Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe

Basler, David

doi:10.1016/j.agrformet.2015.11.007

Cited by 165 publications

(223 citation statements)

References 70 publications

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“…Phenology is a well established field of research and there are numerous model comparison studies which attempt to find the model which best explains a specific phenological event (Basler, 2016;Tang et al, 2016). Many phenology models are well established (Chuine, de Cortazar-Atauri, Kramer, & Hänninen, 2013), yet studies frequently implement a custom codebase to evaluate them.…”

Section: Discussionmentioning

confidence: 99%

pyPhenology: A python framework for plant phenology modelling

Taylor¹

2018

JOSS

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SummaryPhenology, the timing of biological events, is an important aspect of environmental systems and phenology models have been in use since the mid 20th century (Chuine & Régnière, 2017). Phenology is a well established field of research and there are numerous model comparison studies which attempt to find the model which best explains a specific phenological event (Basler, 2016;Tang et al., 2016). Many phenology models are well established (Chuine, de Cortazar-Atauri, Kramer, & Hänninen, 2013), yet studies frequently implement a custom codebase to evaluate them. The pyPhenology package attempts to create a common modelling framework which can be used and referenced in phenology research. This will allow other researchers to check the implementation of mathematical models and add new models without rewriting core functionality.pyPhenology has an object oriented API where the same analysis code can be used regardless of the underlying model. The API is inspired by scikit-learn, having fit() and predict() methods for all models (Pedregosa et al., 2011). This allows for easy and reproducible code for phenology model selection and comparison studies. The package implements model fitting using built-in optimizers from the scipy package, allowing end-users to fit models to their data with just a few lines of code (Jones, Oliphant, Peterson, & Others, 2001). A common phenology model requirement is setting certain parameters to fixed values, for example setting the first day for degree day accumulation to January 1. This is done via a simple argument in model initialization. Fitted models can be saved for later use or model parameters exported as a python dictionary for inclusion other analysis. New models can be easily added and must only implement the actual model equations and a checks for adequate explanatory variables. All other requirements, such as fitting, predictions, or model saving, can be inherited from a parent class.The package comes preloaded with several phenology datasets for users to test and learn with. The first is flower and budburst observations of highbush blueberry (Vaccinium corymbosum) from Harvard Forest (O'Keefe, 2015). The second is flower, budburst, and fall senescence observations of aspen (Populus tremuloides) from the U.S.A. National Phenology Network and the many participants who contribute to its Nature's Notebook program (USA National Phenology Network, 2017). Daily mean temperature for all locations represented in the data was obtained from the PRISM dataset (PRISM Climate Group, 2004). pyPhenology was built with large scale analysis in mind and currently drives the continental scale phenology models on http://phenology.naturecast.org.

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Section: Discussionmentioning

confidence: 99%

pyPhenology: A python framework for plant phenology modelling

Taylor¹

2018

JOSS

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“…The snow cover and the timing of snowmelt vary considerably from year to year. For example, in northern and high-elevation ecosystems, vegetation follows the temporal variation of temperatures, and budburst dates can vary by 40 days (Häkkinen, 1999), but our ability to predict this variation is limited (Basler, 2016). Consequently, phenology remains among the key components causing uncertainties in our estimates of vegetation carbon balances (Richardson et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Webcam network and image database for studies of phenological changes of vegetation and snow cover in Finland, image time series from 2014 to 2016

Peltoniemi

Aurela

Böttcher

et al. 2018

Earth Syst. Sci. Data

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Abstract. In recent years, monitoring of the status of ecosystems using low-cost web (IP) or time lapse cameras has received wide interest. With broad spatial coverage and high temporal resolution, networked cameras can provide information about snow cover and vegetation status, serve as ground truths to Earth observations and be useful for gap-filling of cloudy areas in Earth observation time series. Networked cameras can also play an important role in supplementing laborious phenological field surveys and citizen science projects, which also suffer from observer-dependent observation bias. We established a network of digital surveillance cameras for automated monitoring of phenological activity of vegetation and snow cover in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1-3 cameras. Here, we document the network, basic camera information and access to images in the permanent data repository (http://www.zenodo.org/communities/phenology_camera/). Individual DOI-referenced image time series consist of half-hourly images collected between 2014 and 2016 (https://doi.org/10.5281/zenodo.1066862). Additionally, we present an example of a colour index time series derived from images from two contrasting sites.

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“…This must lead to an overestimation of C*. Surprisingly, the consideration of photoperiodicity during ecodormancy led to better model results [38,39] and mainly to more realistic dates for t 1 and C* [14,34,37]. Experimental studies showed that some late successional species (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…But in temperate climates, the annual variability of air temperature frequently leads to mild spells during winter, which do not have the same effect on plant development as similar temperatures in the beginning of spring. For this reason CF-models usually show the tendency to very late dates of t 1 , in order to avoid the accumulation of higher temperatures in the beginning or mid of winter [34,37]. This must lead to an overestimation of C*.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification and Timing of Dormant and Ontogenetic Phase for Sweet Cherries in Northeast Germany for Modelling Purposes

Fm¹,

Kp²

2017

J Hortic

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Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe

Cited by 165 publications

References 70 publications

pyPhenology: A python framework for plant phenology modelling

pyPhenology: A python framework for plant phenology modelling

Webcam network and image database for studies of phenological changes of vegetation and snow cover in Finland, image time series from 2014 to 2016

Identification and Timing of Dormant and Ontogenetic Phase for Sweet Cherries in Northeast Germany for Modelling Purposes

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