We used a novel data-driven method of Gaussian Process Empirical Dynamic Modeling (GPEDM) to forecast forest and grassland vegetative phenology.
This nonparametric model accounts for nonlinear relationships with environmental variables and past phenology, outperforming traditional parameteric method.
The model allowed us to fill in historical data, forecast future data, infer drivers, estimate green-up dates, and simulate impacts of climate change.
This approach has the promise to improve predictions of phenology and understanding of complex environmental responses.
GPEDM
Gaussian Process time-delay embedding model with a spatial covaraince function layer (Sugihara et al., 2012).
Empirical dynamic modeling through time-delay embedding (Munch et al., 2017).
This study
Study area for forest land cover in the NEON domain Appalachians_Cumberland Plateau. Raster mask represent pixels with forest land cover. Orange points represent locations of PhenoCams.
Study area for grassland land cover in the NEON domain Pacific Southwest. Raster mask represent pixels with grassland land cover. Orange points represent locations of PhenoCams.
Workflow of this study.
One-step ahead predictions
Observed forest EVI in black and predicted forest EVI in purple.
Observed grassland EVI in black and predicted forest EVI in purple.
Accuracy of in-sample predictions and three types of out-of-sample predictions (extrapolation over time, over space, over both time and space)for forest EVI.
Accuracy of in-sample predictions and three types of out-of-sample predictions (extrapolation over time, over space, over both time and space)for grassland EVI.
Multi-steps ahead forecasts
Observed forest EVI in black and forecasted forest EVI in blue starting from black verticle line.
Observed forest EVI in black and forecasted grassland EVI in blue starting from black verticle line.
Inference
Inferred length scale (φ) showing the influence of predictors at different lags on response (grassland EVI) at different day of year, with larger values of φ suggesting more important influence of a predictor on the response.
Inferred functional relationships between the response (forest EVI) and two predictors as examples.
Climate change
Forecasted forest EVI under 1°C increase in temperature at one pixel in two years as an example.
Green-up date
Forest green-up date estimated from observed EVI and EVI from one-stepo ahead predictions, in in-sample tests and three types of out-of-sample tests (extrapolation over time, over space, over both time and space).
Forest green-up dates estimated from observed EVI and EVI from multi-steps ahead forecasts.
Forest green-up dates estimated from observed EVI and EVI from multi-steps ahead simulations under 1°C warming.
PhenoCam greenness
Observed forest PhenoCam greenness in black and forecasted forest PhenoCam greenness in blue starting from black verticle line.
Observed grassland PhenoCam greenness in black and forecasted grassland PhenoCam greenness in blue starting from black verticle line.
