Modeling Spatiotemporal Forest Health Monitoring Data

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When small data beats big data (2018, methodology): “Aggregating the tree level data at site level makes150 sense here, as it simplifies the model and reduces data (Augustin et al. 2009).”
Distinct effects of climate warming on populations of silver fir (Abies alba) across Europe (2015, methodology): “GAMM is a flexible semi-parametric method that characterizes nonlinear patterns observed between a ‘response’ variable and one or several ‘explanatory’ variables (Wood, 2006; Augustin et al., 2009).”
Econometric modelling of the regional knowledge production function in Europe (2015, methodology): “…Regression Splines are adopted as a basis to represent the univariate smooth terms (Wood, 2003, 2006a, 2006b), whereas for bivariate smooth functions such as fðRDr;t;HKr;tÞ, we use the scale-invariant tensor product smooths proposed by Wood (2006c) (Augustin et al., 2009; Longhi et al., 2014).”
GSpartan: a Geospatio-Temporal Multi-task Learning Framework for Multi-location Prediction (2016, background): “Such data are prevalent across many disciplines, including geophysical and environmental sciences [15, 3, 1], medical informatics [12], and computational fluid dynamics [29].”
A spatially explicit height–diameter model for Scots pine in Estonia (2011, background): “Up to now, this topic has been rarely discussed in a forestry context (Brezger and Lang 2006; Augustin et al. 2009).”