Lilac and honeysuckle phenology data 1956–2014

Rosemartin, Alyssa; Denny, Ellen G.; Marsh, Rob; Wilson, Bruce E.; Mehdipoor, Hamed; Zurita-Milla, Raúl; Schwartz, Mark D.

doi:10.1038/sdata.2015.38

Cited by 31 publications

(28 citation statements)

References 24 publications

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“…Similarly, the authors of [19] suggest eliminating data from those who do not submit regularly, as well as those who lack experience. This data quality step is supported by recent research that emphasizes the value of repeated visits at regular intervals to ensure high-quality phenology data [39]. Stricter protocols, such as tracking phenology every 1-2 days, will provide more standardized data and possibly increased data quality but will make it difficult to recruit and retain volunteers.…”

Section: Recommendations For Phenology-focused Citizen Science Effortsmentioning

confidence: 90%

Synergistic Use of Citizen Science and Remote Sensing for Continental-Scale Measurements of Forest Tree Phenology

2016

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Abstract:There is great potential value in linking geographically dispersed multitemporal observations collected by lay volunteers (or "citizen scientists") with remotely-sensed observations of plant phenology, which are recognized as useful indicators of climate change. However, challenges include a large mismatch in spatial scale and diverse sources of uncertainty in the two measurement types. These challenges must be overcome if the data from each source are to be compared and jointly used to understand spatial and temporal variation in phenology, or if remote observations are to be used to predict ground-based observations. We investigated the correlation between land surface phenology derived from Moderate Resolution Imaging Spectrometer (MODIS) data and citizen scientists' phenology observations from the USA National Phenology Network (NPN). The volunteer observations spanned 2004 to 2013 and represented 25 plant species and nine phenophases. We developed quality control procedures that removed observations outside of an a priori determined acceptable period and observations that were made more than 10 days after a preceding observation. We found that these two quality control steps improved the correlation between ground-and remote-observations, but the largest improvement was achieved when the analysis was restricted to forested MODIS pixels. These results demonstrate a high degree of correlation between the phenology of individual trees (particularly dominant forest trees such as quaking aspen, white oak, and American beech) and the phenology of the surrounding forested landscape. These results provide helpful guidelines for the joint use of citizen scientists' observations and remote sensing phenology in work aimed at understanding continental scale variation and temporal trends.

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Section: Recommendations For Phenology-focused Citizen Science Effortsmentioning

confidence: 90%

Synergistic Use of Citizen Science and Remote Sensing for Continental-Scale Measurements of Forest Tree Phenology

2016

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“…The Lilac leafing observations collected across the continental United States from 1956 to 2014 for purple common lilac, a cloned lilac cultivar and two cloned honeysuckle cultivars (Rosemartin et al 2015). Applications of this observational dataset range from detecting regional weather patterns to understanding the impacts of global climate change on the onset of spring at the national scale.…”

Section: Lilac Leafing Observationsmentioning

confidence: 99%

Observed earlier start of the growing season from middle to high latitudes across the Northern Hemisphere snow-covered landmass for the period 2001–2014

Chen

Yang

2020

Environ. Res. Lett.

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Vegetation phenology in spring has received much attention for its importance to terrestrial ecosystem carbon exchange and climate-biosphere interactions studies. Through control on surface water and heat balance, snow cover largely impacts on spring vegetation phenology. However, under the background of global warming and rapid reduction of spring snow cover extent across the Northern Hemisphere (NH), the responses of spring vegetation phenology have not been well documented. Using two satellite-derived land cover dynamic datasets and 420 in situ vegetation phenology observations from five filed datasets, this study evaluated the accuracy of satellite-derived vegetation phenology datasets and explored the changes of start of the growing season (SOS) across the NH snow-covered landmass for the period 2001-2014. Compared with MEaSUREs VIPPHEN, the MODIS SOS maps displayed higher accuracy in capture the real SOS climatology by validating with in situ observations (R 2 =0.67, bias=−3.99 d). Moreover, evidences from MODIS SOS maps pointed out that the SOS advanced by approximately 2.36 d in NH middle to high latitudes (43.5°N-70.0°N), but delayed by about 0.53 d in lower latitudes (33.0°N-43.5°N) from 2001 to 2014. The contrast SOS anomalies across the NH snow-covered landmass were further proved by changes in spring NDVI derived from GIMMS in the corresponding period. In addition, the observed changes in SOS were consistent with the spatiotemporal pattern of spring snow end date found in previous studies, indicating vegetation phenology changes should be taken into account in estimating the impacts of snow in climate-biosphere interactions studies.

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“…The ecological and environmental sciences have been leaders in citizen science, boasting some of the longest-running projects that have contributed meaningful data to science and conservation, including the Cooperative Weather Service (1890), the National Audubon Society's Christmas Bird Count (1900, 200+ publications), the North American Breeding Bird Survey (1966, 670+ publications), the leafing and flowering times of U.S. lilacs and honeysuckles (1956, 50+ publications;Rosemartin et al 2015), and the Butterfly Monitoring Scheme (1976, 100+ publications). These and other successful citizen science projects have increased ecological and environmental knowledge at large geographic scales and at high temporal resolution (McKinley et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Assessing data quality in citizen science (preprint)

Kosmala

Wiggins

Swanson

et al. 2016

Preprint

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Ecological and environmental citizen science projects have enormous potential to advance science, influence policy, and guide resource management by producing datasets that are otherwise infeasible to generate. This potential can only be realized, though, if the datasets are of high quality. While scientists are often skeptical of the ability of unpaid volunteers to produce accurate datasets, a growing body of publications clearly shows that diverse types of citizen science projects can produce data with accuracy equal to or surpassing that of professionals. Successful projects rely on a suite of methods to boost data accuracy and account for bias, including iterative project development, volunteer training and testing, expert validation, replication across volunteers, and statistical modeling of systematic error. Each citizen science dataset should therefore be judged individually, according to project design and application, rather than assumed to be substandard simply because volunteers generated it.

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Lilac and honeysuckle phenology data 1956–2014

Cited by 31 publications

References 24 publications

Synergistic Use of Citizen Science and Remote Sensing for Continental-Scale Measurements of Forest Tree Phenology

Synergistic Use of Citizen Science and Remote Sensing for Continental-Scale Measurements of Forest Tree Phenology

Observed earlier start of the growing season from middle to high latitudes across the Northern Hemisphere snow-covered landmass for the period 2001–2014

Assessing data quality in citizen science (preprint)

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