and the spread of alien plant species.
Plant phenology networks of citizen scientists have a long history and have recently contributed to our understanding of climate change effects on ecosystems. This paper describes the development of ...the Alberta and Canada PlantWatch programs, which coordinate networks of citizen scientists who track spring development timing for common plants. Tracking spring phenology is highly suited to volunteers and, with effective volunteer management, observers will stay loyal to a phenology program for many years. Over two decades beginning in 1987, Alberta PlantWatch volunteers reported 47,000 records, the majority contributed by observers who participated for more than 9 years. We present a quantitative analysis of factors that determine the quality of this phenological data and explore sources of variation. Our goal is to help those who wish to initiate new observer networks with an analysis of the effectiveness of program protocols including selected plant species and bloom stages.
In documenting biological responses to climate change, the Intergovernmental Panel on Climate Change has used phenology studies from many parts of the world, hut data from the high latitudes of North ...America are missing. In the present article, we evaluate climate trends and the corresponding changes in sequential bloom times for seven plant species in the central parklands of Alberta, Canada (latitude 52°–57° north). For the study period of 71 years (1936–2006), we found a substantial warming signal, which ranged from an increase of 5.3 degrees Celsius CC) in the mean monthly temperatures for February to an increase of 1.5°C in those for May. The earliest-blooming species' (Populus tremuloides and Anemone patens) bloom dates advanced by two weeks during the seven decades, whereas the later-blooming species' bloom dates advanced between zero and six days. The early-blooming species' bloom dates advanced faster than was predicted by thermal time models, which we attribute to decreased diurnal temperature fluctuations. This unexpectedly sensitive response results in an increased exposure to late-spring frosts.
Citizen science involves public participation in research, usually through volunteer observation and reporting. Data collected by citizen scientists are a valuable resource in many fields of research ...that require long-term observations at large geographic scales. However, such data may be perceived as less accurate than those collected by trained professionals. Here, we analyze the quality of data from a plant phenology network, which tracks biological response to climate change. We apply five algorithms designed to detect outlier observations or inconsistent observers. These methods rely on different quantitative approaches, including residuals of linear models, correlations among observers, deviations from multivariate clusters, and percentile-based outlier removal. We evaluated these methods by comparing the resulting cleaned datasets in terms of time series means, spatial data coverage, and spatial autocorrelations after outlier removal. Spatial autocorrelations were used to determine the efficacy of outlier removal, as they are expected to increase if outliers and inconsistent observations are successfully removed. All data cleaning methods resulted in better Moran’s
I
autocorrelation statistics, with percentile-based outlier removal and the clustering method showing the greatest improvement. Methods based on residual analysis of linear models had the strongest impact on the final bloom time mean estimates, but were among the weakest based on autocorrelation analysis. Removing entire sets of observations from potentially unreliable observers proved least effective. In conclusion, percentile-based outlier removal emerges as a simple and effective method to improve reliability of citizen science phenology observations.
Annual maps of the remote sensing green-up date derived from SPOT-VEGETATION data were compared to the phenological observations collected by the PlantWatch citizen science project across Canada ...between 1998 and 2012. Green-up dates were found to relate to the leaf-out dates for four woody species (Populus tremuloides, Acer rubrum, Syringa vulgaris, Larix laricina), with a RMSE from 13.6 to 15.6days. This was true for all landcover types except in pixels where agriculture or water bodies were dominant. This is less accurate than the results from previous studies for boreal Eurasia (RMSE=8.7days), with phenology data from an operational network. When data were aggregated at a regional level, the remote sensing green-up date matched well with the interannual variations in leafing and also in flowering of most of the recorded species. These included spring events for trees, shrubs and non-woody plants which were either native to Canada or introduced. For most plants, spring flowering and leafing times are functions of accumulated temperature. For this reason, plant species develop in a predictable sequence, and interannual variations in this cohort of species leafing and flowering are correlated. This explains the correlation with remote sensing green-up. Data from this volunteer PlantWatch network proved consistent with independent satellite data, suggesting that combining the two will strengthen the future capacity to monitor vegetation changes.
•Green-up is compared with citizen phenological observations across Canada.•Green-up correlates with the leaf-out dates of the four tested woody plant species.•Green-up correlates with the herbaceous, shrub and tree flowering dates.•Green-up matches plant community phenology interannual variations.
In documenting biological responses to climate change, the Intergovernmental Panel on Climate Change has used phenology studies from many parts of the world, hut data from the high latitudes of North ...America are missing. In the present article, we evaluate climate trends and the corresponding changes in sequential bloom times for seven plant species in the central parklands of Alberta, Canada (latitude 52 degree degree --57 degree degree north). For the study period of 71 years (1936--2006), we found a substantial warming signal, which ranged from an increase of 5.3 degrees Celsius CC) in the mean monthly temperatures for February to an increase of 1.5 degree degree C in those for May. The earliest-blooming species' (Populus tremuloides and Anemone patens) bloom dates advanced by two weeks during the seven decades, whereas the later-blooming species' bloom dates advanced between zero and six days. The early-blooming species' bloom dates advanced faster than was predicted by thermal time models, which we attribute to decreased diurnal temperature fluctuations. This unexpectedly sensitive response results in an increased exposure to late-spring frosts.
One feature of climate change is the trends to earlier spring onset in many north temperate areas of the world. The timing of spring flowering and leafing of perennial plants is largely controlled by ...temperature accumulation; both temperature and phenological records illustrate changes in recent decades. Phenology studies date back over a century, with extensive databases existing for western Canada. Earlier spring flowering has been noted for many woody plants, with larger trends seen for species that develop at spring's start. Implications for ecosystems of trends to earlier spring arrival include changes in plant species composition, changes in timing and distribution of pests and disease, and potentially disrupted ecological interactions. While Alberta has extensive phenology databases (for species, years, and geographic coverage) for recent decades, these data cannot provide continuous ground coverage. There is great potential for phenological data to provide ground validation for satellite imagery interpretation, especially as new remote sensors are becoming available. Phenological networks are experiencing a resurgence of interest in Canada (www.plantwatch.ca) and globally, and linking these ground-based observations with the view from space will greatly enhance our capacity to track the biotic response to climate changes.
Plant phenology can be used for biomonitoring climate change. The flowering of certain temperate zone plant species occurs in response to accumulated heat. Networks of observers presently provide ...data on the timing of the growth of native and crop plants to Agro-meteorological Departments in Europe and the United States. In Alberta, a phenological survey which began in 1987 records flowering times for 15 native plants. with about 200 volunteers contributing observations annually. Six years of data have been summarized and correlated with temperature measurements. The Alberta phenological data can provide a key to sound decision-making in two ways: by providing proxy data on key variables to which vegetation responds, and by providing a model for transforming simple weather data into biologically meaningful zones.
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