Forest health monitoring schemes were set up across Europe in the 1980’s in response to concern about air pollution related forest die back (Waldsterben) and have continued since then. Recent threats to forest health are climatic extremes likely to be due to global climate change, increased ground ozone levels and nitrogen deposition. We model yearly data on tree crown defoliation, an indicator of tree health, from a monitoring survey carried out in Baden-Württemberg, Germany since 1983. On a changing irregular grid, defoliation and other site specific variables are recorded. In Baden-Württemberg the temporal trend of defoliation differs between areas because of site characteristics and pollution levels, making it necessary to allow for space-time interaction in the model. For this purpose we propose to use generalized additive mixed models (GAMMs) incorporating scale invariant tensor product smooths of the spacetime dimensions. The space-time smoother allows separate smoothing parameters and penalties for the space and time dimensions and hence avoids the need to make arbitrary or ad hoc choices about the relative scaling of space and time. The approach of using a space-time smoother has intuitive appeal, making it easy to explain and interpret when communicating the results to non-statisticians, such as environmental policy makers. The model incorporates a non-linear effect for mean tree age, the most important predictor, allowing the separation of trends in time, which may be pollution related, from trends that relate purely to the aging of the survey population. In addition to a temporal trend due to site characteristics and other conditions modelled with the space-time smooth, we account for random temporal correlation at site level by an auto-regressive moving average (ARMA) process. Model selection is carried out using the Bayes information criterion (BIC) and the adequacy of the assumed spatial and temporal error structure is investigated with the empirical semi-variogram and the empirical auto-correlation function respectively. Our method provides, for the first time, the predicted spatial and temporal trends and their confidence intervals. These trends show that since 2004 there is significant evidence for an increased trend in defoliation in spruce. In addition they suggest that there was a recent switch in the primary drivers of damage: Recent damage can mainly be associated with drought years due to climate change and cumulative effects of pollution, whereas initial damage can solely be associated with pollution.

Modeling spatiotemporal forest health monitoring data

MUSIO, MONICA;
2009-01-01

Abstract

Forest health monitoring schemes were set up across Europe in the 1980’s in response to concern about air pollution related forest die back (Waldsterben) and have continued since then. Recent threats to forest health are climatic extremes likely to be due to global climate change, increased ground ozone levels and nitrogen deposition. We model yearly data on tree crown defoliation, an indicator of tree health, from a monitoring survey carried out in Baden-Württemberg, Germany since 1983. On a changing irregular grid, defoliation and other site specific variables are recorded. In Baden-Württemberg the temporal trend of defoliation differs between areas because of site characteristics and pollution levels, making it necessary to allow for space-time interaction in the model. For this purpose we propose to use generalized additive mixed models (GAMMs) incorporating scale invariant tensor product smooths of the spacetime dimensions. The space-time smoother allows separate smoothing parameters and penalties for the space and time dimensions and hence avoids the need to make arbitrary or ad hoc choices about the relative scaling of space and time. The approach of using a space-time smoother has intuitive appeal, making it easy to explain and interpret when communicating the results to non-statisticians, such as environmental policy makers. The model incorporates a non-linear effect for mean tree age, the most important predictor, allowing the separation of trends in time, which may be pollution related, from trends that relate purely to the aging of the survey population. In addition to a temporal trend due to site characteristics and other conditions modelled with the space-time smooth, we account for random temporal correlation at site level by an auto-regressive moving average (ARMA) process. Model selection is carried out using the Bayes information criterion (BIC) and the adequacy of the assumed spatial and temporal error structure is investigated with the empirical semi-variogram and the empirical auto-correlation function respectively. Our method provides, for the first time, the predicted spatial and temporal trends and their confidence intervals. These trends show that since 2004 there is significant evidence for an increased trend in defoliation in spruce. In addition they suggest that there was a recent switch in the primary drivers of damage: Recent damage can mainly be associated with drought years due to climate change and cumulative effects of pollution, whereas initial damage can solely be associated with pollution.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/96572
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